Gap analysis table

Gaps table

AddSorted ascending View Gap ID Gap type Status Theme Other Themes EV Other EV Gap description Thread RS/In-Situ Editor Ambassador Traceability Purpose Date Review Remedy Feasibility Feasibility rational Impact Impact rational Cost Cost rational Timeframe Time rational Priority Priority rational Recommendation
Add FeedBk View FeedBk 001 Geographical extent (1.1) Detected (1) Climate (CL)   C_TAS   The scarcity of microclimatic data (air temperature) from the beneath of trees. Consultation process (3) TBD (4) Guillem Closa   Pieter De Frenne and Kris Verheyen "Weather stations lack forest data" Find out how temperatures are changing beneath the trees 2016/01/15     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 002 Temporal resolution (2.3) Detected (1) Climate (CL)   C_O3A   Daily monitoring of inorganic compounds in precipitation Research programs targets (2) TBD (4) Guillem Closa   http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf Monitoring of inorganic compounds in precipitation (SO4, NO3, NH4, H+ (pH), Na+, K+, Ca2+, Mg2+, Cl-   Most European countries have measurements, but many do not comply with required time resolution (24h). Increase sampling frequency TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 003 Temporal resolution (2.3) Detected (1) Climate (CL)   C_O3A   Daily/weekly monitoring of heavy metals in precipitation Research programs targets (2) RS (1) Guillem Closa   http://www.msceast.org/reports/2_2016.pdf Monitoring of heavy metals in precipitation As, Cd, Ni, Cd, Pb, Cu, Zn       Medium (2) If a low cost automatic sensor is available Very high (4) Human health Medium (2) If a low cost automatic sensor is available Mid term (2)   None (6)    
Add FeedBk View FeedBk 004 Temporal resolution (2.3) Detected (1) Climate (CL)   C_O3A   Daily monitoring of Inorganic compunds in air Research programs targets (2) RS (1) Guillem Closa   http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf Daily monitoring of inorganic compounds in air. SO2, SO4, NO3, HNO3, NH4,NH3, HCI,NA+, K+, Ca2+, Mg2+   About 100 sites measure inorganic gases and particles in air. Uneven regional coverage, and some operate with insufficient temporal resolution Establish additional sites where needed, and increase temporal resolution to 24h TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 005 Temporal resolution (2.3) Detected (1) Climate (CL)   C_PRE   Daily/hourly monitoring of NO2 in air Research programs targets (2) RS (1) Guillem Closa   http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf Hourly/daily monitoring of NO2   There is extensive monitoring of NO2 across Europe, but with most sites at polluted locations. There is a need to have more sites with good data also on remote locations Strengthen monitoring at remote sites TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 006 Temporal resolution (2.3) Detected (1) Climate (CL)   C_PRE   Monthly monitoring of gas particle ratios of N-species Research programs targets (2) RS (1) Guillem Closa   http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf Monthly monitoring of NH3, NH4, HCI, HNO3, NO3 (in combination with filter pack sampling)   Filteerpack samling has artacts and cannot give the "true" distribution between particles and gases of semi-volitile compounds, but when combining with additional measurements, the distribution ban better be inferred   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 007 Temporal resolution (2.3) Detected (1) Climate (CL)   C_O3A   Hourly monitoring O3 Research programs targets (2) RS (1) Guillem Closa   http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf Hourly monitoring of the ozone contained in the air       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 008 Temporal resolution (2.3) Detected (1) Climate (CL)   C_O3A   Monthly monitoring of PM mass in air PM 2.5, PM 10 Research programs targets (2) RS (1) Guillem Closa   EMEP PROGRESS IN ACTIVITIES IN 2009-2019 AND FUTURE WORK. Level 1 Monthly monitoring   Chemical resolved mass concentrations are only monitored at a limited number of locations in Europe   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 009 Temporal resolution (2.3) Detected (1) Climate (CL)   C_RAS   Not enough temporal monitoring of Precipitation amount in ecosystem observation sites Research programs targets (2) TBD (4) Guillem Closa     Daily and mounthly monitoring       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 809 Temporal resolution (2.3) Detected (1) Climate (CL)   C_TAS   Not enough temporal monitoring of Temperature in ecosystem observation sites Research programs targets (2) TBD (4) Guillem Closa     Daily and mounthly monitoring       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 810 Temporal resolution (2.3) Detected (1) Climate (CL)   C_WAS   Not enough temporal monitoring of Wind direction (dd), wind speed (ff), in ecosystem obs. sites Research programs targets (2) TBD (4) Guillem Closa     Daily and mounthly monitoring       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 811 Temporal resolution (2.3) Detected (1) Climate (CL)   C_WVAS   Not enough temporal monitoring of Relative humidity in ecosystem obs. sites Research programs targets (2) TBD (4) Guillem Closa     Daily and mounthly monitoring       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 812 Temporal resolution (2.3) Detected (1) Climate (CL)   C_PAS   Not enough temporal monitoring of Atmospheric pressure in ecosystem obs. sites Research programs targets (2) TBD (4) Guillem Closa     Daily and mounthly monitoring       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 010 Temporal resolution (2.3) Detected (1) Climate (CL)   C_PRE   Monitoring hourly/daily gas particle ratio (NH3/NH4, HNO3/NO3) and monthly Ammonia in emission areas (NH3) Research programs targets (2) RS (1) Guillem Closa   http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf Acidification and eutrophication: Observations contributes to the assessment of nitrogen chemistry, influence by local emissions and dry deposition fluxes.       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 011 Temporal resolution (2.3) Detected (1) Climate (CL)   C_PRE   Monitoring hourly NOx, hourly Light hydrocarbons, and hourly Methane (Photochemical oxidants). Research programs targets (2) RS (1) Guillem Closa   http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf Observations contributes to the assessment of oxidant precursors   Challenging to measure NOx + VOC concentrations with high precision at low ambient concentrations. Requires good instrumentation and proper QAQC routines associated with research grade monitoring. Develop research infrastructures like ACTRIS to support programmes like EMEP and WMO-GAW. TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 012 Temporal resolution (2.3) Detected (1) Climate (CL)   N_APOL   Heavy metals: Monitoring weekly mercury in precipitation, daily mercury in the air, weekly heavy metals in air Research programs targets (2) RS (1) Guillem Closa   http://www.msceast.org/reports/2_2016.pdf Observations contributes to the assessment of mercury and heavy metals fluxes       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 013 Temporal resolution (2.3) Detected (1) Climate (CL)   N_APOL   Persistent organic pollutants: Monitoring weekly POPs in precipitation and in the air Research programs targets (2) RS (1) Guillem Closa   http://www.msceast.org/reports/3_2016.pdf Observations contributes to the assessment of persistent organic pollutants       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 014 Temporal resolution (2.3) Detected (1) Climate (CL)   C_O3A   Particulate matter: Monitoring daily/weekly: mineral dust in PM10 (Si, Al, Fe, Ca), Elemental and Organic Carbon. Hourly/daily: Aerosol absortion, Aerosol size number distribution (dN/dlogDp), Aerosol scatering. Hourly: Aerosol Optical Depth at 550 nm Research programs targets (2) RS (1) Guillem Closa   http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf Observations contributes to the assessment of particulate matter and its source apportionment   A full description of ambient aerosol properties (chemical and physical) is essential to have. This is currently in place in the framworks of EMEP, GAW and ACTRIS. Long-term funding is a challenge Secure funding for observations TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 015 Temporal resolution (2.3) Detected (1) Climate (CL)   N_APOL   Monitoring hourly: CO, Halocarbons ( CFCs, HCFCsd, HFCs, PFCs, SF6) Research programs targets (2) RS (1) Guillem Closa   EMEP PROGRESS IN ACTIVITIES IN 2009-2019 AND FUTURE WORK. Level 2 Tracers observations contributes to the assessment of individual long-range transport events and their source apportionment   Important to measure to understand climate forcing and ozone layer depletion. Due to long atmospheric life time, site densities can be low. Presition of measurements must however be very high. Secure resources to operate high quality observations at selected "supersites" TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 016 Temporal resolution (2.3) Detected (1) Climate (CL)   N_APOL   Monitoring hourly: Dry deposition flux, Dry deposition flux of O3, Dry deposition flux of VOCs, Greenhouse gases, Hydrogen. Hourly\Daily: Hydrocarbons, NOy chemistry, Vertical profiles, OC fractionation, Major inorganics in both PM2.5 and PM10. Daily/weekly:Mercury speciation , Congener-specific Organic tracers PM2.5 and PM10. Research programs targets (2) RS (1) Guillem Closa   EMEP PROGRESS IN ACTIVITIES IN 2009-2019 AND FUTURE WORK. Level 3 Observations contribute to the understanding of processes relevant for long-range transport of air pollutants and support model development and validation       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 017 Geographical inconsistency (4.1) Detected (1) Energy (EN)   E_LULC   Develop high-resolution global land-cover and land-cover change data sets, based on international community consensus and including a robust accuracy assessment. Research programs targets (2) Both (3) Guillem Closa   CA-01. GEO 2016 WORK PROGRAMME Reduce inconsistencies between land cover products       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 018 No measured (7.1) Detected (1) Energy (EN)   EV   In mineral resources there is the lack dedicated EO system or program and currently use EO systems and programs from other SBAs. Research programs targets (2) TBD (4) Guillem Closa   CA-06. GEO 2016 WORK PROGRAMME Develop global coverage by high-spectral resolution sensors       Low (1) the technology and communication standards are available Very high (4) in-situ data is crucial in early warning Low (1) not much added cost regarding exixting technologies; it's more a matter of real application in practice Short term (1)   High (3)    
Add FeedBk View FeedBk 019 No quality (6.3) Detected (1) Water (WA)   C_WVU C_CLD C_O3A C_RAS C_RIV C_LAK In order to gain an understanding of the physical processes that are related to water vapor, clouds, aerosols and precipitation, a new observation paradigm needs to be established that focuses on the physical processes rather just on the final quantity. Research programs targets (2) TBD (4) Guillem Closa   CA-06. GEO 2016 WORK PROGRAMME Develop an observation strategy to improve the synergistic understanding between water vapor and clouds, and if feasible, aerosols and precipitation.       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 020 No access (6.1) Detected (1) Disaster resilience (DI)   EV   There is not timely and reliable access to in-situ data required in emergency events. Research programs targets (2) TBD (4) Guillem Closa   GEO 2016 WORK PROGRAMME. CA-027. Foster Utilization of Earth Observation Remote Sensing and In Situ Data for All Phases of Disaster Risk Management Promote timely and reliable access to in situ data required in emergency events       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 021 No parameter (7.2) Detected (1) Disaster resilience (DI)   C_RAS C_CLD C_O3A Combine the use of remote sensing and EO to better estimate overfloods Research programs targets (2) TBD (4) Guillem Closa   GEO 2016 WORK PROGRAMME. CA-028 Global Flood Risk Monitoring Develop, test and apply methods to utilize satellite remote sensing and other Earth observations with models and maps to estimate location, intensity and duration of floods globally in real-time and a durable monitoring system of flood risk with climate       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 022 No easy access (5.4) Detected (1) Biodiversity (BI)   EBV   There are many excellent tools, protocols and software in use that facilitate effective biodiversity monitoring but these are not easily discoverable or available to all regions of the planet. As well, current efforts to monitor biodiversity are not interoperable, thereby limiting our ability to detect change and the underlying mechanisms driving change in biodiversity. Research programs targets (2) TBD (4) Guillem Closa   GEOBON- Global Biodiveristy Obvserbation Aims to serve as a tecnology transfer, increase the interoperability and the accesibility of biodiversity data, models and tools       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 023 Geographical extent (1.1) Detected (1) Biodiversity (BI)   C_AGB B_EFNP LIDAR global dataset Research programs targets (2) TBD (4) Joan Maso   ECOPotential WP2 meeting. Cited Herique Pereira Estimate biomass globally and with a good resolution. Carbon sequestration global estimation in forestry 2016/04/06     Medium (2) If a satellite borne is done High (3)   Very high (4)   Long term (3) Requires research High (3)    
Add FeedBk View FeedBk 024 Temporal resolution (2.3) Detected (1) Oceans (OC)   C_SL   Absence of a near real-time operational and timely manner a global coverage Sea Surface Height (SSH) for ocean and coastal areas Research programs targets (2) RS (1) Guillem Closa   Sentinel- 3 Mission Objectives Develop global coverage Sea Surface Height (SSH) for ocean and coastal areas 2016/04/20 This gap is somehow represented in gaps 103-104 (EGL)   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 025 Temporal resolution (2.3) Detected (1) Oceans (OC)   C_SL   Absence of a near real-time operational and timely manner a enhanced resolution SSH products in coastal zones and sea-ice regions Research programs targets (2) RS (1) Guillem Closa   Sentinel- 3 Mission Objectives Enhanced resolution SSH products in coastal zones and sea-ice regions 2016/04/20 In addition this gap is somehow represented in gaps 100-103. (EGL)   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 026 Geographical extent (1.1) Detected (1) Oceans (OC)   C_SST   Absence of in a near real-time operational and timely manner global coverage Sea Surface Temperature (SST) and sea-Ice Surface Temperature (IST) Research programs targets (2) RS (1) Guillem Closa   Sentinel- 3 Mission Objectives Global coverage Sea Surface Temperature (SST) and sea-Ice Surface Temperature (IST) 2016/04/20 As far as i understand all the infrared radiometers (AVHRR, MODIS even in GEOSAT) are in real time, accessible and global coverage is not possible unless some combination with T from microwaves is combined, at least for the SST. The GHRSST Multi-Product Ensemble (GMPE) delivered through CMEMS (Copernicus marine) offers a near real-time product at 0.25x 0.25 degrees. So the gap should perhaps more focused on the type spatial resolution.   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 027 Geographical extent (1.1) Detected (1) Oceans (OC) BI C_OC   Absence of in a near real-time operational and timely manner a global coverage ocean colour and water quality products Research programs targets (2) RS (1) Guillem Closa   Sentinel- 3 Mission Objectives Global coverage ocean colour and water quality products 2016/04/20 According to the gap description, this gap affects the EOV: Ocean Color, not the EO: Sea State !

This gap also should affect "Climate " theme because color is used as a aproxy of phytoplacnton that is a quite relevant C02 sink !

I believe that MODIS and VIIRS on Suomi NPP are operating regularly on such bands. They are delivered in near real time but not at global coverage. In addition they are affected by the same problem as infrared radiometers, cloud cover, that in this case can not be solved by complementary radiometers as in the case of SST. The highest temporal resolution attainable is constraint by night/day conditions. MODIS and VIIRS are operating regularly on such bands
  TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 028 Geographical extent (1.1) Detected (1) Multiple (10) CL OC DI EN C_WAS   Absence of in a near real-time operational and timely manner a global coverage ocean surface wind speed measurements Research programs targets (2) RS (1) Guillem Closa   Sentinel- 3 Mission Objectives Global coverage ocean surface wind speed measurements 2016/04/20 I believe that if this gap is associated to ECV:Wind speed at the surface, the it should also be associated also to [EOV :Sea State] because surface stress is derived/related with.

On the other hand winds at the surface are obtained in near real time by already running scatterometers !.

