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August 26, 2026Climate DynamicsOpen Access

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

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Authors

JRJouni RäisänenMRMika RantanenATAntti Toropainen

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Overview

Model-evaluation analysis demonstrates improved probabilistic temperature estimates from climate-adjusted observation models across 2008–2023, indicating better representation of present climate.

Key Points

  • To evaluate the performance of a model-based method that adjusts past observational baselines to estimate current monthly temperature probability distributions in a warming climate.
  • Assessed probabilistic predictions of monthly mean temperatures over the 16-year verification period of 2008–2023.
  • Evaluated probabilistic fidelity using the continuous ranked probability score (CRPS), logarithmic score (L), and rank histograms.
  • Compared baseline periods, local trend augmentations, and alternative statistical fits, including t-distributions and Stochastically Generated Skewed distributions.
  • Adjusting observations for climate change produced major improvements in CRPS, logarithmic score, and rank histogram balance compared to traditional unadjusted observational baselines.
  • Incorporating local observed temperature trends alongside model estimates lengthened the optimal observational baseline period and further improved verification metrics.
  • Standard t-distributions outperformed more flexible Stochastically Generated Skewed distributions, while an Akaike information criterion blend matched t-distribution performance.

Cite This Study

Räisänen et al. (2026) studied this question.

synapsesocial.com/papers/6a8e9acc451774b83f3b341ahttps://doi.org/10.1007/s00382-026-08336-4
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