Present day greenhouse gases could cause more frequent and longer Dust Bowl heatwaves

Substantial warming occurred across North America, Europe and the Arctic over the early twentieth century1, including an increase in global drought2, that was partially forced by rising greenhouse gases (GHGs)3. The period included the 1930s Dust Bowl drought4,5,6,7 across North America’s Great Plains that caused widespread crop failures4,8, large dust storms9 and considerable out-migration10. This coincided with the central United States experiencing its hottest summers of the twentieth century11,12 in 1934 and 1936, with over 40 heatwave days and maximum temperatures surpassing 44 °C at some locations13,14. Here we use a large-ensemble regional modelling framework to show that GHG increases caused slightly enhanced heatwave activity over the eastern United States during 1934 and 1936. Instead of asking how a present-day heatwave would behave in a world without climate warming, we ask how these 1930s heatwaves would behave with present-day GHGs. Heatwave activity in similarly rare events would be much larger under today’s atmospheric GHG forcing and the return period of a 1-in-100-year heatwave summer (as observed in 1936) would be reduced to about 1-in-40 years. A key driver of the increasing heatwave activity and intensity is reduced evaporative cooling and increased sensible heating during dry springs and summers

The contributions of local and remote atmospheric moisture fluxes to East Asian precipitation and its variability

We investigate the contribution of the local and remote atmospheric moisture fluxes to East Asia (EA) precipitation and its interannual variability during 1979-2012. We use and expand the \citet{Brubaker_etal_JC_1993} method, which connects the area-mean precipitation to area-mean evaporation and the horizontal moisture flux into the region. Due to its large landmass and hydrological heterogeneity, EA is divided into five sub-regions: Southeast (SE), Tibetan Plateau (TP), Central East (CE), Northwest (NW) and Northeast (NE). For each region, we first separate the contributions to precipitation of local evaporation from those of the horizontal moisture flux by calculating the precipitation recycling ratio: the fraction of precipitation over a region that originates as evaporation from the same region. Then, we separate the horizontal moisture flux across the region's boundaries by direction. We estimate the contributions of the horizontal moisture fluxes from each direction, as well as the local evaporation, to the mean precipitation and its interannual variability. We find that the major contributors to the mean precipitation are not necessarily those that contribute most to the precipitation interannual variability. Over SE, the moisture flux via the southern boundary dominates the mean precipitation and its interannual variability. Over TP, in winter and spring, the moisture flux via the western boundary dominates the mean precipitation; however, variations in local evaporation dominate the precipitation interannual variability. The western moisture flux is the dominant contributor to the mean precipitation over CE, NW and NE. However, the southern or northern moisture flux or the local evaporation dominates the precipitation interannual variability over these regions, depending on the season. Potential mechanisms associated with interannual variability in the moisture flux are identified for each region. The methods and results presented in this study can be readily applied to model simulations, to identify simulation biases in precipitation that relate to the simulated moisture supplies and transport

Higher CO₂ concentrations increase extreme event risk in a 1.5 °c world

The Paris Agreement1 aims to ‘pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels.’ However, it has been suggested that temperature targets alone are unable to limit the risks associated with anthropogenic emissions2, 3. Here, using an ensemble of model simulations, we show that atmospheric CO2 increase - a more predictable consequence of emissions compared to global temperature increase - has a significant impact on Northern Hemisphere summer temperature, heat stress, and tropical precipitation extremes. Hence in an iterative climate mitigation regime aiming solely for a specific temperature goal, an unexpectedly low climate response may have corresponding ‘dangerous’ changes in extreme events. The direct impact of higher CO2 concentrations on climate extremes therefore substantially reduces the upper bound of the carbon budget, and highlights the need to explicitly limit atmospheric CO2 concentration when formulating allowable emissions. Thus, complementing global mean temperature goals with explicit limits on atmospheric CO2 concentrations in future climate policy would reduce the adverse effects of high-impact weather extremes

Risk, robustness and water resources planning under uncertainty

Risk-based water resources planning is based on the premise that water managers should invest up to the point where the marginal benefit of risk reduction equals the marginal cost of achieving that benefit. However, this cost-benefit approach may not guarantee robustness under uncertain future conditions, for instance under climatic changes. In this paper, we expand risk-based decision analysis to explore possible ways of enhancing robustness in engineered water resources systems under different risk attitudes. Risk is measured as the expected annual cost of water use restrictions, while robustness is interpreted in the decision-theoretic sense as the ability of a water resource system to maintain performance—expressed as a tolerable risk of water use restrictions—under a wide range of possible future conditions. Linking risk attitudes with robustness allows stakeholders to explicitly trade-off incremental increases in robustness with investment costs for a given level of risk. We illustrate the framework through a case study of London’s water supply system using state-of-the-art regional climate simulations to inform the estimation of risk and robustness