Perhaps the way they measure and the number of scatteropmeters are not enough to produce synoptic winds fields with global coverage. There are already real time scatterometers running with global coverage

  Very high (4)   TBD (9)   TBD (9)   TBD (99)   None (6)    
Add FeedBk View FeedBk 029 Geographical extent (1.1) Detected (1) Oceans (OC) DI C_SS   Absence of in a near real-time operational and timely manner a global coverage significant wave height measurement Research programs targets (2) RS (1) Guillem Closa   Sentinel- 3 Mission Objectives Global coverage significant wave height measurement 2016/04/20 I think that if this gap is also associated to [ECV:Sea State] (EGL)   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 030 Geographical extent (1.1) Detected (1) Climate (CL)   C_O3A   Absence of in a near real-time operational and timely manner a global coverage atmospheric aerosol consistent over land and ocean Research programs targets (2) TBD (4) Guillem Closa   Sentinel- 3 Mission Objectives Global coverage atmospheric aerosol consistent over land and ocean 2016/04/20     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 031 Geographical extent (1.1) Detected (1) Climate (CL)   C_WVU C_WVAS Absence of in a near real-time operational and timely manner a global coverage total column water vapour over land and ocean Research programs targets (2) TBD (4) Guillem Closa   Sentinel- 3 Mission Objectives Global coverage total column water vapour over land and ocean 2016/04/20     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 032 Geographical extent (1.1) Detected (1) Biodiversity (BI)   C_LCV   Absence of in a near real-time operational and timely manner a global coverage vegetation products Research programs targets (2) TBD (4) Guillem Closa   Sentinel- 3 Mission Objectives Global coverage vegetation products 2016/04/20     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 033 Geographical extent (1.1) Detected (1) Climate (CL)   C_ICE   Absence of in a near real-time operational and timely manner a global coverage land ice/snow surface temperature product. Research programs targets (2) TBD (4) Guillem Closa   Sentinel- 3 Mission Objectives Global coverage land ice/snow surface temperature products 2016/04/20     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 034 Geographical inconsistency (4.1) Detected (1) Biodiversity (BI)   EBV   No back-calibration of data archives for coherent time series compounded by changing methodologies. Research programs targets (2) TBD (4) Guillem Closa   O'Connor, B., Secades, C., Penner, J., Sonnenschein, R., Skidmore, A., Burgess, N. D., & Hutton, J. M. (2015). Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote Sensing in Ecology and Conservation, 1(1), 19-28. Standardization in EO data and products 2016/04/20     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 035 No coordination of obs. sites (8.2) Detected (1) Biodiversity (BI)   EBV   No overseeing authority ensuring EO-based biodiversity observations are in line with user needs Research programs targets (2) TBD (4) Guillem Closa   O'Connor, B., Secades, C., Penner, J., Sonnenschein, R., Skidmore, A., Burgess, N. D., & Hutton, J. M. (2015). Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote Sensing in Ecology and Conservation, 1(1), 19-28. Designating leadership and institutional oversight 2016/04/20 GEO BON network for facilitating inter-disciplinary dialogue and IPBES for achieving consensus on what biodiversity and ecosystem services need from EO   Very high (4)   High (3)   Low (1)   Mid term (2)   Very high (2)    
Add FeedBk View FeedBk 036 Uncertainty (3.1) Detected (1) Biodiversity (BI)   EBV   Experts in EO data processing not trained in applied biodiversity concepts. EO data products are not fit for purpose. Research programs targets (2) TBD (4) Guillem Closa   O'Connor, B., Secades, C., Penner, J., Sonnenschein, R., Skidmore, A., Burgess, N. D., & Hutton, J. M. (2015). Earth observation as a tool for tracking progress towards the Aichi Biodiversity Targets. Remote Sensing in Ecology and Conservation, 1(1), 19-28. Providing more opportunities for inter-disciplinary inter-disciplinary collaboration 2016/04/20 Knowledge of established EO data providers must be matched with that of biodiversity conservation policy specialists to enable knowledge transfe   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 037 No measured (7.1) Detected (1) Biodiversity (BI)   B_GCC B_GCA B_GCP B_GCB B_SPD B_SPA B_SPS The missing of Genetic composition data Research programs targets (2) TBD (4) Guillem Closa   Geijzendorffer, I. R., Regan, E. C., Pereira, H. M., Brotons, L., Brummitt, N., Gavish, Y., ... & Schmeller, D. S. (2015). Bridging the gap between biodiversity data and policy reporting needs: An Essential Biodiversity Variables perspective. Journal of Applied Ecology.   2016/04/20     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 038 No measured (7.1) Detected (1) Disaster resilience (DI)   EV   There is the need to improve the availability of EO data to implement disaster risk reduction and resilience measures, during all disaster risk management phases Research programs targets (2) TBD (4) Guillem Closa   GI-16. GEO 2016 WORK PROGRAMME. GEO-DARMA = Data Access for Risk Managemen To increase the availability and accuracy of risk related information, both satellite EO information combined with other sources of data (in-situ ground observations, socio-economic, model outputs) 2016/04/20     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 039 Geographical extent (1.1) Detected (1) Climate (CL)   EOV   Lack of spatial coverage in Indic Ocean and in the south hemishere Research programs targets (2) TBD (4) Joan Maso   http://www.iagos.fr/web/images/map/map_iagos.png Contact indic companies if available       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 040 No quality (6.3) Detected (1) Biodiversity (BI)   B_CCT   While the amount of information on biodiversity has increased greatly in recent years there are still major gaps in understanding which need to be filled, such as those related to taxonomy. Similarly much of the information which is currently available is often incomplete and/or in need of updating Research programs targets (2) TBD (4) Guillem Closa   Aichi targets Compilation. Target 19   2016/05/04     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 041 No open access (6.2) Detected (1) Biodiversity (BI)   EBV   Information and technologies relating to biodiversity should be made more accessible and shared, subject to national legislation, so that it can be put to better use. Much of the information which is available is not effectively used as it is difficult to access. Research programs targets (2) TBD (4) Guillem Closa   Aichi targets Compilation. Target 19 Accessibility could be improved through the further development of the clearing-house mechanism at national and global levels. 2016/05/04     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 042 Uncertainty (3.1) Detected (1) Biodiversity (BI)   EBV   Further efforts are also needed, at multiple scales, to improve biodiversity-related knowledge and reduce uncertainties around the relationship between biodiversity change, ecosystem services and impacts on human well-being Research programs targets (2) TBD (4) Guillem Closa   Aichi targets Compilation. Target 19   2016/05/04     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 043 Geographical extent (1.1) Detected (1) Energy (EN)   E-SSI   Scarcity of accurate in situ measurements in most of the world. Large networks measuring radiation, such as GAW, BSRN have a limited coverage. National meteo networks are by definition limited and in addition, many of them do not measure radiation, except sunshine duration. Observation requirement (1) In-Situ (2) Lucien Wald (LW) ARMINES IEA Solar Heating and Cooling Program, Tasks 36 and 46. GEO Task US-09-01a Various. Ranges from establishing a bankable report for investment seeking to validation / calibration of Copernicus products and others 2016/11/10   Meta-Network: Opportunities exist to get access to in-situ measurements coming from numerous PV plant operators all over Europe. PV plant operators do hold in-situ measurements for their daily work. An extra effort is needed to identify, convince, access and connect their data. As a result one could create a Meta-Network of private providers using open, standard and interoperable technologies. This Meta-Network would complement existing well known meteo networks (GAW, BSRN). Very high (4) No instrument to develop, no specific installation. It is a matter of networking Very high (4) Such data are needed right from the start to develop projects in solar energy. Such data would be used routinely for the validation of Copernicus radiation products Low (1) It can be done with existing sensors. Costs are those for networking and operating a platform implementing the meta-network for interoperability of various small networks TBD (99)   Crucial (1) Crucial for the development of projects in Africa or Asia where data may exist but are yet unknown and unavailable. Crucial also for the development of Copernicus products in solar energy as validation in areas outside Europe will help the uptake of these products by companies as it increases their confidence in products  
Add FeedBk View FeedBk 044 No open access (6.2) Detected (1) Energy (EN)   EREV   No easy access by SMEs to meteorological measurements because of costs Industry-driven challenges (5) Both (3) Lucien Wald (LW) ARMINES ConnectinGEO. Exchanges with companies in various occasions, including Copernicus events Various. Ranges from establishing a bankable report for investment seeking to validation / calibration of Copernicus products and others 2016/11/10 Meteo data may originate from in situ measurements or from meteorological analyses It is likely a networking activity to demonstrate to governments supporting met-offices that providing easy access at very limited costs to companies will foster the development of renewable energy projects and will support their international commitments in climate and environment Very high (4) No instrument to develop, no specific installation. It is a matter of networking High (3) Such data are really needed and their availability would help in developing renewable energy. However, it is not crucial as companies have found ways to cope with this gap. TBD (9) I cannot say. It is a balance at governmental level TBD (99)   High (3) Such data are really needed and their availability would help in developing renewable energy. However, it is not crucial as companies have found ways to cope with this gap.  
Add FeedBk View FeedBk 045 Geographical extent (1.1) Detected (1) Energy (EN)   E-BAT E-TDL E-CUR E-OFL Scarcity of accurate in situ measurements in coastal areas for marine renewable energies. Bathymetry, type of floor, tides, swell, currents. Industry-driven challenges (5) Both (3) Lucien Wald (LW) ARMINES ConnectinGEO. Exchanges with companies in various occasions, including Copernicus events. IREMARE web site, EWTEC 2015, Island Energy Transitions: Pathways for Accelerated Uptake of Renewables 2015 Various. Ranges from establishing a bankable report for investment seeking to validation / calibration of Copernicus products and others 2016/11/10 This gap Is related mainly with gaps 64-70 and gap 28. In my humble opinion this gap could be partially merged with the others just by addding the "Energy Theme", merging together the gap description and/or the "Purpose column" (EGL. LW: Yes, I agree that this gap is consistent with 64, 65, 66, 68, 70, 71 and 72. However, two caveats. 1) this gap includes bathymetry and type of floor; 2) it originates from industry challenge For bathymetry and type of floor, exploit SAR images or images in visible-NIR range together with computer models. For the other variables, see the gaps 64 to 72 Very high (4) Images exist, models exist Very high (4) Such data are needed right from the start to develop projects in marine energy. High (3) Computer resources needed. Validation campaigns needed TBD (99)   Crucial (1) Crucial for the development of projects in marine energy Currently, companies must invest in very costly campaigns for collecting data on local bathymetry, type of floor, swell etc. These campaigns must last approx. 1 year and are very expensive, especailly outside Europe.
Add FeedBk View FeedBk 046 Temporal resolution (2.3) Detected (1) Multiple (10) CL OC WA BI DI C_SST   Satellite observations about Sea Surface Temperature do not cover the diurnal cycle Consultation process (3) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Determine and understanding ocean-atmosphere heat and gas exchanges. In situ measurements necessary to cal/val satellite signals and sensor drift. 2016/05/30 Infrared provides 2-4 measurements per day (or less with cloud covers). Geostacionary satellites (e.g. METEOSAT, GOES) recurrent every 3 hour (clouds affect). Microwave observations from (e.g. AMSR) complement cloud cover areas at lower resolution and far from coast-land areas. To improve the combination of in-situ sensors with presently available infrared observations including geostationary and microwaves and model analysis to better describe the daily cycle. Increasing microwaves constellation would also help. Ocean operational models should be improved to include the dynamics associated with the processes involved (e.g. restratification, convective cooling, Langmuir turbulence, etc.). High (3) While increasing the satellite capacity (e.g. AMSR) and optical recurrency at high spatial resolution are quite expensive, ocean models representing the physical processes are ready but not operational. High (3) Weather/ocean forecast uncertainties reduced. Improvements on the atmosphere-ocean feedbacks and parametrizations into climate models. Assessment improvements on fisheries operations (species associated to thermal fronts). High (3) Improvements can be done with combinations of sensors in place and analysis with models (e.g. GHRSST) however computational costs associated to make operational models resolving such scales are expensive with present computing resources. Benefits and reduction cost could benefit from more in situ observations. Mid term (2)   Medium (4)    
Add FeedBk View FeedBk 047 Geographical inconsistency (4.1) Detected (1) Multiple (10) CL OC WA BI DI C_SST   Differences among SST products near the coasts. Consultation process (3) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Reynolds R. W., Chelton D. B., 2010: Comparisons of Daily Sea Surface Temperature Analyses for 2007-08, Journal of Climate, 23, 3545-3562

Determine and understanding ocean-atmosphere heat and gas exchanges. In situ measurements necessary to cal/val satellite signals and sensor drift. 2016/05/30 Resolution of infrared radiometers are reasonable for coastal areas (aprox 1-2 km). Differencies arise between the analyses and interpolation procedures to obtain high level products. Comprehensive review of procedures and dedicated intercomparisons/experiments against independent data should contribute to characterize and harmonize differences. Very high (4) As more high resolution infrared and microwave instruments are being launched data uncertainties will be reduced and differences among procedures as well. However processing methodologies can benefit from specifically dedicated experiment/exercises to validate products against independent data. High (3) Weather/ocean forecast uncertainties reduced. Improvements on the atmosphere-ocean feedbacks and parametrizations into climate models. Assessment improvements on fisheries operations (species associated to thermal fronts). Medium (2) The costs is between Low and Medium in the sense that intercomparison of procedures are ongoing (e.g. GHRSST) but detailed validation with experiments to obtain new high resolution independent data could be promoted. Mid term (2)   Very high (2)    
Add FeedBk View FeedBk 048 Temporal inconsistency (4.2) Detected (1) Multiple (10) CL OC WA BI DI C_SST   Uncertainties in the adjutsments between different instruments along the time Consultation process (3) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Reynolds R. W., Chelton D. B., 2010: Comparisons of Daily Sea Surface Temperature Analyses for 2007-08, Journal of Climate, 23, 3545-3562

Determine and understanding ocean-atmosphere heat and gas exchanges. In situ measurements necessary to cal/val satellite signals and sensor drift. 2016/05/30 For climate studies the only valuable analysis is based on AVHRR data because extends back to 1981. ATSR and AATSR microwaves on ENVISAT between 1991 and 2012 has contributed to complement infrared observations. To ensure a long time monitoring, a successful combination with new radiometers (e.g. MODIS, SENTINEL) and microwaves instruments is needed providing higher resolutions, global coverage and overcome cloud cover problems of infrared captors. Careful adjustments procedures need to be designed in particular because cloud cover is seasonal and regional dependent and temporal continuity with global coverage can only be assured by conbining radiometers of different characteristics. Additionally to keep long time in situ series is a way to reduce uncertainties in the adjustment procedures. TBD (9) As more high resolution infrared and microwave instruments are being launched data uncertainties will be reduced and differences among procedures as well. Methodologies can benefit from programs to maintain already available long time series of in situ SST observations, and in particular, set up a long time monitoring on those sites where cloud cover is more persistent in time. High (3) Improvements on the atmosphere-ocean feedbacks and parametrizations into climate models. Medium (2) The costs is medium in the sense that intercomparison of procedures are ongoing (e.g. GHRSST) but the maintenance of time series of in situ observations is necessary. TBD (99)   TBD (0)    
Add FeedBk View FeedBk 049 Geographical extent (1.1) Detected (1) Multiple (10) CL OC WA BI DI C_SST   Non-uniform distribution of in situ surface measurements. Argo floats tend to be clustered by topographically constraint areas (e.g. Caribbean Sea, South China Sea,etc.), lack at high latitudes and polar areas and in areas of higher variability (e.g. Falkands/Malvinas, gulf Stream, etc.) Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Real time status of ARGO deployments through JCOMMOPS API, (http://www.jcommops.org/board?t=Argo)

Fiedler E, Mao Ch., Maclaren A. 2015: SST: results and recommendations. E-AIMS Deliverable D4.3.3. Availbale at http://www.euro-argo.eu/content/download/83965/1049581/version/1/file/E-AIMS_4.3_SeaSurfaceTemperature_V2.pdf

Determine and understand ocean-atmosphere heat and gas exchanges. In situ measurements necessary to cal/val satellite signals and sensor drift. 2016/05/30 Somehow national agencies through the Argo program coordinate the strategy to deploy the array of floats. However the scientific community and research projects may have their own objectives which can not be in line with the need of a uniform coverage. Coordination mechanisms between national agencies and the scientific community and needs could be used to have a better coverage either to be more uniform or based on reducing uncertainties. Specific research programs devoted to deal with undersampled regions or to allocate additional funds for a more intensive deployment could be promoted. Very high (4) Near surface Argo measurements has been proved to provide a good estimate of foundation temperature (the remperature minus the diurnal cycle) so there is no technical concern and many floats provides SST while they stay at the surface to transmit data. So there are not technical reasons the feasibility is only a matter of coordination and cost to improve the number of floats. High (3) Reduce uncertainties and errors in SST analysis which therefore impacts the characterization of the atmosphere-ocean feedbacks in weather forecast models, parametrizations into climate models and the earth temperature evolution. Medium (2) According to Fiedler et al., 2015, to achieve an error sampling of 0.02 K across the global ocean, the Argo observations would need to be increased by up to 1300 observations/month. If we assume costs of about $200 per profile we have a rough estimation of near 3 M$ per year in addition to the already available array. TBD (99)   TBD (0)    
Add FeedBk View FeedBk 050 No measured (7.1) Detected (1) Multiple (10) CL OC WA BI DI C_SST   Lack of in situ surface measurements from Argo buoys in marginal seas and shelf seas (i.e Baltic Sea, North Sea, Barents Sea etc.) ad polar areas Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Real time status of ARGO deployments through JCOMMOPS API, (http://www.jcommops.org/board?t=Argo)

W. Walczowski, I. Goszczko 2015: Arctic float final evaluation, E-AIMS Deliverable 2.5.2, available at http://www.euro-argo.eu/content/download/89388/1101132/file/E-AIMS_D2.252.pdf.

Determine and understand ocean-atmosphere heat and gas exchanges. In situ measurements necessary to cal/val satellite signals and sensor drift. 2016/05/30 Lack of Argo in marginal seas is more related with the coordination and efforts of agencies in riparian countries while the lack in shelf seas and shelf areas (< 200 m depth) is more a consequence of Argo planning decissions. On shelf areas the topography may be highly variable and the probability of beaching can be high to interrupt the monitoring. Polar areas covered by ice capes introduces further complexity to make transmission available. Some technical solutions endowing Argo floats with inertial navigation systems via accelerometers may help to self-adjust the sampling to bathymetric changes provided bathymetric charts are also included . Adopting measures of hardware protection of antenna and instruments or alternatively using sensing ice algorithms and procedures would also be necessary in polar areas to avoid equipment dammages. For shelf seas complementary meaures can be adopted by extending actual coastal buoys networks or by collaboration with owners of offshore platforms (e.g. oil and gas rigs, aquaculture installation, etc.) to supply environmental data. High (3) Some technical solutions endowing inertial navigation systems with accelerometers may help to self-adjust the sampling to bathymetric changes. In polar areas some changes in float design would allow to an efficient solution. However as it is usual a compromise between energy consumption and sampling strategy may constraint their performance, so the limiting element is the energy consumption. In polar areas, Argo floats would benefit of improvements in battery storage technologies. To promote collaboration with already existing offshore installations in shelf areas, usually associated to energy production and aquaculture, to deploy fixed instruments and increase the infromation coverage. Very high (4) To provide the most complete coverage of all kind of oceanic regions. Specific impact would be the information on polar regions as being in such areas where environmental changes are relevant footprints of the climate change. High (3) Specific hardware additions and changes in float design should be implemented increasing the costs. In polar regions maintenance, deployment operations and monitoring needs would increase costs. TBD (99)   TBD (0)    
Add FeedBk View FeedBk 051 Vertical resolution (2.2) Detected (1) Multiple (10) CL OC WA BI DI C_SST   Insufficient in situ surface measurements from Argo buoys. The number of measurements close to the surface (0 - 5 m) is around 20% of total profiles Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC Statistics from Coriolis Global Data Assembly Center (GDAC) Determine and understand ocean-atmosphere heat and gas exchanges. In situ measurements necessary to cal/val satellite signals and sensor drift. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 052 Geographical extent (1.1) Detected (1) Multiple (10) CL OC WA BI DI C_SST   Insufficient temporal coverage. Argo deployment started in 2000 and became fully operative in 2005 so less than the WMO 30 years definition of clima. Drop of the number of Argo float deployments benneath 3200 in 2018. Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC Argo program description from the Argo website:

http://www.argo.ucsd.edu/About_Argo.html

Durack P.J., Lee T., Vinogradova N. T., D. Stammer, 2016: Keeping the lights on for global ocean salinity observation, NATURE CLIMATE CHANGE, vol 6. 228-231.

