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

R. Manzanas (), L. Fiwa, C. Vanya, H. Kanamaru and J. M. Gutiérrez
Additional contact information
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)

Climatic Change, 2020, vol. 162, issue 3, No 26, 1437-1453

Abstract: 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.

Keywords: Climate change projections; Statistical downscaling; Bias adjustment; Malawi; Humidity; Extrapolation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10584-020-02867-3

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