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Compound extreme climate events (ECEs) are increasingly recognized for their potential to exacerbate food insecurity risks beyond those posed by isolated events. The notion of ‘compound event’ encompasses not only co-occurring ECEs but also multiple ECEs across (different) growth stages (mECEs). The additional effects of these mECEs on crop yield, particularly considering various types of ECEs and regional scales, remain poorly understood. To close this knowledge gap, we consider droughts, pluvials, heatwaves, and coldwaves, and further identify which types of compound events have additional effects on winter wheat yield in France, using statistical methods and datasets encompassing 94 counties over a 68-year period. Our results indicate co-occurring drought heatwaves in summer and spring, along with co-occurring pluvial heatwaves and pluvial coldwaves in winter, have negative additional effects on yield compared with single ECEs. We further identify the types of mECEs that have intensified effects, with the majority showing negative effects on yield. Key interactions leading to intensified yield loss include droughts in winter or spring combined with summer co-occurring drought heatwaves, pluvials across multiple growth stages, pluvials combined with coldwaves, and the transition between droughts and pluvials, with the most severe anomaly attaining −17.2%. Coldwaves are the main ECE related to intensified yield increases, while their frequency is decreasing. Overall, this study stresses the interactions among ECEs on crop yield, and the identified types of mECEs could serve as foundational information for designing control experiments and improving process-based crop models.

Extreme surface ocean waves are often primary drivers of coastal flooding and erosion over various time scales. Hence, understanding future changes in extreme wave events owing to global warming is of socio-economic and environmental significance. However, our current knowledge of potential changes in high-frequency (defined here as having return periods of less than 1 year) extreme wave events are largely unknown, despite being strongly linked to coastal hazards across time scales relevant to coastal management. Here, we present global climate-modeling evidence, based on the most comprehensive multi-method, multi-model wave ensemble, of projected changes in a core set of extreme wave indices describing high-frequency, extra-tropical storm-driven waves. We find changes in high-frequency extreme wave events of up to ∼50%–100% under RCP8.5 high-emission scenario; which is nearly double the expected changes for RCP4.5 scenario, when globally integrated. The projected changes exhibit strong inter-hemispheric asymmetry, with strong increases in extreme wave activity across the tropics and high latitudes of the Southern Hemisphere region, and a widespread decrease across most of the Northern Hemisphere. We find that the patterns of projected increase across these extreme wave events over the Southern Hemisphere region resemble their historical response to the positive anomaly of the Southern Annular Mode. Our findings highlight that many countries with low-adaptive capacity are likely to face increasing exposure to much more frequent extreme wave events in the future.

The increasing frequency of extreme climate events may significantly alter the species composition, structure, and functionality of ecosystems, thereby diminishing their stability and resilience. This study draws on temperature and precipitation data from 53 meteorological stations across Mongolia, covering the period from 1983 to 2016, along with MODIS normalized difference vegetation index (NDVI) data from 2001 to 2016. The climate anomaly method and the curvature method of cumulative NDVI logistic curves were employed to identify years of extreme climate events and to extract the start of the growing season (SOS) in Mongolia. Furthermore, the study assessed the impact of extreme climate events on the SOS across different vegetation types and evaluated the sensitivity of the SOS to extreme climate indices. The study results show that, compared to the multi-year average green-up period from 2001 to 2016, extreme climate events significantly impact the SOS. Extreme dryness advanced the SOS by 6.9 days, extreme wetness by 2.5 days, and extreme warmth by 13.2 days, while extreme cold delayed the SOS by 1.2 days. During extreme drought events, the sensitivity of SOS to TN90p (warm nights) was the highest; in extremely wet years, the sensitivity of SOS to TX10p (cool days) was the strongest; in extreme warm events, SOS was most sensitive to TX90p (warm days); and during extreme cold events, SOS was most sensitive to TNx (maximum night temperature). Overall, the SOS was most sensitive to extreme temperature indices during extreme climate events, with a predominantly negative sensitivity. The response and sensitivity of SOS to extreme climate events varied across different vegetation types. This is crucial for understanding the dynamic changes of ecosystems and assessing potential ecological risks.

Globally, 2023 was the warmest observed year on record since at least 1850 and, according to proxy evidence, possibly of the past 100 000 years. As in recent years, the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world. Here, we provide an overview of those of 2023, with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change. We also highlight emerging features associated with some of these extreme events. Hot extremes are occurring earlier in the year, and increasingly simultaneously in differing parts of the world (e.g., the concurrent hot extremes in the Northern Hemisphere in July 2023). Intense cyclones are exacerbating precipitation extremes (e.g., the North China flooding in July and the Libya flooding in September). Droughts in some regions (e.g., California and the Horn of Africa) have transitioned into flood conditions. Climate extremes also show increasing interactions with ecosystems via wildfires (e.g., those in Hawaii in August and in Canada from spring to autumn 2023) and sandstorms (e.g., those in Mongolia in April 2023). Finally, we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.

