Journal article

Going with the Trend: Forecasting Seasonal Climate Conditions under Climate Change

Yawen Shao, Quan J Wang, Andrew Schepen, Dongryeol Ryu

MONTHLY WEATHER REVIEW | AMER METEOROLOGICAL SOC | Published : 2021

Abstract

For managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision-making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast postprocessing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical postprocessing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this tr..

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Grants

Awarded by Australian Research Council


Funding Acknowledgements

We thank the European Centre for Medium-Range Weather Forecasts (ECMWF) for supplying the SEAS5 forecast data. We thank the Australian Bureau of Meteorology for the freely available AWAP dataset. We also thank three anonymous reviewers for valuable comments. This study is linked to an ARC Linkage Project (LP170100922) funded by the Australian Research Council and industry partners.