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Probabilistic seasonal forecasting of African drought by dynamical models

Probabilistic seasonal forecasting of African drought by dynamical models
Probabilistic seasonal forecasting of African drought by dynamical models

As a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982-2007) seasonal hydrologic hindcasts run at 0.258, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soilmoisture percentile as indices. In terms ofBrier skill score (BSS), the system is more skillful than climatology out to 3-5 months, except for the forecast of soil moisture drought over centralAfrica. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soilmoisture than the SPI6. Drought forecasts based on SPI6 are generallymore skillful than for soilmoisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soilmoisture drought forecast can bemore skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode fromobservations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land-atmosphere coupling, is necessary.

Atmosphere-ocean interaction, Climate models, Drought, Extreme events, Probability forecasts/models/distribution, Seasonal forecasting
1525-755X
1706-1720
Yuan, Xing
cd29f8ca-815f-4694-9ea9-4489be804294
Wood, Eric F.
ee59ebb9-367e-48ce-beab-22666be5095d
Chaney, Nathaniel W.
bc3ca362-9e26-46af-bd26-f99983445106
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Kam, Jonghun
2ca1444e-be4f-4250-9fa5-c5b9dca224fe
Liang, Miaoling
797ae7aa-b8dd-4eef-9039-83c7fb64f756
Guan, Kaiyu
79efc8b7-8ae3-43c7-a39c-9831af3ae12f
Yuan, Xing
cd29f8ca-815f-4694-9ea9-4489be804294
Wood, Eric F.
ee59ebb9-367e-48ce-beab-22666be5095d
Chaney, Nathaniel W.
bc3ca362-9e26-46af-bd26-f99983445106
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Kam, Jonghun
2ca1444e-be4f-4250-9fa5-c5b9dca224fe
Liang, Miaoling
797ae7aa-b8dd-4eef-9039-83c7fb64f756
Guan, Kaiyu
79efc8b7-8ae3-43c7-a39c-9831af3ae12f

Yuan, Xing, Wood, Eric F., Chaney, Nathaniel W., Sheffield, Justin, Kam, Jonghun, Liang, Miaoling and Guan, Kaiyu (2013) Probabilistic seasonal forecasting of African drought by dynamical models. Journal of Hydrometeorology, 14 (6), 1706-1720. (doi:10.1175/JHM-D-13-054.1).

Record type: Article

Abstract

As a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982-2007) seasonal hydrologic hindcasts run at 0.258, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soilmoisture percentile as indices. In terms ofBrier skill score (BSS), the system is more skillful than climatology out to 3-5 months, except for the forecast of soil moisture drought over centralAfrica. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soilmoisture than the SPI6. Drought forecasts based on SPI6 are generallymore skillful than for soilmoisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soilmoisture drought forecast can bemore skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode fromobservations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land-atmosphere coupling, is necessary.

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More information

Accepted/In Press date: 20 June 2013
Published date: 1 December 2013
Keywords: Atmosphere-ocean interaction, Climate models, Drought, Extreme events, Probability forecasts/models/distribution, Seasonal forecasting

Identifiers

Local EPrints ID: 480767
URI: http://eprints.soton.ac.uk/id/eprint/480767
ISSN: 1525-755X
PURE UUID: 14c1de63-c772-4f41-8e3f-80fd0e1762b8
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 09 Aug 2023 17:11
Last modified: 17 Mar 2024 03:40

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Contributors

Author: Xing Yuan
Author: Eric F. Wood
Author: Nathaniel W. Chaney
Author: Jonghun Kam
Author: Miaoling Liang
Author: Kaiyu Guan

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