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Evaluation of the skill of North-American multi-model ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA

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journal contribution
posted on 03.02.2017, 09:46 by Louise Slater, Gabriele Villarini, Allen Bradley
This paper examines the forecasting skill of eight Global Climate Models (GCMs) from the North-American Multi-Model Ensemble (NMME) project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models’ ability to predict extended periods of extreme climate conducive to eight ‘billion-dollar’ historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecasted than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales.

Funding

This study was supported by NOAA's Climate Program Office's Modeling, Analysis, Predictions, and Projections Program, Grant #NA15OAR4310073.

History

School

  • Social Sciences

Department

  • Geography and Environment

Published in

Climate Dynamics

Volume

53

Issue

12

Pages

7381–7396

Citation

SLATER, L., VILLARINI, G. and BRADLEY, A., 2017. Evaluation of the skill of North-American multi-model ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA. Climate Dynamics, 53(12), pp. 7381–7396.

Publisher

© Springer

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

18/07/2016

Publication date

2016-08-04

Copyright date

2019

Notes

The final publication is available at Springer via http://dx.doi.org/10.1007/s00382-016-3286-1

ISSN

0930-7575

eISSN

1432-0894

Language

en