Relation between precipitation location and antecedent/subsequent soil moisture spatial patterns

<p>Recent evidence has shown that relations between soil moisture and precipitation at spatial and temporal aspect are contrary to each other: afternoon precipitation tends to occur at times in which conditions are overall wet and heterogeneous in the morning, but preferentially over those patches that are relatively drier than the surroundings. This study expands the notion of soil moisture–precipitation spatial coupling by analyzing the preferred precipitation location over a range of different soil moisture patterns. Using global observations of precipitation and observationally-constrained evaporative stress estimates, we confirm that relatively drier patches have more chances of receiving rain, but the preference is weakened under wetter soil conditions. During extremely wet times, wet patches have more chances of receiving rain. Moreover, the preference of precipitation to occur on drier soils is stronger when soil moisture conditions are heterogeneous. Such results indicate that the positive feedback mechanism becomes more positive as soil wetness increases and the negative feedback mechanism becomes more negative as soils become drier and more heterogeneous. The strength of these two feedback mechanisms jointly affects preferential precipitation location. Counterintuitively, analysis from one-day-after- event soil moisture pattern shows that negative soil moisture–precipitation coupling may in turn further heterogenize soil moisture patterns, because dry patch gets extremely wet with no or less rain in surrounding. Although results here do not necessarily imply a causal relationship, this work contributes to enhancing our understanding of soil moisture–precipitation spatial coupling, and exposes the complex nuances of these land–atmosphere interactions.</p

A novel bias correction methodology for climate impact simulations

Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinder any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies, most of which have been criticized for physical inconsistency and the nonpreservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias-corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance of carefully considering statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying changes in past, current and future extremes

Western US high June 2015 temperatures and their relation to global warming and soil moisture

Abstract TheWestern US statesWashington (WA), Oregon (OR) and California (CA) experienced extremely high temperatures in June 2015. The temperature anomalies were so extreme that they cannot be explained with global warming alone. We investigate the hypothesis that soil moisture played an important role as well. We use a land surface model and a large ensemble from the weather@home modelling effort to investigate the coupling between soil moisture and temperature in a warming world. Both models show that May was anomalously dry, satisfying a prerequisite for the extreme heat wave, and they indicate that WA and OR are in a wet-to-dry transitional soil moisture regime. We use two different land surface-atmosphere coupling metrics to show that there was strong coupling between temperature, latent heat and the effect of soil moisture deficits on the energy balance in June 2015 in WA and OR. June temperature anomalies conditioned on wet/dry conditions show that both the mean and extreme temperatures become hotter for dry soils, especially in WA and OR

Western US high June 2015 temperatures and their relation to global warming and soil moisture

Abstract TheWestern US statesWashington (WA), Oregon (OR) and California (CA) experienced extremely high temperatures in June 2015. The temperature anomalies were so extreme that they cannot be explained with global warming alone. We investigate the hypothesis that soil moisture played an important role as well. We use a land surface model and a large ensemble from the weather@home modelling effort to investigate the coupling between soil moisture and temperature in a warming world. Both models show that May was anomalously dry, satisfying a prerequisite for the extreme heat wave, and they indicate that WA and OR are in a wet-to-dry transitional soil moisture regime. We use two different land surface-atmosphere coupling metrics to show that there was strong coupling between temperature, latent heat and the effect of soil moisture deficits on the energy balance in June 2015 in WA and OR. June temperature anomalies conditioned on wet/dry conditions show that both the mean and extreme temperatures become hotter for dry soils, especially in WA and OR

A novel bias correction methodology for climate impact simulations

Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance to carefully consider statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying past, current and future extremes

Higher CO2 concentrations increase extreme event risk in a 1.5C world

The Paris Agreement1 aims to ‘pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels.’ However, it has been suggested that temperature targets alone are unable to limit the risks associated with anthropogenic emissions2, 3. Here, using an ensemble of model simulations, we show that atmospheric CO2 increase - a more predictable consequence of emissions compared to global temperature increase - has a significant impact on Northern Hemisphere summer temperature, heat stress, and tropical precipitation extremes. Hence in an iterative climate mitigation regime aiming solely for a specific temperature goal, an unexpectedly low climate response may have corresponding ‘dangerous’ changes in extreme events. The direct impact of higher CO2 concentrations on climate extremes therefore substantially reduces the upper bound of the carbon budget, and highlights the need to explicitly limit atmospheric CO2 concentration when formulating allowable emissions. Thus, complementing global mean temperature goals with explicit limits on atmospheric CO2 concentrations in future climate policy would reduce the adverse effects of high-impact weather extremes