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While there is evidence for an acceleration in global mean sea level (MSL) since the 1960s, its detection at local levels has been hampered by the considerable influence of natural variability on the rate of MSL change. Here we report a MSL acceleration in tide gauge records along the U.S. Southeast and Gulf coasts that has led to rates (>10 mm yr −1 since 2010) that are unprecedented in at least 120 years. We show that this acceleration is primarily induced by an ocean dynamic signal exceeding the externally forced response from historical climate model simulations. However, when the simulated forced response is removed from observations, the residuals are neither historically unprecedented nor inconsistent with internal variability in simulations. A large fraction of the residuals is consistent with wind driven Rossby waves in the tropical North Atlantic. This indicates that this ongoing acceleration represents the compounding effects of external forcing and internal climate variability. Sea level rise along the U.S. Southeast and Gulf Coast has accelerated since 2010 due to changes in steric expansion and the ocean’s circulation. The acceleration represents the compounding effects of external forcing and natural climate variability.

Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.

The 2019/20 Black Summer bushfire disaster in southeast Australia was unprecedented: the extensive area of forest burnt, the radiative power of the fires, and the extraordinary number of fires that developed into extreme pyroconvective events were all unmatched in the historical record. Australia’s hottest and driest year on record, 2019, was characterised by exceptionally dry fuel loads that primed the landscape to burn when exposed to dangerous fire weather and ignition. The combination of climate variability and long-term climate trends generated the climate extremes experienced in 2019, and the compounding effects of two or more modes of climate variability in their fire-promoting phases (as occurred in 2019) has historically increased the chances of large forest fires occurring in southeast Australia. Palaeoclimate evidence also demonstrates that fire-promoting phases of tropical Pacific and Indian ocean variability are now unusually frequent compared with natural variability in pre-industrial times. Indicators of forest fire danger in southeast Australia have already emerged outside of the range of historical experience, suggesting that projections made more than a decade ago that increases in climate-driven fire risk would be detectable by 2020, have indeed eventuated. The multiple climate change contributors to fire risk in southeast Australia, as well as the observed non-linear escalation of fire extent and intensity, raise the likelihood that fire events may continue to rapidly intensify in the future. Improving local and national adaptation measures while also pursuing ambitious global climate change mitigation efforts would provide the best strategy for limiting further increases in fire risk in southeast Australia. Multiple climate contributors to fire risk in southeast Australia have led to an increase in fire extent and intensity over the past decades that will likely continue into the future, suggests a synthesis of climate variability, long-term trends and palaeoclimatic evidence.

By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change on surface water availability in Iran over the twenty-first century. Our findings show that increasing water withdrawal in Iran, due to population growth and increased agricultural activities, has been the main source of historical water stress. Increased levels of water stress across Iran are expected to continue or even worsen over the next decades due to projected variability and change in precipitation combined with heightened water withdrawals due to increasing population and socio-economic activities. The greatest rate of decreased water storage is expected in the Urmia Basin, northwest of Iran, (varying from ~ − 8.3 mm/year in 2010–2039 to ~ − 61.6 mm/year in 2070–2099 compared with an observed rate of 4 mm/year in 1976–2005). Human activities, however, strongly dominate the effects of precipitation variability and change. Major shifts toward sustainable land and water management are needed to reduce the impacts of water scarcity in the future, particularly in Iran’s heavily stressed basins like Urmia Basin, which feeds the shrinking Lake Urmia.

The interaction between co-occurring drought and hot conditions is often particularly damaging to crop's health and may cause crop failure. Climate change exacerbates such risks due to an increase in the intensity and frequency of dry and hot events in many land regions. Hence, here we model the trivariate dependence between spring maximum temperature and spring precipitation and wheat and barley yields over two province regions in Spain with nested copulas. Based on the full trivariate joint distribution, we (i) estimate the impact of compound hot and dry conditions on wheat and barley loss and (ii) estimate the additional impact due to compound hazards compared to individual hazards. We find that crop loss increases when drought or heat stress is aggravated to form compound dry and hot conditions and that an increase in the severity of compound conditions leads to larger damage. For instance, compared to moderate drought only, moderate compound dry and hot conditions increase the likelihood of crop loss by 8 % to 11 %, while when starting with moderate heat, the increase is between 19 % to 29 % (depending on the cereal and region). These findings suggest that the likelihood of crop loss is driven primarily by drought stress rather than by heat stress, suggesting that drought plays the dominant role in the compound event; that is, drought stress is not required to be as extreme as heat stress to cause similar damage. Furthermore, when compound dry and hot conditions aggravate stress from moderate to severe or extreme levels, crop loss probabilities increase 5 % to 6 % and 6 % to 8 %, respectively (depending on the cereal and region). Our results highlight the additional value of a trivariate approach for estimating the compounding effects of dry and hot extremes on crop failure risk. Therefore, this approach can effectively contribute to design management options and guide the decision-making process in agricultural practices.

