Greenland ice loss is accelerating, but its consequences beyond sea-level rise remain poorly understood. At the same time, the Euro-Mediterranean region is facing unprecedented summer climate extremes where the underlying mechanisms have been long debated. Here we present the first evidence of a far-reaching cascading mechanism by which Greenland ice sheet melting acts as a major forcing of hemispheric-scale, spring-summer atmospheric circulation shifts with potential high-impact effects. This has already exacerbated record-breaking and catastrophic western Euro-Mediterranean climate events, and has taken part in its accelerated warming trend relative to the global mean. Using reanalysis data, a high-resolution climate model ensemble, and process-based diagnostics, we demonstrate that these processes have contributed to recent European atmospheric heatwaves with severe socioeconomic and human impacts, marine heatwaves causing mass mortality events, and extreme convective storms such as the historic Mediterranean derecho that led to serious destruction and loss of human lives. These links situate the mechanism within the broader Earth system and points out anthropogenic climate change as a likely forcing, suggesting that cascading impacts will intensify as ice loss accelerates. We also find that CMIP6 models cannot simulate the cascading mechanism, most likely because of their inability to simulate localised freshwater influx correctly, thus indicating that current projections of future climate may be underestimating these impacts. Our findings thus point out Greenland melting as a previously unreported major driver of spring-summer large-scale circulation changes, with potential for a systemic amplification of long-range regional climate hazards. These hazards include confirmed profound socioeconomic, human and ecological consequences with unforeseeable future effects. Thus, incorporating these processes is essential for forecasts systems, Earth system models, and long-term projections, posing a significant gap in our ability to project future risk and representing a major step towards effective early-warning and mitigation strategies.

Changes in tropical cyclone frequencies as the climate warms is a topic of significant current debate [1, 2]. There is no accepted theory of how tropical cyclogenesis might respond to a warmer ocean-atmosphere system as multiple controlling factors exist [3–7]. Anthropogenic warming of surface ocean temperatures due to increased greenhouse gas concentrations [8] increases the potential for tropical cyclogenesis [9–11]; however, realized cyclogenesis also requires an initial local disturbance [12–16] to develop. Most multi-decadal tropical cyclone permitting climate models (i.e. resolutions of 15-50km) exhibit frequency decreases in warmer climates, despite the increase in tropical cyclogenesis potential [17–25]. In this paper, we analyze the International Best Track Archive for Climate Stewardship, IBTrACS, [26] and the HURSAT-B1 [27] observed tropical storm data with mean shift changepoint and regression methods. Summarizing all of our analyses, we find that the global frequency of tropical cyclones with lifetime maximum sustained winds of 63km/hour or greater having a duration of more than two days (over all storm strengths) has at least very likely decreased. From the IBTrACS dataset, we also find at the global scale that it is extremely likely that the proportion of tropical cyclones that reach Category 3+ on the Saffir-Simpson Hurricane Wind Scale has increased and that it is virtually certain that both the proportion and frequency of Category 4+ storms has increased.

Abrupt snowmelt, triggered by rain-on-snow events or "snow-eater heat waves," can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850-2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

This article combines methods from existing techniques to identify multiple changepoints in non-Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non-Gaussian series, enabling penalized likelihood methods to handle non-Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count-oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by Major League Baseball batters from 1920 to 2023 and 2) a six-dimensional series of tropical cyclone counts from the Earth's basins of generation from 1980 to 2023. In the first series, beta marginal distributions are used to describe the proportions; in the second, Poisson marginal distributions seem appropriate.

Addressing climate change requires knowledge of its impacts on both nature and people. This Review depicts current approaches to the attribution of climate change impacts and potential uses for this information. The discussion covers how impact attribution identifies the drivers of observed changes and events that form links in the causal chain from anthropogenic greenhouse gas emissions and other human-induced climate forcing factors to effects on natural and human systems mediated by changes in climate and weather. Various approaches are presented that use observations and/or model simulations to estimate how a world without climate change could have evolved. In addition, different societal uses of impact attribution results are discussed and how different study designs might support them. This Review also identifies persistent knowledge gaps that call for input from policy experts globally. For example, future tailored designs might enable the attribution of additional impacts and improve quantification of the role of climate change against other drivers, whereas increased transdisciplinary collaboration and organization might provide standardization that benefits data comparability and synthesis. Addressing the remaining challenges is expected to help the impact attribution community to produce targeted answers for well-framed questions that inform development, implementation and operationalization of climate policies. Climate change impact attribution quantifies the observed consequences of climate change through combining climate attribution aspects with impact modelling. Undorf et al. discuss the key steps and methodological choices required, possible societal uses of impact attribution results and ongoing challenges.

