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Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa

Abstract

The skill of North American multimodel ensemble (NMME) seasonal forecasts in East Africa (EA), which encompasses one of the most food and water insecure areas of the world, is evaluated using deterministic, categorical, and probabilistic evaluation methods. The skill is estimated for all three primary growing seasons: March–May (MAM), July–September (JAS), and October–December (OND). It is found that the precipitation forecast skill in this region is generally limited and statistically significant over only a small part of the domain. In the case of MAM (JAS) [OND] season it exceeds the skill of climatological forecasts in parts of equatorial EA (Northern Ethiopia) [equatorial EA] for up to 2 (5) [5] months lead. Temperature forecast skill is generally much higher than precipitation forecast skill (in terms of deterministic and probabilistic skill scores) and statistically significant over a majority of the region. Over the region as a whole, temperature forecasts also exhibit greater reliability than the precipitation forecasts. The NMME ensemble forecasts are found to be more skillful and reliable than the forecast from any individual model. The results also demonstrate that for some seasons (e.g. JAS), the predictability of precipitation signals varies and is higher during certain climate events (e.g. ENSO). Finally, potential room for improvement in forecast skill is identified in some models by comparing homogeneous predictability in individual NMME models with their respective forecast skill.

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Acknowledgments

The NMME forecasts and CPC-URD precipitation data were downloaded from the Institute of Research Institute (IRI) data library (http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/). The authors thank the CPC, IRI and NCAR personnel in creating, updating and maintaining the NMME archive. The NMME project and data dissemination is supported by NOAA, NSF, NASA and DOE. The GPCC Precipitation data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/. The CHIRPS precipitation data was obtained from ftp://ftp.chg.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/global_monthly/ and the CRU temperature data set was obtained from Center of Environmental Data Archival via (https://services.ceda.ac.uk/dj_security/account/signin/?r=; http://browse.ceda.ac.uk/browse/badc/cru/data/cru_ts/cru_ts_3.22). Support for this study comes from the US Geological Survey (USGS) cooperative agreement #G09AC000001, NOAA Award NA11OAR4310151, the USGS Climate and Land Use Change program, NASA Grants NNX15AL46G, NNH12ZDA001 N-IDS and NNX14AD30G and through the SERVIR Applied Sciences Team as part of the NASA Earth Sciences Division Applied Sciences Program, Capacity Building Initiative (Dr. Nancy Searby Manager).

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Correspondence to Shraddhanand Shukla.

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This paper is a contribution to the special collection on the North American Multi-Model Ensemble (NMME) seasonal prediction experiment. The special collection focuses on documenting the use of the NMME system database for research ranging from predictability studies, to multi-model prediction evaluation and diagnostics, to emerging applications of climate predictability for subseasonal to seasonal predictions. This special issue is coordinated by Annarita Mariotti (NOAA), Heather Archambault (NOAA), Jin Huang (NOAA), Ben Kirtman (University of Miami) and Gabriele Villarini (University of Iowa).

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Shukla, S., Roberts, J., Hoell, A. et al. Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa. Clim Dyn 53, 7411–7427 (2019). https://doi.org/10.1007/s00382-016-3296-z

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  • DOI: https://doi.org/10.1007/s00382-016-3296-z

Keywords

  • Forecast Skill
  • Multimodel Ensemble
  • Africa East (EA)
  • Precipitation Forecasts
  • Temperature Forecasts