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Bias adjustment for decadal predictions of precipitation in Europe from CCLM

Li, Jingmin and Pollinger, Felix and Panitz, Hans-Jürgen and Feldmann, Hendrik and Paeth, Heiko (2019) Bias adjustment for decadal predictions of precipitation in Europe from CCLM. Climate Dynamics, 53 (3-4), pp. 1323-1340. Springer. DOI: 10.1007/s00382-019-04646-y ISSN 0930-7575

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Official URL: https://link.springer.com/article/10.1007%2Fs00382-019-04646-y

Abstract

A cross-validated model output statistics (MOS) approach is applied to precipitation data from the high-resolution regional climate model CCLM for Europe. The aim is to remove systematic errors of simulated precipitation in decadal climate predictions. We developed a two-step bias-adjustment approach. In step one, we estimate model errors based on a long-term ‘CCLM assimilation run’ (regionalizing data from a global assimilation run) and observational data. In step two, the resulting transfer function is applied to the complete set of decadal hindcast simulations (285 individual runs). In contrast to lead-time-dependent bias-adjustment approaches, this one is designed for variables with poor decadal prediction skill and without dominant lead-time-dependent bias. In terms of the CCLM assimilation run, MOS is shown to be effective in predictor selection, model skill improvement, and model bias reduction. Yet, the positive effect of MOS correction is accompanied with an underestimation of precipitation variability. After MOS application, an estimated mean square skill score of more than 0.5 is observed regionally. Simulated precipitation in decadal hindcasts is further improved when the MOS is trained on the basis of other decadal hindcasts from the same regional climate model but with a large underestimation in forecast uncertainty. Our results suggest that the MOS system derived from the assimilation run is less effective but allows the potential climate predictability in decadal hindcasts and forecasts to be retained. Using hindcasts itself for training is recommended unless a statistical method is capable of distinguishing biases and predictions within a hindcasts dataset.

Item URL in elib:https://elib.dlr.de/131506/
Document Type:Article
Title:Bias adjustment for decadal predictions of precipitation in Europe from CCLM
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Li, JingminDLR, IPA und Univ. Würzburghttps://orcid.org/0000-0002-7328-5102
Pollinger, FelixUniv. WürzburgUNSPECIFIED
Panitz, Hans-JürgenIMK, KIT Karlsruhehttps://orcid.org/0000-0002-9771-4733
Feldmann, HendrikIMK, KIT KarlsruheUNSPECIFIED
Paeth, HeikoUniv. WürzburgUNSPECIFIED
Date:August 2019
Journal or Publication Title:Climate Dynamics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:53
DOI :10.1007/s00382-019-04646-y
Page Range:pp. 1323-1340
Publisher:Springer
ISSN:0930-7575
Status:Published
Keywords:Bias-adjustment, CCLM, Hindcasts, Decadal prediction, Precipitation, Model output statistics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Atmosphären- und Klimaforschung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Transport Meteorology
Deposited By: Volkert, Dr. Hans
Deposited On:28 Nov 2019 12:37
Last Modified:28 Nov 2019 12:37

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