The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds may be exceeded by future events, which undermines attribution and planning for heatwave impacts. Here, we show how the occurrence and relative probability of observed yet unprecedented events that exceed a priori upper bound estimates, so-called “impossible” temperatures, has changed over time. We find that many unprecedented events are actually within data-driven upper bounds, but only when using modern spatial statistical methods. Furthermore, there are clear connections between anthropogenic forcing and the “impossibility” of the most extreme temperatures. Robust understanding of heatwave thresholds provides critical information about future record-breaking events and how their extremity relates to historical measurements.

The 2021 heatwave in the Pacific Northwest of the United States and Canada was unusual in many regards. In particular, not only was the event deemed impossible prior to the human interference in the climate system, standard out-of-sample non-stationary generalized extreme value (GEV) analyses revealed it to be statistically impossible in 2021 as many observed temperatures were above the upper bound of the upper bound of fitted GEV distributions. Obviously, as the event actually occurred, these statistical models are not fit for the purpose of estimating the influence of climate change on the event’s probability. By expanding the number of physical covariates beyond just greenhouse gas concentrations and by incorporating spatial statistical techniques in a Bayesian hierarchal framework, we are able to construct a statistical model where observed temperatures during this heatwave were not “impossible” and thus estimate the change in their probabilities leading to Granger-type causal inference attribution statements. We further extend this statistical framework to all quality daily GHCN station measurements and find that while many physically plausible outlier temperatures are impossible in the simple non-stationary GEV framework, they can be explained using our more complicated non-stationary Bayesian spatial statistical model embedded in a deep learning machinery.

Abrupt snowmelt, triggered by rain-on-snow events or ``snow-eater heatwaves'', can cause flooding, accelerate snow drought, and impact water availability. Yet the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heatwaves have received relatively little attention. To address this gap, we developed a method to identify snow-eater heatwaves and estimate their melt potential using Twentieth Century Reanalysis Version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850–2015. Melt season snow-eater heatwaves typically last 3–5 days, with 3–5 events, doubling snowmelt rates. Seven of 11 spring superfloods can be linked with snow-eater heatwaves. Since the 1850s, snow-eater heatwaves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heatwave impacts into SNOW-17 improves extreme snowmelt estimates, providing additional tools to support water management.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

As the science of climate attribution continues to gain importance, we should remember that this discipline reaches back to the 1920s, when catastrophic droughts motivated research into the Southern Oscillation (Walker and Bliss 1932). Climate attribution existed before human-induced climate change, and this deep literature belies recent suggestions that limited research categorically constrains contributions to efforts like loss and damage compensation (King et al. 2023). Some hazards, like droughts and extreme temperatures, are much easier to link to climate change (Noy et. al, 2023). ENSO-related droughts, in particular, represent a very important and well-studied type of hazard. Techniques for linking droughts to impacts in food insecure countries are well-developed, and formal attributions of eastern and southern African droughts (e.g. Funk et al. 2016, 2018, 2019, 2023A, 2023B) have supported advances in long-lead forecasting.Building on this work, in this talk we connect ‘modal’ analyses of sea surface temperatures (SST) with an evaluation of reanalysis ‘atmospheric heating’. Our modal framework grows out of analyses of ENSO-residual SST (Compo and Sardeshmukh, 2010; Newman and Solomon 2012; Lyon et al. 2014); most of the variance of observed and simulated global SST can be described an ENSO mode and an ENSO-residual West Pacific Warming Mode (WPWM, Funk and Hoell 2015; Funk 2023B).In this talk we describe observed and CMIP6-simulated changes in ENSO and WPWM Principal Components (PC) time series, and highlight the real-world implications of two key characteristics: the long term increases in both, and their modest inverse correlation on decadal time scales, which leads to more extreme ocean states. Strong El Niños correspond to large ENSO PC values. La Niña events in a warming Pacific Ocean are associated with exceptionally warm west Pacific SST, corresponding to the increasing WPWM PC, and La Niña-related droughts (Funk et al. 2023B).While these PC extremes produce very warm Pacific SST, and strong SST gradients, formally evaluating the impact of these SST patterns can be challenging. Tropical Pacific atmospheric heating, which drives many ENSO-related teleconnections provides a useful metric of ENSO strength. This heating combines diabatic heating due to precipitation, radiation, sensible heating and evaporation and adiabatic heating due to heat convergence. Using 1950-2023 ERA5 reanalyses, and CPC Oceanic Niño Index-based ENSO event definitions, we suggest that when ENSO events occur, ENSO-related atmospheric heating extremes have become substantially and significantly more energetic. Contrasting 1996-2022 and 1950-1996 El Niño events, we find very large increases in January-to-June heating over the equatorial eastern Pacific. A similar contrast for La Niña events indicates large heating increases over the western Pacific that extend from June of into September of the following year. Hence, ENSO events are likely becoming stronger and longer due to climate change. We conclude by showing how CMIP6 SST simulations and statistical heating/SST relationships can be used to estimate climate change-related enhancements to these heating extremes. Facing a future “characterized by unprecedented aridification/wetting punctuated by more severe extremes” (Stevenson et al. 2021), these insights can help support the formal attribution of ENSO-related droughts.

