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ACRE Workshop, June 23-25, 2008. Zurich http://www.mdm.unican.es The ENSEMBLES Statistical Downscaling Web Portal. End2End Tool for Regional Projection Antonio S. Cofiño D. San-Martín, J.M. Gutiérrez, C. Sordo, J. Fernández, D. Frías, M.A. Rodríguez, S. Herrera, R. Ancell, M. Pons, B. Orfila, E. Díez Meteorology & Data Mining Santander Group Motivation http://www.mdm.unican.es There are many projects around the world producing global (GCM) and regional (RCM) simulations of climate change. Many of these projects involve end-uses from impact sectors ... However, it is still difficult for end-users to access the stored simulations and to post-process them to be suitable for their own models: daily resolution, interpolation to prescribed locations, etc. There is a need of friendly interactive tools so users can easily run interpolation/downscaling jobs on their own data using the existing downscaling techniques and simulation datasets (AR4, Prudence, ENSEMBLES, ...). Statistical Downscaling (SD). WHY? http://www.mdm.unican.es Typical resolution of climate change GCMs. ECHAM5/MPI-OM (200 km) There is a gap between the coarse-resolution outputs available from GCMs and the regional needs of the end-users. Surface variables: • Mean precipitation • Precip. 90th percentile • Consecutive dry days • Number of heavy events Typical resolution of Seasonal GCMs. Even if they work at seasonal or climate change scales, end-users normally need daily values interpolated (downscaled) to the local points or grids of interest. Statistical Downscaling (SD). HOW? http://www.mdm.unican.es Emission Scenarios Dynamical Downscaling runs regional climate models in reduced domains with boundary conditions given by the GCMs. Global Predictions GCM RCM A2 Historical Records B2 A2 Climatology (1961-90) Y = f (X;) The form and parameters of the model depend of Statistical Downscaling is based on the different empirical models fitted to data using tecniques used. historical records. A2 SD. Transfer Functions Precipitaion, temperatures, etc. (T(1ooo mb),..., T(500 mb); Z(1ooo mb),..., Z(500 mb); .......; Yn Linear Regression: TMaxn = a+b T850n Neural Networks: Local Records of Tmax http://www.mdm.unican.es H(1ooo mb),..., H(500 mb)) = Xn TMaxn = f (T850n ), Neural Net. Linear model 850mb GCM outputs (closest grid point) Neural networks are non-parametric models inspired in the brain. SD. Weather Types (e.g. analogs) Analog set http://www.mdm.unican.es PC2 frequency mean PC1 Weather Type (cluster) Pforecast (precip > u) = SCk P(precip > u | Ck) Pforecast(Ck) Skill of Statistical Downscaling http://www.mdm.unican.es The variability of the results obtained using different types of downscaling models in some studies suggests the convenience of using as much statistical downscaling methods as possible when developing climate-change projections at the local scale. For some indices and seasons, the spread is very small (e.g. pav in JJA) but for others it is much larger (e.g. pnl90 in DJF). Importantly, for each index the variability among models is of the same order of magnitude as the variability between the two scenarios. DOWNSCALING HEAVY PRECIPITATION OVER THE UNITED KINGDOM: A COMPARISON OF DYNAMICAL AND STATISTICAL METHODS AND THEIR FUTURE SCENARIOS (HAYLOCK ET AL. 2006) http://www.mdm.unican.es www.meteo.unican.es/ensembles 30 users from 20 partners http://www.mdm.unican.es Computing Infraestructure Data Availability. Observations http://www.mdm.unican.es • ECA (European Climate Assessment & Dataset project). Daily datasets of precipitation, temperature, pressure, humidity, cloud cover, sunshine and snow depth since 1900 over networks of 100-1000 stations. • Ensembles 50km gridded daily observation records of precipitation and surface temperature. 1950-2006. http://www.mdm.unican.es Data Availability. GCMs Data Availability. Reanalysis & S2D Data available for the European region: http://www.mdm.unican.es • NCEP/NCAR Reanalysis1. 