A Pragmatic Approach to Build a Reduced Regional Climate Projection Ensemble for Germany Using the EURO-CORDEX 8.5 Ensemble

AbstractThe application of an ensemble reduction technique to the European branch of the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) ensemble at resolution “EUR-11” (~12.5 km) under the RCP8.5 scenario is presented. The technique is based on monthly mean changes between a reference and two future time periods, calculated for eight regions in Germany, of the parameters near-surface air temperature (tas), precipitation totals (pr), contribution of precipitation from very wet days to precipitation totals (R95pTOT), near-surface specific humidity (huss), and surface downwelling shortwave radiation (rsds). The sensitivity of the reduction procedure with respect to a number of tuning parameters is investigated. When the optimal combination of tuning parameters is applied, the technique allows the reduction from 15 to 7 ensemble members, while the reduced ensemble reproduces about 94% of the spread of the full ensemble. Keeping in mind that climate projection ensemble...

[1]  Alex J. Cannon,et al.  Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure , 2016 .

[2]  Reto Knutti,et al.  Addressing interdependency in a multimodel ensemble by interpolation of model properties , 2015 .

[3]  B. Santer,et al.  Statistical significance of climate sensitivity predictors obtained by data mining , 2014 .

[4]  Reto Knutti,et al.  Mapping model agreement on future climate projections , 2011 .

[5]  A. K. Tank,et al.  An Alternative Index for the Contribution of Precipitation on Very Wet Days to the Total Precipitation , 2014 .

[6]  A. Matzarakis,et al.  High-resolution grids of hourly meteorological variables for Germany , 2018, Theoretical and Applied Climatology.

[7]  R. Knutti,et al.  Robustness and uncertainties in the new CMIP5 climate model projections , 2013 .

[8]  P. Cox,et al.  Quantifying future climate change , 2012 .

[9]  Ruth Lorenz,et al.  A climate model projection weighting scheme accounting for performance and interdependence , 2017 .

[10]  Jason P. Evans,et al.  Optimally choosing small ensemble members to produce robust climate simulations , 2013 .

[11]  T. Shepherd,et al.  Towards process-informed bias correction of climate change simulations , 2017 .

[12]  K. Calvin,et al.  The RCP greenhouse gas concentrations and their extensions from 1765 to 2300 , 2011 .

[13]  R. Vautard,et al.  Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble , 2014 .

[14]  Alex J. Cannon.  Selecting GCM Scenarios that Span the Range of Changes in a Multimodel Ensemble: Application to CMIP5 Climate Extremes Indices* , 2015 .

[15]  Thomas Mendlik,et al.  Selecting climate simulations for impact studies based on multivariate patterns of climate change , 2015, Climatic Change.

[16]  Reto Knutti,et al.  The end of model democracy? , 2010 .

[17]  R. Knutti,et al.  Climate model genealogy , 2011 .

[18]  Reto Knutti,et al.  Challenges in Combining Projections from Multiple Climate Models , 2010 .

[19]  Zachary Pirtle,et al.  What does it mean when climate models agree? A case for assessing independence among general circulation models. , 2010 .

[20]  Reto Knutti,et al.  A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble , 2015 .

[21]  Ian T. Jolliffe,et al.  Empirical orthogonal functions and related techniques in atmospheric science: A review , 2007 .

[22]  R. Vautard,et al.  EURO-CORDEX: new high-resolution climate change projections for European impact research , 2014, Regional Environmental Change.