Determine and understand ocean-atmosphere heat and gas exchanges. In situ measurements necessary to cal/val satellite signals and sensor drift. 2016/05/30 This gap has been merged with gap 53 Increase the investment on Argo floats at least to ensure the 30 years period according to the WMO definition of clima. Very high (4) There is almost no technological problems concerning the measurement of in situ SST from Argo. However, of a total of 3887 operational floats in October2016, the number of countries contributing to the Argo program falls to only 30 being a few national agencies from specific countries (e.g. USA, France, Australia,..) the main contributors. Probably much more efforts could be easily increased if more national agencies are involved. Outside the operational activities of national agencies, research plans and programs could be somehow incentivate the use of Argo floats as an indirect way to increase the number of floats. Very high (4) The impact of the Argo program as a multipurpose platform scanning the 3D structure of the ocean is beyond any doubt and affects a great number of communities. Very high (4) The approximate cost for deploying an array of 800 floats per year to maintain a level of around 3000 floats in operation is about 24 M$ TBD (99)   TBD (0)    
Add FeedBk View FeedBk 057 Temporal extent (1.3) Detected (1) Multiple (10) CL OC WA BI C_SSS C_OAS Time series are short because SSS missions and relatively new. SMOS was a proof of concept (2010-present), Aquarius operated in 2011-2015 and SMAP was conceived for soil moisture and now is being exploited for ocean SSS (2015-present). Consultation process (3) RS (1) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

P. E. Land, J. D. Shutler, H.S. Findlay, F. Girard-Ardhuin, R. Sabia, N. Reul, J.-F. Piolle, B. Chapron, Y. Quilfen, J. Salisbury, D. Vandemark, R. Bellerby and P. Bhadury, 2015: Salinity from Space Unlocks Satellite-Based Assessment of Ocean Acidification, Environ. Sci. Technol., 2015, 49 (4), pp 1987\x{2013}1994
DOI: 10.1021/es504849s

SSS is directly essential for climate, monitoring the water cycle changes and to evaluate Evaporation-Precipitation (E-P) fluxes over the ocean from basin to global scales. It is relevant to determine the sea surface density, freshwater transport and coastal ocean dynamics (river discharges). Further, in situ SSS measurements are essential for cal/val satellite signals and sensor drift of new missions. 2016/05/30   To promote coordination among spatial agencies to ensure the continuity of present L-band missions (SMOS-Aquarius-SMAP). The Aquarius mission has prematurely ended, SMOS is still running beyond the nominal mission life-time and SMAP was launched in 2015. High (3) SMOS, the first mission to retrieve SSS, was a proof of concept. After several years of operation, SMOS/Aquarius/SMAP missions have provided enough knowledge, expertise and maturity in terms of technologies, signal retrieval issues and operational capabilities to guarantee a successful continuation of remote sensing SSS measurements. Very high (4) SSS retrieval is as crucial as SST to gain knowledge on the water cycle, in particular to determine freshwater fluxes. A relevant collateral aspect to take into account is that L-band missions needed to retrieve SSS simultaneously serve to retrieve other ECV's. In particular soil moisture and complementary information for the cryosphere (Ice thickness, ice extent,..) and ocean acidification. Very high (4) Similar to many satellite missions. TBD (99)   TBD (0)    
Add FeedBk View FeedBk 058 Geographical extent (1.1) Detected (1) Multiple (10) CL OC WA BI C_SSS   Insufficient spatial coverage. SSS retrieval in marginal seas and cold waters is difficult to obtain due to RFI (Radio-Frequency interference) contamination. SSS is problematic for the land-sea transition. Retrieved signals are good up to several km near the continental coasts (50 km SMAP and 800 km, SMOS). Consultation process (3) RS (1) E. Garcia-Ladona CSIC

Lagerloef G., Kao H.Y., Meissner T., Vazquez J., 2015: Aquarius Salinity Validation Analysis; Data Version 4.0, 30pp. Available at: ftp://podaac-ftp.jpl.nasa.gov/allData/aquarius/docs/v4/AQ-014-PS-0016_AquariusSalinityDataValidationAnalysis_DatasetVersion4.0and3.0.pdf)

Ballabrera J., 2015: Sea Surface Salinity: Results and Recommendations D4.4.3, E-AIMS. Euro-Argo Improvements for the GMES Marine Service, E-AIMS: D4.443-v2. Available at: http://www.euro-argo.eu/content/download/91862/1123452/version/1/file/E-AIMS_D4.443-V2.pdf)

SSS is directly an essential for climate, monitoring the water cycle changes and to evaluate Evaporation-Precipitation (E-P) fluxes over the ocean from basin to global scales. It is relevant to determine the sea surface density, freshwater transport and coastal ocean dynamics (river discharges). Further, in situ SSS measurements are essential for cal/val satellite signals and sensor drift of new missions. 2016/05/30   Three measures would help to improve the retrieval of SSS: a) To promote stronger commitments among countries to improve the applicability of present regulations related with the radiofrequency use in the bands of interest . b) To foster a new generation of instruments to reduce the limitations associated to land-sea transition areas and to promote a continuous series of missions in L-band similarly as the infrared and higher frequency microwaves missions (C-band and above), c) there is room to improve L2 products at the processing level, d)In line with the production of SST analysis to promote a roadmap of L4 products merging satellite and in situ measurements to increase the geographical extent of present products. High (3) SMOS, the first mission to retrieve SSS, was a proof of concept. After several years years of operation, SMOS/Aquarius/SMAP missions have provided enough knowledge, expertise and maturity in terms of technologies, SSS retrieval issues and operational capabilities to guarantee a successful continuation of remote sensing measurements. High level merging products are still under development and specific efforts to derive improved L4 products is a matter of continuity of research teams developing it. Very high (4) The impact of the SSS retrieval is as crucial as SST to gain knowledge on the water cycle. Generically speaking coastal areas are the places where freshwater discharges are produced. SSS at high latitudes can be essential to monitor ice and ice melting particularly in the Artic area. For some marginal and semi-enclosed seas (i.e. Mediterranean, Baltic) salinity and salinity gradients are key element of its dynamics. In marginal and shels seas, and shelf regions, remote sensing of SSS is complementary of the Argo monitoring and where climatological analysis (e.g. Levitus) present less data while they are necessary to constraint climate models. High (3) Cost depend on the mentioned remedies. Ensuring a continuity of any sattelite mission (a) is ranked "Very High" according to the criteria here adopted. However the other two remedies can be considered as "Low". Policy actions (a) to reduce RFI's is quite easy to implement and ensuring the continuity of the experts involved in the improvements and development of L2-L4 products is relatively low. TBD (99)   TBD (0)    
Add FeedBk View FeedBk 059 Geographical inconsistency (4.1) Detected (1) Multiple (10) CL OC WA BI C_SSS   Products differ due to differences of onboard instrument configurations (e.g. real aperture radiometers versus synthetic aperture radiometers). Also different processing strategies produce different high levels products (L3, L4). Biases and non-linear effects at the level of brightness temperature measurements exist between SSS derived from Aquarius and SMOS missions. A similar gap appears in terms of Temporal Inconsistency. Consultation process (3) RS (1) E. Garcia-Ladona CSIC

Pablos M., Piles M., Gonzalez-Gambau V., Vall-llossera M., Camps A., Martinez J., 2014: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 9, 3833-3844.


W. Tang, A. Fore, S. Yueh, T. Lee, A. Hayashi, A. Sanchez-Franks, B, King, D, Baranowski, J, Martinez, submitted:Validating SMAP SSS with in situ measurements, Remote Sensing of Environment.

SSS is directly essential for climate, monitoring the water cycle changes and to evaluate Evaporation-Precipitation (E-P) fluxes over the ocean from basin to global scales. It is relevant to determine the sea surface density, freshwater transport and coastal ocean dynamics (river discharges). Further, in situ SSS measurements are essential for cal/val satellite signals and sensor drift of new missions. 2016/05/30   To improve intercalibration algorithms. High (3) To promote joint synergies among mission teams. An increase of independent in situ measurements would benefit the validation and verification of intercalibrations. Very high (4) To improve the SSS analysis reducing the uncertainties in geographical analysis and obtain more coherent time series. Near real time SSS data may help to warn up on biofouling maintenance of open ocean moored arrays. Medium (2) The increase of in situ near-surface measurements can be ranked as "High" while improvement the intercallibration can be considered as "Low" TBD (99)   TBD (0)    
Add FeedBk View FeedBk 060 Uncertainty (3.1) Detected (1) Multiple (10) CL OC WA BI C_SSS   In marginal and shelf seas and cold regions, inadequate accuracy with respect to the mission target requirements (cold waters, north Atlantic, north and western Pacific, Antartic Circumpolar). This is the result of both instrument properties and RFI contamination translating uncertainties and reducing the accuracy in such affected areas. Consultation process (3) RS (1) E. Garcia-Ladona CSIC Lagerloef G., Kao H.Y., Meissner T., Vazquez J., 2015: Aquarius Salinity Validation Analysis; Data Version 4.0, 30pp. Available at: ftp://podaac-ftp.jpl.nasa.gov/allData/aquarius/docs/v4/AQ-014-PS-0016_AquariusSalinityDataValidationAnalysis_DatasetVersion4.0and3.0.pdf) SSS is directly essential for climate, monitoring the water cycle changes and to evaluate Evaporation-Precipitation (E-P) fluxes over the ocean from basin to global scales. It is relevant to determine the sea surface density, freshwater transport and coastal ocean dynamics (river discharges). Further, in situ SSS measurements are essential for cal/val satellite signals and sensor drift of new missions. 2016/05/30   To promote stronger commitments among countries to improve the applicability of present regulations related with the radiofrequency use in the bands of interest. To explore feasibility of new generation of instruments on other bands (e.g. P-band). TBD (9) Much more research is needed to both dessign new algorithms to mitigate RFI contamination decreasing uncertainties and explore new spectral bands to retrieve SSS. Very high (4) First missions were conceived to have acceptable resolution for climate purposes. Reducing uncertainties will improve the range of applications beyond climate as it could be improvements in predicatibility through data assimilation of SSS. Medium (2) Basic research is needed to make improvements at different levels that require moderate funds at least initially TBD (99)   TBD (0)    
Add FeedBk View FeedBk 061 Boundary conditions issue (4.3) Detected (1) Multiple (10) CL OC WA BI C_SSS   Insufficient spatial coverage by design. Argo does not measure salinity close to the surface (< 5 m) to avoid biofouling. Around 1% of data within 1 m. Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC Ballabrera J., 2015: Sea Surface Salinity: Results and Recommendations D4.4.3, E-AIMS. Euro-Argo Improvements for the GMES Marine Service, E-AIMS: D4.443-v2. Available at: http://www.euro-argo.eu/content/download/91862/1123452/version/1/file/E-AIMS_D4.443-V2.pdf) SSS is essential for climate, monitoring the water cycle changes and to evaluate Evaporation-Precipitation (E-P) fluxes over the ocean from basin to global scales. It is relevant to determine the sea surface density, freshwater transport and coastal ocean dynamics (river discharges). Further, in situ SSS measurements are essential for cal/val satellite signals and sensor drift of new missions. 2016/11/30 The gap type has been changed to better reflect the nature of the gap. This is a technical gap with no satisfactory solution. There is a variety of acoustic, chemical, mechanical and energy methods (laser and ultraviolet irradiance) that partially could help but increase the energy consumption. Research should be needed to investigate different solutions while minimising the energy requirements. High (3) Biofouling has been addressed in many areas and applications since long time. Efficiency and energy consumption are the key elements for a successful implementation into Argo and surface drfiters. TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 062 No measured (7.1) Detected (1) Multiple (10) CL OC WA BI C_SSS   Insufficient spatial coverage related to marginal, shelf areas and shelf seas similar as for the SST Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC Ballabrera J., 2015: Sea Surface Salinity: Results and Recommendations D4.4.3, E-AIMS. Euro-Argo Improvements for the GMES Marine Service, E-AIMS: D4.443-v2. Available at: http://www.euro-argo.eu/content/download/91862/1123452/version/1/file/E-AIMS_D4.443-V2.pdf) In situ SSS measurement essential for cal/val satellite signals and sensor drift. SSS is essential for climate and water cycle changes derived from Evaporation-Precipitation (E-P) fluxes over the ocean from basin to global scales. It is relevant to determine the surface density, freshwater transport and coastal ocean dynamics (river discharges). 2016/01/16 The gap type has been changed to better reflect the nature of the gap. This is a technical gap with no satisfactory solution. There is a variety of acoustic, chemical, mechanical and energy methods (laser and ultraviolet irradiance) that partially could help but increase the energy consumption. Research should be needed to investigate different solutions while minimising the energy requirements. High (3) Biofouling has been addressed in many areas and applications since long time. Efficiency and energy consumption are the key elements for a successful implementation into Argo and surface drfiters. There are some legal concerns about the effects of anti-biofouling substances. Very high (4) It will improve SSS fields complementing satellite SSS retrievals. TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 063 Temporal extent (1.3) Detected (1) Multiple (10) CL OC WA BI C_SSS   Insuficient temporal coverage. Time series are short because SSS missions and relatively new. SMOS was a proof of concept (2010-present). Aquarius ceased in (2011-2015). SMAP was conceived for soil moisture and now is being exploited for ocean SSS (2015-present) Consultation process (3) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

In situ SSS measurement essential for cal/val satellite signals and sensor drift .

SSS is essential for climate and water cycle changes derived from Evaporation-Precipitation (E-P) fluxes over the ocean from basin to global scales. It is relevant to determine the surface density, freshwater transport and coastal ocean dynamics (river discharges).

