Subseasonal prediction performance for South American land–atmosphere coupling in extended austral summerChevuturi, A. ORCID: https://orcid.org/0000-0003-2815-7221, Klingaman, N. P. ORCID: https://orcid.org/0000-0002-2927-9303, Guo, L., Holloway, C. E. ORCID: https://orcid.org/0000-0001-9903-8989, Guimarães, B. S., Coelho, C. A. S., Kubota, P. Y., Young, M., Black, E. ORCID: https://orcid.org/0000-0003-1344-6186, Baker, J. C. A. and Vidale, P. L. ORCID: https://orcid.org/0000-0002-1800-8460 (2022) Subseasonal prediction performance for South American land–atmosphere coupling in extended austral summer. Climate Resilience and Sustainability, 1 (1). e28. ISSN 2692-4587
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1002/cli2.28 Abstract/SummaryLand–atmosphere feedbacks, through water and energy exchanges, provide subseasonal-to-seasonal predictability of the hydrological cycle. We analyse subseasonal land–atmosphere coupling over South America (SA) during extended austral summer for the soil moisture-to-precipitation and soil moisture-to-air temperature feedback pathways. We evaluate subseasonal hindcasts from global forecasting systems from the UK Met Office, the National Centers for Environmental Prediction (NCEP), the European Centre for Medium Range Weather Forecasts and the Center for Weather Forecast and Climate Studies (CPTEC), for the common period of 1999–2010, against two reanalyses. Biases in land–atmosphere states are established in the first week of hindcasts and increase with lead time. By Week 5, all the models only demonstrate good performance over northern, northeastern and southeastern SA for soil moisture and evapotranspiration and over tropical and subtropical SA for temperature. The hindcasts show stronger coupling at longer lead–lag between variables than reanalyses. Our results highlight possible deficiencies in feedbacks between soil moisture and precipitation in CPTEC and NCEP forecasts over the Amazon due to initial dry soil moisture biases, and in feedbacks between soil moisture and temperature for all four investigated models over southeastern SA due to erroneous representations of evapotranspiration.
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