Christopher H. O’Reilly, Daniel J. Befort and Antje Weisheimer
Earth System Dynamics
Work Package 2
Understanding how the climate may change in the coming decades is vital to putting the most effective policies in place to prepare for it, but current multi-decadal predictions are very uncertain. This paper presents new findings on calibrating multi-decadal projections using current observations, a technique previously used for much shorter projections. The team found that calibrated ensembles of climate projections were more accurate and reliable than uncalibrated ensembles, but with a greater degree of spread between individual projections, leading to uncertainty. The calibrated ensembles projected an increase in summer temperature over the next 40 years relative to the 1995-2014 average of between 1.3°C and 2°C across Europe, somewhat less than the uncalibrated ensembles. This method shows promise in improving our future climate projections, helping inform policymakers in producing effective climate adaptation and mitigation policies.
There is a great demand from decision-makers for regional climate projections out to around 50 years in the future, however such projections are currently very uncertain. This limits their usefulness in making climate adaptation and mitigation decisions. There are several ways of reducing this uncertainty, with each having various advantages and disadvantages when used on projections over different areas or timescales or using different types of models. This paper examines the idea of calibrating large ensembles of future climate projections using current climate observations, adjusting and improving the decadal projections. This would improve the information available for determining policies to mitigate and adapt to the effects of future climate change.
The team used calibration methods usually used on monthly and seasonal climate projections, but they still proved effective on a multi-decadal timescale. The calibrated projections were more reliable and accurate than the uncalibrated ensembles, as demonstrated through statistical analysis. This was particularly marked for the calibrated temperature projections, which gave a smaller projected temperature increase for Europe than the uncalibrated ensembles. They project an increase in summer temperature in 2041-2060 relative to the 1995-2014 average of 2°C for Central Europe and the Mediterranean, and 1.3°C for Northern Europe. The calibrated projections of future rainfall, however, were still unreliable owing to the different dynamics of rainfall trends. Although the calibrated ensembles were statistically more reliable, they showed greater uncertainty, or differences between their individual projections, than the uncalibrated ensembles.
This study made use of two large ensembles of climate model projections. The Community Earth System Model version 1 Large Ensemble (CESM1-LE) contains 40 members with simulations starting in 1920, while the Max Planck Institute Grand Ensemble (MPI-GE) contains 100 members starting in 1850 (although one of these was not used). This study focussed on the period 1920-2060, with the 40-year timescale being the primary period of interest for EUCP. Observational data from several datasets was used to calibrate these simulations, choosing those which best match observations between 1920 and 2016 and which should, therefore, simulate the future most accurately. The effectiveness of the calibration was tested using statistical analysis.
This paper demonstrates a promising new technique for improving our projections of how the climate may change in the coming decades. Accurate climate information is crucial for formulating well-informed, effective policies around climate mitigation and adaptation; therefore, this study will help policymakers in government and industry improve their future planning.
This study examines methods of calibrating projections of future regional climate for the next 40–50 years using large single-model ensembles (the Community Earth System Model (CESM) Large Ensemble and Max Planck Institute (MPI) Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an “imperfect model” approach using the historical/representative concentration pathway 8.5 (RCP8.5) simulations from the Coupled Model Intercomparison Project 5 (CMIP5) archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely homogeneous Gaussian regression (HGR), is used for the subsequent analysis. As an extension to the HGR calibration method it is applied to dynamically decomposed data, in which the underlying data are separated into dynamical and residual components (HGR-decomp). Based on the verification results obtained using the imperfect model approach, the HGR-decomp method is found to produce more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes.