Turbulent fluxes represent an efficient way to transport heat and moisture from the surface into the atmosphere. Due to their inherently highly complex nature, they are commonly described by semiempirical relationships. What we show here is that these fluxes can also be predicted by viewing them as the outcome of a heat engine that operates between the warm surface and the cooler atmosphere and that works at its limit.

We determine the point of no return (PNR) for climate change, which is the latest year to take action to reduce greenhouse gases to stay, with a certain probability, within thresholds set by the Paris Agreement. For a 67 % probability and a 2 K threshold, the PNR is the year 2035 when the share of renewable energy rises by 2 % per year. We show the impact on the PNR of the speed by which emissions are cut, the risk tolerance, climate uncertainties and the potential for negative emissions.

Climate change projections of temperature extremes are particularly uncertain in central Europe. We demonstrate that varying soil moisture-atmosphere feedbacks in current climate models leads to an enhancement of model differences; thus, they can explain the large uncertainties in extreme temperature projections. Using an observation-based constraint, we show that the strong drying and large increase in temperatures exhibited by models on the hottest day in central Europe are highly unlikely.

Results show that an additional 6.97 million people will be exposed to droughts in China under a 1.5 ºC target relative to reference period, mostly in the east of China. Demographic change is the primary contributor to exposure. Moderate droughts contribute the most to exposure among 3 grades of drought. Our simulations suggest that drought impact on people will continue to be a large threat to China under the 1.5 ºC target. It will be helpful in guiding adaptation and mitigation strategies.

Earth system models provide simplified accounts of human-Earth interactions. Most current models treat CO₂ emissions as a homogeneously distributed forcing. However, this paper presents a new parameterization, POPEM (POpulation Parameterization for Earth Models), that computes anthropogenic CO₂ emissions at a grid point scale. A major advantage of this approach is the increased capacity to understand the potential effects of localized pollutant emissions on long-term global climate statistics.