Purpose of Review What does recent work say about how changes in convective organization could lead to changes in extreme precipitation? Recent Findings Changing convective organization is one mechanism that could explain variation in extreme precipitation increase through dynamics. In models, the effects of convective self-aggregation on extreme precipitation are sensitive to parameterization, among other factors. In both models and observations, whether or not convective organization influences extreme precipitation is sensitive to the time and space scales analyzed, affecting extreme precipitation on some scales but not others. While trends in observations in convective organization associated with mean precipitation have been identified, it has not yet been established whether these trends are robust or relevant for events associated with extreme precipitation. Summary Recent work has documented a somewhat view of how changes in convective organization could affect extreme precipitation with warming, and it remains unclear whether or not they do.

Extreme temperature events are predicted to become more frequent and intense as climate change continues, with important implications for ecosystems. Accordingly, there has been growing interest in what drives resilience to climatic disturbances. When a disturbance overwhelms the resistance of an ecosystem, it becomes vulnerable during recovery, with implications for ecosystem function and persistence. Understanding what influences ecosystem recovery is particularly important in seagrass ecosystems because of their functional roles, vulnerability, and divergent recovery strategies. Seagrass cover was monitored for 3 yr following a large, heatwave-associated mortality event in Shark Bay, Australia. Although the ecosystem’s historically dominant foundational seagrass, Amphibolis antarctica, is capable of rapid disturbance recovery, this did not occur, likely because of the failure of mechanisms which have driven rapid recovery in other systems (persistence of rhizome beds, sexual reproduction among neighboring beds). Instead, a tropical early successional seagrass, Halodule uninervis, became more common, increasing diversity. These changes in the structure of the Shark Bay seagrass ecosystem, and reduction of biomass and structural complexity, will have important implications for ecosystem services and community dynamics and indicates that this ecosystem is highly vulnerable to future disturbances. More generally, our work suggests that seagrass ecosystems typified by a mix of early and late successional species may be particularly likely to exhibit a mismatch between recovery of cover per se and recovery of function following disturbance. As such, extreme climatic events have the potential to abruptly alter seagrass community dynamics and ecosystem services.

Since the late 1990s, land surface temperatures over Japan have increased during the summer and autumn, while global mean temperatures have not risen in this duration (i.e., the global warming hiatus). In contrast, winter and spring temperatures in Japan have decreased. To assess the impact of both global warming and global-scale decadal variability on this enhanced seasonal temperature contrast, we analyzed the outputs of 100 ensemble simulations of historical and counterfactual non-warming climate simulations conducted using a high-resolution atmospheric general circulation model (AGCM). Our simulations showed that atmospheric fields impacted by the La Nina-like conditions associated with Interdecadal Pacific Oscillation (IPO) have predominantly contributed to the seasonal temperature contrast over Japan. Compared with the impact of negative IPO, the influence of global warming on seasonal temperature contrasts in Japan was small. In addition, atmospheric variability has also had a large impact on temperatures in Japan over a decadal timescale. The results of this study suggest a future increase in heatwave risk during the summer and autumn when La Nina-like decadal phenomena and atmospheric perturbations coincide over a background of global warming.

