Predicted impacts of climate change on crops—including yield declines and loss of conservation lands—could be mitigated by exploiting existing diversity within crops. Here we examine this possibility for wine grapes. Across 1,100 planted varieties, wine grapes possess tremendous diversity in traits that affect responses to climate, such as phenology and drought tolerance. Yet little of this diversity is exploited. Instead many countries plant 70–90% of total hectares with the same 12 varieties—representing 1% of total diversity.

Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change.

Future changes in rainfall have serious impacts on human adaptation to climate change, but quantification of these changes is subject to large uncertainties in climate model projections. To narrow these uncertainties, significant efforts have been made to understand the intermodel differences in future rainfall changes. Here, we show a strong inverse relationship between present-day precipitation and its future change to possibly calibrate future precipitation change by removing the present-day bias in climate models.

Aridity—the ratio of atmospheric water supply (precipitation; P) to demand (potential evapotranspiration; PET)—is projected to decrease (that is, areas will become drier) as a consequence of anthropogenic climate change, exacerbating land degradation and desertification. However, the timing of significant aridification relative to natural variability—defined here as the time of emergence for aridification (ToEA)—is unknown, despite its importance in designing and implementing mitigation policies.

The El Niño/Southern Oscillation (ENSO) has a pronounced influence on year-to-year variations in climate1. The response of fires to this forcing2 is complex and has not been evaluated systematically across different continents. Here we use satellite data to create a climatology of burned-area and fire-emissions responses, drawing on six El Niño and six La Niña events during 1997–2016.

 

A critical question for agricultural production and food security is how water demand for staple crops will respond to climate and carbon dioxide (CO2) changes, especially in light of the expected increases in extreme heat exposure. To quantify the trade-offs between the effects of climate and CO2 on water demand, we use a ‘sink-strength’ model of demand which relies on the vapour-pressure deficit (VPD), incident radiation and the efficiencies of canopy-radiation use and canopy transpiration; the latter two are both dependent on CO2.

Most climate change mitigation scenarios that are consistent with the 1.5–2 °C target rely on a large-scale contribution from biomass, including advanced (second-generation) biofuels. However, land-based biofuel production has been associated with substantial land-use change emissions. Previous studies show a wide range of emission factors, often hiding the influence of spatial heterogeneity. Here we introduce a spatially explicit method for assessing the supply of advanced biofuels at different emission factors and present the results as emission curves.

Mesoscale convective system (MCS)-organized convective storms with a size of ~100 km have increased in frequency and intensity in the USA over the past 35 years, causing fatalities and economic losses. However, their poor representation in traditional climate models hampers the understanding of their change in the future. Here, a North American-scale convection-permitting model which is able to realistically simulate MSCs is used to investigate their change by the end-of-century under RCP8.5.

The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated. Although various physical processes have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern9,10. In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area.

In 2014 and 2015, post-monsoon extremely severe cyclonic storms (ESCS)—defined by the WMO as tropical storms with lifetime maximum winds greater than 46 m s−1—were first observed over the Arabian Sea (ARB), causing widespread damage. However, it is unknown to what extent this abrupt increase in post-monsoon ESCSs can be linked to anthropogenic warming, natural variability, or stochastic behaviour.

Pages