WP21 Calibration and Downscaling Aims To develop & apply bias-correction and downscaling methods that provide localized added-value (for specific users/applications) on GCM seasonal-decadal hindcasts (demonstration/evaluation) & forecasts. To provide downscaled standard climate variables and more advanced climate indices for EUPORIAS case studies To assess and quantify uncertainties (in/introduced by) the downscaling methods (in collaboration with WP31 & 32) To make downscaled/calibrated/bias-corrected data available through The EUPORIAS web-portal Main Regions: Europe and East Africa Main Timescales: Seasonal (1-6 months) some inter-annual (<10 years)

Europe: Seasonal to Decadal hindcasts Relevant downscaled products are either already established with known users and/or further refined through stakeholder dialogue in WP12 GCM runs to be used? ENSEMBLES seasonal hindcasts, EUROSIP, Glosea forecasts, CMIP5 decadal hindcasts?? SPECS hindcast sets?? Others?? Good if we used a common GCM data set (for hindcast period) with a standardized access mechanism (this should be decided early). Need to consider hindcast/forecast drift (bias) as a varying function of forecast lead time and hindcast start date. Would be good if best-observations (per region, variable) were made available across the consortium (e.g. for bias correction) and even a final common set of Europe-wide bias-corrected hindcast fields ?

Methods: A mix of homegrown statistical approaches: Europe: Seasonal to inter-annual timescales. UC: Will target climate indices as derived through user dialogue in WP12. Methods will build on their existing MeteoLab statistical toolbox. KNMI: Advanced delta-method for bias-correction/calibration of GCM- simulated precipitation as input to river Rhine hydrological model. SMHI: ESD of GCM forecast large-scale circulation fields to basin-scale seasonal discharge for hydropower stakeholders. Use of bias-corrected precip (via DBS method) in E-HYPE to provide European Flood risk estimates. EDF: Analogue methodology for downscaling GCM precip/temperature as input to EDF hydrology models. MF: 2 SD methods (physically-based bias correction and weather-type sampler) used to downscale seasonal forecasts to drive SVAT and river routing model MeteoSwiss: Assess added-value of advanced SD data (emanating from this task) compared to simpler CIIs derived direct from GCM hindcasts (presumably same GCM set) with emphasis on insurance sector.

Where possible it would be good to share a given SD method to provide data into another application/region. e.g. can SMHI circulation-to-catchment scale precipitation SD method be used for KNMI Rhine River application? etc etc Can we achieve a project-accepted bias correction method for a given variable/time-frequency to apply once to a shared GCM hindcast data set Bias-correction procedure for S2D forecasts is (somewhat) different from bias-correcting standard climate projections. In the latter a single climatological (annual-cycle) bias-correction can be derived. With S2D forecasts the same can be done, but must be for each forecast lead time The bias-correction for April(Y1) of a forecast initialized the November(Y0) before will not be the same as the bias correction for April(Y2) from the same forecast start date. Nor will April (Y1) bias correction be the same if the forecast was initialized in June(Y0) instead of Nov(Y0). A bias-correction data set needs to sample: All start dates, All forecasts lead-times and sufficient number of years and ensemble members per start date to form a forecast-lead time climatological bias correction.

Nov 2000 Nov 2001 Nov 2002 Nov 2003 Nov 2004 Nov 2005 Nov 2006 CMIP5: ensemble forecast systems using an initialized ESM Ensemble initialized near-term predictions Forecast time 5 years Core Tier 1

observational dataset } re-forecasts Common period Time Climatology of the first forecast year Estimating the climate for each forecast lead time

© Crown copyright Met Office (Hawkins and Sutton, 2011) Projection Uncertainties Large uncertainties in model response to external forcing Need multi-model ensembles and to understand physical mechanisms Projections of Dec-Feb decadal rainfall

Task 21.2 Statistical-Dynamical downscaling of GCM seasonal forecasts over East Africa as input to WFP Africa LEAP system 4 RCMs contribute by downscaling the same (EC-Earth) GCM Seasonal forecasts. RCMs: RCA4(SMHI), COSMO(DWD), RegCM4(ENEA), WRF(UC, UL-IDL, IPMA) Original idea: Use anomaly-initialization (AI) with EC-Earth and DD an ensemble of seasonal forecasts (~6 months). This will limit model drift with forecast lead time with RCMs forecasting anomalies from the climatology for the region. A parallel set of EC-Earth/RCM DD runs using period of the CMIP5 EC- Earth run used for anomaly initialization provides a climatology for each EC- Earth/RCM couplet to which the forecast anomalies can be compared. Forecast anomalies will also be compared to observed anomalies for the same hindcast seasons. Extra tasks: The above will use a representative set of (~4-5 different) years for downscaling that satisfy (i) Good observational cover, (ii) high societal importance and (iii) climate anomalies of differing sign (wet/dry), likely linked to ENSO phases Design a method to select a sub-ensemble (of GCM forecasts) for DD (e.g. reduce a set of ~30 ensemble members to a representative set of ~10 for downscaling). Compare DD results to GCM data and SD methods from 21.1 (UC) applied to the same GCM hindcasts for East Africa.

(Smith et al. submitted) Reducing Model forecast drift: Full field versus anomaly initialization In anomaly initialization, observed anomalies (e.g ocean & sea-ice state) are added to the model’s climatology to create an initial state: Drift during forecast should be reduced

Observed Nino composite Model composites Hindcast skill (Smith et al. submitted) DJF hindcast precipitation for ENSO composites months 2-4 forecast lead-time UK Met Office system using both anomaly and full field initialization

Full field initialized GCM hindcasts better capture seasonal timescale ENSO teleconnections (higher correlations over S.America & E. Africa). Likely due to more accurate atmospheric state in FF runs for teleconnective (wave) motions. Anomaly-initialization method seems more promising for dynamical downscaling as it reduces drift of (model level) fields used as boundary conditions for RCMs. But seems overall less accurate on seasonal timescales 2 nd option: Use full-field initialization, then bias-correct GCM hindcast/forecast SST/SIC (using a ~ set of hindcasts) and repeat GCM runs for same period using bias-corrected SST/SIC that retain GCM forecast anomalies. This technique has been used before for climate projections we are presently testing such an approach for CMIP5 decadal hindcasts with EC-Earth