The Climate Modelling User Group's overall feedback and evolution

Transcription

1 4 th Colocation Meeting The Climate Modelling User Group's overall feedback and evolution Roger Saunders on behalf of CMUG

2 CMUG Assessments of CCI CDRs Why does CMUG assess CCI datasets? Provide an independent view of the datasets and associated uncertainties Study consistency between ECVs Demonstrate applications for climate modelling to accelerate use by the climate/ reanalysis communities

3 CMUG assessments to date Methodology used for assessment of ECVs Climate Model (single, ensemble) Assessment of precursors (see CMUG D3.1 report series) O 3( (IASI), Land Cover (GlobCover), SSH (AVISO), Cloud (ISCCP), Fire (GFEDv3) Initial assessment of CCI CDRs to date O 3, Land Cover, SM, Cloud Re-analyses SST (HadISST), O 3 (ERA) O 3, SM, Aerosols, GHG, SSH Precursor datasets OC, SSH, SST Independent satellite or in situ measurements Related observations (surface and TOA fluxes, temperature, water vapour) SST (ARC) Fire (GFEDv3 ) SST, O 3, OC SM Assimilation GlobColor OC

4 Climate dataset production an iterative process Assess version version n+1 n+2 n with with models CMUG CMUG Feedback to CCI team Satellite world Climate world Reprocess version n to n+1

5 CMUG finding Issues with the Data Time stamps in NetCDF incorrect or not present for many datasets No data on 29 th February No data on the dateline Consistency between algorithms (e.g. flags) Quality Flags on level 3 data

6 CMUG assessments of ECVs Sea Surface Temperature

7 Overview CCI SST validation against drifting buoys and comparison with results from pre-cursor ARC-CCI dataset (v 0.9) (diff fom v1.1 is v. small). Main difference is in retrieval methodology (statistical vs OE). All results for ATSR-2 and AATSR (Jun 1995 Dec 2009) Note that ARC data is on 0.1 deg grid (typically 100 pixels) vs CCI on 0.05 deg grid (25 pixels). This will increase the st. deviations for CCI. Ideally the assessment should be done on the same grid.

8 Bias-map CCI Precursor

9 Bias-Hovmoller

10 Uncertainty

11 Conclusions on assessment of SST CCI Scope for users getting confused with times associated with AATSR SSTs at depth. 2ch biases in SST significantly higher than for ARC 3ch bias in SST slightly higher than ARC Uncertainties of CCI product suggest they are reasonable but less matchups for nightime cases than for ARC Feedback suggests AVHRR SST dataset is an improvement over the pathfinder SST dataset. This is good news to extend the time series back before 1995.

12 CMUG assessments of ECVs Ocean Colour

13 Overview Comparing OC-CCI V1 and GlobColour chlorophyll Observations from September 1997 to July 2012 Created monthly climatologies for 1998 to 2011 from each product, by averaging observations on to a 1 x1 grid Calculated error covariances for each product using 2003 data Quality controlled and superobbed observations using these covariances and climatologies Control run with no assimilation from ( as spin-up) Performed runs assimilating each product for 2003 In the process of running full assimilative reanalyses for Preliminary results from 2003 runs shown here

14 log 10 (chlorophyll) observations 01/01/2003 after QC/superobbing GlobColour ( superobs) CCI ( superobs) log 10 (mg/m 3 )

15 April chlorophyll ( mean) GlobColour CCI mg/m 3

16 Model log 10 (chlorophyll) 31/01/2003 Control Assimilating GlobColour Assimilating CCI log 10 (mg/m 3 )

17 Global fco 2 error (2003) RMS error (µatm) Correlation Control GlobColour assim 71.4 (-10%) 0.36 CCI assim 68.5 (-13%) In situ fco 2 observations from SOCAT µatm

18 CMUG assessments of ECVs Clouds

19 Crown copyright Met Office Cloud droplet effective radius: CCI merged product and MODIS

20 Crown copyright Met Office Cloud liquid water path: CCI MODIS CloudSat - SSM/I

21 CMUG assessments of ECVs Ozone

22 Ozone (Merged) L3 Dataset Availability Period assessed Reanalysis TCO 3 Apr 1996 Jun 2011 Apr 1996 Jun 2011 streams ERA-Interim MACC JRA-25 Nadir Profile O 3 Jan-Dec 1997 Jan-Dec 2008 Jan-Dec 1997 Jan-Dec 2008 ERA-Interim MACC Limb O 3 Jan 1997-Dec 2008 Jan 1997-Dec 2008 ERA-Interim MACC Use CMF developed at ECMWF: interactive web-based tool to compare pre-calculated statistics of model and observations

23 Global mean total column O 3 CCI Sdev ERA-Interim is 10DU lower than MACC or CCI and annual cycle is much less. MACC reduction in ozone in Autumn is more rapid than CCI.