2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 064 Temporal resolution (2.3) Detected (1) Multiple (10) CL OC DI EN HU C_SS   Poor temporal coverage from altimeters for the involved scales. Consultation process (3) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Improve and validate sea state forecasts. Essential for marine security and marine trade. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 065 Spatial resolution (2.1) Detected (1) Multiple (10) CL OC DI EN HU C_SS   Lack of enough horizontal resolution (100 km) from SAR spectral and wave energy capabilities Consultation process (3) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Improve and validate sea state forecasts. Essential for marine security and marine trade for regional applications. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 066 Temporal resolution (2.3) Detected (1) Multiple (10) CL OC DI EN HU C_SS   Lack of enough temporal (6 h) from SAR spectra and wave energy capabilities Consultation process (3) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Improve and validate sea state forecasts for regional applications. Essential for marine security and marine trade. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 067 No measured (7.1) Detected (1) Multiple (10) CL OC DI HU C_SS   No data on spectra and nor directional information from altimeters. Several parameters not measured by present meteo-ocean buoys (wave breaking, whitcapping, rogue waves, Stokes drift) Consultation process (3) Both (3) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Improve and validate sea state forecasts. Essential for marine security and marine trade. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 068 Geographical extent (1.1) Detected (1) Multiple (10) CL OC DI EN HU C_SS   Poor offshore coverage Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Improve and validate sea state forecasts. Essential for marine security and marine trade. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 069 No parameter (7.2) Detected (1) Multiple (10) CL OC DI HU C_SS   Not much meteo-ocean buoys with directional spectra capabilities Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Improve and validate sea state forecasts. Essential for marine security and marine trade. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 070 Non well known format (5.5) Detected (1) Multiple (10) CL OC DI EN HU C_SS   Lack of standardization in data reports with biases between networks of buoys. Consultation process (3) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Improve and validate sea state forecasts. Essential for marine security and marine trade. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 071 Spatial resolution (2.1) Detected (1) Multiple (10) OC WA BI DI EN HU C_OC   Lack of enough resolution. Currents derived from SSH lacks of enough resolution to address ocean submesoscale processes. Consultation process (3) RS (1) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

P.-Y. Le Traon, D. Antoine, A. Bentamy, H. Bonekamp, L.A. Breivik, B.
Chapron, G. Corlett, G. Dibarboure, P. DiGiacomo, C. Donlon, Y. Faugre, J. Font, F. Girard-Ardhuin, F. Gohin, J.A. Johannessen, M. Kamachi, G. Lagerloef, J. Lambin, G. Larnicol, P. Le Borgne, E. Leuliette, E. Lindstrom, M.J. Martin, E. Maturi, L. Miller, L. Mingsen, R. Morrow, N. Reul, M.H. Rio, H. Roquet, R. Santoleri & J. Wilkin (2015) Use of satellite observations for operational oceanography: recent achievements and future prospects, Journal of Operational Oceanography, 8:sup1, s12-s27, DOI: 10.1080/1755876X.2015.1022050

A. Schiller, M. Bell, G. Brassington, P. Brasseur, R. Barciela, P. De Mey, E. Dombrowsky, M. Gehlen, F. Hernandez, V. Kourafalou, G. Larnicol, P.-Y. Le Traon, M. Martin, P. Oke, G. C. Smith, N. Smith, H. Tolman, K. Wilmer-Becker, 2015: Synthesis of new scientific challenges for GODAE OceanView, Journal of Operational Oceanography, 8:sup2, s259-s271, DOI:

10.1080/1755876X.2015.1049901

Submesoscale processes may appear as key processes to better understand ocean-atmosphere exchanges and surface transport of properties (momentum, heat, gases). High resolution velocity fields are needed to resolve submesoscale variability with large impact on seaborne commerce, fishing, storm surges, marine ecosystems, .. 2016/05/30   At technical level the solution may come from a virtual constellation of altimeters combined with new generation of sensors, platforms and new satellite capabilities (e.g. 2d-altimetry, Doppler radar). However improvements to extract better information from all in situ available instruments should potentially improve present analysis. High (3) Improvements by enlarging the present constellation of altimeters is highly feasible in terms of technology. Improvements with new instruments, sensors and platforms is quite mature in terms of concept but need to be tested. Research activities on new procedures to better exploit data are ongoing but still need to be systematically validated. Very high (4) Accessing the scales at the submesoscale will help to clarify and to understand the ocean-atmosphere fluxes, tracer advection and transport processes (e.g. pollution events, ecosystem response, ...). At long scales necessary for climate change and at short scales very important for weather forecast. High (3) Improvements on satellite constellation and new technologies could be very expensive, while improving information knowledge will be relatively low cost. TBD (99) New generation of altimeters are on-going. However new missions about 2-d altimetry should be promoted and scheduled as soon as possible. Note however that research activities to improve the present exploitation of data to achieve higher resolutions can be addressed in mid term. TBD (0) The impact of improving ocean currents is very high because affects several socio-economic sectors. Tourism, marine trade, marine security and pollution, fisheries, management, etc.  
Add FeedBk View FeedBk 072 Geographical extent (1.1) Detected (1) Multiple (10) OC WA BI DI EN HU C_OC   Insufficient spatial coverage of sea surface measurements. In coastal regions VHF radar measurements mainly cover USA coasts and few locations in Europe. In open ocean where coverage is mainly done with drifters (SVP program) fixed morings and ADCP onboard R/V the highest rate are approximately 1 data per 5 box from drifters. Consultation process (3) Both (3) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

P.R. Oke, G. Larnicol, E.M. Jones, V. Kourafalou, A.K. Sperrevik, F. Carse, C.A.S. Tanajura, B. Mourre, M. Tonani, G.B. Brassington, M. Le Henaff, G.R. Halliwell Jr., R. Atlas, A.M. Moore, C.A. Edwards, M.J. Martin, A.A. Sellar, A. Alvarez, P. De Mey & M. Iskandarani (2015) Assessing the impact of observations on ocean forecasts and reanalyses:Part 2, Regional applications, Journal of Operational Oceanography, 8:sup1, s63-s79, DOI:10.1080/1755876X.2015.1022080

To cover the range of space and time variability of coastal currents. Proved impact on forecasting products via data assimilation techniques at least for regional applications 2016/05/30   Similarly to GAP number 71 a virtual constellation of altimeters combined with new generation of sensors, platforms and new satellite capabilities (e.g. 2d-altimetry, Doppler radar) should help to improve both resolution and coverage, particularly to solve the land-sea contamination in radar signals. At the same time improvements to better extract the information from all in situ available sources, mainly through improved dynamical models, should be of great help to progressively increase the quality and coverage of resolution interpolated fields. Particularly for coastal areas, extending the deployment of VHF radar infrastructures (e.e, CODAR, WERA) would be a good remedy. TBD (9) Improvements by extending the present constellation of altimeters is highly feasible in terms of technology. Improvements with new instruments, sensors and platforms is quite mature in terms of concept but need to be tested. On the other hand research activities on new procedures to better exploit data is an active area of research. Present VHF technologies are mature enough to be systematically deployed. Furthemore, alliances and collaborations with private sectors (e.g. energy and aquaculture) to share platforms for increasing the observational coverage should also contribute efficiently to lower costs (e.g maintenance and communications) Very high (4) The impact of improving ocean currents in coastal areas is very high because affects many socio-economic sectors. Tourism, marine trade, marine security, marine pollution, fisheries, management and assessment of coastal ecosystems, sea level rise impacts are, among many others, some specific sectors that would benefit. High (3) Improvements on satellite constellation could be very expensive, while improving information knowledge will be relatively low cost. Increasing the coverage of coastal areas with VHF radars scale with the amount of area to be covered, usually sectors of 50 x 50 km squares. TBD (99) The coastal area to be covered is huge. Primary focus should be associated with coastal regions with sensible areas related with marine protected areas and fragile ecosystems , oil-gas and related infrastructures, marine security (e.g. straits), etc. TBD (0) Crucial for coastal management. Coastal areas are the places were antropic impact is more evident.  
Add FeedBk View FeedBk 073 Temporal extent (1.3) Detected (1) Multiple (10) OC WA DI BI HU C_OC   Recovery of times series of surface currents is affected by lack of at least 4 altimeters working simultaneously in several periods needed to accurately assess ocean mesoscale variability. Consultation process (3) RS (1) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Pascual A., Faugere Y., Larnicol G., Le Traon P-Y., 2006: Geophysical Research Letters, vol. 33, L02611, doi:10.1029/2005GL024633
Resolving adequately the variability of sea surface mesoscale currents is fundamental to infer non-local mass, energy and momentum transport for both short and long time scales 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 074 No coordination of obs. sites (8.2) Detected (1) Multiple (10) OC WA DI BI HU C_OC   Lack of an international organism coordinating ocean surface currents. Consultation process (3) Both (3) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf To coordinate measurements and information from many heterogeneous ways and technologies to obtain sea surface currents. 2016/01/16 This gap has been merged with gap number 95   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 075 Temporal extent (1.3) Detected (1) Multiple (10) CL OC WA HE C_OAS   Insufficient data to cover the extent of variability that organisms observe. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Newton J.A., Feely R.A., Jewett E.B., Williamson P., Mathis J., 2015: Global Ocean Acidification Observing Network: Requirements and Governance Plan.
Second Edition, GOA-ON. Available at: http://www.goa-on.org/docs/GOA-ON_plan_print.pdf.
Understanding global acidification conditions and improving the understanding of the ecosystem impacts and response to ocean acidification for warn-water coral reefs. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 076 Vertical extent (1.2) Detected (1) Multiple (10) CL OC WA HE C_OAS   Insufficient spatial coverage in Artic, Southern Oceans, "coral triangle" (south-east Asia) and off Peru. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Newton J.A., Feely R.A., Jewett E.B., Williamson P., Mathis J., 2015: Global Ocean Acidification Observing Network: Requirements and Governance Plan.
Second Edition, GOA-ON. Available at: http://www.goa-on.org/docs/GOA-ON_plan_print.pdf.

Understanding global acidification conditions and improving the understanding of the ecosystem impacts and response to ocean acidification for warn-water coral reefs. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 077 No parameter (7.2) Detected (1) Multiple (10) CL OC WA HE C_OAS   Integration of physical, chemical and biological sensing. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Newton J.A., Feely R.A., Jewett E.B., Williamson P., Mathis J., 2015: Global Ocean Acidification Observing Network: Requirements and Governance Plan.
Second Edition, GOA-ON. Available at: http://www.goa-on.org/docs/GOA-ON_plan_print.pdf.

Need of colocation of environmental data to solve the Ecosystem function characterized by primary and secondary production, orsganism interaction, nutrient cycling an material exchange. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 078 No measured (7.1) Detected (1) Multiple (10) CL OC WA HE C_OAS   Identification of hot spots in the sense of threatened ecosystems Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Newton J.A., Feely R.A., Jewett E.B., Williamson P., Mathis J., 2015: Global Ocean Acidification Observing Network: Requirements and Governance Plan.
Second Edition, GOA-ON. Available at: http://www.goa-on.org/docs/GOA-ON_plan_print.pdf.

Understanding global acidification conditions and improving the understanding of the ecosystem impacts and response to ocean acidification for warn-water coral reefs. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 079 No catalogue (5.1) Detected (1) Multiple (10) CL OC WA HE C_OC   Lack of development and sharing of in situ databases and derived products of sufficient quality to use in cal/val satellite products. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Relevant to determine the marine albedo and assess the ocean ecosystem health and productivity and the role of the oceans in the global carbon cycle. Important to manage living marine resources and to quantify the impacts of climate variability and change. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 080 No interdisciplinary coord. (8.1) Detected (1) Multiple (10) CL OC WA HE C_OC   Limited linkage between ocean colour and ecosystem variables Research programs targets (2) RS (1) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Relevant to determine the marine albedo and assess the ocean ecosystem health and productivity and the role of the oceans in the global carbon cycle. Important to manage living marine resources and to quantify the impacts of climate variability and change. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 081 TBD (?.?) Detected (1) Multiple (10) CL OC WA HE C_OC   Risk of continuity of climate-research quality ocean colour radiance observations. Research programs targets (2) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Relevant to determine the marine albedo and assess the ocean ecosystem health and productivity and the role of the oceans in the global carbon cycle. Important to manage living marine resources and to quantify the impacts of climate variability and change 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 082 TBD (?.?) Detected (1) Multiple (10) CL OC WA HE C_OC   Difficulty in sustaining projects for cross-calibrating and merging OCR data across satellite sensors to support global and regional products Research programs targets (2) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Relevant to determine the marine albedo and assess the ocean ecosystem health and productivity and the role of the oceans in the global carbon cycle. Important to manage living marine resources and to quantify the impacts of climate variability and change 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 083 No processable (5.6) Detected (1) Multiple (10) CL OC WA HE C_OC   Need of continued R+T development (data streams, algorithms, products) for waters of type II where optical properties are not dominated by phytoplankton. Research programs targets (2) RS (1) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Relevant to determine the marine albedo and assess the ocean ecosystem health and productivity and the role of the oceans in the global carbon cycle. Important to manage living marine resources and to quantify the impacts of climate variability and change 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 084 Vertical extent (1.2) Detected (1) Multiple (10) CL OC WA BI EN HU C_TD C_SALD Insufficient vertical coverage of measurements down 2000 m (more of the 50% of the ocean volume is whithin the layer deeper than 2000 m). XBT regular sections are concentrated around the first 750 m and in general below 700 m data are too sparse. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

JCOMMOPS (http://www.jcommops.org/board?t=Argo),

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley
(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Available at: http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf

To characterize deep water masses, to monitor the ocen heat content and to determine the general structure of the ocean circulation and the conveyor belt. Necessary to determine the water cycle, heat and mass gesotrophic transports and the steric component of the sea level change, and indirectly to understand changes in the marine biology and biogeochemistry. Essential for data assimilation into ocean circulation models. 2016/05/30 Merged gap for both EV Temperature and Salinity (Subsurface) To deploy a fraction of Argo profilers with the ability to increase the vertical extent downwards. Very high (4) Argo technology for CTD recording (conductivity, temperature and pressure) is not a problem. However some aspects concerning the need of pressure-ressistance equipment and energy storage may be problematic in terms of cost. Very high (4) The Argo program is a key oceanic observation system with considerable impact on the quality of forecasts and analysis of present ocean models. In 10 years (2006-2016) the Argo program has collected more data than in the previous century (1900-2000). High (3) Deployment of deep profilers implies a significant increase of costs. Already comercial deep floats increases a factor of 10 with respect the current Argo floats. TBD (99)

If enough funds are allocated the solution can be implemented quite fast, however the sampling should guarantee at least enough time for climate purposes.

TBD (0) In addition to temperature and salinity this gap affects several EOV variables (e.g. nutrients, oxygen, pH, CO2 associated variables, etc.). Immediate impact would be on detecting trends in deep ocean waters properties (e.g. overturning circulation, ocean acidification), a better estimation of the steric contribution to sea level and improvements in ocean forecasting.  
Add FeedBk View FeedBk 085 Geographical inconsistency (4.1) Detected (1) Multiple (10) CL OC WA BI EN HU C_TD   Sub-surface temperatures estimates from available products have variations at different times and for differents periods. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley
(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Available at: http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf
To characterize deep water masses, to monitor the ocen heat content and to determine the general structure of the ocean circulation. Necessary to determine the geostrophic circulation, heat transport and steric sea level and indirectly to understand changes in the marine biology and biogeochemistry. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 086 Bad metadata (6.5) Detected (1) Multiple (10) CL OC WA BI EN HU C_TD   XBT metadata not always available. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf To characterize deep water masses, to monitor the ocen heat content and to determine the general structure of the ocean circulation. Necessary to determine the geostrophic circulation, heat transport and steric sea level and indirectly to understand changes in the marine biology and biogeochemistry. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 087 Geographical extent (1.1) Detected (1) Multiple (10) CL OC WA BI EN HU C_TD C_SALD, C_SST, C_SSS Non-uniform distribution of in situ measurements. Argo profilers by design provide data up to 2000 m leaving inaccessible topographically constraint areas (Caribbean Sea, South China Sea,etc.) and for high latitudes if dedicated deployments are not scheduled. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

JCOMMOPS (http://www.jcommops.org/board?t=Argo)

To characterize water masses, to monitor the ocen heat content and to determine the general structure of the ocean circulation. Necessary to determine the geostrophic circulation, heat transport and steric sea level and indirectly to understand changes in the marine biology and biogeochemistry. 2016/05/30 Merged for both EV Temperature and Salinity (Subsurface) Probably the most satisfactory way of solve this gap is to reinforce the Argo program. TBD (9) Argo technology for CTD recording (conductivity, temperature and pressure) is not a problem. Deploying strategies are in part designed by national agencies for operational needs and some initiatives to analyze the impact have already addressing it (e.g. EAIMS). Very high (4) The Argo program is a key oceanic observation system with considerable impact on the quality of forecasts and analysis of present ocean models. In 10 years (2006-2016) the Argo program has collected more data than in the previous century (1900-2000). Medium (2) Cost depends on the number of devices needed to have a satisfactory sampling. However promoting studies to evaluate the needs and the impact in order to optimize deployment strategies are relatively cheap. TBD (99) Once the regions affected by this gap are detected the solution can be planned in relatively short to mid term. TBD (0) The solution of this gap mainly affects to reduce uncertainties in some specific undersampled regions.  
Add FeedBk View FeedBk 088 Geographical extent (1.1) Detected (1) Multiple (10) CL OC WA BI EN HU C_TD C_SALD, C_SST, C_SSS Lack of enough in situ surface and subsurface measurements in shelf seas, marginal seas (e.g. Baltic Sea, North Sea, Barents Sea, Mediterranean Sea, etc.) Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

JCOMMOPS (http://www.jcommops.org/board?t=Argo)

Ballabrera J., 2015: Sea Surface Salinity: Results and Recommendations D4.4.3, E-AIMS. Euro-Argo Improvements for the GMES Marine Service, E-AIMS: D4.443-v2. Available at: http://www.euro-argo.eu/content/download/91862/1123452/version/1/file/E-AIMS_D4.443-V2.pdf)

Real time status of ARGO deployments through JCOMMOPS API, (http://www.jcommops.org/board?t=Argo)

Fiedler E, Mao Ch., Maclaren A. 2015: SST: results and recommendations. E-AIMS Deliverable D4.3.3. Availbale at http://www.euro-argo.eu/content/download/83965/1049581/version/1/file/E-AIMS_4.3_SeaSurfaceTemperature_V2.pdf

To characterize water masses, to monitor the ocen heat content and to determine the general structure of the ocean circulation. Necessary to determine the geostrophic circulation, heat transport and steric sea level and indirectly to understand changes in the marine biology and biogeochemistry. 2016/12/05

This gap affects many variables.

Probably the main measure is to reinforce the Argo program as the most satisfactory way of solve this gap. Somehow national agencies through the Argo program coordinate the strategy to deploy the array of floats. However the scientific community and research projects may have their own interests which can not be in line with the need of a uniform coverage. The lack of Argo sampling can however be partially compensated by exploiting observational synergies with offshore platforms devoted to oil-gas, marine energy and aquaculture activities.

