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Few studies explore extreme wintertime European cold waves (CW) despite their huge economic, and social impacts and a recent decade punctuated by CW (February 2012, January 2017 or March 2018), while Europe bets on mild winter to avoid an energy crisis in 2023. Here, we investigate potential early CW warning signals and model biases over the winters (November-to-March) of the 1950–2005 period, calculating the atmospheric and surface conditions of key climate variables (e.g. snow, incoming radiation, cloudiness, pressure, winds and sea surface temperatures variables) both in reanalyses and climate models, before and during CW events. We show that global coupled climate models systematically overestimate the number of CW in the present-day period. Until 30 days before CW, some robust and significant early patterns emerge, both in models and reanalyses: weak atmospheric blocking, anomalously negative North Sea surface temperatures (SST) and a deficit of incoming longwave radiation. Downward shortwave radiation is anomalously positive during and before CW which weakens arguments for direct negative solar forcing on winter extreme cold events in Europe. Climate models share in their great majority (> 80%) these patterns (for dynamical and radiative-related variables, spatial correlation r > 0.7) and can correctly simulate the sign of climate variables anomalies during and at least 7 days before CW. We find that excess of European snow cover and snow depth are unlikely to cause occurrences of CW, but the advection of cold air masses (North-East) emerges as a potential precursor signal of a majority of CW. In a context of climate change, fossil fuels scarcity and increased uncertainty due to geopolitical events, it is crucial to study the sensitivity of the energy, health and agriculture sectors to compound extreme weather, including cold waves. [ABSTRACT FROM AUTHOR]

In the autumn, the French Mediterranean area is frequently exposed to heavy precipitation events whose daily accumulation can exceed 300 mm. One of the key processes contributing to these precipitation amounts is deep convection, which can be explicitly resolved by state-of-the-art convection-permitting models to reproduce heavy rainfall events that are comparable to observations. This approach has been tested and performed at climate scale in several studies in recent decades for different areas. In this research, we investigate the added value of using an ensemble of three climate simulations at convection-permitting resolution (approx. 3 km) to replicate extreme precipitation events at both daily and shorter timescales over the south of France. These three convection-permitting simulations are performed with the Weather Research and Forecasting (WRF) Model. They are forced by three EURO-CORDEX simulations, which are also run with WRF at the resolution of 0.11 ∘ (approx. 12 km). We found that a convection-permitting approach provides a more realistic representation of extreme daily and 3-hourly rainfall in comparison with EURO-CORDEX simulations. Their similarity to observations allows use for climate change studies and its impacts. [ABSTRACT FROM AUTHOR]

The estimation of hub-height wind speed is critical to a comprehensive wind resource assessment, particularly for the evaluation of future energy mix scenarios. However, gridded datasets of wind speeds are often limited to near-surface winds, especially when it comes to climate model projections, which is a real limitation for using climate models. This study develops a transfer method to calculate 100 m wind speed using three machine learning methods, including the Least Absolute Shrinkage Selector Operator, Random Forest (RF) and extreme Gradient Boost (XGBoost). Compared with the traditional algorithm, based on empirical formulae, the tested machine learning-based algorithms allow much more accurate estimates of 100 m wind speeds. RF and XGBoost have good performance on the hourly scale, and correct the major biases of the classical, simplified algorithms, especially in the diurnal cycle of hub-height wind speeds. RF appears to be the best algorithm when compared with the reanalysis data. In addition, the machine learning transfer model is applied to 19 regional climate projections. Results show that the 100 m wind speed has decreased in most of Europe during 1979–2019, which is consistent with the observed stilling of surface winds in recent years. This trend is projected to increase in the future, under an uncurbed greenhouse gas emission scenario, which indicates adverse effects for the development of wind power generation in Europe. The approach established in this study can be applied to obtain numerical climate model outputs accurately, which is critical to the estimation of the long-term changes of global renewable energy resources. • A transfer model to calculate hub-height wind speed using machine learning methods. • Random Forest corrects the poor performance of the diurnal cycle simulation. • Wind power potential will decrease in the future along the 21st century in Europe. • Our methodology will benefit to wind power potential and drought risk assessment. [ABSTRACT FROM AUTHOR]

