Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitati
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Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi R. Manzanas1
5 ´ · L. Fiwa2 · C. Vanya3 · H. Kanamaru4 · J. M. Gutierrez
Received: 20 August 2019 / Accepted: 11 September 2020 / © Springer Nature B.V. 2020
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
1 Introduction Agriculture remains key for the socio-economic development of many countries in Africa, contributing with more than one-third to their total gross domestic product. This is the case R. Manzanas
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Climatic Change
for Malawi, where the agricultural sector is the engine for economic growth and household food, nutrition, and income security (IEET 2013). However, because irrigation infrastructure is largely underdeveloped or not fully exploited (Wiyo et al. 1999), this country exhibits a strong over-dependence on rain-fed systems (see, e.g., Wiyo et al. 2000; Grist 2015). As such, reliable regional climate change projections which allow the development of intelligent adaptation policies are essential for Malawi, since they can contribute to guaranteeing the sustainability of the agricultural sector during the next decades, avoidi
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