ESA-CCI CMUG Workpackages
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1 ESA-CCI CMUG Workpackages WP3.9 Assessments of glaciers, land cover and sea-level data for hydrological modelling of the Arctic ocean drainage basi n WP3.8 Cross-assessments of clouds, soil moisture, water vapour and radiation in observations and regional climate models WPO3.4 Cross-assessment of clouds, aerosols, sea level, ocean colour, SST, water vapour and radiation in observations and global climate models WP3.10 Cross assessment of SST, SI, ocean colour and sea-level data for ocean model Omar Bellprat WP4.2 Assessing CCI SST and Sea-Ice datasets as boundary conditions in CCI4MIP simulations. Serge Planton WP5.1 Benchmarking models with ESA-CCI data: Implement metrics from WPO3.4 into ESMValtool. Mattia Righi
2 WP3.8 Cross-assessment of clouds, soil-moisture, water vapour and radiation for regional climate variability David Lindstedt, Petter Lind, Ulrika Willén Evaluate precipitation, cloud and soil moisture interactions on regional scale. Harmonie Non-hydrostatic regional Climate model (HCLIM) run over Europe, horizontal reolution 15km, with ERA-Interim boundary data. Waiting to analyse a 30 year 6km simulations to be ready July 2016
3 WP3.8 Cross-assessment of ECV's for regional climate variability Comparing Degree of Saturation (DoS) DoS = 0 for completely dry soil. DoS = 1 for a totally wet soil (at porosity).. HCLIM SM Winter CCI-SM HCLIM Top SM layer 1cm Mediterranean monthly mean land Soil Moisture (SM) Spring. Summer. SM shifted Autum n SM Anomaly Month number Jan 2003-Dec 2007
4 WP3.8 Cross-assessment of ECV's for regional climate variability Comparing HCLIM, CCI, CLARA-a2 and ERA-Interim Cloud cover (no simulator...) HCLIM Clouds Cloud cover CCI-Clouds Cloud anomaly Month number Jan 2003-Dec 2007
5 WP3.8 Cross-assessment of ECV's for regional climate variability Comparing HCLIM and EOBS (gridded surface data) precipitation HCLIM Precip EOBS Precip Precipitation Precipitation anomalies Month number Jan 2003-Dec 2007
6 WP3.8 Cross-assessment of ECV's for regional climate variability - Bias obs/model for SM and Clouds + Identify seasonal model/obs errors SM shifted Precipitation Cloud Month number Jan Dec Observed and modelled variabilty similar for each ECV and across ECV's... Soil Moisture Anomaly Precipitation Anomaly Cloud Anomaly Month number Jan Dec 2007
7 WPO3.4 Cross-assessment of clouds, SST, sea level, (ocean colour, aerosols), water vapour and radiation for global climate variability Ulrika Willén, Martin Evaldsson, Abhay Devasthale Evaluate teleconnection (ENSO, IOD, NAO) patterns in observations using CCI ECVs and other ECVs, investigating known relationships, climate indices, such as between SST and rainfall for ENSO index and aim to derive new metrics, observational constraints. IWV ERIM and GPS stations correlation with ENSO IWV ERIM and GPS stations correlation with NAO
8 WPO3.4 Cross-assessment of ECV's for global climate variability Example: ENSO correlations for CCI SST and Clouds with TRMM and GPCP precipitation and CERES OLR all different timeperiods CCI SST&TRMM precip: CCI SST&GPCP precip: CCI SST&CCI Cloud: CCI SST&CERES OLR:
9 WPO3.4 Cross-assessment of ECV's for global climate variability Nino3.4 SST (NOAA), Clouds(CCI and CLARA) and Sea level(cci) indices: Index=Anomaly(X)/std(X) Nino34 SST anomalies from
10 WPO3.4 Cross-assessment of ECV's for global climate variability Nino3.4: SST (NOAA), Clouds( CCI,CLARA top), ( ERIM, EC-Earth bottom) level(cci)
11 Conclusions When evaluating climate models and/or processes 1a Use different types of observations for same ECV Passive/active satellites, ground-based 1b Use interacting ECV's for process studies Clouds-Soil moisture-precipitation Clouds-Aerosols-Radiation-Temperature 2 Use uncertainties 3 Know what you are comparing... and know assumptions in simulators Directly Simplistic Simulators/ observational proxies (spatial, temporal sampling) Advanced Simulators, COSP (3D fields online) 4 Work together with data experts/ producers Bridge the gap CCI and CCI+
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13 Cross ECV interactions Aerosol Cloud GHG O3 SSH SST OC SI Fire LC Glaciers Ant. IS Arc. IS SM
14 WPO3.4 Cross assessment of of clouds, SM, aerosols, GHG, SST, water vapour ad radiation global climate variability and trends Timeseries, scatterplots ENSO, IOD, 2D corr, Cross assessment observations, reanalysis and models. Investigate indices, process, co-varying variables. SST Nino3.4 and Clouds 3.4 correlations Strong signal for positive index... OLR...
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