Seasonal variability and predictability of monsoon precipitation in Southern Africa

Matthew F. Horan; Fred Kucharski; Moetasim Ashfaq

doi:10.1088/1748-9326/ad2737

Seasonal variability and predictability of monsoon precipitation in Southern Africa

Matthew F. Horan, Fred Kucharski, Moetasim Ashfaq

Computational Earth Sciences Group

Research output: Contribution to journal › Article › peer-review

Abstract

Rainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Niño-Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%-75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key in predicting monsoon precipitation with a zero to five-month lead. Seasonal forecasting systems accurately represent the interplay of the three forcings but show varying skills in representing their teleconnection over SA. This makes them less effective at predicting monsoon precipitation than the empirical model.

Original language	English
Article number	034010
Journal	Environmental Research Letters
Volume	19
Issue number	3
DOIs	https://doi.org/10.1088/1748-9326/ad2737
State	Published - Mar 1 2024

Funding

Notice: This manuscript has been authored by employees of UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE). Accordingly, the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (www.energy.gov/downloads/doe-public-access-plan). This research used the OLCF resources, a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This work is supported by the U.S. Air Force Numerical Weather Modeling Program and NCCR Center, located within the NNCCS at the ORNL and supported under a Strategic Partnership Project 2316T849‐08 between DOE and NOAA. Notice: This manuscript has been authored by employees of UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE). Accordingly, the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( www.energy.gov/downloads/doe-public-access-plan ).

Funders	Funder number
DOE Public Access Plan
U.S. Department of Energy
National Oceanic and Atmospheric Administration
Office of Science	DE-AC05-00OR22725
Oak Ridge National Laboratory
U.S. Air Force

Keywords

Southern Africa monsoon
predictability
seasonal forecasting
teleconnections

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10.1088/1748-9326/ad2737

Cite this

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title = "Seasonal variability and predictability of monsoon precipitation in Southern Africa",

abstract = "Rainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Ni{\~n}o-Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%-75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key in predicting monsoon precipitation with a zero to five-month lead. Seasonal forecasting systems accurately represent the interplay of the three forcings but show varying skills in representing their teleconnection over SA. This makes them less effective at predicting monsoon precipitation than the empirical model.",

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author = "Horan, {Matthew F.} and Fred Kucharski and Moetasim Ashfaq",

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AU - Horan, Matthew F.

AU - Kucharski, Fred

AU - Ashfaq, Moetasim

PY - 2024/3/1

Y1 - 2024/3/1

N2 - Rainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Niño-Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%-75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key in predicting monsoon precipitation with a zero to five-month lead. Seasonal forecasting systems accurately represent the interplay of the three forcings but show varying skills in representing their teleconnection over SA. This makes them less effective at predicting monsoon precipitation than the empirical model.

AB - Rainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Niño-Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%-75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key in predicting monsoon precipitation with a zero to five-month lead. Seasonal forecasting systems accurately represent the interplay of the three forcings but show varying skills in representing their teleconnection over SA. This makes them less effective at predicting monsoon precipitation than the empirical model.

KW - Southern Africa monsoon

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KW - seasonal forecasting

KW - teleconnections

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Seasonal variability and predictability of monsoon precipitation in Southern Africa

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Keywords

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