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Numerical experiments on sensitivity to land surface initializations are frequently conducted to investigate the predictability and uncertainties of hydrometeorological extremes. However, the conventional approaches to soil initialization often assume synchronized extremes over the target region, creating initial conditions that violate the intrinsic spatial pattern of hydrometeorological variability. Here we propose a “Slope” approach to accommodate unsynchronized anomalies, which creates initial conditions of a variable (soil temperature or soil moisture) across a large domain based on the slopes of linear regression between the variable averaged over a small target region and at each grid point in the surrounding regions. Within the target region, the “Slope” approach produces spatial patterns and temporal evolutions of hydrometeorological responses similar to the conventional approach, but generates stronger signals probably due to the nonlocal impact (excluded from the conventional approach). In the surrounding regions, the hydrometeorological responses in the “Slope” approach are consistent with the spatiotemporal variability of the model climate. Slope‐based experiments targeting different adjacent regions produce similar results, suggesting that one ensemble of experiments targeting one region may be sufficient to represent the responses from multiple ensembles each targeting a different region and thus providing the basis for increasing the computational efficiency of some land‐atmosphere interaction studies. While South America is used to demonstrate the concept in this study, the new approach offers the most advantages in regions with spatially unsynchronized or even anti‐phased hydrometeorological extremes.

 

Flash drought often leads to devastating effects in multiple sectors and presents a unique challenge for drought early warning due to its sudden onset and rapid intensification. Existing drought monitoring and early warning systems are based on various hydrometeorological variables reaching thresholds of unusually low water content. Here, we propose a flash drought early warning approach based on spaceborne measurements of solar-induced chlorophyll fluorescence (SIF), a proxy of photosynthesis that captures plant response to multiple environmental stressors. Instead of negative SIF anomalies, we focus on the subseasonal trajectory of SIF and consider slower-than-usual increase or faster-than-usual decrease of SIF as an early warning for flash drought onset. To quantify the deviation of SIF trajectory from the climatological norm, we adopt existing formulas for a rapid change index (RCI) and apply the RCI analysis to spatially downscaled 8-d SIF data from GOME-2 during 2007–2018. Using two well-known flash drought events identified by the operational US Drought Monitor (in 2012 and 2017), we show that SIF RCI can produce strong predictive signals of flash drought onset with a lead time of 2 wk to 2 mo and can also predict drought recovery with several weeks of lead time. While SIF RCI shows great early warning potential, its magnitude diminishes after drought onset and therefore cannot reflect the current drought intensity. With its long lead time and direct relevance for agriculture, SIF RCI can support a global early warning system for flash drought and is especially useful over regions with sparse hydrometeorological data. 

Abstract This study assesses the ecohydrological effects of recent meteorological droughts in tropical South America based on multiple sources of data, and investigates the possible mechanisms underlying the drought response and recovery of different ecohydrological systems. Soil drought response and recovery lag behind the meteorological drought, with delays longer in the dry region (Nordeste) than in the wet region (Amazonia), and longer in deep soil than in shallow soil. Evapotranspiration (ET) and vegetation in Nordeste are limited by water under normal conditions and decrease promptly in response to the onset of shallow soil drought. In most of the Amazon where water is normally abundant, ET and vegetation indices follow an increase-then-decrease pattern, increase at the drought onset due to increased sunshine and decrease when the drought is severe enough to cause a shift from an energy-limited regime to a water-limited regime. After the demise of meteorological droughts, ET and vegetation rapidly recover in Nordeste with the replenishment of shallow soil moisture (SM), but take longer to recover in southern Amazon due to their dependence on deep SM storage. Following severe droughts, the negative anomalies of ET and vegetation indices in southern Amazon tend to persist well beyond the end of soil drought, indicating drought-induced forest mortality that is slow to recover from. Findings from this study may have implications on the possibility of a future forest dieback as drought is projected to become more frequent and more severe in a warmer climate. 

Abstract This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates. 

South America, especially the Amazon region, is considered a hotspot of biosphere–atmosphere interactions and presents a unique challenge for regional climate modeling. Here, we evaluate the performance of a regional model in simulating the climate–vegetation system in South America and use the model to investigate the potential role of large‐scale warming in the recently observed trend of hydroclimate and vegetation. Compared with prescribing vegetation based on observational data, adding the predictive vegetation capacity to the regional climate model enabled the model to simulate the vegetation response to climate while sustaining the model performance in reproducing the mean, variability and extremes of the regional climate. The coupled vegetation–climate model captures the recent trends in hydroclimate and vegetation productivity and their spatial contrasts, including a trend toward warmer, drier, and less productive conditions in the Amazon and Nordeste regions and a trend toward cooler, wetter, and more productive condition in the La Plata region. Results from sensitivity experiment driven by detrended boundary forcing for the regional climate suggest that much of the trends in the Amazon and Nordeste regions can be attributed to the effects of large‐scale warming, but contribution from decadal variability also plays a role especially for the most recent decade. However, the trend in the La Plata region cannot be attenuated by the removal of the boundary forcing trend, indicating the role of large‐scale circulation pattern changes. The recent trends in vegetation productivity may be early manifestation of future changes in the Amazon and surrounding regions.