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Heatwaves with large impacts have increased in the recent past and will continue to increase under future warming. However, the implication for population exposure to severe heatwaves remains unexplored. Here, we characterize maximum potential human exposure (without passive/active reduction measures) to severe heatwaves in India. We show that if the global mean temperature is limited to 2.0 °C above pre-industrial conditions, the frequency of severe heatwaves will rise by 30 times the current climate by the end-21st century. In contrast, the frequency is projected to be about 2.5 times more (than the low-warming scenario of 2 °C) under conditions expected if the RCP8.5 'business-as-usual' emissions scenario is followed. Under the 2.0 °C low-warming target, population exposure to severe heatwaves is projected to increase by about 15 and 92 times the current level by the mid and end-21st century respectively. Strategies to reduce population growth in India during the 21st century may provide only limited mitigation of heatwave exposure mostly late in the century. Limiting global temperatures to 1.5 °C above preindustrial would reduce the exposure by half relative to RCP8.5 by the mid-21st century. If global temperatures are to exceed 1.5 °C then substantial measures will be required to offset the large increase in exposure to severe heatwaves in India.

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.

Human input to autumn 2000 floods Human emissions of greenhouse gases — and related warming — are recognized as an influence on global and regional warming and on broad-scale precipitation changes. But to date, it has proved difficult to assess the human impact on specific weather events. Now Pardeep Pall and colleagues use publicly contributed climate simulations to show that increased greenhouse-gas emissions substantially increased the risk of flood occurrence during the extensive flooding that occurred in England and Wales in autumn 2000. Human emissions of greenhouse gasses — and related warming — have been shown to be an influence on global and regional warming and on broad-scale precipitation changes. But so far, assessing the human imprint on specific weather events has proven difficult. Now, publicly contributed climate simulations are used to show that increased greenhouse gas emissions substantially increased the risk of flood occurrence during the catastrophic 2000 England and Wales floods. Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing 1 . Yet climate models used to study the attribution problem typically do not resolve the weather systems associated with damaging events 2 such as the UK floods of October and November 2000. Occurring during the wettest autumn in England and Wales since records began in 1766 3 , 4 , these floods damaged nearly 10,000 properties across that region, disrupted services severely, and caused insured losses estimated at £1.3 billion (refs 5 , 6 ). Although the flooding was deemed a ‘wake-up call’ to the impacts of climate change at the time 7 , such claims are typically supported only by general thermodynamic arguments that suggest increased extreme precipitation under global warming, but fail 8 , 9 to account fully for the complex hydrometeorology 4 , 10 associated with flooding. Here we present a multi-step, physically based ‘probabilistic event attribution’ framework showing that it is very likely that global anthropogenic greenhouse gas emissions substantially increased the risk of flood occurrence in England and Wales in autumn 2000. Using publicly volunteered distributed computing 11 , 12 , we generate several thousand seasonal-forecast-resolution climate model simulations of autumn 2000 weather, both under realistic conditions, and under conditions as they might have been had these greenhouse gas emissions and the resulting large-scale warming never occurred. Results are fed into a precipitation-runoff model that is used to simulate severe daily river runoff events in England and Wales (proxy indicators of flood events). The precise magnitude of the anthropogenic contribution remains uncertain, but in nine out of ten cases our model results indicate that twentieth-century anthropogenic greenhouse gas emissions increased the risk of floods occurring in England and Wales in autumn 2000 by more than 20%, and in two out of three cases by more than 90%.

When a damaging extreme meteorological event occurs, the question often arises as to whether that event was caused by anthropogenic greenhouse gas emissions. The question is more than academic, since people affected by the event will be interested in recurring damages if they find that someone is at fault. However, since this extreme event could have occurred by chance in an unperturbed climate, we are currently unable to properly respond to this question. A solution lies in recognising the similarity with the cause-effect issue in the epidemiological field. The approach there is to consider the changes in the risk of the event occurring as attributable, as against the occurrence of the event itself. Inherent in this approach is a recognition that knowledge of the change in risk as well as the amplitude of the forcing itself are uncertain. Consequently, the fraction of the risk attributable to the external forcing is a probabilistic quantity. Here we develop and demonstrate this methodology in the context of the climate change problem. [PUBLICATION ABSTRACT]

In the midsummer of 2013, Central and Eastern China (CEC) was hit by an extraordinary heat event, with the region experiencing the warmest July-August on record. To explore how human-induced greenhouse gas emissions and natural internal variability contributed to this heat event, we compare observed July-August mean surface air temperature with that simulated by climate models. We find that both atmospheric natural variability and anthropogenic factors contributed to this heat event. This extreme warm midsummer was associated with a positive high-pressure anomaly that was closely related to the stochastic behavior of atmospheric circulation. Diagnosis of CMIP5 models and large ensembles of two atmospheric models indicates that human influence has substantially increased the chance of warm mid-summers such as 2013 in CEC, although the exact estimated increase depends on the selection of climate models.

Here, in PNAS, Hansen et al. document an observed planet-wide increase in the frequency of extremely hot months and a decrease in the frequency of extremely cold months, consistent with earlier studies. This analysis is achieved through aggregation of gridded monthly temperature measurements from all over the planet. Such aggregation is advantageous in achieving statistical sampling power; however, it sacrifices regional specificity. Lastly, in that light, we find the conclusion of Hansen et al. that “the extreme summer climate anomalies in Texas in 2011, in Moscow in 2010, and in France in 2003 almost certainly would not have occurred in the absence of global warming” to be unsubstantiated by their analysis.

This paper describes five sets of regions intended for use in summarizing extreme weather over Earth’s land areas from a climate perspective. The sets differ in terms of their target size: ∼10 Mm2, ∼5 Mm2, ∼2 Mm2, ∼0.5 Mm2, and ∼0.1 Mm2 (where 1 Mm2= 1 million km2). The regions are based on political/economic divisions, and hence are intended to be primarily aligned with geographical domains of decision-making and disaster response rather than other factors such as climatological homogeneity. This paper describes the method for defining these sets of regions; provides the final definitions of the regions; and performs some comparisons across the five sets and other available regional definitions with global land coverage, according to climatological and non-climatological properties.

An important and under-quantified facet of the risks associated with human-induced climate change emerges through extreme weather. In this paper, we present an initial attempt to quantify recent costs related to extreme weather due to human interference in the climate system, focusing on economic costs arising from droughts and floods in New Zealand during the decade 2007–2017. We calculate these using previously collected information about the damages and losses associated with past floods and droughts, and estimates of the “fraction of attributable risk” that characterizes each event. The estimates we obtain are not comprehensive, and almost certainly represent an underestimate of the full economic costs of climate change, notably chronic costs associated with long-term trends. However, the paper shows the potential for developing a new stream of information that is relevant to a range of stakeholders and research communities, especially those with an interest in the aggregation of the costs of climate change or the identification of specific costs associated with potential liability.

Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.