Skip to main content

Advertisement

SpringerLink
Log in

Assessing Climate-system Historical Forecast Project (CHFP) seasonal forecast skill over Central Africa

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

The present study investigates the predictive skill of the Climate-system Historical Forecast Project (CHFP) seasonal forecast in Central Africa (CA) using deterministic and categorical evaluation methods with focus on rainfall. The skill is evaluated for all the seasons December–February (DJF), March–May (MAM), June–August (JJA), September–November (SON) at 1- and 4-month lead-time (lead-1 and lead-4) that are consistent with many regional climate outlooks. It is found that for DJF and JJA at lead-1, the 8 models of the CHFP represent well the seasonal mean rainfall in CA with correlations greater than 0.7. For MAM and SON seasons, the scores are less good and the Japan Meteorological Research Institute version 1 (JMAMRI1) model presents the best scores. For the MAM season at lead-4, the JMAMRI1 model is better. The CHFP Multi-model ensemble (MME) mean captures the spatial differences in the seasonal mean climatology of precipitation and clearly resolves the bi-modal and uni-modal natures of observed precipitation. For the DJF season, at lead-1, the CHFP MME correctly captures the maximum rainfall observed in the Southern Democratic Republic of Congo (DRC) and northern Angola. The rainfall intensity is slightly overestimated. Results indicate that for DJF and MAM, the Probability Of Detection (POD), accuracy, Success Ratio (SR), and Equitable Threat Score (ETS) are higher for the less than precipitation climatology than for greater than precipitation climatology events. This indicates that CHFP forecasts may be more useful in forecasting less than precipitation climatology conditions than greater than precipitation climatology events conditions. That is, the CHFP forecast ensemble is better able to capture the dominant mechanisms responsible for years of decreased rainfall rather than increased rainfall. It follows that the CHFP models appear to be a valuable tool that can provide some key seasonal features up to 4 months in advance, which can thus help decision-makers of this region to take appropriate adaptation and mitigation measures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Binam JN, Tonye J, Nyambi G, Akoa M, et al. (2004) Factors affecting the technical efficiency among smallholder farmers in the slash and burn agriculture zone of Cameroon. Food Polic 29(5):531–545

    Article  Google Scholar 

  • Clover J (2003) Food security in sub-saharan Africa: feature. African Secur Rev 12:5–15

    Article  Google Scholar 

  • Doblas-Reyes FJ, Hagedorn R, Palmer T (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting–II. Calibration and combination. Tellus A 57:234–252

    Google Scholar 

  • Fotso-Nguemo TC, Vondou DA, Tchawoua C, Haensler A (2017) Assessment of simulated rainfall and temperature from the regional climate model REMO and future changes over Central Africa. Clim Dynam 48 (11-12):3685–3705

    Article  Google Scholar 

  • Fotso-Nguemo TC, Chamani R, Yepdo ZD, Sonkoué D, Matsaguim CN, Vondou DA, Tanessong RS (2018) Projected trends of extreme rainfall events from CMIP5 models over Central Africa. Atmos Sci Lett 19 (2):e803

    Article  Google Scholar 

  • Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A et al (2015) The climate hazards infrared precipitation with stations–a new environmental record for monitoring extremes. Sci Data 2:150,066

    Article  Google Scholar 

  • Gbetnkom D, Khan SA (2002) Determinants of agricultural exports: the case of Cameroon, vol 120. African Economic Research Consortium

  • Hagedorn R, Doblas-Reyes FJ, Palmer T (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting–I. Basic concept. Tellus A 57:219–233

    Google Scholar 

  • Hillbruner C, Moloney G (2012) When early warning is not enough–lessons learned from the 2011 Somalia Famine. Global Food Secur 1:20–28

    Article  Google Scholar 

  • Huffman GJ, Adler RF, Bolvin DT, Gu G (2009) Improving the global precipitation record: GPCP version 2.1. Geophys Res Lett 36:17

    Article  Google Scholar 

  • Kirtman BP (2003) The COLA anomaly coupled model: ensemble ENSO prediction. Month Weather Rev 131:2324–2341

    Article  Google Scholar 

  • Kirtman B, Pirani A (2009) The state of the art of seasonal prediction: outcomes and recommendations from the First World Climate Research Program Workshop on Seasonal Prediction. Bull Am Meteorol Soc 90(4):455–458

    Article  Google Scholar 

  • Kirtman BP, Min D, Infanti JM, Kinter IIIJL, Paolino DA, Zhang Q, Van Den Dool H, Saha S, Mendez MP, Becker E et al (2014) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95:585–601

    Article  Google Scholar 

  • Nikulin G, Jones C, Giorgi F, Asrar G, Büchner M, Cerezo-Mota R, Christensen OB, Déqué M, Fernandez J, Hänsler A et al (2012) Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. J Climate 25(18):6057–6078

    Article  Google Scholar 

  • Nkendah R (2010) The informal cross-border trade of agricultural commodities between Cameroon and its CEMAC’s Neighbours. In: Paper for the NSF/AERC/IGC conference

