• Archer, E., W. Landman, J. Malherbe, M. Tadross, and S. Pretorius, 2019: South Africa’s winter rainfall region drought: A region in transition? Climate Risk Manage., 25, 100188, https://doi.org/10.1016/j.crm.2019.100188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Batté, L., C. Ardilouze, and M. Déqué, 2018: Forecasting West African heat waves at subseasonal and seasonal time scales. Mon. Wea. Rev., 146, 889907, https://doi.org/10.1175/MWR-D-17-0211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blamey, R. C., and C. J. C. Reason, 2009: Numerical simulation of a mesoscale convective system over the east coast of South Africa. Tellus, 61A, 1734, https://doi.org/10.1111/j.1600-0870.2008.00366.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Driver, P., and C. J. C. Reason, 2017: Variability in the Botswana High and its relationships with rainfall and temperature characteristics over southern Africa. Int. J. Climatol., 37, 570581, https://doi.org/10.1002/joc.5022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Engelbrecht, C. J., 2017: Exploring subseasonal dynamic predictability of extreme events: A case study of the January and February heat waves. 33rd Annual Conf. of the South African Society for Atmospheric Sciences, Polekwane, South Africa, South African Society for Atmospheric Sciences.

  • Engelbrecht, C. J., and W. A. Landman, 2016: Interannual variability of seasonal rainfall over the Cape south coast of South Africa and synoptic type association. Climate Dyn., 47, 295313, https://doi.org/10.1007/s00382-015-2836-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Engelbrecht, C. J., W. A. Landman, R. Graham, and P. McLean, 2017: Seasonal prediction skill of intraseasonal synoptic type variability over the Cape south coast of South Africa by making use of the Met Office Global Seasonal Forecast system 5. Int. J. Climatol., 37, 19982012, https://doi.org/10.1002/joc.4830.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fauchereau, N., S. Trzaska, M. Rouault, and Y. Richard, 2003: Rainfall variability and changes in southern Africa during the 20th century in the global warming context. Nat. Hazards, 29, 139154, https://doi.org/10.1023/A:1023630924100.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gozzo, L. F., R. P. Da Rocha, M. S. Reboita, and S. Sugahara, 2014: Subtropical cyclones over the southwestern South Atlantic: Climatological aspects and case study. J. Climate, 27, 85438562, https://doi.org/10.1175/JCLI-D-14-00149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimm, A. M., and C. J. C. Reason, 2015: Intraseasonal teleconnections between South America and South Africa. J. Climate, 28, 94899497, https://doi.org/10.1175/JCLI-D-15-0116.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hart, N. C. G., C. J. C. Reason, and N. Fauchereau, 2010: Tropical–extratropical interactions over southern Africa: Three cases of heavy summer season rainfall. Mon. Wea. Rev., 138, 26082623, https://doi.org/10.1175/2010MWR3070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joubert, A. M., S. J. Mason, and J. S. Galpin, 1996: Droughts over southern Africa in a doubled-CO2 climate. Int. J. Climatol., 16, 11491156, https://doi.org/10.1002/(SICI)1097-0088(199610)16:10<1149::AID-JOC70>3.0.CO;2-V.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kraaij, T., J. A. Baard, J. Arndt, L. Vhengani, and B. W. van Wilgen, 2018: An assessment of climate, weather, and fuel factors influencing a large, destructive wildfire in the Knysna region, South Africa. Fire Ecol., 14, 4, https://doi.org/10.1186/s42408-018-0001-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landman, W. A., and L. Goddard, 2002: Statistical recalibration of GCM forecasts over southern Africa using model output statistics. J. Climate, 15, 20382055, https://doi.org/10.1175/1520-0442(2002)015<2038:SROGFO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landman, W. A., and A. Beraki, 2012: Multi-model forecast skill for mid-summer rainfall over southern Africa. Int. J. Climatol., 32, 303314, https://doi.org/10.1002/joc.2273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lyon, B., 2009: Southern Africa summer drought and heat waves: Observations and coupled model behaviour. J. Climate, 22, 60336046, https://doi.org/10.1175/2009JCLI3101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malherbe, J., F. A. Engelbrecht, W. A. Landman, and C. J. Engelbrecht, 2012: Tropical systems from the southwest Indian Ocean making landfall over the Limpopo River Basin, southern Africa: A historical perspective. Int. J. Climatol., 32, 10181032, https://doi.org/10.1002/joc.2320.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, S., and M. Jury, 1997: Climatic variability and change over southern Africa: A reflection on underlying processes. Prog. Phys. Geogr., 21, 2350, https://doi.org/10.1177/030913339702100103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mastrangelo, D., and P. Malguzzi, 2019: Verification of two years of CNR-ISAC subseasonal forecasts. Wea. Forecasting, 34, 331344, https://doi.org/10.1175/WAF-D-18-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mundhenk, B. D., E. A. Barnes, E. D. Maloney, and C. F. Baggett, 2018: Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden–Julian Oscillation and quasi-biennial oscillation. Climatic Atmos. Sci., 1, 20177, https://doi.org/10.1038/s41612-017-0008-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Osman, M., and M. S. Alvarez, 2018: Subseasonal prediction of the heat wave of December 2013 in southern South America by the POAMA and BCC-CPS models. Climate Dyn., 50, 6781, https://doi.org/10.1007/s00382-017-3582-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pascale, S., B. Pohl, S. B. Kapnick, and H. Zhang, 2019: On the Angola low interannual variability and its role in modulating ENSO effects in southern Africa. J. Climate, 32, 47834803, https://doi.org/10.1175/JCLI-D-18-0745.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phakula, S., W. A. Landman, and A. Beraki, 2018: Forecasting seasonal rainfall characteristics and onset months over South Africa. Int. J. Climatol., 38, e889e900, https://doi.org/10.1002/joc.5417.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phakula, S., W. A. Landman, C. J. Engelbrecht, and T. Makgoale, 2020: Forecast skill of minimum and maximum temperatures on subseasonal timescales over South Africa. Earth Space Sci., 7, e2019EA000697, https://doi.org/10.1029/2019EA000697.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reason, C. J. C., and A. Keibel, 2004: Tropical Cyclone Eline and its unusual penetration and impacts over the southern African mainland. Wea. Forecasting, 19, 789805, https://doi.org/10.1175/1520-0434(2004)019<0789:TCEAIU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reason, C. J. C., and M. Rouault, 2005: Links between the Antarctic Oscillation and winter rainfall over western South Africa. Geophys. Res. Lett., 32, L07705, https://doi.org/10.1029/2005GL022419.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rouault, M., and Y. Richard, 2005: Intensity and spatial extent of droughts in southern Africa. Geophys. Res. Lett., 32, L15702, https://doi.org/10.1029/2005GL022436.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and et al. , 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seo, E., and et al. , 2019: Impact of soil moisture initialization on boreal summer subseasonal forecasts: Mid-latitude surface air temperature and heat wave events. Climate Dyn., 52, 16951709, https://doi.org/10.1007/s00382-018-4221-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmonds, I., and K. Keay, 2000: Mean Southern Hemisphere extratropical cyclone behavior in the 40-Year NCEP–NCAR Reanalysis. J. Climate, 13, 873885, https://doi.org/10.1175/1520-0442(2000)013<0873:MSHECB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singleton, A. T., and C. J. C. Reason, 2007: A numerical model study of an intense cutoff low pressure system over South Africa. Mon. Wea. Rev., 135, 11281150, https://doi.org/10.1175/MWR3311.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sousa, P. M., R. C. Blamey, C. J. C. Reason, A. M. Ramos, and R. M. Trigo, 2018: The ‘day zero’ Cape Town drought and the poleward migration of moisture corridors. Environ. Res. Lett., 13, 124025, https://doi.org/10.1088/1748-9326/aaebc7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taljaard, J. J., 1967: Development, distribution and movement of cyclones and anticyclones in the Southern Hemisphere during the IGY. J. Appl. Meteor., 6, 973987, https://doi.org/10.1175/1520-0450(1967)006<0973:DDAMOC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, D., E. F. Wood, and X. Yuan, 2017: CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States. Hydrol. Earth Syst. Sci., 21, 14771490, https://doi.org/10.5194/hess-21-1477-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and et al. , 2017: The Subseasonal to Seasonal (S2S) prediction project database. Bull. Amer. Meteor. Soc., 98, 163173, https://doi.org/10.1175/BAMS-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, C. J., D. Hudson, and O. Alves, 2014: ENSO, the IOD and the intraseasonal prediction of heat extremes across Australia using POAMA-2. Climate Dyn., 43, 17911810, https://doi.org/10.1007/s00382-013-2007-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, C. J., and et al. , 2017: Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteor. Appl., 24, 315325, https://doi.org/10.1002/met.1654.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Subseasonal Deterministic Prediction Skill of Low-Level Geopotential Height Affecting Southern Africa

