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Downscaling large-scale climate variability using a regional climate model: the case of ENSO over Southern Africa

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Abstract

This study documents methodological issues arising when downscaling modes of large-scale atmospheric variability with a regional climate model, over a remote region that is yet under their influence. The retained case study is El Niño Southern Oscillation and its impacts on Southern Africa and the South West Indian Ocean. Regional simulations are performed with WRF model, driven laterally by ERA40 reanalyses over the 1971–1998 period. We document the sensitivity of simulated climate variability to the model physics, the constraint of relaxing the model solutions towards reanalyses, the size of the relaxation buffer zone towards the lateral forcings and the forcing fields through ERA-Interim driven simulations. The model’s internal variability is quantified using 15-member ensemble simulations for seasons of interest, single 30-year integrations appearing as inappropriate to investigate the simulated interannual variability properly. The incidence of SST prescription is also assessed through additional integrations using a simple ocean mixed-layer model. Results show a limited skill of the model to reproduce the seasonal droughts associated with El Niño conditions. The model deficiencies are found to result from biased atmospheric forcings and/or biased response to these forcings, whatever the physical package retained. In contrast, regional SST forcing over adjacent oceans favor realistic rainfall anomalies over the continent, although their amplitude remains too weak. These results confirm the significant contribution of nearby ocean SST to the regional effects of ENSO, but also illustrate that regionalizing large-scale climate variability can be a demanding exercise.

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Acknowledgments

This article is part of the first author’s MSc thesis funded by the FABER program of the Burgundy region and is a contribution to the LEFE/IDAO VOASSI program funded by CNRS. The authors thank C. Cassou, F. Chauvin, J-P. Duvel, M. Rouault and J. Vialard for helpful discussions. Comments and suggestions from three anonymous reviewers greatly helped improve the manuscript. WRF was provided by the University Corporation for Atmospheric Research website (http://www.mmm.ucar.edu/wrf/users/download/get_source.html). Rain-gauge records were provided by the South African Water Research Commission. ERA40 data were provided by the ECMWF Meteorological Archival and Retrieval System (MARS). The Multivariate ENSO Index was downloaded from http://www.esrl.noaa.gov/psd/enso/mei/. Calculations were performed using HPC resources from DSI-CCUB, université de Bourgogne.

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  1. Julien Crétat

    Present address: Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA

Authors and Affiliations

  1. Centre de Recherches de Climatologie, UMR6282 Biogéosciences, CNRS/Université de Bourgogne, 6 boulevard Gabriel, 21000, Dijon, France

    Damien Boulard, Benjamin Pohl, Julien Crétat & Nicolas Vigaud

  2. Atmospheric Physics Department, Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam

    Thanh Pham-Xuan

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  1. Damien Boulard
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  2. Benjamin Pohl
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Correspondence to Benjamin Pohl.

Appendix: Sensitivity to forcing reanalyses and period

Appendix: Sensitivity to forcing reanalyses and period

This appendix briefly presents the regional experiments performance over the period 1979–1998, corresponding to the satellite era. Over this period, additional integrations forced by state-of-the-art ERA-Interim were also conducted, allowing us to assess the influence of lateral forcings on simulated interannual variability and regional ENSO effects in SA. Results (Table 3) can be summarized as follows:

Table 3 As Table 2 but for 1979–1998
Full size table
  1. 1.

    With respect to the 1971–1998 period, analyses computed over the only satellite era are qualitatively unchanged (see Table 2 for comparison purpose). The statistical significance of the interannual correlations is unaffected, although correlation values are slightly modified due to a lower number of degrees of freedom.

  2. 2.

    Exp. EI presents a very similar skill compared to the simulations forced by ERA40, including correlations with observed rainfall variability and ENSO descriptor. It can be noted that correlations between EI and other WRF exp. are as strong as the correlations between all simulations forced by ERA40. Although further analyses are required to fully document the incidence of the lateral forcings on WRF solutions (especially in terms of model biases and basic state) it can be concluded that the model poor performance at the interannual timescale is not only attributable to one single set of large-scale atmosphere forcings, further confirming the robustness of the conclusions raised in this study.

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Boulard, D., Pohl, B., Crétat, J. et al. Downscaling large-scale climate variability using a regional climate model: the case of ENSO over Southern Africa. Clim Dyn 40, 1141–1168 (2013). https://doi.org/10.1007/s00382-012-1400-6

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