Open AccessWeather extremes over Europe under 1.5 and 2.0 °C global warming from HAPPI regional climate ensemble simulations
Author(s) -
Kevin Sieck,
Christine Nam,
Laurens M. Bouwer,
Diana Rechid,
Daniela Jacob
Publication year - 2021
Publication title -
earth system dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.807
H-Index - 39
eISSN - 2190-4979
pISSN - 2190-4987
DOI - 10.5194/esd-12-457-2021
Subject(s) - climatology , environmental science , precipitation , percentile , climate model , global warming , climate change , mean radiant temperature , meteorology , statistics , geography , mathematics , ecology , biology , geology
. This paper presents a novel dataset of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. This is a unique and physically consistent dataset, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100×10-year simulations and 25×10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. To demonstrate how adaptation-relevant information can be derived from the HAPPI dataset, changes in four climate indices for periods with 1.5 and2.0 ∘C global warming are quantified. These indices includenumber of days per year with daily mean near-surface apparent temperature of >28 ∘C (ATG28); the yearly maximum 5-day sum ofprecipitation (RX5day); the daily precipitation intensity of the 50-yearreturn period (RI50yr); and the annual consecutive dry days (CDDs). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature-related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 ∘C.