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  • 1 South African Weather Service, Pretoria, South Africa
  • | 2 Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa
  • | 3 Global Change Institute, University of the Witwatersrand, Johannesburg, South Africa
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Abstract

The NCEP CFSv2 and ECMWF hindcasts are used to explore the deterministic subseasonal predictability of the 850-hPa circulation of a large domain over the Atlantic and Indian Oceans that is relevant to the weather and climate of the southern African region. For NCEP CFSv2, 12 years of hindcasts, starting on 1 January 1999 and initialized daily for four ensemble members up to 31 December 2010 are verified against ERA-Interim reanalysis data. For ECMWF, 20 years of hindcasts (1995–2014), initialized once a month for all the months of the year are employed in a parallel analysis to investigate the predictability of the 850-hPa circulation. The ensemble mean for 7-day moving averages is used to assess the prediction skill for all the start dates in each month of the year, with a focus on the start dates in each month that are representative of the week-3 and week-4 hindcasts. The correlation between the anomaly patterns over the study domain shows skill over persistence up into the week-3 hindcasts for some months. The spatial distribution of the correlation between the anomaly patterns show skill over persistence to notably reduce over the domain by week 3. A prominent area where prediction skill survives the longest, occur over central South America and the adjacent Atlantic Ocean.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0008.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Christien J. Engelbrecht, christien.engelbrecht@weathersa.co.za

Abstract

The NCEP CFSv2 and ECMWF hindcasts are used to explore the deterministic subseasonal predictability of the 850-hPa circulation of a large domain over the Atlantic and Indian Oceans that is relevant to the weather and climate of the southern African region. For NCEP CFSv2, 12 years of hindcasts, starting on 1 January 1999 and initialized daily for four ensemble members up to 31 December 2010 are verified against ERA-Interim reanalysis data. For ECMWF, 20 years of hindcasts (1995–2014), initialized once a month for all the months of the year are employed in a parallel analysis to investigate the predictability of the 850-hPa circulation. The ensemble mean for 7-day moving averages is used to assess the prediction skill for all the start dates in each month of the year, with a focus on the start dates in each month that are representative of the week-3 and week-4 hindcasts. The correlation between the anomaly patterns over the study domain shows skill over persistence up into the week-3 hindcasts for some months. The spatial distribution of the correlation between the anomaly patterns show skill over persistence to notably reduce over the domain by week 3. A prominent area where prediction skill survives the longest, occur over central South America and the adjacent Atlantic Ocean.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0008.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Christien J. Engelbrecht, christien.engelbrecht@weathersa.co.za

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