Large-ensemble climate model data for impact attribution and projections

Attributing observed and projecting future impacts of climate change is key for risk assessment and corresponding action. Climate data provide only one, but crucial, input for these studies. Standardised global climate model output is often practical for this, but needs for many impact modelling studies to be bias-corrected, and ideally available at spatial resolutions that allow consideration of non-climate factors varying at spatial scales higher than typical for, say, CMIP output. Such processed climate data are available typically only for a very small subset of available climate model simulations. Making large-ensemble climate data available for impact modelling can reduce the gap between state-of-the-art climate science and the climate information informing societally highly relevant impact studies.

Here, we present a method, code, and data of bias-corrected and statistically downscaled large-ensemble CMIP6 data from the CMIP, ScenarioMIP, and DAMIP subprojects. First, we present a method tweak that allows the Bias Adjustment and Statistical Downscaling (BASD) code used in the Intersectoral Impact Modelling Intercomparison Project (ISIMIP) Phase 3 to be scientifically better applicable to large-ensemble climate data. Specifically, instead of correcting a single ensemble member for the bias of the same-labelled (e.g., r1i1p1f1) member of the historical simulations compared to observationally-derived data, we correct each member for the bias of the respective model’s full ensemble (r*i*p*f*) to preserve ensemble spread. Second, we provide a user-friendly software that implements this ISIMIP3BASD-LE method and the complete pre-/postprocessing at an improved computational speed. Third, we present an overview of the CMIP6 simulations newly processed with this method and code.

The method and code are easily applicable to upcoming CMIP7 data, as well as any other climate model data available. Current challenges regard the availability of CMIP6 data in terms of individual ensemble members/variables, as well as decisions regarding the ensemble size for bias correction with incomplete variable coverage compared to what is desired from an impact modelling perspective. Compared to native high-resolution simulations and dynamical downscaling, the thus-processed climate data retain all disadvantages associated with the original method/data. Nonetheless, uptake of the method, code, and data will allow impact attribution to make a step forward, and impact projections to be on a climate-scientifically much sounder basis.