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Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi

Author

Listed:
  • R. Manzanas

    (Universidad de Cantabria)

  • L. Fiwa

    (Lilongwe University of Agriculture and Natural Resources (LUANAR))

  • C. Vanya

    (Department of Climate Change and Meteorological Services (DCCMS))

  • H. Kanamaru

    (Food and Agriculture Organization (FAO) of the United Nations, Regional Office for Asia and the Pacific)

  • J. M. Gutiérrez

    (Instituto de Física de Cantabria (CSIC - Universidad de Cantabria))

Abstract

Statistical downscaling (SD) and bias adjustment (BA) methods are routinely used to produce regional to local climate change projections from coarse global model outputs. The suitability of these techniques depends on the particular application of interest and, especially, on the required spatial resolution. Whereas SD is appropriate for local (e.g., gauge) resolution, BA may be a good alternative when the gap between the predictor and predictand resolution is small. However, the different sources of uncertainty affecting SD such as reanalysis uncertainty, the choice of suitable predictors, climate model, and/or statistical approach may yield implausible projections in particular situations for which BA techniques may offer a compromise alternative, even for local resolution. In this work, we consider a case study with 41 rain gauges over Malawi and show that, despite producing similar results for a historical period, the use of different predictors may lead to large differences in the future projections obtained from SD methods. For instance, using temperature T (specific humidity Q) produces much drier (wetter) conditions than those projected by the raw global models for the target area. We demonstrate that this can be partially alleviated by substituting T+Q by relative humidity R, which simultaneously accounts for both water availability and temperature, and yields regional projections more compatible with the global one. Nevertheless, large local differences still persist, lacking a physical interpretation. In these situations, the use of simpler approaches such as empirical BA may lead to more plausible (i.e., more consistent with the global model) projections.

Suggested Citation

  • R. Manzanas & L. Fiwa & C. Vanya & H. Kanamaru & J. M. Gutiérrez, 2020. "Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi," Climatic Change, Springer, vol. 162(3), pages 1437-1453, October.
  • Handle: RePEc:spr:climat:v:162:y:2020:i:3:d:10.1007_s10584-020-02867-3
    DOI: 10.1007/s10584-020-02867-3
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    References listed on IDEAS

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    1. B. Hewitson & J. Daron & R. Crane & M. Zermoglio & C. Jack, 2014. "Interrogating empirical-statistical downscaling," Climatic Change, Springer, vol. 122(4), pages 539-554, February.
    2. R. Manzanas & L. Amekudzi & K. Preko & S. Herrera & J. Gutiérrez, 2014. "Precipitation variability and trends in Ghana: An intercomparison of observational and reanalysis products," Climatic Change, Springer, vol. 124(4), pages 805-819, June.
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    Cited by:

    1. Alvar-Beltrán, J. & Heureux, A. & Soldan, R. & Manzanas, R. & Khan, B. & Dalla Marta, A., 2021. "Assessing the impact of climate change on wheat and sugarcane with the AquaCrop model along the Indus River Basin, Pakistan," Agricultural Water Management, Elsevier, vol. 253(C).
    2. Jie Chen & Xunchang John Zhang, 2021. "Challenges and potential solutions in statistical downscaling of precipitation," Climatic Change, Springer, vol. 165(3), pages 1-19, April.
    3. Alessandro Dosio & Christopher Lennard & Jonathan Spinoni, 2022. "Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations," Climatic Change, Springer, vol. 170(1), pages 1-24, January.

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