We analyze projected changes in climate extremes (extreme temperatures and heavy precipitation) in the multimodel ensembles of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). The results reveal close similarity between both ensembles in the regional climate sensitivity of the projected multimodel mean changes in climate extremes, that is, their projected changes as a function of global warming. This stands in contrast to widely reported divergences in global (transient and equilibrium) climate sensitivity in the two multimodel ensembles. Some exceptions include higher warming in the South America monsoon region, lower warming in Southern Asia and Central Africa, and higher increases in heavy precipitation in Western Africa and the Sahel region in the CMIP6 ensemble. The multimodel spread in regional climate sensitivity is found to be large in both ensembles. In particular, it contributes more to intermodel spread in projected regional climate extremes compared with the intermodel spread in global climate sensitivity in CMIP6. Our results highlight the need to consider regional climate sensitivity as a distinct feature of Earth system models and a key determinant of projected regional impacts, which is largely independent of the models' response in global climate sensitivity. Plain Language Summary Many articles analyze and compare global climate sensitivity in climate models, that is, how their global warming differs at a given level of CO2 concentrations. However, global warming is only one quantity affecting impacts. To assess human‐ and ecosystem‐relevant impacts, it is essential to evaluate the regional climate sensitivity of climate models, that is, how their regional climate features differ at a given level of global warming. We analyze here regional climate sensitivity in the new multimodel ensemble that will underlie the conclusions of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This ensemble of model projections is called the “Sixth Coupled Model Intercomparison Project” or CMIP6. We find that differences in regional climate sensitivity between models in CMIP6 often contribute more to the uncertainty of regional extremes projections than the uncertainty in global mean warming between models. Overall, the regional climate sensitivity features in the CMIP6 models' projections ensemble are very similar to those of the prior ensemble (CMIP5), although the model ensembles have been highlighted to differ in their global climate sensitivity over the 21st century. Key Points Changes in climate extremes as a function of global warming are quasilinear and determine a “regional climate sensitivity” in CMIP5 and CMIP6 The regional climate sensitivity of climate extremes is found to be very similar in CMIP5 and CMIP6, unlike global climate sensitivity Model spread in regional climate sensitivity in CMIP6 contributes more to uncertainty of projected extremes than global climate sensitivity

Over the past century, the world has seen a sustained decline in the proportion of people living in poverty, but climate change could challenge poverty reduction efforts. On the Poverty and Welfare Impacts of Climate Change: Quantifying the Effects, Identifying the Adaptation Strategies surveys the relevant research on how climate change may affect global poverty rates and presents country-specific studies with implications for low-income rural populations as well as governments' risk management programs.An evidence review examines three main strands of the literature. Unsurprisingly, the impacts of climate change are shown to be generally regressive-falling more heavily on the poor than on the rich. However, most estimates have tended to ignore the effect of aggregate economic growth on poverty and household welfare. With continued growth, the evidence suggests that the poverty impact will be relatively modest and will not reverse the major decline in poverty expected over the next 40 years. Sector-specific studies-focusing on how climate change may affect agricultural yields-are generally poor predictors of national-level poverty impacts because of heterogeneity in the ability of households to adapt. That heterogeneity features prominently in studies of how weather shocks affect rural households in Indonesia and Mexico. Erratic deviations from long-term weather patterns affect growing cycles and thereby rural households' consumption (per capita expenditure) and health indicators. In Indonesia, the affected households appeared able to protect food expenditures at the expense of nonfood expenditures, and their access to credit and community public-works projects had the strongest moderating effects. In Mexico, weather shocks affected both food and nonfood consumption in ways that varied by both region and timing. The affected households' ability to smooth consumption depended on factors including proximity to bus stations. In some regions, weather shocks also had measurable stunting effects on the stature of children between 12 and 47 months of age, perhaps from changes in household income, increases in communicable diseases, or both. Overall, more region-specific analyses within more finely tuned climate categories will help researchers to better estimate the effects of climate change on poverty and the effectiveness of government-level strategies to address those effects.This book will be of interest to academics, and decision makers in government and nongovernmental organizations, seeking to design climate-smart poverty alleviation and safety net programs based on evidence.

With global climate warming, extreme climate events are becoming more frequent, posing a great threat to crop production. In this study, twelve extreme climate indices (ECIs) were defined to characterize climate events prone to occurring during key phenological stages of wheat. Additionally, eighteen Global Climate Models (GCMs) from the Coupled Model Inter-comparison Project phase 6 (CMIP6) were selected to analyze the spatial–temporal characteristics and trends of these ECIs under four emission scenarios of the future Shared Socioeconomic Pathway (SSP). The Delta Change Method (DCM) was used to correct the bias of GCM data, and the arithmetic mean and Independence Weighted Mean (IWM) were used to aggregate the results of different GCMs to improve the projection accuracy of ECIs. Overall, the IWM ensemble results can better reproduce historical changes of ECIs than multi-model arithmetic mean and any individual GCM. The results indicated that the ECIs across wheat growing area in China were significant spatial heterogeneity during the historical period from 1981 to 2010. Under future climate scenarios, the frequency of extreme high temperature events would significantly increase in most regions, and the intensity will increase by 0.13–0.99 ° and 0.44–2.41 ℃ during 2031–2060 and 2071–2100. However, the stress of extreme low temperature events during wheat growth periods would decrease. Although the changes of extreme precipitation events under different climate scenarios were not significant, these showed considerably spatial differences across wheat growing area. In order to maintain high and stable yield of wheat, it is important to take measures to mitigate the effects of future extreme climate events on wheat production.