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Climate predictability on seasonal timescales over South America from the NMME models

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Abstract

This paper evaluates the predictability and skill of the models from the North American Multi-Model Ensemble project (NMME) in South America on seasonal timescales using analysis of variance (ANOVA). The results show that the temperature variance is dominated by the multi-model ensemble signal in the austral autumn and summer and by the inter-model biases in the austral spring and winter. The temperature predictability is higher at low latitudes, although moderate values are found in extratropical latitudes in the austral spring and summer. The predictability of precipitation is lower than that of temperature because noise dominates the variance. The highest levels of precipitation predictability are reached in tropical latitudes with large inter-seasonal variations. Southeastern South America and Patagonia present the highest predictability at midlatitudes. The NMME skill of temperature is better than that of precipitation, and it is better at low latitudes for both variables. At extratropical latitudes, the skill is moderate for temperature and low for precipitation, although precipitation reaches a local maximum in southeastern South America.

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Data availability

Raw data can be found at http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/. Data supporting the findings of this article is available upon request.

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Acknowledgements

We thank the two anonymous reviewers for their comments. We acknowledge the agencies that support the NMME-Phase II system, and we thank the climate modeling groups (Environment Canada, NASA, NCAR, NOAA/GFDL, NOAA/NCEP, and University of Miami) for producing and making available their model output. NOAA/NCEP, NOAA/CTB, and NOAA/CPO jointly provided coordinating support and led development of the NMME-Phase II system.

Funding

The research was supported by UBACyT20020170100428BA, PDE 46 2019, PICT-2018-03046, CLIMAT-AMSUD 21-CLIMAT-05 and the CLIMAX Project funded by Belmont Forum/ANR-15-JCL/-0002-01. LGA is supported by a fellowship grant from CONICET.

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Author notes

  1. Marisol Osman

    Present address: Karlsruhe Institute of Technology, Karlsruhe, Germany

Authors and Affiliations

  1. Departamento de Ciencias de la Atmósfera y los Océanos, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina

    Luciano G. Andrian, Marisol Osman & Carolina S. Vera

  2. CONICET-Universidad de Buenos Aires, Centro de Investigaciones del Mar y la Atmósfera (CIMA), Buenos Aires, Argentina

    Luciano G. Andrian, Marisol Osman & Carolina S. Vera

  3. CNRS-IRD-CONICET-UBA, Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL3351 IFAECI), Buenos Aires, Argentina

    Luciano G. Andrian, Marisol Osman & Carolina S. Vera

Authors
  1. Luciano G. Andrian
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  2. Marisol Osman
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  3. Carolina S. Vera
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Contributions

LGA: data curation; formal analysis; investigation; methodology; visualization; writing—original draft; writing—review and editing. MO: conceptualization; data curation; supervision; methodology; writing—review and editing. CSV: conceptualization; funding acquisition; methodology; project administration; resources; supervision; writing—review and editing.

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Correspondence to Luciano G. Andrian.

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All codes used in this work can be found at https://github.com/LucianoAndrian/tesis

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Andrian, L.G., Osman, M. & Vera, C.S. Climate predictability on seasonal timescales over South America from the NMME models. Clim Dyn 60, 3261–3276 (2023). https://doi.org/10.1007/s00382-022-06506-8

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