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non‐stationary multivariate process. For instance, future mean sea‐level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deep‐landslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors. Plain Language Summary Many societal and environmental impacts from events such as droughts and storms arise from a combination of weather and climate factors referred to as a compound event. Considering the complex nature of these high‐impact events is crucial for an accurate assessment of climate‐related risk, for example to develop adaptation and emergency preparedness strategies. However, compound event research has emerged only recently, therefore our ability to analyze these events is still limited. In practice, studying compound events is a challenging task, which often requires interaction between experts across multiple disciplines. Recently, compound events were divided into four types to aid the framing of research on this topic, but guidelines on how to study these four types are missing. Here, we take a pragmatic approach and—focusing on case studies of different compound event types—illustrate how to address specific research questions that could be of interest to users. The results allow identifying recommendations for compound event analyses. Furthermore, through the case studies, we highlight the relevance that compounding effects have for the occurrence of landslides, flooding, vegetation impacts, and crop failures. The guidelines emerged from this work will assist the development of compound event analysis across disciplines and sectors. Key Points Using case studies representative of four main compound event types we show how compound event‐related research questions can be tackled We present user‐friendly guidelines for compound event analysis applicable to different compound event types We demonstrate that compound events cause vegetation impacts, coastal flooding, landslides, and continental‐scale crop yield failures

Soil moisture (SM) during the vegetation growing season largely affects plant transpiration and photosynthesis, and further alters the land energy and water balance through its impact on the energy partition into latent and sensible heat fluxes. To highlight the impact of strong vegetation activity, we investigate global SM–climate interactions over the peak growing season (PGS) during 1982–2015 based on multisource datasets. Results suggest widespread positive SM–precipitation (P), SM–evapotranspiration (ET), and negative SM–temperature (T) interactions with non-negligible negative SM–P, SM–ET, and positive SM–T interactions over PGS. Relative to the influence of individual climate factors on SM, the compounding effect of climate factors strengthens SM–climate interactions. Simultaneously, variations of SM are dominated by precipitation from 50°N toward the south, by evapotranspiration from 50°N toward the north, and by temperature over the Sahara, western and central Asia, and the Tibetan Plateau. Importantly, the higher probability of concurrent SM wetness and climate extremes indicates the instant response of SM wetness to extreme climate. In contrast, the resistance of vegetation partially contributes to a consequent slower response of SM dryness to extreme climate. We highlight the significance of the compounding effects of climate factors in understanding SM–climate interaction in the context of strong vegetation activity, and the response of SM wetness and dryness to climate extremes.

Preserving skeletal muscle function is essential to maintain life quality at high age. Calorie restriction (CR) potently extends health and lifespan, but is largely unachievable in humans, making “CR mimetics” of great interest. CR targets nutrient-sensing pathways centering on mTORC1. The mTORC1 inhibitor, rapamycin, is considered a potential CR mimetic and is proven to counteract age-related muscle loss. Therefore, we tested whether rapamycin acts via similar mechanisms as CR to slow muscle aging. Here we show that long-term CR and rapamycin unexpectedly display distinct gene expression profiles in geriatric mouse skeletal muscle, despite both benefiting aging muscles. Furthermore, CR improves muscle integrity in mice with nutrient-insensitive, sustained muscle mTORC1 activity and rapamycin provides additive benefits to CR in naturally aging mouse muscles. We conclude that rapamycin and CR exert distinct, compounding effects in aging skeletal muscle, thus opening the possibility of parallel interventions to counteract muscle aging. The anti-aging intervention calorie restriction (CR) is thought to act via the nutrient-sensing multiprotein complex mTORC1. Here the authors show that the mTORC1-inhibitor rapamycin and CR use largely distinct mechanisms to slow mouse muscle aging.

Flooding is of particular concern in low-lying coastal zones that are prone to flooding impacts from multiple drivers, such as oceanographic (storm surge and wave), fluvial (excessive river discharge), and/or pluvial (surface runoff). In this study, we analyse, for the first time, the compound flooding potential along the contiguous United States (CONUS) coastline from all flooding drivers, using observations and reanalysis data sets. We assess the overall dependence from observations by using Kendall's rank correlation coefficient (τ) and tail (extremal) dependence (χ). Geographically, we find the highest dependence between different drivers at locations in the Gulf of Mexico, southeastern, and southwestern coasts. Regarding different driver combinations, the highest dependence exists between surge–waves, followed by surge–precipitation, surge–discharge, waves–precipitation, and waves–discharge. We also perform a seasonal dependence analysis (tropical vs. extra-tropical season), where we find higher dependence between drivers during the tropical season along the Gulf and parts of the East Coast and stronger dependence during the extra-tropical season on the West Coast. Finally, we compare the dependence structure of different combinations of flooding drivers, using observations and reanalysis data, and use the Kullback–Leibler (KL) divergence to assess significance in the differences of the tail dependence structure. We find, for example, that models underestimate the tail dependence between surge–discharge on the East and West coasts and overestimate tail dependence between surge–precipitation on the East Coast, while they underestimate it on the West Coast. The comprehensive analysis presented here provides new insights on where the compound flooding potential is relatively higher, which variable combinations are most likely to lead to compounding effects, during which time of the year (tropical versus extra-tropical season) compound flooding is more likely to occur, and how well reanalysis data capture the dependence structure between the different flooding drivers.

The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a managed water system in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present an impact-focused multivariate statistical framework to model the dependence between these flooding drivers and the resulting return periods of inland water levels. This framework is applied to the same managed water system using the aforementioned large ensemble. Composite analysis is used to guide the selection of suitable predictors and to obtain an impact function that optimally describes the relationship between high inland water levels (the impact) and the explanatory predictors. This is complex due to the high degree of human management affecting the dynamics of the water level. Training the impact function with subsets of data uniformly distributed along the range of water levels plays a major role in obtaining an unbiased performance. The dependence structure between the defined predictors is modelled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. Regarding the return levels, this is quantified by a root-mean-square deviation of 0.02 m. The proposed framework is able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. Even though the framework has only been applied and validated in one study area, it shows great potential to be transferred to other areas. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50-year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.