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest the possibility of significant changes in both large-scale aspects of the ocean and atmospheric circulations and in the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of the High-Resolution Model Intercomparison Project (HighResMIP1) was successful at producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions on the order of 1° that are typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis of a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose the High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances, along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various digital twin initiatives. It also has the potential to be used as training and validation data for the fast-evolving machine learning climate models.

The future state of the global water cycle and the prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation Experiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing, and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling, and experimental research and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here, we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2-3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multiscale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

The role climate change plays in increasing the burden placed on governments and insurers to pay for recovery has not been extensively explored and is the focus of this study. This study examines the impacts of climate change attributed flooding on federal disaster aid disbursement in Harris County, Texas following Hurricane Harvey in 2017. Our approach uses flood models to estimate the amount of flood damages attributable and not attributable to climate change under two climate change attribution scenarios from peer reviewed studies: 20% and 38% increases in rainfall associated with the hurricane due to climate change. These estimates are combined with census tract-level disbursement data for FEMA’s National Flood Insurance Program (NFIP) and the Individual Assistance (IA) part of the Individuals and Households Program. We employ spatial lag regression models with direct and spatial spillover effects to analyze the relationship between a tract’s flood damages—both attributed and not attributed to climate change—and federal disaster aid. We find that both types of flood damage shape federal aid disbursements, but that climate change attributed damages tend to have larger effect sizes (elasticities) especially for IA. Specifically, for a 1% increase in additional climate change attributed damages per household in a census tract (under the 20% scenario), expected NFIP levels in that census tract are 0.26% higher and IA levels are 0.3% higher. Implications center on federal funding in an era of climate change.

While growing bodies of scholars have examined the separate effects of extreme heat and COVID-19 on migrant farmworkers in the United States, we are unaware of any study examining their potential combined impact on agricultural labor supply. We analyzed the combined effect of extreme heat and COVID-19 on farmworkers’ decisions to work and the number of hours they work. We collected survey data from 280 randomly selected migrant farmworkers in three communities in California in 2020. We employed Heckman´s selection model to control for self-selection to participate in the agricultural labor market, despite the dual burden of the presence of COVID-19 and heat waves, which may be associated with unobservable factors such as different power standings between employers and farmworkers. We selected the variables used in our models using the Least Absolute Shrinkage and Selection Operator (LASSO), which is a Machine Learning technique to build simpler models by filtering out unimportant predictors. We found that extreme heat alone and the combination of heat and COVID-19 increase the probability of workers participating in the agricultural labor market, while COVID-19 alone does not. Our findings also show that, once workers enter the agricultural labor market under extreme conditions, the number of hours they work decreases in response to extreme heat, COVID-19, and the combined impact of both.

Abstract Tropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979–2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24‐hr in regions close to the coast. Analysis of the observed large‐scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high‐resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification.

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.