To reinforce coordination mechanisms among national agencies, the scientific community to agree on plans to have a better and uniform coverage based on reducing uncertainties. To promote systematic detailed studies on the number of Argo floats needed to optimize Argo deployment in such regions. Specific research programs devoted to deal with undersampled regions or to allocate additional funds for a more intensive deployment could be promoted. To promote collaboration with already existing offshore installations in shelf areas, usually associated to the energy sector and aquaculture facilities in order to share observational instruments and increase the information coverage. High (3) Concerning the Argo technology the technology is ready. For shelf and polar areas, some technical solutions endowing inertial navigation systems with accelerometers may help to self-adjust the sampling to bathymetric changes characteristic of these regions. In polar areas some changes in float design would allow efficient solutions. However as it is usual a compromise between energy consumption and sampling strategy may constraint their performance where the limiting element is the energy consumption. Argo floats would benefit of improvements in battery storage technologies. On the other hand for deep marginal seas (e.g. Mediterranean, Black Sea..) there is no need of such implementations and feasibility is "Very high" Very high (4) The Argo program is a key oceanic observation system with considerable impact on the quality of forecasts and analysis of present ocean models. In 10 years (2006-2016) the Argo program has collected more data than in the previous century (1900-2000). To increase measurements in shelf areas has a high impact because constitutes the boundaries of the open ocean and where anthropic pressures and impacts are more evident. High (3) Cost depends on the number of devices needed to have a satisfactory sampling. Synergies for instrumenting offshore platforms could be adopted at lower costs. TBD (99) The time frame depend on the adopted solution and the kind of region. Thus in polar areas some technological issues are not at all satisfactory. Other strategies non based on Argo can be solved relatively fast. TBD (0) The priority also depend of the region. In some cases the areas are usually covered by networks of coastal buoys and similar (e,g. North Sea). For polar areas and for regional seas adjacent to poorer countries lacking coastal networks priority should be very high to adequately monitor changes.  
Add FeedBk View FeedBk 089 Temporal extent (1.3) Detected (1) Multiple (10) CL OC WA BI EN HU C_TD C_SALD, C_SSS Insufficient temporal coverage. Argo deployment started in 2000 and became fully operative in 2005-2006, so less than the WMO 30 years definition of clima. Note however that regular sections sections and measurements (bathythermographs, CTD and XBT sections) are available since 1980s and before. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Argo program description from the Argo website:

http://www.argo.ucsd.edu/About_Argo.html

Durack P.J., Lee T., Vinogradova N. T., D. Stammer, 2016: Keeping the lights on for global ocean salinity observation, NATURE CLIMATE CHANGE, vol 6. 228-231.

To characterize water masses, to monitor the ocen heat content and to determine the general structure of the ocean circulation. Necessary to determine the geostrophic circulation, heat transport and steric sea level and indirectly to understand changes in the marine biology and biogeochemistry. 2016/12/01

This gap affects many variables.

This gaps has been merged with gap 91

Probably the main measure is to reinforce the Argo program as the most satisfactory way of solve this gap. Note however that for the SST there exist complentary in situ sampling comming from regular XBT sections.

To maintain, or even increase, the investment on Argo floats at least to ensure the 30 years period according to the WMO definition of clima. Very high (4) There is almost no technological problems concerning the measurement of in situ SST from Argo. However, of a total of 3887 operational floats in October 2016, the number of countries contributing to the Argo program falls to only 30 being a few national agencies from specific countries (e.g. USA, France, Australia,..) the main contributors. Probably much more efforts could be easily increased if more national agencies are involved. Outside the operational activities of these agencies, research plans and programs could be somehow incentivated the use and deploy of Argo floats as an indirect way to increase the operational number of floats. Very high (4) Generically speaking the impact of the Argo program as a multipurpose platform scanning the 3D structure of the ocean is beyond any doubt and affects a great number of communities. In particular for the sea surface temperature, extending the time series will provide a better description of sub-decadal variability of the ocean heat content impacting our present knowledge of ocean warning to sea level rise. Very high (4)

According to Argo program estimations, the cost of maintainning an array of 300 requires to deply 600 unities per year which is equivalent to $24m per year.

(http://www.argo.ucsd.edu/FAQ.html#cost)

TBD (99) Argo program reached the coverage objectives in 2006 so at least 20 years more would be the time necessary according to WMO definition of clima TBD (0)

This gap affects many variables

Probably the main measure is to reinforce the Argo program as the most satisfactory way of solve this gap. Note however that for temperature there exist scomplentary in situ sampling comming from regular XBT sections.

 
Add FeedBk View FeedBk 090 Temporal resolution (2.3) Detected (1) Multiple (10) CL OC WA BI EN HU C_TD C_SALD, C_SSS, C_SST Lack of high temporal resolution of in situ observations to cover diurnal cycle. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

To characterize deep water masses, to monitor the ocen heat content and to determine the general structure of the ocean circulation. Necessary to determine the geostrophic circulation, heat transport and steric sea level and indirectly to understand changes in the marine biology and biogeochemistry.

2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 091 TBD (?.?) Detected (1) Multiple (10) CL OC WA BI EN HU C_TD   Drop of the number of Argo float deployments benneath 3200 in 2018. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC Durack P.J., Lee T., Vinogradova N. T., D. Stammer, 2016: Keeping the lights on for global ocean salinity observation, NATURE CLIMATE CHANGE, vol 6. 228-231.   2016/12/01 This gap has been merged with gap 89. It should be discarded.   TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 092 Geographical extent (1.1) Detected (1) Multiple (10) CL OC WA DI EN HU C_CD C_C Lack of sufficient spatial coverage for many climatic applications, specially in the Southern hemisphere. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Holloway G., Nguyen A., Zeliang W., 2011: Oceans and ocean models as seen by current meters, Journal of Geophysical Research, VOL. 116, C00D08, doi:10.1029/2011JC007044

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley
(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Available at: http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf

Currents are essential to determine the transport of mass, energy and many other properties (nutrients, O2, sediments, etc.) from basin to global scales. They are necessary to determine absolute velocity fields complementing the geostrophic field from temperature and salinity measurements. Direct measurements of lateral and bottom boundary currents ara important to resolve Ekman transport of properties to constraint large-scale and basin ocean currents, from small to climate scales. Important for model validation. 2016/05/30   To extent spatially the number of mooring sites with currentmeters at least for key dynamic areas (e.g. main energetic currents, Aghulas retroflection, Malvinas confluence) particularly in the Southern ocean. High (3) Technology is ready but feasibility in this case is more related to the capacity, mainly in terms of efforts and costs, of national agencies to maintain mooring sites. Very high (4) Variability of currents in the Southern ocean is a key aspect to understand changes in the climate system. Medium (2) Of course cost depend on the number of sites to be covered and maintained. Mid term (2) New mooring sites should be maintained to get long time series to resolve interanual and decadal variability. Very high (2) Necessary to better characterize and understand the variability of several ocean variables  
Add FeedBk View FeedBk 093 Temporal extent (1.3) Detected (1) Multiple (10) CL OC WA DI EN HU C_CD C_C Lack of sufficient temporal coverage and extent for many climatic applications, in particular to monitor the meridional overturning circulation. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Holloway G., Nguyen A., Zeliang W., 2011: Oceans and ocean models as seen by current meters, Journal of Geophysical Research, VOL. 116, C00D08, doi:10.1029/2011JC007044

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley
(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Available at: http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf

Currents are essential to determine the transport of mass, energy and many other properties (nutrients, O2, sediments, etc.) from basin to global scales. They are necessary to determine absolute velocity fields complementing the geostrophic field from temperature and salinity measurements. Direct measurements of lateral and bottom boundary currents ara important to resolve Ekman transport of properties to constraint large-scale and basin ocean currents, from small to climate scales. Important for model validation. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 094 No measured (7.1) Detected (1) Multiple (10) CL OC WA DI EN HU C_CD C_C Present observing systems are inadequate to directly measure the vertical component of currents. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Vertical currents are essential to determine the transport of mass, energy and many other properties (nutrients, O2, sediments, etc.). In particular vertical current are essential to quantify vertical fluxes of nutrients to the photic zone (upwelling sytems). 2016/05/30     Medium (2)   High (3)   Medium (2)   Mid term (2) Requires reseach High (3)    
Add FeedBk View FeedBk 095 No coordination of obs. sites (8.2) Detected (1) Multiple (10) CL OC WA DI EN HU C_CD C_C Lack of an international organism coordinating such kind of measurements at global scale. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf Currents are essential to determine the transport of mass, energy and many other properties (nutrients, O2, sediments, etc.) from basin to global scales. They are necessary to determine absolute velocity fields complementing the geostrophic field from temperature and salinity measurements. Direct measurements of lateral and bottom boundary currents ara important to resolve Ekman transport of properties to constraint large-scale and basin ocean currents, from small to climate scales. Important for model validation. 2016/12/02 Ocean currents is probably the only variable that can be measured through many different direct and indirect methods and techniques (drifters, Doppler effect, dynamic topography, mechanical methods, etc). To promote international cooperation to stablish a coordination organism specific for ocean current measurements. Very high (4) Quite feasible if promoted from already existing international organisms (GOOS, WMO, GEO, etc.) Alternatively an initial set-up could arise from specific R+I initiatives (e.g. national research plans or regional alliances) or reserach programs (SVP, GLOBCURRENT,etc) Very high (4) To coordinate procedures and best practices to archive, process and deliver such kind of data. It will help to recover much more historical records from field cruises and research experiments presently not much centralised. TBD (9) To set up a coordination organism can be ranked as relatively low. TBD (99) The time necessary to set up and coordinate different initiatives on the frame of several international organism. TBD (0) The ocean velocity field is as crucial as the other scalars (temperature, salinity) for several themes and SBAs.

Stablishing a coordination organism around the ocean velocity variable will accelerate improvements in data quality products, data standardization and data sharing necessary to hold all the information from quite diverse ways to measure it.

 
Add FeedBk View FeedBk 096 Vertical extent (1.2) Detected (1) Multiple (10) CL OC WA HE C_OOD   Insufficient vertical coverage of measurements down 2000 m (more of the 50% of the ocean volume is whithin the layer deeper than 2000 m). Historical classical Winkler method was based on discrete samples from ship cruises so measurements have long history but have limitations. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

JCOMMOPS (http://www.jcommops.org/board?t=Argo)

Determine the evolution of O2 necessary to sustain the life in the ocean. To assess the risk of ocean deoxygenation and the impact on marine ecosystems eventually as a response to global warming but also to eutrophication. To identify ocean water masses related with ocean circulation patterns. 2016/05/30   Increase the number of Argo profilers equipped with O2 sensors. Very high (4) Technology is ready and proved but the implementation in Argo floats increments the costs of manufacturing and increases energy consumption. High (3)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 097 Geographical extent (1.1) Detected (1) Multiple (10) CL OC WA HE C_OOD   Non-uniform distribution of in situ surface measurements. Argo profilers by design provide data up to 2000 m leaving inaccessible topographically constraint areas (Caribbean Sea, South China Sea,etc.) and for high latitudes if dedicated deployments are not scheduled. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

JCOMMOPS (http://www.jcommops.org/board?t=Argo)
Determine the evolution of O2 necessary to sustain the life in the ocean. To assess the risk of ocean deoxygenation and the impact on marine ecosystems eventually as a response to global warming but also to eutrophication. To identify ocean water masses related with ocean circulation patterns. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 098 No measured (7.1) Detected (1) Multiple (10) CL OC WA HE C_OOD   Lack of in situ measurements from Argo buoys in shelf seas (i.e Baltic Sea, North Sea, Barents Sea etc.) and marginal seas Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

JCOMMOPS (http://www.jcommops.org/board?t=Argo)
Determine the evolution of O2 necessary to sustain the life in the ocean. To assess the risk of ocean deoxygenation and the impact on marine ecosystems eventually as a response to global warming but also to eutrophication. To identify ocean water masses related with ocean circulation patterns. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 099 Temporal extent (1.3) Detected (1) Multiple (10) CL OC WA HE C_OOD   Insufficient temporal coverage. Argo deployment started in 2000 and became fully operative in 2005 so half the WMO temporal definition of clima (30 years). Salinity observations is the third most-oft-observed water quality parameter after temperature and salinity. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

JCOMMOPS (http://www.jcommops.org/board?t=Argo)
Determine the evolution of O2 necessary to sustain the life in the ocean. To assess the risk of ocean deoxygenation and the impact on marine ecosystems eventually as a response to global warming but also to eutrophication. To identify ocean water masses related with ocean circulation patterns. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 100 Geographical extent (1.1) Detected (1) Multiple (10) CL OC DI EN HU C_SL   Spatial coverage. Insufficient number of stations. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Global Sea-Level Observing System (GLOSS) Implementation Plan - 2012, UNESCO/IOC, 41pp. 2012. IOC Technical Series No.100.
Impact on coastal and islands communities and settlements. Essential for coastal infrastructure dessign, protection and maintenance (risk assessment) and for marine security (storm surges, tsunamis, etc). Sea Level is presently a key variable in data assimilation systems into ocean models. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 101 No metadata (6.6) Detected (1) Multiple (10) CL OC DI EN HU C_SL   Lack of metadata in the position of gauges affect uncertainties. Research programs targets (2) In-Situ (2) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Global Sea-Level Observing System (GLOSS) Implementation Plan - 2012, UNESCO/IOC, 41pp. 2012. IOC Technical Series No.100.
Impact on coastal and islands communities and settlements. Essential for coastal infrastructure dessign, protection and maintenance (risk assessment) and for marine security (storm surges, tsunamis, etc). Sea Level is presently a key variable in data assimilation systems into ocean models. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 102 Geographical inconsistency (4.1) Detected (1) Multiple (10) CL OC DI EN HU C_SL   Reconcile altimetry measurements (SSH) and in situ sea level gauges for intercalibration purposes and reconstruct long tme series of sea level. Research programs targets (2) Both (3) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Global Sea-Level Observing System (GLOSS) Implementation Plan - 2012, UNESCO/IOC, 41pp. 2012. IOC Technical Series No.100.
Sea level signal helps to identify, detect, surface mesoscale features adequately resolved with several altimeters working simulatenously. High resolution velocity fields are needed to resolve submesoscale motions for many applications related with marine trade and security. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 103 No parameter (7.2) Detected (1) Multiple (10) CL OC DI EN HU C_SL   Not always guarantee the operation of enough simulatenous of altimeters (capacity ?) Research programs targets (2) RS (1) E. Garcia-Ladona CSIC

World Meteorological Organization (WMO), 2015: Status of the Global Observing System for Climate, October 2015, GCOS-195. Available at: http://www.wmo.int/pages/prog/gcos/Publications/GCOS-195_en.pdf

