Clim2Power WP2: the next six months Project Meeting Part 2, April 6th 2018 Jennifer Ostermöller & Kristina Fröhlich

T2.2 Generation of downscaled seasonal forecasts Evaluate different methods of stat. downscaling Test with reference data set Apply to seasonal forecasts Structure is also kind of an outline of the talk Literature research (this talk)

Downscaling of seasonal forecast Methods Dynamical downscaling  up to now: limited skill of the forecast  uncertainties would propagate to the RCM Calculations expensive Statistical downscaling  find representative circulation weather types  resampling of the time series of the evaluation run Physical consistency of the variables?

Methods: empirical-statistical downscaling ADVANTAGES CHALLENGES Based on the assumption that physical relationships remain unchanged in the future  assumption should hold for seasonal time scales Only small bias expected Low computational costs Maybe even reduce/subsample global ensemble (NAO index) Seasonal forecast  GCM drift expected Methods are developed and optimized for other applications Less variables Climate projections Smaller domains Maybe downscaling for each of the four case studies necessary? (rather than for the whole EURO-CORDEX region)

Methods: empirical-statistical downscaling Statistical downscaling methods STARS (Potsdam-Institut für Klimafolgenforschung Orlowsky et al. 2008, Hoffmann et al. 2014) WETTREG (Climate & Environment Consulting Potsdam GmbH Kreienkamp et al. 2013) EPISODES (DWD, Kreienkamp et al. 2018, accepted )  new method, promises consistent time series used at DWD

Methods: statistical downscaling WETTREG (WETTerlagen-basierte REGionalisierungsmethode, Kreienkamp et al. 2013) Circulation weather types Regressions Weather generator Temperature and precipitation classes (each grid point, divided by seasons) Classes are associated with circ. types Determine CWT from global model  assign class at each grid point  resampling: synthetic time series calculated by weather generator Figures: Kreienkamp et al. (2013)

Methods: statistical downscaling EPISODES (Kreienkamp et al. 2018) Further development of WETTREG Is applicable to climate projections, decadal and seasonal forecast as well Two-step procedure Day-by-day downscaling (GCM  regional) uses perfect prognosis method to find analogue days  regression of predictand and predictor  daily time series of meteorological variables Weather generator creates synthetic time series  merge climatology, climate change signal and short-term variability (not necessary for seasonal predictions) Relative humidity needed…

Methods: statistical-dynamical downscaling Statistical-dynamical downscaling of 10-m wind speed to calculate wind energy output (Reyers et al. ,2015) 77 weather classes (based on CWT and geostrophic flow) Representative days of each class simulated by CCLM (DD) Weather class frequencies recombined to PDFs Figures: Reyers et al. (2015)

Methods: statistical-dynamical downscaling For the whole domain of Europe, 30 or even 77 weather classes could be way too many It is very likely that some classes are underrepresented  reduce number of weather classes  weather regimes Weather regimes maintain several weeks Take advantage of climate indices (e.g. NAO) NAO > 0 (cyclonic regime) NAO < 0 (blocked regime)  Reduce number of ensemble members of global forecast Determine weather regimes for Europe Grams et al. (2017): 7 weather regimes in the Atlantic-European region Cyclonic: AT, ZO, ScTr Blocked: AR, EuBL, ScBL, GL

Other possible methods Principal Component Analysis (PCA) using transfer functions between predictor and predictand and subsequent regression (Krähenmann et al., in prep.) Calculated separately for each variable PCA of HR field (CCLM eva.) for each month  PC Loadings Adaption of PC Loadings to LR fields (global seas. forecast) Regression with PC Loadings as predictors for each time step Calculation of background fields Calculation of residuals Inverse Distance Weighted interpolation of residuals

Tools and support CWT tools from MiKlip project (decadal predictions) available https://www-miklip.dkrz.de/ Experiences from the ReKliEs (Regionale Klimaprojektionen Ensemble) project Collaboration with the developers of WETTREG/ EPISODES at DWD Potsdam

Outlook to the next six months Delivering the full reference data set on ESGF Further research and tests of methods Decision making for preferred method (autumn 2018) First downscaling of seasonal forecasts (winter 2018/2019)

Thank you for your attention Project Clim2Power is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), FCT (PT), EPA (IE), ANR (FR) with co-funding by the European Union (Grant 690462).

References M. Reyers, J. Pinto and J. Moemken, Statistical-dynamical downscaling for wind energy potentials: evaluation and applications to decadal hindcasts and climate change, International Journal of Climatology, 35: 229-244 (2015) C. M. Grams, R. Beerli, S. Pfenninger, I. Staffell and H. Wernli, Balancing Europe’s wind-power output through spatial deployment informed by weather regimes, Nature Climate Change, 7: 557-564 (2017) F. Kreienkamp, A. Paxian, B. Früh, P. Lorenz and C. Matulla, Evaluation of the Empirical-Statistical Downscaling method EPISODES, Climate Dynamics, accepted (2018) F. Kreienkamp, A. Spekat and W. Enke, The Weather Generator Used in the Empirical Statistical Downscaling Method, WETTREG, Atmosphere, 4: 169-197 (2013) B. Orlowsky, F. W. Gerstengarbe and P. C. Werner, A resamplingscheme for regional climate simulations and its performance compared to a dynamical RCM, Theor. Appl. Climatol., 92: 209—223 (2008)