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An Overview of Dynamical Downscaling of Global Reanalyses for Improved Climate Reconstruction Over Data-Sparse Regions

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Part of the Sustainable Development Goals Series book series (SDGS)

Abstract

Skillful and high-resolution datasets are critical if climate change is to be fully assimilated into water resources management and planning strategies. This is, however, a challenge for data-poor parts of the globe like Africa where adaptive capacity is also a challenge. Global datasets are also often not sufficient, as they come in coarse resolutions that are limited. Global reanalyses continue to help alleviate this challenge when they are downscaled to useful resolution, especially at the basin scale. This chapter provides an overview of dynamical downscaling of global reanalyses to useful resolution. The usefulness of dynamical downscaling through providing relatively high resolution and skillful datasets is also highlighted as very important for climate impact studies, especially with regard to data-sparse regions of Africa.

Keywords

  • Africa
  • Data poor
  • Dynamically consistent
  • High resolution
  • Re-analysis

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Acknowledgements

The authors would like to acknowledge the University of New South Wales, Sydney where the first author recently completed his Ph.D., especially as this chapter is a follow-up to his Ph.D. work. Acknowledgement is also given to the University of Botswana, where all the authors are based.

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Correspondence to D. B. Moalafhi .

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Moalafhi, D.B., Kenabatho, P.K., Parida, B.P. (2020). An Overview of Dynamical Downscaling of Global Reanalyses for Improved Climate Reconstruction Over Data-Sparse Regions. In: Matondo, J.I., Alemaw, B.F., Sandwidi, W.J.P. (eds) Climate Variability and Change in Africa . Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-030-31543-6_3

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