Abstract
Future climate changes could alter hydrometeorological patterns and change the nature of droughts at global to regional scales. However, there are considerable uncertainties in future drought projections. Here, we focus on agricultural drought by analyzing surface soil moisture outputs from CMIP5 multi-model ensembles (MMEs) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios. First, the annual mean soil moisture by the end of the 21st century shows statistically significant large-scale drying and limited areas of wetting for all scenarios, with stronger drying as the strength of radiative forcing increases. Second, the MME mean spatial extent of severe drought is projected to increase for all regions and all future RCP scenarios, and most notably in Central America (CAM), Europe and Mediterranean (EUM), Tropical South America (TSA), and South Africa (SAF). Third, the model uncertainty presents the largest source of uncertainty (over 80%) across the entire 21st century among the three sources of uncertainty: internal variability, model uncertainty, and scenario uncertainty. Finally, we find that the spatial pattern and magnitude of annual and seasonal signal to noise (S/N) in soil moisture anomalies do not change significantly by lead time, indicating that the spreads of uncertainties become larger as the signals become stronger.
Highlights
Future drought risks could be exacerbated by spatiotemporal changes in hydrometeorological variables due to climate change[1,2,3]
We investigate future agricultural drought change by calculating the multi-model mean percentage change in annual/seasonal mean surface soil moisture for the period of 2071–2100 (RCP forcing) relative to 1976–2005 for each emission scenario (Fig. 1)
Since we perform multiple comparisons using the Wilcoxon signed-rank test and calculate the p-values for more than 3000 grids for each scenario, we control for the false discovery rate (FDR) for multiple comparisons at a significance level of 0.05 and adjust the p-value for each grid cell following the method of Benjamini and Hochberg[32] (Fig. 1)
Summary
Future drought risks could be exacerbated by spatiotemporal changes in hydrometeorological variables due to climate change[1,2,3]. Soil moisture is an important indicator for agricultural drought since it can reflect the total effects of precipitation and evapotranspiration, represent the status of agriculture, and determine the available water supply for healthy plant growth[18,21,22]. Future soil moisture changes depend on the total interaction of temperature and precipitation, the complex surface hydrological process, as well as other factors, such as wind speed, vegetation, land use/cover change, and atmospheric CO2 (influence plant stomatal conductance and plant transpiration)[23]. We use soil moisture as an integrative variable to reflect the change in agricultural drought risks. Prior studies have assessed and quantified the uncertainty associated with primary climate variables like surface air temperature[24,25] and precipitation[26,27]. Quantifying and partitioning uncertainty associated with drought is very important for decision makers to understand the scope and direction for narrowing the uncertainty through investment in climate science[24]
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