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

2. 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)

3. 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?

4. 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)

5. 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

6. 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)

7. 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…

8. 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)

9. 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

10. 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

11. Tools and support
CWT tools from MiKlip project (decadal predictions) available
Experiences from the ReKliEs (Regionale Klimaprojektionen Ensemble) project
Collaboration with the developers of WETTREG/ EPISODES at DWD Potsdam

12. 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)

13. 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 ).

14. 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: (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: (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: (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)