Mid‐tropospheric deep depressions in summer over the North Atlantic are shown to have strongly increased in the eastern and strongly decreased in the western North Atlantic region. This evolution is linked to a change in baroclinicity in the west of the North Atlantic ocean and over the North American coast, likely due to the increased surface temperature there. Deep depressions in the Eastern North Atlantic are linked to a temperature pattern typical of extreme heat events in the region. The same analysis is applied to a sample of CMIP6 model outputs, and no such trends are found. This study suggests a link between the observed increase of summer extreme heat events in the region and the increase of the number of Atlantic depressions. The failure of CMIP6 models to reproduce these events can consequently also reside in an incorrect reproduction of this specific feature of midlatitude atmospheric dynamics. Plain Language Summary: Extreme temperatures events in Western Europe have been rising fast, and current global climate models are not able to reproduce this excess. There are different hypotheses to explain this discrepancy. One is that the large‐scale atmospheric dynamics, responsible for the local weather, is not correctly represented by the models: indeed, the frequency and amplitude of some specific weather phenomena have been shown to be insufficiently reproduced, especially in summer. Here, we study one such phenomenon, namely the transient deep depressions, or extratropical cyclones, that travel across the Atlantic basin. A significant large increase of the number of these events is found in summer in the region of the North Atlantic off the western European coast. Depressions in that region are accompanied by high temperatures in continental western Europe. An ensemble of state of the art climate models are also analyzed and none of them is able to correctly reproduce the frequency of deep depressions nor their large trend, which suggests a common origin with the insufficient prediction of western European extreme heat events. Great caution should be used when analyzing climate change predictions in the region, and even more so when studying changes in complex dynamical phenomena. Key Points: Deep depression occurrences have significantly increased over the eastern side, and decreased over the western side of the North AtlanticDeep depressions are linked to high surface temperature patterns in western continental Europe but have little impact on the mean warmingGlobal Climate Models fail to reproduce the observed trends in deep depressions correctly [ABSTRACT FROM AUTHOR]

Since 2019, Central South America (CSA) has been reeling under drought conditions, with the last 4 months of 2022 receiving only 44% of the average total precipitation. Simultaneously to the drought, a series of record-breaking heat waves has affected the region. The rainfall deficit during October–November-December (OND) is highly correlated with the Niño3.4 index, indicating that the OND 2022 rainfall deficit is partly driven by La Niña, as observed in previous droughts in this region. To identify whether human-induced climate change was also a driver of the OND 2022 rainfall deficit, we analysed precipitation over the most impacted region. Our findings revealed a pattern of decreased rainfall over the past 40 years, although we cannot definitively conclude whether this trend exceeds what would be expected from natural variations. To clarify if this trend can be attributed to climate change, we looked at 1-in-20-year low rainfall events over the same region in climate models. The models show that the severity of low rainfall events decreases (i.e. they become wetter, the opposite of the trend observed in most weather records), although this trend is again not significant and is compatible with natural variability. Therefore, we cannot attribute the low rainfall to climate change. Moreover, our analysis of effective precipitation potential (evapotranspiration minus rainfall) shows that, in climate models, the increase in temperature does partly compensate for the increase in rainfall but only to offset the wetting, and does not lead to a significant climate change signal in effective precipitation. However, higher temperatures in the region, which have been attributed to climate change, decreased water availability in the models in late 2022, indicating that climate change probably reduced water availability over this period also in the observations, increasing agricultural drought, although this study did not quantify this effect. This means that even though the reduced rainfall is within the natural variability, the consequences of drought are becoming more severe due to the strong increase in extreme heat. The case of the OND 2022 rainfall deficit and the ongoing drought in CSA is a clear example of the interplay between climate variability and human-induced climate change. This shows the importance of considering not only those aspects associated with climate change but also climate variability in order to understand and attribute particular events or trends at the regional level. [ABSTRACT FROM AUTHOR]

