Regionalización dinámica: la experiencia española

Transcription

1 Regionalización dinámica: la experiencia española William Cabos Universidad de Alcalá de Henares Madrid, Spain Thanks to: D. Sein M.A. Gaertner J. P. Montávez J. Fernández M. Domínguez L. Fita M. García-Díez G. Liguori N. López de la Franca P. Jiménez-Guerrero R. Romera E. Sánchez... 8º Congreso internacional de la AEC Septiembre 2012, Salamanca

2 OUTLINE ESCENA project Some questions about the model domains Project results Conclussions

3 Aims ESCENA PROJECT Dynamical downscaling of global historical and scenario simulations from AR4 (plus ERA-Interim evaluation simulations) Coordination of Spanish regional modelling groups Funding Strategic Action on Energy and Climate Change Ministerio de Agricultura, Alim. y Medio Ambiente

4 PARTNERS Coordination of Spanish regional modelling groups Univ. de Castilla-La Mancha (PROMES) Univ. de Murcia (MM5) Univ. de Cantabria (WRF) Univ. de Alcalá de Henares (REMO)

5 PROMES REGIONAL CLIMATE MODELS PROnóstico a MESoescala, by UCLM MM5 WRF Mesoscale Model version 5, by PSU/NCAR Weather Research & Forecasting Model, by NCAR 2 different PBL schemes: WRF-A and WRF-B REMO REgional MOdel, by MPI-M

6 Atmospheric General Circulation Climate Model. -developed in the Max Planck institute of Metereology -Hidrostatic ( non hydrostatic soon!) - maximal grid resolution: 1/10 degree. This grid resolution enables the model to resolve the most important topographic features -Sofisticated vegetation scheme -State of the art soil, hydrographic and glaciar models -Widely used for climatic and impact studies in Europe -Echam4 physics -Rotated coordinates REMO

7 DOMAINS

8 QUESTIONS Is there added value in such a project? Why not use ENSEMBLES (EUROCORDEX)? Domain location? Which resolution? 50, 25, 11, km? RESOURCES!! At which extent regional climate should reproduce the signal from global models? internal variability (RCM) over external variability (GCM)?

9 RCM ADDED VALUE AGAINST GCM JJA total precipitation biases (Model CRU/HOAPS). MPI-ESM (left), REMO uncoupled (center), REMO coupled with MPIOM (right)

10 DOMAIN LOCATION PROMES / MM5 / WRF Lambert Conic 25Km REMO Rotated 0.22º ENSEMBLES Project model domains

11 DOMAIN LOCATION Advantages of ESCENA with respect to previous projects: - Domain centered over Iberian Peninsula - Includes Canary Islands and Madeira EUROCORDEX Region (center of boundaries

12 INTERNAL VS EXTERNAL VARIABILITY: THE ARCTIC OCEAN (Sein el al, 2014) REMO / Global MPIOM coupled setups Colored spherical rectangles show REMO domains. FIVE REMO SETUPS THE SAME OCEAN REMO setups MPIOM grid (every 12th grid line is shown)

13 Mean ( ) ERA40 DJF 2m temperature and difference Model ERA40 [K]

14 Mean DJF common (upper row) and internal (center row) sea level pressure variability. The black polygon indicates the common area of all the five coupled domains. Lower row: log10 of relative variability (common (CV) divided by internal (IV))

15 PROJECT RESULTS EXPERIMENTS DATA SET FOR VALIDATION MODEL DATA OUTREACH ENSEMBLE EVALUATION CLIMATE SIGNAL

16 EXPERIMENTS Evaluation simulations ECMWF ERA-Interim Reanalysis Period: Historical + scenario simulations AR4 / CMIP3 GCMs 3 GCMs (ECHAM5, ARPEGE3, HadCM3) 2 param. perturbations (high / low temp. sensitivity) Historical GHG forcing (20C3M, ) Scenario GHG forcing (SRES A1B/A2/B1, )

17 OBSERVED DATA FOR MODEL EVALUATION SPAIN02 dataset (Herrera et al., 2010; U. de Cantabria) Variables: daily precipitation, daily maximum/minimum temperature Period: Regular lat-lon grid (0.2º resolution) Observation stations used

18 GCM/RCM matrix The GCM/RCM matrix was filled. 2 RCMs (PROMES, MM5) were nested to all GCMs and scenarios 1 GCM forced by 1 scenario (ECHAM5-A1B) was downscaled by all RCMs This enabled a number of sensitivity studies

19 OUTPUT DATA A large number of surface and upper-air variables are available. The frequency stored depends on the variable (mainly 3h and daily) Other variables and/or frequencies are available from the groups. [ Contact the groups directly ]

20 PROJECT WEBSITE

21 PROJECT DATA SERVER A THREDDS data catalog serves the data on the native grid

22 ENSEMBLE EVALUATION: TMAX BIAS DAILY MAXIMUM TEMPERATURE BIAS (ºC) 2 papers: Jiménez-Guerrero et al. (Clim. Res, 2013) Domínguez et al. (Clim. Res, 2013)

23 ENSEMBLE EVALUATION: PRECIPITATION BIAS PRECIPITATION BIAS (%)

24 ENSEMBLE EVALUATION: basin spatial average of precipitation extreme indices

25 ENSEMBLE EVALUATION: DJF BIASES Adapted from: Jiménez-Guerrero et al., Clim. Res.

26 2-3 degrees by 2050 CLIMATE CHANGE SIGNAL: TMAX Projected delta changes in summer

27 EOF1 as climate change signal Fernández et al., in preparation

28 PROJECTED MAX. TEMPERATURE GCM direct output ESCENA RCMs

29 5 Tmax (anual) Winter (DEF) Verano (JJA) 0

30 Change sign not clear PROJECTED PRECIPITATION: WINTER Projected delta changes in winter

31 Change sign not clear PROJECTED PRECIPITATION: SUMMER Projected delta changes in summer

32 50% Precip. (anual) Invierno (DEF) Verano (JJA) 0

33 WIND POWER (Gómez et al, 2015) Regions of the Iberian Peninsula with coherent temporal variability in wind speed in each of the models are identified and analyzed using cluster analysis. These regions are continuous in each model and exhibit a high degree of overlap across the models. Identied regions are similar for ERA-INTERIM and GCM forcing ERA INTERIM ECHAM6 MPIOM

34 WIND POWER CHANGE A1B ESCENARIO: ( ) - ( ) Using the identified regions we quantified the impact of climate change on wind power in a robust way

35 CONCLUSIONS ESCENA provides an ensemble of RCM simulations over Spain. The ensemble explores the sensitivity to: Emission scenario GCM formulation GCM parameter perturbations RCM formulation RCM parameterizations (PBL) RCMs provide a wide range of physically-consistent climate variables, but RCMs have biases, which need to be taken into account when using the data in impact studies.

36 CONCLUSIONS 2 The RCMs essentially follow the changes projected by the GCMs, however... regional details depend on the RCM (even the sign, in the case of precipitation) Analyses using these data should propagate the uncertainty by using as many simulations as possible (ideally, the 19 available).

37 Gracias! Contact: More info on ESCENA: visit

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39 Projected precipitation

40 Projected precipitation

41 Projected precipitation

42 1-2 degrees by 2050 Projected min. temperature Projected delta changes in winter

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