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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-04-04

AUTHORS

J. Kim, Duane E. Waliser, Chris A. Mattmann, Cameron E. Goodale, Andrew F. Hart, Paul A. Zimdars, Daniel J. Crichton, Colin Jones, Grigory Nikulin, Bruce Hewitson, Chris Jack, Christopher Lennard, Alice Favre

ABSTRACT

Monthly-mean precipitation, mean (TAVG), maximum (TMAX) and minimum (TMIN) 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 TAVG and TMAX than for TMIN, 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. More... »

PAGES

1189-1202

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-013-1751-7

DOI

http://dx.doi.org/10.1007/s00382-013-1751-7

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https://app.dimensions.ai/details/publication/pub.1035489971


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