Does Quantile Mapping of Simulated Precipitation Correct for Biases in Transition Probabilities and Spell Lengths?

Climate impact studies constitute the basis for the formulation of adaptation strategies. Usually such assessments apply statistically postprocessed output of climate model projections to force impact models. Increasingly, time series with daily resolution are used, which require high consistency, f...

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Bibliographic Details
Published in:Journal of climate 2016-03, Vol.29 (5), p.1605-1615
Main Authors: Rajczak, Jan, Kotlarski, Sven, Schär, Christoph
Format: Article
Language:English
Subjects:
Atmospheric models
Bias
Climate
Climate and weather
Climate change
Climate models
Climate studies
Consortia
Global warming
Hydrology
Mapping
Markov analysis
Markov chains
Modelling
Precipitation
Regional climate models
Regional climates
Sequencing
Simulation
Statistical analysis
Studies
Time series
Topography
Transition probabilities
Weather
Weather stations
Wet days
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Summary:Climate impact studies constitute the basis for the formulation of adaptation strategies. Usually such assessments apply statistically postprocessed output of climate model projections to force impact models. Increasingly, time series with daily resolution are used, which require high consistency, for instance with respect to transition probabilities (TPs) between wet and dry days and spell durations. However, both climate models and commonly applied statistical tools have considerable uncertainties and drawbacks. This paper compares the ability of 1) raw regional climate model (RCM) output, 2) bias-corrected RCM output, and 3) a conventional weather generator (WG) that has been calibrated to match observed TPs to simulate the sequence of dry, wet, and very wet days at a set of long-term weather stations across Switzerland. The study finds systematic biases in TPs and spell lengths for raw RCM output, but a substantial improvement after bias correction using the deterministic quantile mapping technique. For the region considered, bias-corrected climate model output agrees well with observations in terms of TPs as well as dry and wet spell durations. For the majority of cases (models and stations) bias-corrected climate model output is similar in skill to a simple Markov chain stochastic weather generator. There is strong evidence that bias-corrected climate model simulations capture the atmospheric event sequence more realistically than a simple WG.
ISSN:0894-8755
1520-0442
DOI:10.1175/jcli-d-15-0162.1