The extent of the Amazon rainforest is projected to drastically decrease in future decades because of land-use changes. Previous climate modelling studies have found that the biogeophysical effects of future Amazonian deforestation will likely increase surface temperatures and reduce precipitation locally. However, the magnitude of these changes and the potential existence of tipping points in the underlying relationships is still highly uncertain. Using a regional climate model at a resolution of about 50 km over the South American continent, we perform four ERAinterim-driven simulations with prescribed land cover maps corresponding to present-day vegetation, two deforestation scenarios for the twenty-first century, and a totally-deforested Amazon case. In response to projected land cover changes for 2100, we find an annual mean surface temperature increase of 0:5 C over the Amazonian region and an annual mean decrease in rainfall of 0.17 mm/day compared to present-day conditions. These estimates reach 0:8 C and 0.22 mm/day in the total-deforestation case. We also compare our results to those from 28 previous (regional and global) climate modelling experiments. We show that the historical development of climate models did not modify the median estimate of the Amazonian climate sensitivity to deforestation, but led to a reduction of its uncertainty. Our results suggest that the biogeophysical effects of deforestation alone are unlikely to lead to a tipping point in the evolution of the regional climate under present-day climate conditions. However, the conducted synthesis of the literature reveals that this behaviour may be model-dependent, and the greenhouse gas-induced climate forcing and biogeochemical feedbacks should also be taken into account to fully assess the future climate of this region.

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Irrigation affects climate conditionsand especially hot extremesin various regions across the globe. Yet how these climatic effects compare to other anthropogenic forcings is largely unknown. Here we provide observational and model evidence that expanding irrigation has dampened historical anthropogenic warming during hot days, with particularly strong effects over South Asia. We show that irrigation expansion can explain the negative correlation between global observed changes in daytime summer temperatures and present-day irrigation extent. While global warming increases the likelihood of hot extremes almost globally, irrigation can regionally cancel or even reverse the effects of all other forcings combined. Around one billion people (0.79-1.29) currently benefit from this dampened increase in hot extremes because irrigation massively expanded throughout the 20 th century. Our results therefore highlight that irrigation substantially reduced human exposure to warming of hot extremes but question whether this benefit will continue towards the future.

During the industrial period, many regions experienced a reduction in forest cover and an expansion of agricultural areas, in particular North America, northern Eurasia, and South Asia. Here, results from the Land-Use and Climate, Identification of Robust Impacts (LUCID) and CMIP5 model intercomparison projects are compared in order to investigate how land-cover changes (LCC) in these regions have locally impacted the biophysical land surface properties, like albedo and evapotranspiration, and how this has affected seasonal mean temperature as well as its diurnal cycle. The impact of LCC is extracted from climate simulations, including all historical forcings, using a method that is shown to capture well the sign and the seasonal cycle of the impacts diagnosed from single-forcing experiments in most cases. The model comparison reveals that both the LUCID and CMIP5 models agree on the albedo-induced reduction of mean winter temperatures over midlatitudes. In contrast, there is less agreement concerning the response of the latent heat flux and, subsequently, mean temperature during summer, when evaporative cooling plays a more important role. Overall, a majority of models exhibit a local warming effect of LCC during this season, contrasting with results from the LUCID studies. A striking result is that none of the analyzed models reproduce well the changes in the diurnal cycle identified in present-day observations of the effect of deforestation. However, overall the CMIP5 models better simulate the observed summer daytime warming effect compared to the LUCID models, as well as the winter nighttime cooling effect.