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The VALUE perfect predictor experiment: Evaluation of temporal variability
Author(s) -
Maraun Douglas,
Huth Radan,
Gutiérrez José M.,
Martín Daniel San,
Dubrovsky Martin,
Fischer Andreas,
Hertig Elke,
Soares Pedro M. M.,
Bartholy Judit,
Pongrácz Rita,
Widmann Martin,
Casado Maria J.,
Ramos Petra,
Bedia Joaquín
Publication year - 2019
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5222
Subject(s) - downscaling , climatology , quantile , environmental science , precipitation , residual , term (time) , climate change , econometrics , statistics , meteorology , computer science , mathematics , geography , geology , oceanography , physics , algorithm , quantum mechanics
Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short‐term variations, spells and variability from interannual to long‐term trends. The EU‐COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long‐term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors.