Editorial Type:
Article
Article Type:
Research Article

Probabilistic Seasonal Forecasting of African Drought by Dynamical Models

Xing Yuan Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Eric F. Wood Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Nathaniel W. Chaney Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Justin Sheffield Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Jonghun Kam Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Miaoling Liang Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Kaiyu Guan Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey

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Print Publication:
01 Dec 2013
Collections:
Advancing Drought Monitoring and Prediction
DOI:
https://doi.org/10.1175/JHM-D-13-054.1
Page(s):
1706–1720
Restricted access

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 0.25°, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soil moisture percentile as indices. In terms of Brier skill score (BSS), the system is more skillful than climatology out to 3–5 months, except for the forecast of soil moisture drought over central Africa. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soil moisture than the SPI6. Drought forecasts based on SPI6 are generally more skillful than for soil moisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soil moisture drought forecast can be more 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 from observations 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.

Corresponding author address: Xing Yuan, Department of Civil and Environmental Engineering, Princeton University, E318 Engineering Quad, Princeton, NJ 08544. E-mail: xingy@princeton.edu

This article is included in the Advancing Drought Monitoring and Prediction Special Collection.

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 0.25°, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soil moisture percentile as indices. In terms of Brier skill score (BSS), the system is more skillful than climatology out to 3–5 months, except for the forecast of soil moisture drought over central Africa. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soil moisture than the SPI6. Drought forecasts based on SPI6 are generally more skillful than for soil moisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soil moisture drought forecast can be more 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 from observations 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.

Corresponding author address: Xing Yuan, Department of Civil and Environmental Engineering, Princeton University, E318 Engineering Quad, Princeton, NJ 08544. E-mail: xingy@princeton.edu

This article is included in the Advancing Drought Monitoring and Prediction Special Collection.

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  • Barnston, A. G., Thiao W. , and Kumar V. , 1996: Long-lead forecasts of seasonal precipitation in Africa using CCA. Wea. Forecasting, 11, 506–520, doi:10.1175/1520-0434(1996)011<0506:LLFOSP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Barnston, A. G., Tippett M. K. , L'Heureux M. L. , Li S. , and DeWitt D. G. , 2012: Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing? Bull. Amer. Meteor. Soc., 93, 631–651, doi:10.1175/BAMS-D-11-00111.1.

    • Search Google Scholar
    • Export Citation

  • Batté, L., and Déqué M. , 2011: Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: Skill of the ENSEMBLES project multimodel ensemble forecasts. Tellus, 63A, 283–299, doi:10.1111/j.1600-0870.2010.00493.x.

    • Search Google Scholar
    • Export Citation

  • Bowden, J., and Semazzi F. , 2007: Empirical analysis of intraseasonal climate variability over the Greater Horn of Africa. J. Climate, 20, 5715–5731, doi:10.1175/2007JCLI1587.1.

    • Search Google Scholar
    • Export Citation

  • Bretherton, C. S., Smith C. , and Wallace J. M. , 1992: An intercomparison of methods for finding coupled patterns in climate data. J. Climate, 5, 541–560, doi:10.1175/1520-0442(1992)005<0541:AIOMFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Camberlin, P., Janicot S. , and Poccard I. , 2001: Seasonality and atmospheric dynamics of the teleconnection between African rainfall and tropical sea-surface temperature: Atlantic vs. ENSO. Int. J. Climatol., 21, 973–1005, doi:10.1002/joc.673.

    • Search Google Scholar
    • Export Citation

  • Cook, K. H., 2000: The South Indian Convergence Zone and interannual rainfall variability over Sothern Africa. J. Climate, 13, 3789–3804, doi:10.1175/1520-0442(2000)013<3789:TSICZA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dutra, E., Magnusson L. , Wetterhall F. , Cloke H. L. , Balsamo G. , Boussetta S. , and Pappenberger F. , 2012: The 2010–2011 drought in the Horn of Africa in ECMWF reanalysis and seasonal forecast products. Int. J. Climatol., 33, 1720–1729, doi:10.1002/joc.3545.

    • Search Google Scholar
    • Export Citation

  • Gimeno, L., and Coauthors, 2012: Oceanic and terrestrial sources of continental precipitation. Rev. Geophys., 50, RG4003, doi:10.1029/2012RG000389.

    • Search Google Scholar
    • Export Citation

  • Hoerling, M., and Kumar A. , 2003: The perfect ocean for drought. Science, 299, 691–694, doi:10.1126/science.1079053.

  • Hoskins, B. J., and Karoly D. , 1981: The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci., 38, 1179–1196, doi:10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Landman, W. A., DeWitt D. , Lee D. , Beraki A. , and Lotter D. , 2012: Seasonal rainfall prediction skill over South Africa: One- versus two-tiered forecasting systems. Wea. Forecasting, 20, 5715–5731.

    • Search Google Scholar
    • Export Citation

  • Li, H., Luo L. , Wood E. F. , and Schaake J. , 2009: The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting. J. Geophys. Res., 114, D04114, doi:10.1029/2008JD010969.

    • Search Google Scholar
    • Export Citation

  • Liang, X., Wood E. F. , and Lettenmaier D. P. , 1996: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modifications. Global Planet. Change, 13, 195–206, doi:10.1016/0921-8181(95)00046-1.

    • Search Google Scholar
    • Export Citation

  • Luo, L., and Wood E. F. , 2007: Monitoring and predicting the 2007 U.S. drought. Geophys. Res. Lett., 34, L22702, doi:10.1029/2007GL031673.

    • Search Google Scholar
    • Export Citation

  • Luo, L., Wood E. F. , and Pan M. , 2007: Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. J. Geophys. Res., 112, D10102, doi:10.1029/2006JD007655.

    • Search Google Scholar
    • Export Citation

  • Mason, S. J., 1998: Seasonal forecasting of South African rainfall using a non-linear discriminant analysis model. Int. J. Climatol., 18, 147–164, doi:10.1002/(SICI)1097-0088(199802)18:2<147::AID-JOC229>3.0.CO;2-6.

    • Search Google Scholar
    • Export Citation

  • McCabe, G. J., Palecki M. A. , and Betancourt J. L. , 2004: Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc. Natl. Acad. Sci. USA, 101, 4136–4141, doi:10.1073/pnas.0306738101.

    • Search Google Scholar
    • Export Citation

  • McKee, T. B., Doesken N. J. , and Kleist J. , 1993: The relationship of drought frequency and duration to time scales. Preprints, Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179–184.

  • McKee, T. B., Doesken N. J. , and Kleist J. , 1995: Drought monitoring with multiple time scale. Preprints, Ninth Conf. on Applied Climatology, Dallas, TX, Amer. Meteor. Soc., 233–236.

  • Mo, K., Shukla S. , Lettenmaier D. P. , and Chen L. , 2012: Do Climate Forecast System (CFSv2) forecasts improve seasonal soil moisture prediction? Geophys. Res. Lett., 39, L23703, doi:10.1029/2012GL053598.

    • Search Google Scholar
    • Export Citation

  • Peterson, T. C., Stott P. A. , and Herring S. , 2012: Explaining extreme events of 2011 from a climate perspective. Bull. Amer. Meteor. Soc., 93, 1041–1067, doi:10.1175/BAMS-D-12-00021.1.

    • Search Google Scholar
    • Export Citation

  • Pozzi, W., and Coauthors, 2013: Towards global drought early warning capability: Expanding international cooperation for the development of a framework for global drought monitoring and forecasting. Bull. Amer. Meteor. Soc., 94, 776–785, doi:10.1175/BAMS-D-11-00176.1.

    • Search Google Scholar
    • Export Citation

  • Saha, S., and Coauthors, 2013: The NCEP Climate Forecast System version 2. J. Climate, in press.

  • Schubert, S. D., Suarez M. J. , Pegion P. J. , Koster R. D. , and Bacmeister J. T. , 2004: Causes of long-term drought in the U.S. Great Plains. J. Climate, 17, 485–503, doi:10.1175/1520-0442(2004)017<0485:COLDIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Schubert, S. D., and Coauthors, 2009: A U.S. CLIVAR project to assess and compare the responses of global climate models to drought-related SST forcings patterns: Overview and results. J. Climate, 22, 5251–5272, doi:10.1175/2009JCLI3060.1.

