ESA Climate Change Initiative Climate Modelling User Group CMUG www ESA Climate Change Initiative Climate Modelling User Group CMUG www.cci-cmug.org

Overview What are the issues for climate modelling? What is the role of CMUG? What is the added value of CMUG? (requirements, errors, data formats, exploitation, lessons learnt) Some examples..

Uses of satellite data for climate To ascertain decadal and longer term changes in the climate Detection & attribution of observed variations to natural and anthropogenic forcings Evaluate the physical processes most relevant to reducing uncertainty in climate prediction To develop, constrain and validate climate models thus gaining confidence in projections of future change Input or comparison to reanalyses (e.g. ERA-CLIM, EURO4M) Seasonal and decadal model initialisation (ocean, land surface, stratosphere) To identify biases in current and past in situ measurements (e.g. radiosondes, buoys)

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

Model resolutions are increasing… E.g. the new Met Office model, HadGEM3, will have a horizontal resolution of ~ 60 km and 85 vertical levels

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 models are becoming increasingly complex… A fully coupled Earth System Model includes: Atmosphere, ocean, sea- ice, land surface Land ecosystems: vegetation, soils Ocean ecosystems: plankton Aerosols: sulphate, black carbon, organic carbon, dust, sea salt Tropospheric chemistry: ozone, methane, oxidants W. Collins et al., 2008

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

Decadal prediction:Global mean surface temperature anomaly Observations Forecast Forecast from 2007 Smith, D. M., S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris and J. M. Murphy, 2007, Improved surface temperature prediction for the coming decade from a global climate model, Science, 317, 796-799, doi:10.1126/science.1139540 Thames Barrier was designed to cope with tidal levels that were anticipated by 2030. A new Thames Barrier is being planned and this one will have to last into next century. They’ll need to know about uncertainty in predicted climate change to assess the risks and make a good decision. What is the measure of uncertainty? Requires data for both initialisation and verification of forecasts. D. Smith et al., Science 2007 © Crown copyright Met Office

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 (ERA-CLIM) 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

The way we use data for model evaluation is evolving CloudSat Forward modelling of measured quantities (radiances, radar reflectivities) rather than high-level products Increased focus on using observations to investigate physical processes in greater detail Aim is to improve the representation of these processes in climate models from A. Bodas-Salcedo et al., 2009

Objectives of CMUG Support integration within the CCI programme Through requirements and user assessment from the Climate Modelling Community Through feedback from a “climate system” perspective Foster the exploitation of Global Satellite Data Products within the Climate Modelling Community By promoting the use of CCI data sets to climate modellers By building partnership and links with existing research organisations, networks and scientific bodies of the Climate Modelling Community. Assess quality and impact of individual/combined Global Satellite Data Products in Climate Model and Data Assimilation context By assessing suitability of products for climate applications (e.g. climate modelling, decadal prediction, reanalysis, etc) By quantifying their incremental value on model performance in an objective manner.

Met Office Hadley Centre CMUG Consortium Met Office Hadley Centre HadGEM, FOAM, HadISST Roger Saunders Mark Ringer Paul Van Der Linden ECMWF IFS, ERA, MACC MPI-Meteorology ECHAM, JSBACH MétéoFrance Arpege, MOCAGE, CNRM-CM, Mercator Dick Dee David Tan Alex Loew Silvia Kloster Stefan Kinne Serge Planton Thierry Phulpin

CMUG Consortium and models ESA CCI projects Sea-level Sea surface temperature Ocean Colour Glaciers and ice caps Land Cover Fire disturbance Cloud properties Ozone Aerosols Greenhouse Gases Climate Modellers Reanalyses

Main Activities of CMUG Refining of scientific requirements derived from GCOS for climate modellers. Provide technical feedback to CCI projects Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia Look specifically at required consistencies across ECVs from a user viewpoint. Promote and report on the use of the CCI datasets by climate modellers Interact with related climate modelling and reanalysis initiatives.

