Estimating present temperature climate in a warming world: probabilistic verification of a model-based approach for years 2008–2025

Le résumé fourni par la source

Abstract When climate changes, statistics derived from past observations become unrepresentative of the true present climate. In 2008, Räisänen and Ruokolainen proposed a method for alleviating this bias, combining global mean temperature change with model-based regression coefficients that translate the global warming to changes in local mean temperature and temperature variability. Here, the fidelity of this method in predicting the probability distributions of monthly mean temperatures is evaluated, focusing on the 18 years 2008–2025 that post-date the original study. Compared with the traditional approach in which the distributions are estimated directly from observations, a major improvement is found in both the continuous ranked probability score ( CRPS ) and the logarithmic score ( L ). The rank histograms that describe the positions of the verifying observations within the predicted distributions also become far more balanced. In addition, the optimal length of the baseline period from which observations are used increases when the observed temperatures are adjusted for climate change. The verification statistics are further improved when augmenting the model-based climate change estimates with information from local observed temperature trends. Both CRPS and L are degraded when the t -distributions fitted to the climate-change-adjusted observations are replaced with more flexible Stochastically Generated Skewed distributions, but an objective blend between the two distributions yields statistics slightly better than those for the t -distribution.

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