1948-2007 • ERA40 ECMWF: 1957-2002 • JRA25 Japanese Reanalysis: 1979-2004 A smaller worldwide dataset is also available Available for Europe • DEMETER. Multi-model seasonal prediction experiment including seven models ran for six months four times a year using 9 different perturbed initial conditions (9 members). • ENSEMBLES Stream 1. Check the help in the portal for updated information about this dataset. Data Availability. ACC Daily worldwide datasets obtained from different sources: http://www.mdm.unican.es • CERA • IPCC data centre (PCMDI) • Local Providers. • PCMDI_CGCM3. Canadian Centre for Climate Modelling and Analysis, including 20th century (from 1951 to 2000) and scenarios A1B, B1 (periods 2046-2065 and 2081-2100). • CERA_MPI-ECHAM5, including 20th century data (1961-2000) and scenarios A1B, B1, and A2 (2001-2100). • CNRM-CM3 (local provider), including 20th century (1961-2000) and scenarios A1B, B1, and A2 (2001-2100). We will continue including datasets as they become available. http://www.mdm.unican.es Data Access Portal 60,Potential Vorticity,PV 129,Geopotential,Z 130,Temperature,T 131,U velocity,U 132,V velocity,V 133,Specific humidity,Q 136,Total Column Water,TCW 137,Total Column Water Vapour,TCW 138,Relative vorticity,VO 142,Large Scale Precipitation,LSP 143,Convective Precipitation,CP 151,MSLP,MSL 155,Divergence,D 157,Relative humidity,R 165,10m E-Wind Component,10U 166,10m N-Wind Component,10V 167,2m Temperature,2T 168,2m Dew Point,2D 1000, 925, 850, 700, 500, 300 mb 00, 06, 12, 18 , 24 UTC 1.125ºx1.125º resolution http://www.mdm.unican.es Data Access: s2d & acc Statistical Downscaling Portal http://www.mdm.unican.es Problem: Local climate change prediction for Madrid (Spain): maximum temperature Goal: Provide daily local values for the summer season june-august 2010-2040 in a suitable format (e.g., text file, or Excel file). Predictors (T(1ooo mb),..., T(500 mb); Regional zone Local zone Z(1ooo mb),..., Z(500 mb); H(1ooo mb),..., H(500 mb)) Xn Downscaling Model Regres, CCA, … Yn = WT Xn Predictands Precipitation Temperature Yn This is the structure followed in the portal’s design: predictors + predictand + downscaling method. Demo... My Home http://www.mdm.unican.es The “My Home” tab allows the user to explore: 1. The zones (pre-defined regions). 2. The profile with the account information. 3. The status of the jobs: queued, running, finished. Demo... Predictors http://www.mdm.unican.es A simple zone with a single predictor parameter: T850mb was created. New zones can be easily defined by clicking in the “new zone” button. http://www.mdm.unican.es Demo... predictand http://www.mdm.unican.es Demo... Downscaling Method http://www.mdm.unican.es Demo... Validation http://www.mdm.unican.es Demo... Regional Projection Demo...Time to Compute http://www.mdm.unican.es Scheduling the job Five minutes later ... http://www.mdm.unican.es Climate Change Scenarios Max. Temp Seasonal validation (RSA) http://www.mdm.unican.es Precip Tmax ... windows of opportunity with ENSO http://www.mdm.unican.es Teleconnections with ENSO may bring some seasonal predictability to the extra-tropics and, thus, some window of opportunity for operational seasonal forecast. http://www.mdm.unican.es ... windows of opportunity with ENSO Current Status • Support for users in ENSEMBLES project. http://www.mdm.unican.es • Data access for Reanalysis, Seasonal2Decadal and Climate Change models. • Users can use common observations datasets or upload their own data for downscaling. • Data access control based on user authentication and authorization. • User can choose predictors, predictands and transfer function to be used in the downscaling process. • Quality assessment of the downscaling. • Download the downscaled data. Future actions http://www.mdm.unican.es • Start to open the tool to wider community outside ENSEMBLES project and Europe. • Data access and storage: towards remote accessing of datasets based on OPeNDAP. Reanalysis, S2D & ACC simulations. • Incorporate more statistical downscaling tools. • Ongoing work on geographically distributed computing and storage based on GRID technologies (EGEE, EELA,…). Thank you !!! http://www.mdm.unican.es [email protected]