  • Novella NS, Thiaw WM (2013) African rainfall climatology version 2 for famine early warning systems. J Appl Meteorol Climatol 52(3):588–606

    Article  Google Scholar 

  • Ogallo L, Oludhe C (2009) Climate information in decision-making in the Greater horn of Africa: lessons and experiences. World Meteorological Organization (WMO) Bulletin 58:184

    Google Scholar 

  • Osman M, Vera C (2017) Climate predictability and prediction skill on seasonal time scales over South America from CHFP models. Climate Dynam 49(7-8):2365–2383

    Article  Google Scholar 

  • Palmer T, Doblas-Reyes F, Hagedorn R, Alessandri A, Gualdi S, Andersen U, Feddersen H, Cantelaube P, Terres J, Davey M et al (2004) Development of a European multimodel ensemble system for seasonal-to-interannual prediction (DEMETER). Bull Am Meteorol Soc 85:853–872

    Article  Google Scholar 

  • Palmer T, Doblas-Reyes F, Weisheimer A, Rodwell M (2008) Toward seamless prediction: calibration of climate change projections using seasonal forecasts. Bull Am Meteorol Soc 89:459–470

    Article  Google Scholar 

  • Saha S, Nadiga S, Thiaw C, Wang J, Wang W, Zhang Q, Van den Dool H, Pan HL, Moorthi S, Behringer D et al (2006) The NCEP climate forecast system. J Climate 19:3483–3517

    Article  Google Scholar 

  • Shukla S, Roberts J, Hoell A, Funk CC, Robertson F, Kirtman B (2016) Assessing North American multimodel ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa. Climate Dynam, 1–17

  • Tanessong RS, Igri PM, Vondou DA, Tamo PK, Kamga FM (2014) Evaluation of probabilistic precipitation forecast determined from WRF forecasted amounts. Theor Appl Climatol 116:649–659

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res: Atmos 106(D7):7183–7192

    Article  Google Scholar 

  • Thober S, Kumar R, Sheffield J, Mai J, Schäfer D, Samaniego L (2015) Seasonal soil moisture drought prediction over Europe using the North American Multi-Model Ensemble (NMME). J Hydrometeorol 16(6):2329–2344

    Article  Google Scholar 

  • Tompkins AM, Ortiz De Zárate MI, Saurral RI, Vera C, Saulo C, Merryfield WJ, Sigmond M, Lee WS, Baehr J, Braun A et al (2017) The climate-system historical forecast project: providing open access to seasonal forecast ensembles from centers around the Globe. Bull Am Meteorol Soc 98(11):2293–2301

    Article  Google Scholar 

  • Wilks DS (2011) Statistical methods in the atmospheric sciences. In: 3rd ed international geophysics series 100:704 pp

  • Yuan X, Roundy JK, Wood EF, Sheffield J (2015) Seasonal forecasting of global hydrologic extremes: system development and evaluation over GEWEX basins. Bull Am Meteorol Soc 96(11):1895–1912

    Article  Google Scholar 

Download references

Acknowledgements

We greatly acknowledge the WCRP/CLIVAR Working Group on Seasonal to Interannual Prediction (WGSIP) for establishing the Climate-system Historical Forecast Project and the Centro de Investigaciones del Mar y la Atmósfera (CIMA) for providing the model outputs. We also thank the data providers for making the updated model outputs available through CHFP.

Author information

Authors and Affiliations

  1. School of Wood, Water and Natural Resources, Faculty of Agronomy and Agricultural Sciences, University of Dschang, PO Box 786, Ebolowa, Cameroon

    Roméo S. Tanessong

  2. Climate Change Research Laboratory, National Institute of Cartography, P.O. Box 157, Yaoundé, Cameroon

    Thierry C. Fotso-Nguemo

  3. Laboratory of Mechanics and Modeling of Physical Systems, Department of Physics, Faculty of Science, University of Dschang, PO Box 67, Dschang, Cameroon

    A. J. Komkoua Mbienda & G. M. Guenang

  4. Higher Teacher Technical School, University of Buea, Buea, Cameroon

    A. Tchakoutio Sandjon

  5. Laboratory of Environmental Modeling and Atmospheric Physics, Department of Physics, Faculty of Science, University of Yaounde 1, Yaoundé, Cameroon

    S. Kaissassou & Derbetini A. Vondou

Authors
  1. Roméo S. Tanessong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thierry C. Fotso-Nguemo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. J. Komkoua Mbienda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. M. Guenang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. Tchakoutio Sandjon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. S. Kaissassou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Derbetini A. Vondou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roméo S. Tanessong.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tanessong, R.S., Fotso-Nguemo, T.C., Mbienda, A.J.K. et al. Assessing Climate-system Historical Forecast Project (CHFP) seasonal forecast skill over Central Africa. Theor Appl Climatol 140, 1515–1526 (2020). https://doi.org/10.1007/s00704-020-03176-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-020-03176-6

Keywords

Search

Navigation