Global Sea-Level Observing System (GLOSS) Implementation Plan - 2012, UNESCO/IOC, 41pp. 2012. IOC Technical Series No.100.
Sea level signal helps to identify, detect, surface mesoscale features adequately resolved with several altimeters working simulatenously. High resolution velocity fields are needed to resolve submesoscale motions for many applications related with marine trade and security. 2016/05/30     TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 104 Unknown semantics (5.7) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Missing agreement on levels of data and associated names across domains Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.3 GCOS AOPC Seidel et al., 2013         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 105 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Unknown suitability of measurement maturity assessment Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.3         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 106 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Missing evaluation criteria for assessing existing observing capabilities Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.1         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 107 No quality (6.3) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Lack of a comprehensive review of current sub-orbital observing capabilities for all the study of ECVs in atmospheric, ocean and land domains Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.4, D1.6, D1.8         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 108 No catalogue (5.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Lack of unified tools showing all the existing observing capabilities for measuring ECVs with respect to satellite spatial coverage Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.4, D1.6, D1.8         High (3) Demostrated in GAIA Clim High (3)   Medium (2)   Short term (1)   Very high (2)    
Add FeedBk View FeedBk 109 Bad metadata (6.5) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Lack of a common effort in metadata harmonization Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.4, D1.6, D1.8         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 110 No quality (6.3) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Need for a scientific approach for the assessment of gaps in the existing networks measuring ECVs Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.9         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 111 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Evaluation of the effect of missing data or missing in temporal coverage of full traceability data provided by ground-based networks Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.9 Whiteman et al., 2011         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 112 Vertical extent (1.2) Detected (1) Climate (CL)   N_ACO   CO limited availability of quantitative profiles; Insufficient verification of vertical information in satellite products Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.2         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 113 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Insufficiently traceable uncertainty estimates Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.3 Immler et al., 2010         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 114 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Traceable uncertainty estimates from baseline and comprehensive networks Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.1, D1.4 Immler et al., 2010         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 115 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Propagate uncertainty from well-characterized locations and parameters to other locations and parameters. Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 116 Catalogue saturation (5.2) Detected (1) Climate (CL)   C_WVU   Water vapor measurements with the lidar and microwave radiometer are often provided in a sparse way and under an uncoordinated effort Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D1.1, D2.1         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 117 Geographical extent (1.1) Detected (1) Climate (CL)   C_O3A C_WVU C_WNU C_TU There is currently limited aircraft data, for example in Eastern Europe. Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 118 Catalogue saturation (5.2) Detected (1) Climate (CL)   C_O3A   Northern Hemisphere bias in NDACC and PANDORA network sites distribution Observation requirement (1) TBD (4)   BIRA GAIA-CLIM H2020- D1.1, D2.1         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 119 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   24/7 operation of lidar systems Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 120 Vertical resolution (2.2) Detected (1) Climate (CL)   C_O3A   Lidar incomplete altitude coverage Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.2, D2.4         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 121 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Incomplete collocation of sun and moon photometers with day and night time aerosol lidars Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 122 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Missing continued intercomparison with reference systems Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.2 Wandinger et al., 2015         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 123 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A   Lack of rigorous aerosol lidar error budget availability Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D?.?; Earlinet         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 124 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A   Need of Raman lidars or better multi-wavelength systems Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.2 Veselovskii et al., 2012         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 125 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A   Need for assimilation experiments of lidar measurements Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.2 EU project website ACTRIS2: www.actris.eu         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 126 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A   Reducing calibration uncertainties using a common reference standard Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.2 Leblanc et al., 2008 ?ISSI report? Is it also for aerosol?         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 127 Temporal resolution (2.3) Detected (1) Climate (CL)   C_WVU   Continuous operation of water vapor Raman lidars limited during daytime Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 128 Vertical resolution (2.2) Detected (1) Climate (CL)   C_O3A   Tropospheric O3 profile data is limited Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 129 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A   Lack of rigorous tropospheric O3 lidar error budget availability Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Leblanc et al., 2008 ?ISSI report?         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 130 Uncertainty (3.1) Detected (1) Climate (CL)   C_TU   Lack of rigorous temperature lidar error budget availability Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Leblanc et al., 2008 ?ISSI report?         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 131 Uncertainty (3.1) Detected (1) Climate (CL)   C_TU C_WVU, C_CLD MWR Missing standards maintained by National/International Measurement Institutes Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.1 Walker et al., 2011         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 132 Uncertainty (3.1) Detected (1) Climate (CL)   C_TU C_WVU, C_CLD Uncertainty of the MW absorption spectrum used in MWR retrievals Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.1         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 133 No quality (6.3) Detected (1) Climate (CL)   C_TU C_WVU, C_CLD Automated MWR data quality control Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.1 EU Cost action TOPROF         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 134 No quality (6.3) Detected (1) Climate (CL)   C_TU C_WVU, C_CLD Calibration best practices and instrument error characterization Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.1 EU Cost action TOPROF         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 135 Temporal extent (1.3) Detected (1) Climate (CL)   C_TU C_WVU, C_CLD Homogenization of retrieval method Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.1 EU Cost action TOPROF         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 136 Temporal extent (1.3) Detected (1) Climate (CL)   C_WVU C_O3A, C_GHG Agreement on systematic vs. random part of the uncertainty and how to evaluate each part (H20, O3, CH4) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- NORS_D4.3_UB.pdf         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 137 Temporal extent (1.3) Detected (1) Climate (CL)   C_WVU C_O3A, C_GHG Line of sight and vertical averaging kernel are only approximations of the real 3D averaging kernel of a retrieval (H20, O3, CH4) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- NORS_D4.2_DUG.pdf         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 138 Temporal extent (1.3) Detected (1) Climate (CL)   C_WVU C_O3A, C_GHG Spectroscopic uncertainties (H20, O3, CH4) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Hase et al., 2012 Frankenberg et al., 2011         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 139 Temporal extent (1.3) Detected (1) Climate (CL)   C_CO2 C_GHG Current spectroscopic databases contain uncertainties (CO2, CH4) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Wunsch et al., 2011         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 140 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A C_CO2, C_GHG Cell measurements carried out to characterize ILS have their own uncertainties (H20, O3, CH4) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Hase et al, 2012 Hase et al., 2013         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 141 Temporal extent (1.3) Detected (1) Climate (CL)   C_GHG   possible SZA dependence in the retrieval during polar vortex overpasse (CH4) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 142 Temporal extent (1.3) Detected (1) Climate (CL)   C_CO2 C_GHG In-situ calibration can be verified by involving new data (CO2, CH4) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Wunsch et al., 2011         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 143 Temporal extent (1.3) Detected (1) Climate (CL)   C_WVU C_CO2, C_GHG TCCON calibration w.r.t. standards (H20, CO2, CH4; column) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 144 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Uncertainty of the O3 cross section used in the spectral fit Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- NORS_D4.3_UB.pdf NDACC_UVVIS-WG_O3settings_v2.pdf         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 145 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Random uncertainty of the O3 section in spectral fit and AMF calculations Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- NORS_D4.3_UB.pdf NDACC_UVVIS-WG_O3settings_v2.pdf         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 146 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Uncertainty in a priori profile of O3 shape for AMF calculation Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Hendrick et al., 2011         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 147 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Uncertainty in vertical averaging kernels for O3 Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Eskes and Boersma, 2003         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 148 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Uncertainty in PANDORA measurements for O3 columns Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Herman et al., 2015         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 149 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Information content of MAX-DOAS tropospheric O3 (tropospheric column) measurements Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.1; Liu et al., 2006 Irie et al, 2011 Gomez et al., 2014         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 150 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   MAX-DOAS tropospheric O3 (tropospheric column) retrieval method Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Same as for G2.31         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 151 Temporal extent (1.3) Detected (1) Climate (CL)   C_O3A   Random and systematic uncertainties of MAX-DOAS tropospheric O3 column measurements Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D2.1; Liu et al., 2006 Irie et al, 2011         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 152 Temporal extent (1.3) Detected (1) Climate (CL)   C_WVU   Uncertainties of ZTD, given by a 3rd party (IGS) (H20 column) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Ning, 2012         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 153 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Incomplete knowledge of spatiotemporal atmospheric variability at the scale of the inter-comparisons (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D3-1 (incl. Annex 1, 2 and 3)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 154 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Limited quantification of the impact of co-location criteria. (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D3-1 (incl. Annex 1, 2 and 3)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 155 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Missing generic and specific standards for co-location criteria in validation work. (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D3-1 (incl. Annex 1, 2 and 3)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 156 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Limited characterization of the multi-dimensional (spatiotemporal) smoothing and sampling properties of atmospheric remote sensing systems, and of the resulting uncertainties. (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D3-1 (incl. Annex 1, 2 and 3)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 157 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Representativeness uncertainty assessment missing for higher-level data based on averaging of individual measurements. (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D3-1 (incl. Annex 1, 2 and 3)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 158 Uncertainty (3.1) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Missing comparison error budget decomposition including errors due to sampling and smoothing differences. (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D3-1 (incl. Annex 1, 2 and 3)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 159 Uncertainty (3.1) Detected (1) Climate (CL)   C_TU   Lack of traceable uncertainty estimates for NWP and reanalysis fields & equivalent TOA radiances. (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Bell et al., 2008 Bohrmann et al., 2013 Doherty et al., 2015 Geer et al., 2010 Lu et al., 2011         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 160 Uncertainty (3.1) Detected (1) Climate (CL)   C_WVU   Lack of traceable uncertainty estimates for NWP and reanalysis fields & equivalent TOA radiances Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- Same as for G4.01         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 161 Uncertainty (3.1) Detected (1) Climate (CL)   C_TU C_WVU Where traceable uncertainty estimates exist for a model or reanalysis quantity, it is often limited to a few locations and parameters where reference datasets are available. Comprehensiveness is lacking for extension to locations and parameters where reference datasets are not available Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- n/a         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 162 Uncertainty (3.1) Detected (1) Climate (CL)   C_TU C_WVU Datasets from baseline and comprehensive networks provide valuable spatiotemporal coverage, but often lack the characteristics needed to facilitate traceable uncertainty estimates Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- WPs 1,2,3         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 163 Uncertainty (3.1) Detected (1) Climate (CL)   C_TU C_WVU Limited knowledge about how to propagate uncertainty from well-characterized locations and parameters to other locations and parameters. Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- WP4 (+ Task 1.4/1.5)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 164 Uncertainty (3.1) Detected (1) Climate (CL)   C_TU C_WVU Difficulty to assess the importance of natural variability in the total model-observation error budget Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- WP4 (+ Task 1.4/1.5)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 165 No easy access (5.4) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Access to data in multiple locations with different data policies and accessibility (e.g. speed of retrieving and unpacking, passwords) (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- http://www.gruan.org http://tccon.ornl.gov/ http://www.ndsc.ncep.noaa.gov/data         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 166 No easy access (5.4) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Access to data in multiple data format and structure (e.g. granularity of data). Lack of standardized metadata (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- http://www.ucar.edu/tools/applications_desc.jsp         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 167 Cannot be viewed (5.3) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Efficient data management to collocate observations needs to be improved (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- CCI toolbox Giovanni GSICS         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 168 Catalogue saturation (5.2) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Usability of reference database needs to be ascertained: subset definition (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- WP5         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 169 Non well known format (5.5) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Usability of reference database needs to be ascertained: format (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- WP5         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 170 Cannot be viewed (5.3) Detected (1) Climate (CL)   C_O3A C_WVU C_CO2 C_GHG C_TU Need for analysis tools to exploit reference database (visualization, intercomparison, statistics, etc.) (H2O, O3, T, CO2, CH4, aerosols) Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- ICARE multibrowse and associated graphical modules? Felyx project NOAA NPROVS         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 171 No provenance (6.4) Detected (1) Climate (CL)   ECVA   Incomplete development and/or application and/or documentation of an unbroken traceability chain of Cal/Val data manipulations for atmospheric ECV validation systems. Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D5.1 Keppens et al., 2015 (traceability chain) QA4ECV: http://www.qa4ecv.eu/ QA4EO: http://qa4eo.org/         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 172 Temporal extent (1.3) Detected (1) Climate (CL)   ECVA   Missing quantification of additional uncertainties introduced in the comparison results due to differences in (multi-dimensional) sampling and smoothing of atmospheric inhomogeneity Research programs targets (2) TBD (4)   BIRA GAIA-CLIM H2020- D5.1, D3.1 Lambert et al., 2012 Verhoelst et al., 2015 Fasso et al., 2014 Ignaccolo et al., 2015 ?EU FP6 GEOmon Technical Notes D4.2.1 and D4.2.2 (2008-2011)?         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 173 No fast access to big data (5.8) Solved (6) All (11)       Lack of broadband connectivity for big-data fast visualization and processing Industry-driven challenges (5) RS (1) M. Miguel-Lago EARSC Presented at ConnectinGEO -product award WP.5.5. Connectivity 2016/06/15 Developing Improve client software to transmit only the necessary data. Use cloud or High Processing Computing (HPC) data processing. High (3)   High (3)   Unknown (0)   Short term (1)   High (3)   Feasibility. Need financing for final stage
Add FeedBk View FeedBk 174 Geographical extent (1.1) Solved (6) Disaster resilience (DI) HU E-ELEV   Lack of continuity and uniform temporal sampling in time series. Industry-driven challenges (5) RS (1) M. Miguel-Lago EARSC Presented at ConnectinGEO -product award WP.5.5. Temporal series 2016/06/15 Operational Implement data fussion techniques to generated regular interpolated samples. Very high (4)   Very high (4)   Low (1)   Short term (1)   Low (5)   Operational. Need awareness
Add FeedBk View FeedBk 175 Temporal extent (1.3) Solved (6) Oceans (OC)   C_SL   Lack of tidal, ocean currents and water elevation prediction services Industry-driven challenges (5) RS (1) M. Miguel-Lago EARSC Presented at ConnectinGEO -product award WP.5.5. (Ocean. Tidal atlas) Tidal monitoring 2016/06/15 Operational Implement a forecast system based on recent data Very high (4)   High (3)   Low (1)   Short term (1)   Low (5)   Operational. Need awareness
Add FeedBk View FeedBk 176 Catalogue saturation (5.2) Solved (6) All (11)       Lack of tools for Big Data analysis: merge timeseries, proper map and statistics visual representation Industry-driven challenges (5) RS (1) M. Miguel-Lago EARSC Presented at ConnectinGEO -product award WP.5.5. Big data 2016/06/15 Operational Develope the right tools for big data analysis and visualization Very high (4)   High (3)   Low (1)   Short term (1)   Low (5)   Operational. Need awareness
Add FeedBk View FeedBk 177 Geographical extent (1.1) Detected (1) Climate (CL)   C_GLA   Glacier, Accumulation - performed in situ on only a small fraction of glaciers globally. Component of mass balance Observation requirement (1) In-Situ (2) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php Glacier mass balance is a climate indicator and also impacts water resources 2016/11/03   the most accurate estimate of accumulation is in situ measurements but ranging by lidar and radar are increasingly being used. In situ will still be needed for validation. Networks in Alpine regions should be increased instead of being closed. High (3) Long term In situ networks are funded through national hydromet services High (3) Most glaciers are losing mass which is impacting water resources and sea level rise Medium (2) In some regions the facilities are in place (huts, etc.) and only the personnel costs are needed to revive them. Mid term (2) (time frame to implement, or to maintain?) High (3) see impact rationale  
Add FeedBk View FeedBk 178 Geographical extent (1.1) Detected (1) Climate (CL)   C_GLA   Glacier, Facies, snowline - can be estimated from imagery, approximates equlibrium line Observation requirement (1) RS (1) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php facies are the visible expression of the processes determining surface mass balance 2016/11/03   facies can be determined from radar and visible imagery with adequate resolution Very high (4) only a matter scheduling acquisitions at end of ablation season for all the world's glaciers High (3) Most glaciers are losing mass which is impacting water resources and sea level rise Medium (2) depends on source, some data are free, some carry cost Short term (1)   High (3) also requires people and/or algorithms to interpret the data  
Add FeedBk View FeedBk 179 Uncertainty (3.1) Detected (1) Climate (CL)   C_GLA   Glacier, Glacier area - global inventory of glaciers is mostly complete but quality and accuracy varies widely Observation requirement (1) RS (1) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php short term contribution to runoff and sea level rise depends on area (volume is needed to estimate future flows) 2016/11/03   glacier area is the simplest parameter to measure from satellite data and this has been done for most of the world's glaciers (Randolph inventory). However, quality varies greatly by source and region. Very high (4) only a matter scheduling acquisitions for all the world's glaciers Medium (2)   Medium (2) cost is mostly for personnel to run algorithms and quality check Short term (1)   Medium (4) existing inventory needs to be updated as glacier areas are changing  