Main Activities of CMUG Refining of scientific requirements derived from GCOS for climate modellers. Provide technical feedback to CCI projects Provide reanalysis data to CCI projects Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia Look specifically at required consistencies across ECVs from a user viewpoint. Promote and report on the use of the CCI datasets by modellers Interact with related climate modelling and reanalysis initiatives.

Implications for requirements The new ECV datasets must have added value over existing ones and future proof for model evolutions Start from GCOS Tables as much has been done there Be clear about applications for specific dataset as this drives the required accuracy: Climate monitoring high stability, precision and accuracy Change detection high stability, precision Evaluate processes in model high precision and accuracy Model validation high stability, precision Assimilation high precision Datasets must be globally complete (spatially and temporally) Uncertainty estimates are as important as product itself for all applications. Correlation of errors in space/time also important

Lessons learnt from past Recognise move of modellers to using lower level of products (e.g. level 1 radiances). This is especially true for reanalyses (N.B. Importance of GSICS and CLARREO) It took more than 15 years to get ISCCP cloud and ATSR SST datasets used for climate Observation simulators are important for some satellite products to compare apples with apples (e.g. clouds ..) Good statistical summaries of TCDRs help CCI projects should provide colocated datasets Essential to include error characteristics Easy access to data and simple format to read

Observation simulators Ensures comparison of equivalent model variable with observations This was the key for use of ISCCP clouds Note additional source of error from simulator in comparisons

COSP CFMIP Observational Simulator Package CloudSat CALIPSO ISCCP MISR MODIS STATS MODEL WORLD OBSERVATIONS Altitude (km) STATS Radar Reflectivity COMMON GROUND

Users are being consulted On-line questionnaire is available at: http://survey.euro.confirmit.com/wix/p416267727.aspx until 4th July. To date replies from about 25 respondents Also meeting at EGU General Assembly to gather inputs IS-ENES to provide input to questionnaire and help analyse results BADC providing advice on data format issues Climate modelling centres consulted: Hadley, UEA,MPI, IPSL, MF CNRM, Rossby Centre, GFDL, GISS, NCAR, JPL, JAMSTEC, NCEO, MRI-JMA, CMA, CAWCR, NCMWRF, KMA, …. Reanalysis centres: ECMWF, JMA, NCEP, GMAO, CIRES IS-ENES=Infrastructure for European Network for Earth System Modelling EU FP7 project 18 partners 10 countries

Speaking the same language Definition of variables has in the past been top-down New communities are more bottom up via internet fora We need to bridge the gap between EO data providers and climate modellers CMOR NetCDF is an example from the climate world Satellite Climate world world CMOR=Climate Model Output Rewriter CMOR was not designed to serve as an all-purpose writer of CF-compliant netCDF files, but simply to reduce the effort required to prepare and manage MIP model output. Although MIPs encourage systematic analysis of results across models, this is only easy to do if the model output is written in a common format with files structured similarly and with sufficient metadata uniformly stored according to a common standard. Individual modeling groups store their data in different ways, but if a group can read its own data, then it should easily be able to transform the data, using CMOR, into the common format required by the MIPs. The adoption of CMOR as a standard code for exchanging climate data will facilitate participation in MIPs because after learning how to satisfy the output requirements of one MIP, it will be easy to prepare output for other MIPs.

Data Format Issues (inputs from EGU meeting) Access: FTP, Web browser, OpenDAP,.. Level of processing: Level 1 (swath) for model assessment (N.B. needs model observation operator ideally in COSP) Level 2 (swath) for model process studies and inferring trends Level 3 (gridded) for generic model evaluation Format: CF compliant NetCDF (but what about swath data?) Projection: Lat/Long preferred Tools for reading: Dataset producers should provide these Consistency for all products produced in CCI

Main Activities of CMUG Refining of scientific requirements derived from GCOS for climate modellers. Provide technical feedback to CCI projects Provide reanalysis data to CCI projects Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia Look specifically at required consistencies across ECVs from a user viewpoint. Promote and report on the use of the CCI datasets by modellers Interact with related climate modelling and reanalysis initiatives.