24 Global Nadir Profiles 5 hpa CCI NPO3 ERA-Interim MACC 10 hpa 30 hpa 100 hpa CCI NPO3 SDEV ERA-20C ES

25 Global LPO3 CCI LPO3 ERA-Interim MACC CCI LPO3 SDEV ERA-20C ES

26 Summary on O 3 products: Ø Merged Total Column Ozone (TCO3): v v v Generally good annual variability, but not in phase with MACC è maybe lagged in time (~ 1 month in the global mean); Generally good long-term homogeneity: two situations where the TCO3 time series shows sudden changes (Mar 1997, Sep 2002) are likely to be related to real atmospheric signals; Differences with ERA-Interim are ~ 10DU, a few DU from MACC O3 rean. v Quality flags may be useful also on L3. Ø Merged (Nadir and Limb) Ozone Profiles (NPO3 and LPO3): v v v Annual variability and values are reasonable (though only two years available). NPO3: values seem underestimated around 30 hpa compared with reanalyses. Near the tropopause, values are similar to ERA-Interim, but (~20%) lower than MACC. Good agreement between NPO3 uncertainties and ERA-20C ES. LPO3: good agreement with ERA-Interim and MACC at most levels

27 CMUG assessments of ECVs Aerosols

28 Aerosols Name / version Parameter Period Provider Acronym AATSR_ADV / 1.42 AOD FMI ADV AATSR_ORAC / AOD 2008 Uni. Oxford / RAL ORAC 2.02 AATSR_SU / 4.0 AOD 2008 Uni. Swansea SU AATSR_SU / 4.1 AOD Uni. Swansea SU AATSR_SU / 4.2 AOD 2008 Uni. Swansea SU SU : Improved SU nm 659nm 670nm 865nm 870nm 1610nm 1640nm over Oceans ADV Y Y Y ORAC Y Y SU Y Y Y Y MACC Y Y Y Y

29 Global Mean Total AOD:

30 550 nm over oceans: MACC +ve bias against Aeronet in 2008 (~20% globally, % over Southern Oceans). SU4.2 at 550nm over ocean shows 50-80% -ve bias from MACC, like ORAC2.02 Assimilation could improve future AOD reanalysis.

31 Long-term data quality (550 nm): Global Oceans Land Good level of agreement in the global mean SU4.1-ADV1.42, but actually

32 CMUG assessments of ECVs Soil Moisture

33 CCI - SM as a proxy for soil moisture & rainfall dynamics Soil moisture vs. precipitation anomalies ECV_SM a good proxy for precipitation anomalies MPI-ESM soil moisture vs. ECV_SM 1 ECV_SM good proxy for global SM dynamics Max-Planck-Institut für Meteorologie 1 precipitation impact removed Loew et al., 2013

34 CMUG assessments of ECVs Land cover

35 Global land 2m temperature impact minus CRU MPI-ESM minus CRU (global mean 2m temperat Relative model skill ECV_LC GCV Max-Planck-Institut für Meteorologie ctrl mean model 8% better relative to median of all simulations worse better ECV_LC has impact on global mean land temperature simulations Model skill score (Gleckler et al., 2008) ECV_LC improves global 2m temperature simulations compared to precursor (GlobCover, GCV) and standard model setup (ctrl)

36 CMUG Outlook End phase 1 The CMUG is independently assessing the CCI datasets available on 1 st Jan 2014 using observations, precursors, climate models and reanalyses. To deliver report on assessment of CCI products by 31 March 2014 Integration meeting at Met Office Exeter 2-4 June with climate modellers. Satellite world Climate world

37 CMUG-2 Climate Models Institute Model name Model components Hadley Centre MPI-M MétéoFrance HadGEM2-ES UKESM-1 GLOSEA JULES HadSST MPI-ESM, JSBACH MPI-ESM, MPIOM MPI-ESM, HAMOCC MPI-ESM, ECHAM CNRM-RCSM CNRM-CM ATM OCEAN LAND CARBON BUDGET CHEM CMIP5?!! Yes Yes Yes SMHI EC-Earth RCA HARMONIE ()! Yes DLR EMAC IPSL ORCHIDEE IPSL-CM5 Yes ECMWF ERA MACC-II ORA

38 CMUG Outlook Phase 2 Task 1: Meeting the evolving needs of the climate community Task 2: Providing integrated view of CCI & feedback to ESA and CCI teams Task 3: Assessing consistency and quality of CCI products across ECVs NEW Task 4: Exploiting CCI products in MIP experiments Task 5: Adaptation of community climate evaluation tools for CCI needs NEW Task 6: Coordination and Outreach Task 7: Interface to the European Climate Service CMUG will assess both end of phase 1 products and mid-term phase 2 products NEW NEW

39 Cross-ECV consistency SST SL Cl Sice OC Aero GHG LV Fire Ozone Glaci IC SM SST x x X X x x Sea level x x x Clouds x x X x x X x X Sea ice X x x X x x Ocean col X x x x Aerosol x X X x X x GHG x x x X Landcover x x x x x Fire x x x X x x Ozone x x X Glaciers x X x Ice Sheets X SoilM x x Strong Weaker

40 Initial proposal Cross-ECV dataset assessments Cross-Assessment of marine ECVs (SST, OC, SSH, SI) Assessment of the carbon budget using CCI datasets (LC, SM, ) Integrated assessment of the aerosol, GHG, and ozone datasets Integrated exploitation of CCI terrestrial ECVs (LC, Fire, SM) Cross-Assessment of ECVs from sea-ice with atmospheric ECVs Cross-Assessment of Aerosols, Cloud and Radiation CCI ECVs Cross assessment of clouds, radiation, aerosols, GHGs, soil moisture and SSTs Exploiting CCI products in CMIP like experiments Assessing CCI datasets as boundary conditions in CMIP5-like atmosphere simulations Adaption of community climate evaluation tools Benchmarking models with ESA CCI data in the era of CMIP6 Development of community climate dataset evaluation tools Workshops and side events

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