Volume 42, Issue 9 p. 4953-4971
RESEARCH ARTICLE

SEAS5 skilfully predicts late wet-season precipitation in Central American Dry Corridor excelling in Costa Rica and Nicaragua

Katherine M. Kowal

Corresponding Author

Katherine M. Kowal

Department of Geography and the Environment, University of Oxford, Oxford, UK

Correspondence

Katherine Kowal, Department of Geography and the Environment, University of Oxford, Oxford, UK.

Email: [email protected]

Contribution: Conceptualization, Data curation, Formal analysis, Funding acquisition, ​Investigation, Methodology, Project administration, Visualization, Writing - original draft, Writing - review & editing

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Louise J. Slater

Louise J. Slater

Department of Geography and the Environment, University of Oxford, Oxford, UK

Contribution: Conceptualization, Methodology, Supervision, Writing - review & editing

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Anne F. Van Loon

Anne F. Van Loon

Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

Contribution: Methodology, Supervision, Writing - review & editing

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Christian Birkel

Christian Birkel

Department of Geography, University of Costa Rica, San Jose, Costa Rica

Contribution: Methodology, Writing - review & editing

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First published: 24 December 2021
Citations: 3

Funding information: Rhodes Scholarships

Abstract

Better drought preparedness is critically needed in the Central American Dry Corridor (CADC). Seasonal forecasts can be used to build this preparedness but need localized evaluations to ensure they are relevant and useful. This study provides a CADC-focused assessment of the SEAS5 seasonal forecasting system produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). We evaluate SEAS5 predictions of the mean, variability, and extremes of precipitation across the CADC at 1–7-month lead times. We assess differences in regional forecast quality across seasons and lead times by evaluating spatial and temporal associations with El Niño–Southern Oscillation (ENSO) phase, topography, and continentality. Results show that SEAS5 precipitation forecasts often have better skill primarily during the mid to late wet season (July–October). In these months, low/normal precipitation forecasts outperform the climatological mean (1982–2016) up to 5- or 6-month lead times in some subregions. Forecast skill is often worse, however, for predicting precipitation during the early wet season, primarily in June. Forecast skill varies spatially across the region, with higher skill concentrated in the southeast (Costa Rica and Nicaragua). Forecast skill is significantly related to continentality and topography, and together these factors account for at least a quarter of the spatial variance in annual skill at all lead times. Forecast accuracy varies depending on ENSO phase: predictions are often worse in El Niño (warm ENSO) periods during the early wet season when ENSO also has a weaker association with cumulative precipitation relative to the later wet season. SEAS5 could be a particularly useful tool during the second half of the wet season in the southeast CADC, skilfully alerting of upcoming precipitation variability with over 3-month lead times.

CONFLICT OF INTEREST

The authors declare no potential conflict of interest.

DATA AVAILABILITY STATEMENT

The forecast data are publicly accessible via the MARS website hosted by the European Centre for Medium-Range Weather Forecasts. All reference data are publicly available as described in section 2.