Finding plausible and diverse variants of a climate model. Part 1: establishing the relationship between errors at weather and climate time scales

The main aim of this two-part study is to use a perturbed parameter ensemble (PPE) to select plausible and diverse variants of a relatively expensive climate model for use in climate projections. In this first part, the extent to which climate biases develop at weather forecast timescales is assesse...

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Bibliographic Details
Published in:Climate dynamics 2019-07, Vol.53 (1-2), p.989-1022
Main Authors: Sexton, D. M. H., Karmalkar, A. V., Murphy, J. M., Williams, K. D., Boutle, I. A., Morcrette, C. J., Stirling, A. J., Vosper, S. B.
Format: Article
Language:English
Subjects:
Climate
Climate models
Climatology
Computer simulation
Correlation
Correlation analysis
Earth and Environmental Science
Earth Sciences
Ensemble forecasting (Meteorology)
Environmental aspects
Errors
Errors, Scientific
Geophysics/Geodesy
Management
Oceanography
Parameter identification
Process parameters
Systematic errors
Temporal integration
Varieties
Weather
Weather forecasting
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Summary:The main aim of this two-part study is to use a perturbed parameter ensemble (PPE) to select plausible and diverse variants of a relatively expensive climate model for use in climate projections. In this first part, the extent to which climate biases develop at weather forecast timescales is assessed with two PPEs, which are based on 5-day forecasts and 10-year simulations with a relatively coarse resolution (N96) atmosphere-only model. Both ensembles share common parameter combinations and strong emergent relationships are found for a wide range of variables between the errors on two timescales. These relationships between the PPEs are demonstrated at several spatial scales from global (using mean square errors), to regional (using pattern correlations), and to individual grid boxes where a large fraction of them show positive correlations. The study confirms more robustly than in previous studies that investigating the errors on weather timescales provides an affordable way to identify and filter out model variants that perform poorly at short timescales and are likely to perform poorly at longer timescales too. The use of PPEs also provides additional information for model development, by identifying parameters and processes responsible for model errors at the two different timescales, and systematic errors that cannot be removed by any combination of parameter values.
ISSN:0930-7575
1432-0894
DOI:10.1007/s00382-019-04625-3