    • Search Google Scholar
    • Export Citation

  • Sheffield, J., and Wood E. F. , 2011: Drought: Past Problems and Future Scenarios. Earth Ltd., 210 pp.

  • Sheffield, J., Goteti G. , and Wood E. F. , 2006: Development of a 50-yr high-resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 3088–3111, doi:10.1175/JCLI3790.1.

    • Search Google Scholar
    • Export Citation

  • Sheffield, J., Andreadis K. M. , Wood E. F. , and Lettenmaier D. P. , 2009: Global and continental drought in the second half of the twentieth century: Severity-area-duration analysis and temporal variability of large-scale events. J. Climate, 22, 1962–1981, doi:10.1175/2008JCLI2722.1.

    • Search Google Scholar
    • Export Citation

  • Shukla, S., and Lettenmaier D. P. , 2011: Seasonal hydrologic prediction in the United States: Understanding the role of initial hydrologic conditions and seasonal climate forecast skill. Hydrol. Earth Syst. Sci., 15, 3529–3538, doi:10.5194/hess-15-3529-2011.

    • Search Google Scholar
    • Export Citation

  • Smith, D. M., Scaife A. A. , and Kirtman B. P. , 2012: What is the current state of scientific knowledge with regard to seasonal to decadal forecasting. Environ. Res. Lett., 7, 015602, doi:10.1088/1748-9326/7/1/015602.

    • Search Google Scholar
    • Export Citation

  • Smith, T. M., Reynolds R. W. , Peterson T. C. , and Lawrimore J. , 2008: Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880–2006). J. Climate, 21, 2283–2296, doi:10.1175/2007JCLI2100.1.

    • Search Google Scholar
    • Export Citation

  • Svoboda, M., and Coauthors, 2002: The Drought Monitor. Bull. Amer. Meteor. Soc., 83, 1181–1190.

  • Tarhule, A., and Lamb P. J. , 2003: Climate research and seasonal forecasting for West Africans: Perceptions, dissemination, and use? Bull. Amer. Meteor. Soc.,84, 1741–1759, doi:10.1175/BAMS-84-12-1741.

  • Trenberth, K. E., 1984: Some effects of finite sample size and persistence on meteorological statistics. Part II: Potential predictability. Mon. Wea. Rev., 112, 2369–2379, doi:10.1175/1520-0493(1984)112<2369:SEOFSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., and Caron J. M. , 2000: The Southern Oscillation revisited: Sea level pressures, surface temperatures and precipitation. J. Climate, 13, 4358–4365, doi:10.1175/1520-0442(2000)013<4358:TSORSL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wallace, J. M., Smith C. , and Bretherton C. S. , 1992: Singular value decomposition of wintertime sea-surface temperature and 500-mb height anomalies. J. Climate, 5, 561–576, doi:10.1175/1520-0442(1992)005<0561:SVDOWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wang, A., Bohn T. J. , Mahanama S. P. , Koster R. D. , and Lettenmaier D. P. , 2009: Multimodel ensemble reconstruction of drought over the continental United States. J. Climate, 22, 2694–2712, doi:10.1175/2008JCLI2586.1.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 676 pp.

    • Search Google Scholar
    • Export Citation

  • Yoon, J. H., Mo K. , and Wood E. F. , 2012: Dynamic-model-based seasonal prediction of meteorological drought over the contiguous United States. J. Hydrometeor., 13, 463–481, doi:10.1175/JHM-D-11-038.1.

    • Search Google Scholar
    • Export Citation

  • Yuan, X., Wood E. F. , Luo L. , and Pan M. , 2011: A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction. Geophys. Res. Lett., 38, L13402, doi:10.1029/2011GL047792.

    • Search Google Scholar
    • Export Citation

  • Yuan, X., Wood E. F. , Roundy J. K. , and Pan M. , 2013: CFSv2-based seasonal hydroclimatic forecasts over conterminous United States. J. Climate, 26, 4828–4847, doi:10.1175/JCLI-D-12-00683.1.

    • Search Google Scholar
    • Export Citation
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