Examples of CMUG input Ensure CDRs proposed are useful for climate or reanalysis aplications Ensure proposed datasets are consistent with requirements Provide a consistent framework for specification of errors Assess the need for observation simulators or other tools for exploitation

Error characterisation of CDRs An estimate of the errors for each CDR produced is essential for use in climate applications There are several types of errors Precision Accuracy Stability Representativeness 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 See next slide for definitions

Errors associated with CDRs Accuracy is the measure of the non-random, systematic error, or bias, that defines the offset between the measured value and the true value that constitutes the SI absolute standard Precision is the measure of reproducibility or repeatability of the measurement without reference to an international standard so that precision is a measure of the random and not the systematic error. Suitable averaging of the random error can improve the precision of the measurement but does not establish the systematic error of the observation. Stability is a term often invoked with respect to long-term records when no absolute standard is available to quantitatively establish the systematic error - the bias defining the time-dependent (or instrument-dependent) difference between the observed quantity and the true value. Representativity is important when comparing with or assimilating in models. Measurements are typically averaged over different horizontal and vertical scales compared to model fields. If the measurements are smaller scale than the model it is important. The sampling strategy can also affect this term.

Main Activities of CMUG Refining of scientific requirements derived from GCOS for climate modellers. Provide technical feedback to CCI projects Provide reanalysis data to CCI projects Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia Look specifically at required consistencies across ECVs from a user viewpoint. Promote and report on the use of the CCI datasets by modellers Interact with related climate modelling and reanalysis initiatives.

CMUG specific assessments ? ?

Integrated view of ECVs 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) Through comparisons of CDRs for different ECVs (e.g. SST, sea-level, sea-ice and ocean colour) 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 observation simulators for some ECVs Pre-cursors of ECVs will be used for preparation. Through studying teleconnections (e.g. El-Nino SST shows consistent impact on cloud fields, fires). Through assimilation of CDRs and to assess impact on analyses and predictions (e.g. SST in ERA-Interim)

Integrated view (TBD) ?

Landcover Land cover product ECV landcover will provide land cover information, but no land surface parameters associated with it. Model parameters Surface paramters per grid cell and PFT: e.g. Albedo Background albedo LAI faPAR Forest ratio Soil parameter Roughness length How far we can go will depend on the data we will get Best exploit high resolution observational data

Landcover Land cover product ECV landcover will provide land cover information, but no land surface parameters associated with it. Objective Generation of a consistent land surface parameter data set to be used in climate models Variable in time (no pure climatology) Additional information about variability of surface parameters per PFT at the model grid scale Evaluate the impact of the new surface parameter set on climate model simulations using ECHAM6 Provision of the data set to the climate modelling community for assessment in their models Model parameters Surface paramters per grid cell and PFT: e.g. Albedo Background albedo LAI faPAR Forest ratio Soil parameter Roughness length How far we can go will depend on the data we will get Best exploit high resolution observational data

Data used in ERA-Interim Understanding the effect of changes in the observing systems is key to understanding reanalysis quality 34

Total ozone content over the period 2000-2005 (Tesseydre et al, 2007) NIWA climatology MOCAGE-Climat Evolution des moyennes zonales du contenu total en ozone de l’atmosphère entre 2000 et 2005. Le modèle est intégré en T21 L60. Les données climatologiques sont issues du NIWA (New Zealand). Il s’agit du résultat d ’une assimilation de données combinant 4 instruments TOMS (Total Ozone Mapping Spectrometer), 3 restitutions différentes d’instruments GOME (Global Ozone Monitoring Experiment), et des données d’instruments SBUV (Solar Backscatter Ultra-Violet). Cette climatologie couvre la période 1978-2005 avec une résolution de 1,25° en longitude et 1 ° en latitude. On note les maxima de concentration aux hautes latitudes à la fin de l’hiver et l’apparition du « trou » d’ozone au printemps, début de l’été (octobre-novembre dans l’HS. Le modèle sur-estime l’accumulation d’ozone à la fin de l’hiver dans la basse à moyenne statosphère en raison notamment d’une circulation de Brewer-Dobdon trop rapide. Il n’y a pas de biais dans l’UTLS. The variability of the ozone column, both spatially and temporarily, is satisfactory. However, because the Brewer-Dobson circulation is too fast, too much ozone is accumulated in the lower to mid-stratosphere at the end of winter. Ozone in the UTLS region does not show any systematic bias. In the troposphere better agreement with ozone sonde measurements is obtained at mid and high latitudes than in the tropics and differences with observations are the lowest in summer (Tesseydre et al, 2007).

