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Estimating present‐day European seasonal mean rainfall by combining historical data and climate model simulations, for risk assessment
Author(s) -
Jewson Stephen,
Comola Francesco,
Parkes Ben
Publication year - 2021
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.2031
Subject(s) - climate model , climatology , climate change , probabilistic logic , environmental science , consistency (knowledge bases) , econometrics , meteorology , computer science , statistics , geography , mathematics , geology , artificial intelligence , oceanography
Abstract Building risk models for present‐day climate requires an understanding of recent climate trends. To estimate the climate change driven component of recent rainfall trends in Europe, we introduce a novel methodology for combining trend estimates from observed data, a climate model ensemble and a default trend of zero. The methodology weights the different trend estimates based on their uncertainty and consistency with observations. We find that the methodology puts low weights on the observational estimates of recent rainfall trends because they are so uncertain and puts higher weights on the trends estimated using the climate model ensemble mean and the default trend of zero. This demonstrates the value of ensemble simulations of past climate for this application. The methodology we describe establishes a probabilistic framework for estimating uncertain climate change trends based on combining estimates from observed data and climate models and could be applied in many other situations.

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