Identifying patterns of spatial variability within the EuroCORDEX ensemble
Authors: Barnes Clair , Chandler Richard , Brierley Chris , Alegre Raquel 
Published: March 2022

&lt;p&gt;Ensembles of regional climate projections provide information about the range of possible scenarios of future climate change at the local scale, with more detail and better representation of fine-scale processes than can be provided by lower-resolution global circulation models (GCMs). The CORDEX ensembles are multi-model ensembles, with each member obtained by using a GCM to drive a higher-resolution regional climate model (RCM). Due to resource limitations however, users of regional climate information typically do not want to use an entire ensemble and must select a sample of its members for their purposes. To preserve as much information as possible, such a sample should be chosen to be representative of the variation within the ensemble.&lt;/p&gt;&lt;p&gt;Analysis of variance (ANOVA) has often been used to characterise ensemble variation by apportioning the total variation to differences between the GCMs or between the RCMs (Yip et al., 2011; D&amp;#233;qu&amp;#233; et al., 2012), and to produce maps of the geographical regions where variance between the runs is ascribed to one or other model component (Christensen and Kjellstr&amp;#246;m, 2020). However, traditional ANOVA methods require a balanced ensemble in which all possible GCM-RCM pairs are available. The analysis of unbalanced ensembles therefore typically proceeds either by discarding surplus runs or imputing missing ones, or by using computationally intensive Bayesian methods to account for the lack of balance.&lt;/p&gt;&lt;p&gt;We here propose two enhancements to the existing techniques for analysis of ensemble variation. The first is a modification of the standard ANOVA approach, based on the underlying statistical model, that can be applied directly to unbalanced ensembles: the modification is computationally cheap and hence suitable for routine application, and provides ranges of variation that are potentially attributable to the different sources.&lt;/p&gt;&lt;p&gt;The second enhancement adds further detail to the partitioning of variation, using an eigenanalysis that characterises the principal spatial modes of variation within an ensemble. As well as identifying the dominant spatial patterns of variation associated with the GCMs and RCMs, the analysis characterises the contribution from each model, for example by identifying models with different treatments of orography, rain shadows, or urban heat island effects. As well as informing the selection of subsets of ensemble members, this enhancement offers the possibility of emulating missing ensemble members where the GCM-RCM matrix is only partially filled. The method is applied to the EuroCORDEX ensemble with a focus on the UK.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Christensen, O. and Kjellstr&amp;#246;m, E. (2020). Partitioning uncertainty components of mean climate and climate change in a large ensemble of European regional climate model projections. &lt;em&gt;Climate Dynamics&lt;/em&gt;, 54:4293&amp;#8211;4308.&lt;br&gt;D&amp;#233;qu&amp;#233;, M., Somot, S., Sanchez-Gomez, E. et al. (2012). The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability. &lt;em&gt;Climate Dynamics&lt;/em&gt;, 38:951&amp;#8211;964 (2012).&lt;br&gt;Yip, S., Ferro, C. A. T., Stephenson, D. B., and Hawkins, E. (2011). A simple, coherent framework for partitioning uncertainty in climate predictions. &lt;em&gt;Journal of Climate&lt;/em&gt;, 24(17):4634&amp;#8211;4643.&lt;/p&gt;