Add FeedBk View FeedBk 180 Geographical extent (1.1) Detected (1) Disaster resilience (DI)   C_GLA   Glacier, Glacier dammed lakes - near continuous global mapping needed Observation requirement (1) Both (3) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php due to accelerating melt rates of many mountain glaciers, glacier lakes are an increasing hazard since many are in remote locations and hard to monitor 2016/11/03   traditionally accomplished with visible imagery, but possible also with high resolution SAR Very high (4) only a matter scheduling acquisitions and implementing automated change detection algorithms Very high (4) recently entire villages in Nepal were wiped out by GLOFs Medium (2) depends on source, some data are free, some carry cost Short term (1)   Very high (2) the number of dangerous lakes is growning and existing monitoring programs are inadequate  
Add FeedBk View FeedBk 181 Geographical extent (1.1) Detected (1) Climate (CL)   C_GLA   Glacier, Glacier ice thickness - a limiting factor in ability to forecast glacier contribution to runoff Observation requirement (1) RS (1) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php future runoff from glaciers having negative mass balance depends on remaining volume, for which thickness is needed 2016/11/03   various remote sensing methods using radar and gravity measurements are available Very high (4) mostly a matter of cost. for global coverage need many aircraft hours High (3) Most glaciers are losing mass which is impacting water resources and sea level rise High (3)   Mid term (2)   High (3) Current monitoring networks provide estimates of runoff but knowledge of ice thickness is needed to determine when negative mass balances will eventually result in disappearance, so this measure is important for long-term forecasting.  
Add FeedBk View FeedBk 183 Geographical extent (1.1) Detected (1) Climate (CL)   C_GLA   Glacier, Glacier topography - inadequate resolution in most places Observation requirement (1) RS (1) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php changes in mass balance are reflected in surface topography 2016/11/03   various remote sensing methods using lidar, SAR and stereoscopy of visible imagery can be used. Very high (4) only a matter scheduling (and paying for in the case of TerraSAR -X) acquisitions High (3) Most glaciers are losing mass which is impacting water resources and sea level rise Medium (2)   Short term (1)   High (3)    
Add FeedBk View FeedBk 184 Geographical extent (1.1) Detected (1) Climate (CL)   C_GLA   Glacier, Glacier velocity - has been determined for only a fraction of glaciers globally Observation requirement (1) RS (1) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php velocity is important measure of glacier dynamics 2016/11/03   feature tracking in visible imagery or inSAR can supply necessary dat Very high (4) only a matter scheduling acquisitions and applying algorithms High (3) mostly pertains to outlet glaciers of the Greenland and West Antarctic Ice Sheets which have the potential to raise sea levels by 6m or more Medium (2)   Mid term (2)   Very high (2) the fate of the GIS and WAIS is of critical, global importance  
Add FeedBk View FeedBk 185 Geographical extent (1.1) Detected (1) Climate (CL)   C_ICE   Ice sheet, Ice sheet mass change - requires knowledge of basal melt, surface melt, accumulation, velocity, calving rate Observation requirement (1) RS (1) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php ice sheets are continental-scale masses of glacier ice with outlets mostly terminating at the ocean, and thus loose mass by calving, and in the case of floating ice shelves, through basal melting 2016/11/03   all of the measurements describe in gap #177-184 High (3) various satellite and aircraft missions needed to carry out the measureents Very high (4) potential for global scale human disaster of unimaginable proportions High (3)   Long term (3)   Very high (2)    
Add FeedBk View FeedBk 186 Geographical extent (1.1) Detected (1) Climate (CL)   C_PFR   Permafrost, Active layer depth - in situ monitoring network is sparse Observation requirement (1) Both (3) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php We need to increase in-situ networks or use in-SAR satellite data (with in-situ validation again) 2016/11/03   Increase the in-situ network density High (3) We have the in-situ technology High (3) On Greenhouse liberation of gasses. Infrastructures maintenance. Hidrological balance. Even in disaster forcasting... Medium (2) Deploy in-situ in remote places in addition to established stations Mid term (2)   Very high (2) Indicators to Climate Change  
Add FeedBk View FeedBk 188 Geographical extent (1.1) Detected (1) Climate (CL)   C_PFR   Permafrost, Soil temperature - in situ monitoring network is sparse Observation requirement (1) In-Situ (2) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php We need to increase in-situ networks 2016/11/03   Increase the in-situ network density High (3) We have the in-situ technology High (3) On Greenhouse liberation of gasses. Infrastructures maintenance. Hidrological balance. Even in disaster forcasting... Medium (2) Deploy in-situ in remote places in addition to established stations Mid term (2)   Very high (2)    
Add FeedBk View FeedBk 189 Geographical extent (1.1) Detected (1) Climate (CL)   C_PFR   Permafrost, Subsea permafrost distribution Observation requirement (1) Both (3) SJS Khalsa   GCW = http://globalcryospherewatch.org/reference/obs_requirements.php there is currently no program to monitor subsea permafrost 2016/11/03   deploy monitoring network in hear-coastal regions High (3) ocean borehole drilling Medium (2) massive flux of CH4 would ensue if melted High (3) ocean drilling is costly Mid term (2)   High (3)    
Add FeedBk View FeedBk 193 Uncertainty (3.1) Detected (1) Climate (CL)   C_SNC   Land surface, Snow water equivalent Observation requirement (1) RS (1) SJS Khalsa   OSCAR = http://www.wmo-sat.info/oscar/themes/view/5 snow melt runoff is a major source of water in many regions and while the area covered by snow is mapped daily from satellite, this does not revveal the amount of water in the snow pack is unknown 2016/11/03   existing in situ (e.g. snotel, GPS), airborne (gamma ray, lidar), and satellite (radar, lidar) methods each have limitations. new satellite mission dedicated to SWE is needed High (3) both NASA and ESA have programs to develop spaceborne snow missions Very high (4) More than one-sixth of the world\x{2019}s population relies on seasonal snowpack and glaciers for water. Knowing amount and timing of runoff is of critical importance Very high (4) new mission Short term (1) the technologies are mostly ready Very high (2)    
Add FeedBk View FeedBk 194 Temporal resolution (2.3) Detected (1) Oceans (OC)   C_SICE   Sea surface, Sea-ice cover Observation requirement (1) RS (1) SJS Khalsa   OSCAR = http://www.wmo-sat.info/oscar/themes/view/5 done operationally, but temporal resolution is inadequate for many purposes 2016/11/03   sea ice cover is critical part of climate system and important for shipping and sustenance harvesting by indigenous communities Very high (4) radar provides all-weather mapping of sea ice, but coverage and access to data an issue High (3)   Medium (2) Sentinel-1 and other SAR mission data can fill gap, but Short term (1)   Medium (4) the operational ice services should be queried to learn if current data is sufficient  
Add FeedBk View FeedBk 195 Temporal resolution (2.3) Detected (1) Oceans (OC)   C_SICE   Sea surface, Sea-ice motion Observation requirement (1) RS (1) SJS Khalsa   OSCAR = http://www.wmo-sat.info/oscar/themes/view/5 done operationally, but temporal resolution is inadequate for many purposes 2016/11/03   ice motion important for predicting ice concentration and ice pack evolution Very high (4) radar provides all-weather mapping of sea ice, but coverage and access to data an issue High (3)   Medium (2) Sentinel-1 and other SAR mission data can fill gap, but Short term (1)   Medium (4) the operational ice services should be queried to learn if current data is sufficient  
Add FeedBk View FeedBk 197 Uncertainty (3.1) Detected (1) Oceans (OC)   C_SICE   Sea surface, Sea-ice thickness Observation requirement (1) RS (1) SJS Khalsa   OSCAR = http://www.wmo-sat.info/oscar/themes/view/5 estimated by measuring freeboard using remote sensing, but require also estimating ice density and snow cover 2016/11/03   ice thickness important in forecasting ice pack evolution and energy fluxes Very high (4) cryosat is already producing ice thickness maps, but better accuracy and resolution needed High (3)   Medium (2) Sentinel-3 altimetry will advance Mid term (2)   High (3) disappearing Arctic ice pack a global concern  
Add FeedBk View FeedBk 198 Uncertainty (3.1) Detected (1) Oceans (OC)   C_SICE   Sea surface, Sea-ice type - i.e. first year or multi-year Observation requirement (1) RS (1) SJS Khalsa   OSCAR = http://www.wmo-sat.info/oscar/themes/view/5 sea ice age an important determinant of its dynamical, chemical and thermodynamical properties 2016/11/03   ice age important in forecasting ice pack evolution and energy fluxes High (3) tracking old ice from time of sea ice minimum can distinguish first year from multiyear ice. thickness and backscatter can also be used High (3)   Medium (2)   Mid term (2)   High (3) disappearing Arctic ice pack a global concern  
Add FeedBk View FeedBk 199 Uncertainty (3.1) Detected (1) Oceans (OC)   C_SICE   Sea surface, Snow on ice depth Observation requirement (1) RS (1) SJS Khalsa   OSCAR = http://www.wmo-sat.info/oscar/themes/view/5 essential for understanding sea ice surface energy balance and in determining sea ice thickness from surface elevation derived from satellite 2016/11/03   snow on ice confounds ability to estimate ice thickness from radar or lidar measurements. It also strongly influences heat fluxes and albedo High (3)   High (3)   Medium (2)   Mid term (2)   High (3) (I am less familiar with available methods for measuring this variable)  
Add FeedBk View FeedBk 200 Non well known format (5.5) Detected (1) Multiple (10)   C_CO2 C_PRE N_NOI Lack of interoperability in mobile sensor data, regarding standards and data models (CO2, NOX) Industry-driven challenges (5) In-Situ (2) M. Rieke 52North To be documented in ConnectinGEO deliverable (i.e. D6.x + D7.2) Harmonization of access to mobile sensor data (e.g. vessels, gliders, cars, drones, ...)       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 201 Non well known format (5.5) Detected (1) Multiple (10)   EV   Lack of interoperability in crowd-sourced data (e.g. ground-truth data, sightings, etc.), regarding standards and data models Industry-driven challenges (5) Both (3) M. Rieke 52North To be documented in ConnectinGEO deliverable (i.e. D6.x + D7.2) Harmonization of crowd-sourced / Citizen Science data models       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 202 No easy access (5.4) Detected (1) Climate (CL)   C_LCV   Heterogeneous drought data among European countries; access is not centralized Consultation process (3) Both (3) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #1) Climatic analysis of drought in the Europe       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 203 No open access (6.2) Detected (1) Water (WA)   C_RIV   Missing River discharge data (historical, daily resolution) from all countries and regular updates of the time-series on annual or biennial frequency Consultation process (3) Both (3) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #6, #23)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 204 Spatial resolution (2.1) Detected (1) Water (WA)   C_WTS C_LAK Time series of small water bodies is not available for pastoral domain of Senegal due to spatial resolution Consultation process (3) RS (1) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #9) insufficient temporal resolution since the data are provided in 1 km and naturel water bodies do not exceed generally 100m length       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 205 Temporal resolution (2.3) Detected (1) Climate (CL)   C_CLD   Cloud coverage: Missing data, insufficient temporal resolution Consultation process (3) RS (1) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #12, #13) energy balance modelling at field level in areas with moderate cloud cover; European Phenological analysis at high spatial resolution       Low (1) there are several existing satellite missions capable or retrieve cloud cover information High (3)   Medium (2) low-medium cost using and aggregating existing satellite data Short term (1) low-medium time cost using and aggregating existing satellite data Medium (4)    
Add FeedBk View FeedBk 206 No easy access (5.4) Detected (1) All (11)       No automated download of satellite images for real-time classification, mosaicking, change detection analysis, etc. Consultation process (3) RS (1) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #25)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 207 No measured (7.1) Detected (1) Climate (CL)   C_WVAS C_WAS W_EVA Missing data on water vapor and cloud coverage for correlation Consultation process (3) Both (3) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #26) vertical profiles of water vapor in the lower troposphere are very poorly (actually not really at all) measured, and essential variables such as surface evaporation, or atmospheric winds are also not measured.       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 208 Spatial resolution (2.1) Detected (1) Multiple (10)   C_LCV   High resolution land cover/use data not available Consultation process (3) RS (1) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #28, #50, #59, #62, #65, #80)         Medium (2) high resolution data implies huge amount of data High (3) land cover is related to many natural and human-induced processes High (3) hih resolution images are still not freely available. however many new satellites are increasing spatial resolution, such as Sentinel Short term (1) high resolution images are currently available High (3) land cover is related to many natural and human-induced processes  
Add FeedBk View FeedBk 209 No open access (6.2) Detected (1) Climate (CL)   ECVA   Historical meteorological data not publicly available Consultation process (3) Both (3) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #30) Meteorological climate reanalysis       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 210 Temporal resolution (2.3) Detected (1) Multiple (10)   E_LULC   Incomplete or low quality data on historical urban fabrics data (both physical and social) Consultation process (3) Both (3) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #42)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 211 No easy access (5.4) Detected (1) All (11)       Web-service based access to GEOSS data is not always possible Consultation process (3) Both (3) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #43)         TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 212 Temporal resolution (2.3) Detected (1) Water (WA)   W_Q   Historical water quality data not available Consultation process (3) In-Situ (2) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #47) Historic analysis of water quality for region north of Portugal       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 213 Uncertainty (3.1) Detected (1) Multiple (10)       Low quality of historical coastal data Consultation process (3) RS (1) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #51) Historic assessment of coastal change       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 214 Uncertainty (3.1) Detected (1) Climate (CL)   C_SICE C_GLA Low quality and scarcity of arctic sea ice thickness data Consultation process (3) RS (1) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #52) Analysis of Arctic sea ice mass trends.       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 215 Temporal resolution (2.3) Detected (1) Oceans (OC)   C_PLK   Lacking temporal and spatial resolution of phytoplankton biodiversity data Consultation process (3) RS (1) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #55) interested in understanding how algal blooms develop and why harmful algae sometimes dominate blooms: combine data on phytoplankton biodiversity at the species level together with in situ data from automated sensors and satellite remote sensing.       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 216 Temporal resolution (2.3) Detected (1) Disaster resilience (DI)   EV   Missing Near-/Quasi-Real-Time data on natural hazards Consultation process (3) Both (3) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #68)         Low (1)   Very high (4) Save many lifes Very high (4) Cover all hazards is costly Long term (3)   Medium (4)    
Add FeedBk View FeedBk 217 Temporal resolution (2.3) Detected (1) Multiple (10)   C_LCV   Too low temporal resolution of vegetation index data (no seasonal analysis possible) Consultation process (3) RS (1) M. Rieke 52North ConnectinGEO "User Needs and Gaps Survey" - http://twiki.eneon.net/foswiki/bin/view/ConnectinGEOIntranet/UserNeedsAndGapsSurvey (Response #77, #79) Temporal (various years) and seasonal evolution for vegetation index's and surface temperature (if no better, brightness surface temperature) for agricultural sparse woody crops.       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 218 No measured (7.1) Detected (1) Multiple (10)   N_APOL N_ACO C_O3A C_PRE Missing data about traffic pollutant emission Industry-driven challenges (5) In-Situ (2) S. Jirka 52North To be documented in ConnectinGEO deliverable (i.e. D6.x + D7.2) Currently there is a lack in information to derive the air quality impact of traffic; especially data about pollutant emission of cars is necessary to augment the sparse stationary air pollution networks.       TBD (9)   TBD (9)   TBD (9)   TBD (99)   TBD (0)    
Add FeedBk View FeedBk 219 No interdisciplinary coord. (8.1) Detected (1) All (11)   EV   No European in-situ cross-domain coordination initiative Research programs targets (2) In-Situ (2) Joan Mas\x{fffd}   Horizon 2020 Work Programme 2014-2015 in the area of Climate action, environment, resource efficiency and raw materials. SC5-18-2014/2015 Have a single initiative where network can discuss integration and translate their demands to funding agencies 2016/11/25   The European Network of Earth Observation Networks High (3) Already created Very high (4)   Low (1)   Mid term (2)   Very high (2)    
Add FeedBk View FeedBk 220 No interdisciplinary coord. (8.1) Detected (1) All (11)   EV   No Global in-situ cross-domain coordination initiative Research programs targets (2) In-Situ (2) Joan Mas\x{fffd}   GEOSS Work Programme 2017-2019 Have a single initiative to ensure completeness and data sharing in GEOSS 2016/11/25   A GEOSS fundational task High (3) Already created Very high (4)   Low (1)   Mid term (2)   Very high (2)    
Add FeedBk View FeedBk 221 No measured (7.1) Detected (1) Climate (CL)   O_NUT O_CAR O_TRTR No biogeochemistry measures of the deap occean Consultation process (3) In-Situ (2) Joan Mas\x{fffd}   In the ENVRIplus 3rt workshop verbally Even if there argo floats are capturing information from deap see, they do not do biogeochemical measurements 2016/11/17   Install biogeochemical sensors in Argo floats High (3) the thechnology and the argos are there Medium (2)   Medium (2)   Mid term (2)   High (3)    
Add FeedBk View FeedBk 222 Spatial resolution (2.1) Detected (1) Climate (CL)   EBV   Missing high resolution data for terrestrial ecosystems structure and terrestrial ecosystems function Research programs targets (2) RS (1) Joan Mas\x{fffd}   EOEP-4 Data User Element (DUE), GlobDiversity There are products are more corse resolution but not at this resolutions. Sentinel 2 based high level products can potentially deliver some EBV 2016/07/11   Create services and methodologies to calculate high level products Medium (2) Requires reserach and innovation to mature the algoritms Medium (2)   Medium (2)   Short term (1)   High (3)    
Add FeedBk View FeedBk 223 Geographical inconsistency (4.1) Accepted (3) Biodiversity (BI)   B_EFDR   Forestry Harvest: Spatially explicit information on used/unused forests lacking Observation requirement (1) Both (3) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract Allows for tracking trends in afforestation, deforestation and reforestation globally 2016/12/07   Data on wilderness or intact forests might provide proxies Medium (2) country level stats exist (FAO) and spatial products exist on forests, but managed and unmanaged forests are mixed together - with time series you can get at some of this Medium (2) impact is moderate, as this gap prevents us really from knowing how much intact forest we have with direct relation to biodiversity Medium (2) costs not negligible but could be borne by many actors - new satellites on board or coming will help Mid term (2) short to medium term - new tech coming will help Crucial (1) we have good stats at country level - but downscaling needed asap EO community should work on this in near future!
Add FeedBk View FeedBk 224 Geographical inconsistency (4.1) Accepted (3) Biodiversity (BI)   C_LCV   Tree Species: Spatially explicit data on plantations lacking Observation requirement (1) Both (3) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract Allows for discerning natural from managed forests - with GHG implications 2016/12/12   Sentinel 1,2 combination could certainly address this to some extent but strong need for insitu data here Medium (2) tree species is challenging but possible TBD (9)   TBD (9)   TBD (99)   High (3) important to move beyond just land cover - need species for monitoring shifts in regard to CC, to calculate emissions from fire, biodiversity, etc. Insitu data needed
Add FeedBk View FeedBk 225 Uncertainty (3.1) Accepted (3) Agriculture (AG)   AgV   Grazing and Mowing Harvest Observation requirement (1) RS (1) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract GHG management 2016/12/15   insitu data needed here Low (1) extreme uncertainty TBD (9)   TBD (9)   TBD (99)   Low (5)   Insitu data needed
Add FeedBk View FeedBk 226 Uncertainty (3.1) Accepted (3) Agriculture (AG)   AgV   Crop Harvest Observation requirement (1) RS (1) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract GHG management 2016/12/15   insitu data needed Low (1) many intricacies TBD (9)   TBD (9)   TBD (99)   Crucial (1)   Insitu data needed
Add FeedBk View FeedBk 227 Uncertainty (3.1) Accepted (3) Agriculture (AG)   AgV   Crop Species Observation requirement (1) RS (1) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract Usefull for food security 2016/12/15   again sentinel data may help, along with insitu, citizen science Medium (2) lacking global crop calendars TBD (9)   TBD (9)   TBD (99)   High (3)   Insitu data needed
Add FeedBk View FeedBk 228 Uncertainty (3.1) Accepted (3) Agriculture (AG)   AgV   N Fertilization Observation requirement (1) RS (1) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract Important to understand when and where this is happening - N leaching important for water systems 2016/12/15   insitu required Medium (2) livestock data error prone, no data outside croplands TBD (9)   TBD (9)   TBD (99)   Very high (2)   Insitu data needed
Add FeedBk View FeedBk 229 Uncertainty (3.1) Accepted (3) Agriculture (AG)   AgV   Tillage Observation requirement (1) Both (3) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract Relates to GHG emissions from soil 2016/12/15   assume that all crops are tilled Low (1) no data on tillage TBD (9)   TBD (9)   TBD (99)   Low (5)   Insitu data needed
Add FeedBk View FeedBk 230 Uncertainty (3.1) Accepted (3) Agriculture (AG)   AgV   Irrigation Observation requirement (1) TBD (4) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract Water use a key environmental indicator meanwhile 2016/12/15   insitu data required, but again sentinel examples exist Medium (2) amount of water actually used is difficult to assess TBD (9)   TBD (9)   TBD (99)   Very high (2)   Insitu data needed
Add FeedBk View FeedBk 231 Geographical inconsistency (4.1) Accepted (3) Agriculture (AG)   AgV   Artificial Wetland Drainage Observation requirement (1) Both (3) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract Strong climate linkages - drainage of wetlands in tropics for palm oil 2016/12/15   insitu data Low (1) poor data, all drainage covered, not only wetlands TBD (9)   TBD (9)   TBD (99)   Low (5)   Insitu data needed
Add FeedBk View FeedBk 232 Uncertainty (3.1) Accepted (3) Multiple (10)   C_FIRE   Fire as a Management Tool Observation requirement (1) Both (3) I. McCallum IIASA http://onlinelibrary.wiley.com/doi/10.1111/gcb.13443/abstract Fire important both for monitoring agricultural activity but also in regards to emissions 2016/12/15   insitu data Medium (2) scarce data for prescribed fires TBD (9)   TBD (9)   TBD (99)   Low (5)   Insitu data needed
Add FeedBk View FeedBk 233 Spatial resolution (2.1) Accepted (3) Biodiversity (BI) CL C_LAI   Lack of a high resolution LAI global coverage and methodology Consultation process (3) Both (3) J. Masó CREAF ECOPotential internal discussions LAI is important for understanding tree canopy structure that is related to the CO2 absortiion capacity 2017/01/31   insitu data Medium (2) requires a RS big data calculation with insitu validation High (3)   Medium (2) requieres a cloud infrastructure and sentinel 2 data Mid term (2)   High (3)    

