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Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors

Climate dynamics, 2014-03, Vol.42 (5-6), p.1189-1202 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg 2013 ;2015 INIST-CNRS ;COPYRIGHT 2014 Springer ;Springer-Verlag Berlin Heidelberg 2014 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0930-7575 ;EISSN: 1432-0894 ;DOI: 10.1007/s00382-013-1751-7 ;CODEN: CLDYEM

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Title:
Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors
Author: Kim, J. ; Waliser, Duane E. ; Mattmann, Chris A. ; Goodale, Cameron E. ; Hart, Andrew F. ; Zimdars, Paul A. ; Crichton, Daniel J. ; Jones, Colin ; Nikulin, Grigory ; Hewitson, Bruce ; Jack, Chris ; Lennard, Christopher ; Favre, Alice
Subjects: Atmospheric models ; Climate change ; Climate models ; Climate science ; Climatology ; Climatology. Bioclimatology. Climate change ; Earth and Environmental Science ; Earth Sciences ; Earth, ocean, space ; Environmental Sciences ; Exact sciences and technology ; External geophysics ; Geophysics. Techniques, methods, instrumentation and models ; Geophysics/Geodesy ; Global Changes ; Global temperature changes ; Impact analysis ; Measurement ; Meteorology ; Oceanography ; Sciences of the Universe ; Studies
Is Part Of: Climate dynamics, 2014-03, Vol.42 (5-6), p.1189-1202
Description: Monthly-mean precipitation, mean (T AVG ), maximum (T MAX ) and minimum (T MIN ) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for T AVG and T MAX than for T MIN , and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
Language: English
Identifier: ISSN: 0930-7575
EISSN: 1432-0894
DOI: 10.1007/s00382-013-1751-7
CODEN: CLDYEM
Source: AUTh Library subscriptions: ProQuest Central
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Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors

Springer-Verlag Berlin Heidelberg 2013 ;2015 INIST-CNRS ;COPYRIGHT 2014 Springer ;Springer-Verlag Berlin Heidelberg 2014 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0930-7575 ;EISSN: 1432-0894 ;DOI: 10.1007/s00382-013-1751-7 ;CODEN: CLDYEM

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