Decomposition of skill scores for conditional verification – Impact of AMO phases on the predictability of decadal temperature forecasts
We present a decomposition of skill scores for the conditional verification of weather and climate forecast systems. Aim is to evaluate the performance of such a system individually for predefined subsets with respect to the overall performance. The overall skill score is decomposed into: (1) the su...
| Main Authors: | , , |
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| Format: | Text |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/egusphere-2023-2582 https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2582/ |
| Summary: | We present a decomposition of skill scores for the conditional verification of weather and climate forecast systems. Aim is to evaluate the performance of such a system individually for predefined subsets with respect to the overall performance. The overall skill score is decomposed into: (1) the subset skill score assessing the performance of a forecast system compared to a reference system for a particular subset; (2) the frequency weighting accounting for varying subset size; (3) the reference weighting relating the performance of the reference system in the individual subsets to the performance of the full data set. The decomposition and its interpretation is exemplified using a synthetic data set. Subsequently we use it for a practical example from the field of decadal climate prediction: An evaluation of the Atlantic-European near-surface temperature forecast from the German initiative Mittelfristige Klimaprognosen (MiKlip) decadal prediction system conditional on different Atlantic Meridional Oscillation (AMO) phases during initialization. With respect to the chosen Western European North Atlantic sector, the decadal prediction system preop-dcpp-HR performs better than the un-initialized simulations mostly due to performance gain during a positive AMO phase. Compared to the predecessor system ( preop-LR ), no overall performance benefits are made in this region, but positive contributions are achieved for initialization in neutral AMO phases. Additionally, the decomposition reveals a strong imbalance among the subsets (defined by AMO phases) in terms of reference weighting allowing for sophisticated interpretation and conclusions. This skill score decomposition framework for conditional verification is a valuable tool to analyze the effect of physical processes on forecast performance and consequently supports model development and improvement of operational forecasts. |
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