Gap view by code

Gap ID:

 


Gap type: Geographical extent (1.1)
Status: Detected (1)
Theme: Climate (CL)
Other Themes:
EV: C_TAS
Other EV:
Gap description: The scarcity of microclimatic data (air temperature) from the beneath of trees.
Thread: Consultation process (3)
RS/In-Situ: TBD (4)
Editor: Guillem Closa
Ambassador:
Traceability: Pieter De Frenne and Kris Verheyen "Weather stations lack forest data"
Purpose: Find out how temperatures are changing beneath the trees
Date: 2016/01/15
Review:
Remedy:
Feasibility: TBD (9)
Feasibility rational:
Impact: TBD (9)
Impact rational:
Cost: TBD (9)
Cost rational:
Timeframe: TBD (99)
Time rational:
Priority: TBD (0)
Priority rational:
Recommendation:

NOTE: The URL to this gap is: http://twiki.connectingeo.net/foswiki/bin/view/ConnectinGEOIntranet/GapAnalysisTable?gapID=001#Gap_view_by_code

Gap types

Gap type Name
1.1 Geographical extent (1.1)
1.2 Vertical extent (1.2)
1.3 Temporal extent (1.3)
2.1 Spatial resolution (2.1)
2.2 Vertical resolution (2.2)
2.3 Temporal resolution (2.3)
3.1 Uncertainty (3.1)
4.1 Geographical inconsistency (4.1)
4.2 Temporal inconsistency (4.2)
4.3 Boundary conditions issue (4.3)
5.1 No catalogue (5.1)
5.2 Catalogue saturation (5.2)
5.3 Cannot be viewed (5.3)
5.4 No easy access (5.4)
5.5 Non well known format (5.5)
5.6 No processable (5.6)
5.7 Unknown semantics (5.7)
5.8 No fast access to big data (5.8)
6.1 No access (6.1)
6.2 No open access (6.2)
6.3 No quality (6.3)
6.4 No provenance (6.4)
6.5 Bad metadata (6.5)
6.6 No metadata (6.6)
7.1 No measured (7.1)
7.2 No parameter (7.2)
8.1 No interdisciplinary coord. (8.1)
8.2 No coordination of obs. sites (8.2)
?.? TBD (?.?)

Essential Variables

The ones that are used in the Gaps table has been associated with a Alfanumeric code. The others have still numerical codes only

EV Code EV Name Equivalences
EBV EBV: All  
B_GCC EBV: Co-ancestry (Genetic composition)  
B_GCA EBV: Allelic diversity (Genetic composition)  
B_GCP EBV: Population genetic differentiation (Genetic composition)  
B_GCB EBV: Breed and variety div. (Genetic composition)  
B_SPD EBV: Species distribution (Species populations)  
B_SPA EBV: Population abundance (Species populations)  
B_SPS EBV: Population structure by age/size class (Species populations)  
B_STPH EBV: Phenology (Species traits)  
B_STB EBV: Body mass (Species traits)  
B_STN EBV: Natal dispersion distance (Species traits)  
B_STM EBV: Migratory behavior (Species traits)  
B_STD EBV: Demographic traits (Species traits)  
B_STP EBV: Physiological traits (Species traits)  
B_CCT EBV: Taxonomic diversity (Community composition)  
B_CCS EBV: Species interactions (Community composition)  
B_EFNP EBV: Net primary productivity (Ecosystem function)  
B_EFSP EBV: Secondary productivity (Ecosystem function)  
B_EFNR EBV: Nutrient retention (Ecosystem function)  
B_EFDR EBV: Disturbance regime (Ecosystem function)  
B_ESH EBV: Habitat structure (Ecosystem structure)  
B_ESE EBV: Ecosys. extent and fragmentation (Ecosystem structure)  
B_ESC EBV: Ecosys. composition by functional type (Ecosystem structure)  
ECV ECV: All  
ECVA ECV: Atmospheric  
C_TAS ECV: Air temperature (Atmosphere surface) EREV: Surface air temperature
C_WAS ECV: Wind speed and direction (Atmosphere surface) EOV: Surface Wind (Physical surface), EREV: Wind speed and direction
C_WVAS ECV: Water vapour (Atmosphere surface) EREV: Surface humidity
C_PAS ECV: Pressure (Atmosphere surface) EOV: Sea Level Pressure (Physical surface), EREV: Surface atmospheric pressure
C_RAS ECV: Precipitation (Atmosphere surface) EREV: Precipitation; WaV: Precipitation
C_SRB ECV: Surface radiation budget (Atmosphere surface)  
N_APOL NEW: Atmospheric pollutants: Heavy metals, Persistent organic pollutants, Tracers (Atmosphere)  
N_ACO NEW: Atmospheric pollutants: CO  
C_TU ECV: Temperature (Atmosphere upper-air)  
C_WNU ECV: Wind speed and direction (Atmosphere upper-air)  
C_WVU ECV: Water vapour (Atmosphere upper-air)  
C_CLD ECV: Cloud properties (Atmosphere upper-air) EREV: Cloud cover (demand in energy)
C_ERB ECV: Earth radiation budget, including solar irradiance (Atmosphere upper-air)  
C_CO2 ECV: Carbon dioxide (Atmosphere composition)  
C_GHG ECV: Methan, and other long-lived greenhouse gases. Including nitrous oxide (N2O), chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), sulphur hexafluoride (SF6), and perfluorocarbons (PFCs). (Atmosphere composition)  
C_O3A ECV: Ozone and aerosol (Atmosphere composition) ECV: Aerosols (aerosol mass, size distribution (or at least mass at 3 fraction sizes: 1, 2.5 and 10 micron), speciation and chemical composition, Aerosol Optical Depth (AOD) at multiple wavelengths, AAOD, water content, ratio of mass to AOD, vertical distribution of extinction)
C_PRE Precursors (supporting the Aerosols and Ozone ECVs) In particular nitrogen dioxide (NO2), sulphur dioxide (SO2), formaldehyde (HCHO) and carbon monoxide (CO). (Atmosphere composition) ECV: Reactive Gases, Trace gases (incl GHG), Ozone Precursors (Total ozone, profile ozone, surface ozone, NO, NO2 (surface, column, profile), PAN, HNO3, NH3, CO, VOC (isoprene, terpenes, alcohols, aldehydes, ketones, alkanes, alkenes, alkynes, aromatics
C_SST ECV: Sea-surface temperature (Ocean surface) EOV: Sea Surface Temperature (Physical surface), EREV: Temperature, sea-surface, sub-surface and deep-sea
C_SSS ECV: Sea-surface salinity (Ocean surface) EOV: Sea Surface Salinity (Physical surface)
C_SL ECV: Sea level (Ocean surface). EOV: Sea Level (Physical surface)
C_SS ECV: Sea state (Ocean surface) EOV: Sea State (Physical surface)
C_SICE ECV: Sea ice (Ocean surface) EOV: Sea Ice (Physical surface)
C_C ECV: Surface current (Ocean surface) EOV: Surface Current (Physical surface), EREV: Ocean, fixed and floating offshore wind, wave, tidal, currents, OTEC
C_OC ECV: Ocean colour (Ocean surface) EOV: Ocean Color (Physical surface)
C_CO2P ECV: Carbon dioxide partial pressure (Ocean surface) EOV: Carbon Dioxide Partial Pressure (Physical surface)
C_OAS ECV: Ocean acidity (Ocean surface) EOV: Ocean acidity (Physical surface)
C_PLK ECV: Phytoplankton (Ocean surface)    
C_TD ECV: Temperature (Ocean sub-surface) EOV: Temperature (Physical sub-surface), EREV: Temperature, sea-surface, sub-surface and deep-sea
C_SALD ECV: Salinity (Ocean sub-surface) EOV: Salinity (Physical sub-surface)
C_CD ECV: Current (Ocean sub-surface) EOV: Current (Physical sub-surface)
C_NUTD ECV: Nutrients (Ocean sub-surface)  
C_CO2D ECV: Carbon dioxide partial pressure (Ocean sub-surface) EOV: Carbon Dioxide partial pressure (Physical sub-surface)
C_OAD ECV: Ocean acidity (Sub-surface) EOV: Ocean Acidity (Physical sub-surface)
C_OOD ECV: Oxygen (Ocean sub-surface) EOV: Oxygen (Physical sub-surface)
C_TRD ECV: Tracers (Ocean sub-surface) EOV: Tracers (Physical sub-surface)
C_RIV ECV: River discharge (Land) WaV: Runoff/streamflow/river discharge
C_WTS ECV: Water use (Land) WaV: Water us/demand (agriculture, hydrology, energy, urbanization)
C_GWAT ECV: Groundwater (Land) WaV: Groundwater
C_LAK ECV: Lakes (Land) WaV: Lakes/reservoir levels and aquifer volumetric change
C_SNC ECV: Snow cover (Land) WaV: Snow cover
C_GLA ECV: Glaciers and ice caps (Land) WaV: Glaciers/ice sheets
C_ICE ECV: Ice sheets (Land) WaV: Glaciers/ice sheets
C_PFR ECV: Permafrost (Land)  
C_ALB ECV: Albedo (Land)  
C_LCV ECV: Land cover,including vegetation type (Land)  
C_FAPR ECV: FAPAR (Land)  
C_LAI ECV: LAI (Land)  
C_AGB ECV: Above-ground biomass (Land)  
C_SC ECV: Soil carbon (Land)  
C_FIRE ECV: Fire disturbance (Land)  
C_SM ECV: Soil moisture (Land) WaV: Soil Moisture/Temperature
EOV EOV: All  
O_AIR EOV: Upper-Air (Physical surface)  
O_O EOV: Oxygen (Physical surface)  
O_TRA EOV: Tracers (Physical surface)  
O_GHC EOV: Global Ocean Heat Content (Physical sub-surface)  
O_OBIO EOV: Oxygen (Biogeochemical)  
O_NUT EOV: Macro Nutrients: NO3, PO4, Si, NH4, NO2 (Biogeochemical)  
O_CAR EOV: Carbonate System: DIC, Total Alkalinity, pCO2 and ph, at least 2 of 4 (Biogeochemical)  
O_TRTR EOV: Trascient Tracers: CFC-12, CFC-11, SF6, tritium, 3He, 14C, 39Ar (Biogeochemical)  
O_SUSP EOV: Suspended particulates (POC, PON or POM) and PIC ++ laboratory, beam attenuation, backscatter, acidiflabile, beam attenuation (Biogeochemical)  
O_PMAT EOV: Particulate Matter Export: POC export, CaCO3 export, BSi export (Biogeochemical)  
O_NITO EOV: Nitrous Oxide (Biogeochemical)  
O_C13 EOV: Carbon-13: 13C/12C of dissolved inorganic carbon (Biogeochemical)  
O_DOM EOV: DOM: Dissolved organic matter, DOC, DON, DOP (Biogeochemical)  
O_CHL EOV: Chlorophyll (Biology and Ecosystems)  
O_CRL EOV: Coral Cover (Biology and Ecosystems)  
O_MGV EOV: Mangrove Area (Biology and Ecosystems)  
O_HAB EOV: Harmful Algal Blooms HABs (Biology and Ecosystems)  
O_ZPLK EOV: Zooplankton:biomass/abundance (Biology and Ecosystems)  
O_SMA EOV: Salt Marsh Area (Biology and Ecosystems)  
O_LMV EOV: Large marine vertebrates: abundance/distribution (Biology and Ecosystems)  
O_SGRA EOV: Seagrass Area (Biology and Ecosystems)  
O_LMVT EOV: Tags and Tracking of species of value/large marine vertebrates (Biology and Ecosystems)  
O_ZPKK EOV: Zooplankton, Krill (Biology and Ecosystems)  
AgV AgV: All  
A_CA AgV: Crop Area  
A_CT AgV: Crop Type  
A_CC AgV: Crop Condition  
A_CPH AgV: Crop Phenology  
A_CY AgV: Crop Yield (current and forecast)  
A_CM AgV: Crop Management and agricultural practices  
EREV EREV: All  
E-SSI EREV: Solar Surface Irradiance and its components (global, direct, diffuse)  
E-SUN EREV: Sunshine duration (demand in energy)  
E_LULC EREV: Land use, Land cover, including urbanization, hydrology, grid description  
E-ELEV EREV: Elevation, Orography  
E-LST EREV: Land surface temperature  
E-WAVE EREV: Wave, height, direction, period  
E-TDL EREV: Tidal (min, max, sea surface elevation)  
E-CUR EREV: See current, speed, direction  
E-BAT EREV: Ocean bathymetry  
E-OFL EREV: Ocean floor type  
E-URB EREV: Urbanization  
HeV HeV: All  
H_FAM HeV: Famine early warning  
H_DES HeV: Short term forecasting of comunicating diseases  
WaV WaV: All  
W_EVA WaV: Evaporation and Evapotranspiration  
W_Q WaV: Water quality  
EV All of them  
  None of them  
N_NOI NEW: Acoustic pollutants  
?01 ECV: All Global Numerical Weather Prediction (NWP) variables (e.g., PBL + Tropopause height) and others yet to be determined by WMO/GAW. NOTE: Requires more work to accept it
?02 ECV: Others: Actinic flux, fire radiative power, land proxies, lightning, dry and wet deposition, pollen (key species), OCS NOTE: Requires more work to accept it

Threads

Code Thread
1 Observation requirement (1)
2 Research programs targets (2)
3 Consultation process (3)
4 GEOSS DAB analysis (4)
5 Industry-driven challenges (5)

RS/In-Situ

Code RS/In-Situ
1 RS (1)
2 In-Situ (2)
3 Both (3)
4 TBD (4)

Status

Code Status
1 Detected (1)
2 Reviewed (2)
3 Accepted (3)
4 Discarded (4)
5 Prioritized (5)
6 Solved (6)

Themes or SBAs (old ones)

Code Theme
CL Climate (CL)
OC Oceans (OC)
WA Water (WA)
BI Biodiversity (BI)
DI Disaster resilience (DI)
EN Energy (EN)
AG Agriculture (AG)
HE Health (HE)
HS Human settlements (HU)
10 Multiple (10)
11 All (11)

Feasibility

Code Description
9 TBD (9)
4 Very high (4) There is a mature technique
3 High (3) There was already research and maturing the technique is need
2 Medium (2) There is an idea to fill the gap that needs research
1 Low (1) There is not technology forseen to fill the gap
0 Unknown (0)

Impact

Code Description
9 TBD (9)  
4 Very high (4) Most of the communities or topics will be impacted
3 High (3) More than one community or topic will be impacted
2 Medium (2) A community or topic is identified
1 Low (1) Not able to identify a community of topic
0 Unknown (0)  

Cost

Code Description
9 TBD (9)  
4 Very high (4) more than 20MEUR
3 High (3) more than 5MEUR less than 20MEUR
2 Medium (2) more that 0.5MEUR less than 5MEUR
1 Low (1) less that 0.5MEUR
0 Unknown (0)  

Timeframe to implement a solution

Code Period Definition
9 TBD (9)  
4 Long term (3) more than 10 years
2 Mid term (2) less than 5 and more than 2 years
1 Short term (1) less than 2 years
0 Unknown (0)  

Priority

Code Priority
0 TBD (0)
1 Crucial (1)
2 Very high (2)
3 High (3)
4 Medium (4)
5 Low (5)
6 None (6)
-- JoanMaso - 25 Nov 2016

Durack P.J., Lee T., Vinogradova N. T., D. Stammer, 2016: Keeping the lights on for global ocean salinity observation, NATURE CLIMATE CHANGE, vol 6. 228-231.
Topic revision: r86 - 31 Jan 2017, IvetteSerral
 

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