A large area including the central-northern part of Argentina, southern Bolivia, central Chile, and most of Paraguay and Uruguay, experienced record-breaking temperatures during two consecutive heatwaves in late November and early December 2022. During the second heatwave, nine locations in northern Argentina registered their highest maximum temperature of December since at least 1961. Our analysis based on observational and reanalysis datasets indicate that South America, like the rest of the world, has experienced heatwaves increasingly frequently in recent years. The December 2022 heatwave has an estimated return time of 1 in 20 years in the current climate, meaning it has about a 5% chance of happening each year. To estimate how human-caused climate change has influenced the likelihood and intensity of the observed heatwave, we combined climate models with the observation-based data. We found that human-caused climate change made the event about 60 times more likely. A heatwave with a return period of 20 years would be about 1.4 °C less hot in a world without anthropogenic global warming. Heatwaves this early in the summer season pose a substantial risk to human health and are potentially lethal. This risk is aggravated by climate change, but also by other factors such as an aging population, urbanisation and the built environment, and individual behavior and susceptibility to the heat. This highlights the importance of attribution studies in a region already threatened and vulnerable to climate change. [ABSTRACT FROM AUTHOR]

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific Northwest areas of the US and Canada, leading to spikes in sudden deaths and sharp increases in emergency calls and hospital visits for heat-related illnesses. Here we present a multi-model, multi-method attribution analysis to investigate the extent to which human-induced climate change has influenced the probability and intensity of extreme heat waves in this region. Based on observations, modelling and a classical statistical approach, the occurrence of a heat wave defined as the maximum daily temperature (TXx) observed in the area 45–52 ∘ N, 119–123 ∘ W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lay far outside the range of historical temperature observations. This makes it hard to state with confidence how rare the event was. Using a statistical analysis that assumes that the heat wave is part of the same distribution as previous heat waves in this region led to a first-order estimation of the event frequency of the order of once in 1000 years under current climate conditions. Using this assumption and combining the results from the analysis of climate models and weather observations, we found that such a heat wave event would be at least 150 times less common without human-induced climate change. Also, this heat wave was about 2 ∘ C hotter than a 1-in-1000-year heat wave would have been in 1850–1900, when global mean temperatures were 1.2 ∘ C cooler than today. Looking into the future, in a world with 2 ∘ C of global warming (0.8 ∘ C warmer than today), a 1000-year event would be another degree hotter. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future. [ABSTRACT FROM AUTHOR]

The mechanisms leading to the occurrence of extreme weather and climate events are varied and complex. They generally encompass a combination of dynamic and thermodynamic processes, as well as drivers external to the climate system, such as anthropogenic greenhouse gas emissions and land use change. Here we present the ExtremeX multi-model intercomparison experiment, which was designed to investigate the contribution of dynamic and thermodynamic processes to recent weather and climate extremes. The numerical experiments are performed with three Earth system models: CESM, MIROC, and EC-Earth. They include control experiments with interactive atmosphere and land surface conditions, as well as experiments wherein the atmospheric circulation, soil moisture, or both are constrained using observation-based data. The temporal evolution and magnitude of temperature anomalies during heatwaves are well represented in the experiments with a constrained atmosphere. However, the magnitude of mean climatological biases in temperature and precipitation are not greatly reduced in any of the constrained experiments due to persistent or newly introduced biases. This highlights the importance of error compensations and tuning in the standard model versions. To show one possible application, ExtremeX is used to identify the main drivers of heatwaves and warm spells. The results reveal that both atmospheric circulation patterns and soil moisture conditions substantially contribute to the occurrence of these events. Soil moisture effects are particularly important in the tropics, the monsoon areas, and the Great Plains of the United States, whereas atmospheric circulation effects are major drivers in other midlatitude and high-latitude regions. [ABSTRACT FROM AUTHOR]

