Presentation on theme: "CMUG Climate Modelling User Group Roger Saunders Met Office Hadley Centre."— Presentation transcript:
CMUG Climate Modelling User Group Roger Saunders Met Office Hadley Centre
Overview and Meeting Aims Some key points from climate modelling perspective Meeting aims Inputs and outputs Wider perspective
Met Office Hadley Centre Climate Modelling NWP HadGEM3, FOAM, HadSST ECMWF Reanalyses NWP IFS (ERA-Interim) MACC MPI-Hamburg Climate Modelling MPI-M/ESM, JSBACH MétéoFrance Climate Modelling NWP Arpege, MERCATOR CNRM-CM, MOCAGE Climate Modellers Reanalyses Sea-ice Sea-level Sea surface temperature Ocean Colour Glaciers and ice caps Land Cover Fire disturbance Cloud properties Ozone Aerosols Greenhouse Gases CMUG is here to facilitate
CMUG folks here ECMWF IFS, ERA, MACC Dick Dee MPI-Meteorology ECHAM, JSBACH MétéoFrance Arpege, MOCAGE, CNRM-CM, Mercator Met Office Hadley Centre HadGEM, FOAM, HadISST Thierry Phulpin Paul Van Der Linden Alex LoewSerge PlantonDavid Tan Roger SaundersMark Ringer Silvia KlosterStefan Kinne Iryna Khlystova X X
Issues for climate modelling Higher resolution (horiz, vertical, time) Regional climate prediction (e.g. UKCP) More physical processes Seasonal to decadal prediction Use of reanalyses for climate Seamless prediction - weather prediction to climate change using same model Metrics developed to evaluate models – CCI datasets can help here The way we use observational data is evolving
Climate monitoring and attribution Different groups can produce defensible, but statistically inconsistent estimates of trends. Need for better error characterisation
Error characterisation of CDRs An estimate of the errors for each CDR produced is essential for use in climate applications The types of errors recently defined by GCOS Accuracy: The rms difference between the single or averaged values of a variable and the truth. Stability: The extent to which accuracy of a time average remains constant over a longer time period (e.g., annual average relative to decadal average). The importance of specifying each depends on the application Errors should be specified on a FOV basis. Aggregated error estimates are not sufficient Single sensor products are simpler than merged products Error correlations are also important to document
Use of observations evolving.. Forward modelling of measured quantities (radiances, skin SST, radar reflectivities) rather than high-level products (profile retrievals, bulk SST, cloud properties) Ensures more direct comparison of equivalent model variable with observations This was the key for use of ISCCP clouds Observation simulator
HadGEM1 (MO)MMF 4km (CSU)CloudSat MMF 1km (CSU)LMDZ (CNRS)dBZ>-25 (Bodas-Salcedo et al., submitted to BAMS) Multi-model analysis using satellite simulators
Implications for requirements The new ECV datasets must have added value over existing ones and future proof for model evolutions Datasets should have global coverage and for some applications >15 years Be clear about applications for specific dataset as this drives the required accuracy: Climate trend monitoring high stability and accuracy Change detection high stability Evaluate processes in model high accuracy Model validation high stability and accuracy Assimilation high accuracy (and stability) Uncertainty estimates are as important as product itself for all applications. Correlation of errors in space/time also important
Validation of SST Coverage of buoys Buoy validation of ARC SST But what about ocean colour?
Meeting Aims Check ECV project URDs are consistent with the needs of Climate Research Groups and GCOS requirements, including source traceability Allow ECV teams to explain how their projects address the integrated perspective for consistency between the ECVs to avoid gaps Start review of product specifications Discuss how to deal with uncertainties in products Finalise the ECV projects data needs for ECMWF reanalysis data Start a discussion on ECV data set validation Maintain oversight of the position within the international framework in which CMUG/CCI is operating
URDs: Common Issues CMUG report on CCI URDs D2.1: Define period of TCDRs (1 month-30 years?) Clear specification of requirements for which application Some ECVs need clearer error specifications Merged vs single sensor products More interaction with climate modellers in some cases Consideration of model simulators where required Consistency between ECVs
Integrated view of ECVs 1.Through ensuring common input datasets are used for CDR creation and in some cases common pre-processing (e.g. geolocation, land/sea mask, cloud detection) 2.Through comparisons of CDRs for different ECVs (e.g. SST, sea-level, sea-ice and ocean colour) 3.Through comparisons of CDRs with model fields (e.g. GHG and Ozone CDRs and MACC model profiles/total column amounts) CMUG will be involved in development of some observation simulators. Pre-cursors of ECVs will be used for preparation. 4.Through studying teleconnections (e.g. El-Nino SST shows consistent impact on cloud fields, fires). 5.Through assimilation of CDRs and to assess impact on analyses and predictions (e.g. SST in ERA-Interim)
Outputs from meeting Meeting report of actions agreed by ECV projects [including updates to URDs and Product Spec. docs] Meeting report describing strategic position of the CMUG, within CCI, in the international arena Material to inform revision of CMUG reports Clarity on requests for ECMWF reanalysis data Clarity on early demonstration of products (if feasible) to modellers.
Related Activities 1.GCOS, GSICS (Jan/Feb 2011) 2.EUMETSAT CAF/CMSAF and SCOPE-CM 3.NOAA-NASA initiatives (e.g. JPL CMIP5) 4.WCRP Observation and Assimilation Panel (Apr 11) 5.Reanalyses (ERACLIM, JRA-55, EURO4M) 6.Coupled Model Intercomparison Project and follow-on activities (Exeter, June 11) 7.Inputs to IPCC AR-5/6 (interaction with authors) 8.EU IS-ENES, METAFOR, … 9.EU GMES (MACC, MyOcean, Climate, ….)
We dont want to leave our climate research scientists like this! But like this!
Any questions? Please visit www.cci-cmug.org firstname.lastname@example.org email@example.com
Proposed CMIP5 model runs AR-5 CCI datasets could start to be used in the evaluation of these results Proposed CMIP5 model runs
Example of different errors Bias 0.1K Accuracy 0.19K Stability 0.05K/decade Time SST Buoy Representativity and sampling