No abstract

Tropical and sub-tropical South America are highly susceptible to extreme droughts. Recent events include two droughts (2005 and 2010) exceeding the 100-year return value in the Amazon and recurrent extreme droughts in the Nordeste region, with profound eco-hydrological and socioeconomic impacts. In 2015–2016, both regions were hit by another drought. Here, we show that the severity of the 2015–2016 drought ("2016 drought" hereafter) is unprecedented based on multiple precipitation products (since 1900), satellite-derived data on terrestrial water storage (since 2002) and two vegetation indices (since 2004). The ecohydrological consequences from the 2016 drought are more severe and extensive than the 2005 and 2010 droughts. Empirical relationships between rainfall and sea surface temperatures (SSTs) over the tropical Pacific and Atlantic are used to assess the role of tropical oceanic variability in the observed precipitation anomalies. Our results indicate that warmer-than-usual SSTs in the Tropical Pacific (including El Niño events) and Atlantic were the main drivers of extreme droughts in South America, but are unable to explain the severity of the 2016 observed rainfall deficits for a substantial portion of the Amazonia and Nordeste regions. This strongly suggests potential contribution of non-oceanic factors (e.g., land cover change and CO2-induced warming) to the 2016 drought.

In this study, we take an ensemble modeling approach using the regional climate model RegCM4.3.4‐CLM‐CN‐DV (RCM) to study the impact of including vegetation dynamics on model performance in simulating present‐day climate and on future climate projections over West Africa. The ensemble consists of four global climate models (GCMs) as lateral boundary conditions for the RCM, and simulations with both static and dynamic vegetation are conducted. The results demonstrate substantial sensitivity of the simulated precipitation, evapotranspiration, and soil moisture to vegetation representation. Although including dynamic vegetation in the model eliminates potential inconsistencies between surface climate and the bioclimatic requirements of the prescribed vegetation, it enhances model biases causing climate drift. For present‐day climate, the ensemble average generally outperforms individual members due to cancelation of model biases. For future changes, although the original GCMs project contradicting future rainfall trends over West Africa, the RCMs‐produced trends are generally consistent. The multimodel ensemble projects significant decreases of rainfall over a major portion of West Africa and significant increases over eastern Sahel and East Africa. Projected future changes of evapotranspiration and soil moisture are consistent with those of precipitation, with significant decreases (increases) for western (eastern) Sahel. Accounting for vegetation‐climate interactions has localized but significant impacts on projected future changes of precipitation, with a wet signal over a belt of projected increase of woody vegetation cover; the impact on the projected future changes of evapotranspiration and soil moisture over west and central Africa is much more profound.

Using the Regional Climate Model (RegCM) coupled with the Community Land Model (CLM) including modules of carbon–nitrogen cycling (CN) and vegetation dynamics (DV), this study evaluates the performance of the model with different capacity of representing vegetation processes in simulating the present-day climate over China based on three 21-yr simulations driven with boundary conditions from the ERA-Interim reanalysis data during 1989–2009. For each plant functional type (PFT), the plant pheonology, density, and fractional coverage in RegCM-CLM are all prescribed as static from year to year; RegCM-CLM-CN prescribes static fractional coverage but predicts plant phenology and density, and RegCM-CLM-CN-DV predicts plant phenology, density, and fractional coverage. Compared against the observational data, all three simulations reproduce the present-day climate well, including the wind fields, temperature and precipitation seasonal cycles, extremes, and interannual variabilities. Relative to RegCM-CLM, both RegCM-CLM-CN and RegCM-CLM-CN-DV perform better in simulating the interannual variability of temperature and spatial distribution of mean precipitation, but produce larger biases in the mean temperature field. RegCM-CLM-CN overestimates leaf area index (LAI), which enhances the cold biases and alleviates the dry biases found in RegCM-CLM. RegCM-CLM-CN-DV underestimates vegetation cover and/or stature, and hence overestimates surface albedo, which enhances the wintertime cold and dry biases found in RegM-CLM. During summer, RegCM-CLM-CN-DV overestimates LAI in south and east China, which enhances the cold biases through increased evaporative cooling; in the west where evaporation is low, the albedo effect of the underestimated vegetation cover is still dominant, leading to enhanced cold biases relative to RegCM-CLM.

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 1980-2010s, 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. 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 Scenarios 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.