In October 2020, Central Vietnam was struck by heavy rain resulting from a sequence of 5 tropical depressions and typhoons. The immense amount of water led to extensive flooding and landslides that killed more than 200 people, injured more than 500 people, and caused direct damages valued at approximately 1.2 billion USD. Here, we quantify how the intensity of the precipitation leading to such exceptional impacts is attributable to anthropogenic climate change. First, we define the event as the regional maximum of annual maximum 15-day average rainfall (Rx15day). We then analyse the trend in Rx15day over Central Vietnam from the observations and simulations in the PRIMAVERA and CORDEX-CORE ensembles, which pass our evaluation tests, by applying the generalised extreme value (GEV) distribution in which location and scale parameters exponentially covary with increasing global temperatures. Combining these observations and model results, we find that the 2020 event, occurring about once every 80 years (at least 17 years), has not changed in either probability of occurrence (a factor 1.0, ranging from 0.4 to 2.4) or intensity (0%, ranging from −8 to +8%) in the present climate in comparison with early-industrial climate. This implies that the effect of human-induced climate change contributing to this persistent extreme rainfall event is small compared to natural variability. However, given the scale of damage of this hazard, our results underline that more investment in disaster risk reduction for this type of rainfall-induced flood hazard is of importance, even independent of the effect of anthropogenic climate change. Moreover, as both observations and model simulations will be extended with the passage of time, we encourage more climate change impact investigations on the extreme in the future that help adaptation and mitigation plans and raise awareness in the country. [ABSTRACT FROM AUTHOR]

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific northwest areas of the U.S. and Canada, leading to spikes in sudden deaths, and sharp increases in hospital visits for heat-related illnesses and emergency calls. Here we present a multi-model, multi-method attribution analysis to investigate to what extent human-induced climate change has influenced the probability and intensity of extreme heatwaves in this region. Based on observations and modeling, the occurrence of a heatwave with maximum daily temperatures (TXx) as observed in the area 45° N-52° N, 119° W-123° W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lie far outside the range of historically observed temperatures. This makes it hard to quantify with confidence how rare the event was. In the most realistic statistical analysis, which uses the assumption that the heatwave was a very low probability event that was not caused by new nonlinearities, the event is estimated to be about a 1 in 1000 year event in today's climate. With this assumption and combining the results from the analysis of climate models and weather observations, an event, defined as daily maximum temperatures (TXx) in the heatwave region, as rare as 1 in a 1000 years would have been at least 150 times rarer without human-induced climate change. Also, this heatwave was about 2 °C hotter than a 1 in 1000-year heatwave that at the beginning of the industrial revolution would have been (when global mean temperatures were 1.2 °C cooler than today). Looking into the future, in a world with 2 °C of global warming (0.8 °C warmer than today), a 1000-year event would be another degree hotter. It would occur roughly every 5 to 10 years in such global warming conditions. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being, and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future. [ABSTRACT FROM AUTHOR]

The mechanisms leading to the occurrence of extreme weather and climate events are varied and complex. They generally encompass a combination of dynamical and thermodynamical processes, as well as drivers external to the climate system, such as anthropogenic greenhouse gas emissions and land-use change. Here we present the ExtremeX multi-model intercomparison experiment, which was designed to investigate the contribution of dynamic and thermodynamic processes to recent weather and climate extremes. The numerical experiments are performed with three Earth System Models: CESM, MIROC, and EC-Earth. They include control experiments with interactive atmosphere and land surface conditions, and experiments where either the atmospheric circulation, soil moisture or both are constrained using observation-based values. The temporal evolution and magnitude of temperature anomalies during heatwaves is well represented in the experiments with constrained atmosphere. However, mean climatological biases in temperature and precipitation are not substantially reduced in any of the constrained experiments, highlighting the importance of error compensations and tuning in the standard model versions. The results further reveal that both atmospheric circulation patterns and soil moisture conditions substantially contribute to the occurrence of heat extremes. Soil moisture effects are particularly important in the tropics, the monsoon areas and the Great Plains of the United States. [ABSTRACT FROM AUTHOR]