Use of ISCCP to evaluate models Low level cloud: CTP < 680 hPa ISCCP HadGEM1 HadCM3 Thick “Stratus” OD > 23 Medium “Stratocu” 3<OD<23 Martin et al. (2006)‏

Main Activities of CMUG Refining of scientific requirements derived from GCOS for climate modellers. Provide technical feedback to CCI projects Provide reanalysis data to CCI projects Assess the global satellite climate data records (CDRs) produced from the 10 CCI consortia Look specifically at required consistencies across ECVs from a user viewpoint. Promote and report on the use of the CCI datasets by modellers Interact with related climate modelling and reanalysis initiatives.

Related Activities GCOS and GSICS EU (IS-ENES, EUGENE, ..) EUMETSAT (CM-SAF) and SCOPE-CM NOAA-NASA initiatives (e.g. JPL CMIP5) WCRP Observation and Assimilation Panel Reanalyses (ERA-CLIM, MACC) + EURO4M Coupled Model Intercomparison Project and follow-on activities IPCC AR-5 and AR-6

Proposed CMIP5 model runs CCI datasets could start to be used in the evaluation of these results AR-5

Summary CMUG has now started to support the ESA CCI. We are seeking input from the climate modelling and reanalysis communities. It is crucial the products produced are ‘fit for purpose’ otherwise this will be a lost opportunity (and wasted money). If interested please get in touch via cmug@metoffice.gov.uk

Any questions? www.cci-cmug.org cmug@metoffice.gov.uk

Definition of terms Essential Climate Variables are defined as geophysical variables required to characterise the state of the atmosphere and surface and its variability over decadal and longer time periods. Climate Data Records are a long time series dataset e.g. from several consecutive satellite instruments, where the same processing has been applied to all the data and overlap periods have been used to remove any biases so it can be used for climate monitoring. Note also FCDRs (level 1b) and TCDRs (products). Global Satellite Data Product is used for any generic satellite product that is already available (e.g. for NWP assimilation) but has not been reprocessed with climate applications in mind. However some satellite products although not suitable for climate trends can be valuable for studying processes in climate models (e.g. ISCCP). There are some data sets that are produced for climate applications but not necessarily for climate monitoring, of which ISCCP is a good example. ISCCP makes a good point, namely that a data set that is very useful for model development and evaluation doesn’t necessarily have to be capable of detecting /quantifying trends. We need to ensure that in pursuing the latter people don’t lose sight of the former.

GCOS ECVs Ocean Atmosphere Land Sea-Surface Temperature Precipitation Earth Radiation Budget Upper-air Temperature Upper-air Wind Surface Wind Speed and Direction Water Vapour Cloud Properties Carbon Dioxide Methane Other GHGs Ozone (tropospheric) Ozone (stratospheric) Aerosol Properties Snow Cover (Extent, Snow Water Equivalent) Glaciers and Ice Caps Permafrost and seasonally -frozen ground River Discharge Lake levels Albedo Land Cover fAPAR Leaf Area Index Biomass Fire Disturbance Soil Moisture (surface and root zone) Sea-Surface Temperature Sea Level Sea Ice Sea State Ocean Salinity Ocean Colour The individual consortium proposals for each ECV are now starting work. It will be important to ensure that different ECVs are consistent with others (e.g. clouds and aerosols) and the satellite datasets produced are of use to climate modellers.