Jacob Schewe

Climate extremes are increasingly disrupting livelihoods and shaping patterns of human mobility. Their longer-term and compounding relationships with migration remain understudied, particularly at the subnational scale. In this study, by analysing global subnational net migration data, we show that associations between climate extremes and migration vary by hazard type, timing, consecutive events, income level and pre-existing migration context. Floods are associated with decreased net migration across contexts, with effects lasting several years and appearing stronger in lower-income areas. Droughts, by contrast, show associations with net migration primarily in higher-income settings, with effects generally short-term. Tropical cyclones correspond to decreased net migration primarily in lower-income areas, with effects lasting over several years. For compound exposure, droughts show the clearest patterns, with prolonged exposure linked to reduced migration inflows in population-attracting areas. These results highlight how climate extremes interact with local socioeconomic contexts to shape mobility.

This paper describes the climate-related forcings (CRFs), i.e. change in climate comprising the atmosphere and the ocean, coastal water levels, and atmospheric composition (CO₂ and methane concentrations), provided as input data within the “b” part of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). While ISIMIP3a comprises historical impact models simulations forced by observational Direct Human Forcings (DHF), such as changes in population and asset distributions, land use, fishing efforts, agricultural and water management driven by socio-economic development or climate protection strategies, and observational CRF, the ISIMIP3b CRFs are based on climate model simulations generated within the sixth phase of the Coupled Model Intercomparison Project (CMIP6). In a first set of experiments covering the pre-industrial (1601–1849) and historical period (1850–2014) (ISIMIP3b, group I) the CMIP6-based CRFs for the historical period are combined with historical observation-based DHF also considered in ISIMIP3a. These group I simulations allow for the quantification of impacts of historical climate change by comparison to simulations where the observational DHF are combined with simulated pre-industrial CRFs. In addition, the impacts of observed changes in CRFs can be compared to the impacts of simulated changes in CRFs by comparing the ISIMIP3a simulations to the ISIMIP3b, group I simulations. The second group of experiments (ISIMIP3b, group II) comprises future projections assuming constant observational direct human forcings at 2015 levels to estimate the impact of climate change given today's DHF for the low emission scenario SSP1-2.6, the high and the very high emission scenarios SSP3-7.0, SSP5-8.5, and reference simulations based on pre-industrial CRF, respectively. The very high emissions scenarios and the assumption of fixed present day DHF particularly allow for testing the scalability of impacts in terms of global temperature change. The provided CRFs comprise atmospheric CO₂ and CH₄ concentrations, atmospheric and oceanic climate data, coastal water levels, tropical cyclone (TC) tracks and their associated wind speed and precipitation fields. In addition to the CRFs data, this paper describes the experiments belonging to group I and II and the rationale behind them. Another set of future projections accounting for changing DHFs (ISIMIP3b, group III) is in preparation and will be described in another paper.

The Permian Delaware Tight, located in Texas (USA), is the largest identified carbon bomb worldwide. Carbon bombs are defined as 425 fossil fuel megaprojects, of which nearly 60% are already in operation. With potential emissions of 27.8 GtCO₂, the Permian Delaware Tight alone would release three-quarters of current annual global CO₂ emissions and consume over ten percent of the remaining global carbon budget compatible with limiting warming to 1.5 °C. Overall, the cumulative potential emissions from all identified carbon bombs exceed at least twice the remaining carbon budget consistent with the Paris Agreement.However, quantifying the specific climate impacts attributable to individual fossil fuel projects remains a major scientific and legal challenge. Such attribution is central not only for understanding the long-term consequences of continued fossil fuel expansion, but also for informing emerging forms of climate litigation in which plaintiffs seek to establish causal links between emissions, harms, and responsibility. Within the Source2Suffering project, we develop a modelling framework that converts CO₂ and CH₄ emissions from any fossil fuel project into lifetime exposure to six categories of high-impact climate extremes, including heatwaves, droughts, and floods. In addition, the framework quantifies each project's contribution to committed glacier mass loss and multi-century sea-level rise. By explicitly incorporating uncertainty, the model provides probabilistic impact estimates that can support evidence-based arguments in legal contexts where causal strength, foreseeability, and proportionality are scrutinised.Crucially, the framework reveals how the impacts of individual projects propagate unequally across generations and countries. This integrated approach provides new quantitative tools for bridging geosciences and legal practice by making project-level climate responsibility scientifically traceable, comparable, and communicable within litigation and regulatory processes.

Carbon bombs comprise 425 fossil fuel megaprojects whose cumulative potential emissions exceed by at least a factor of two the remaining global carbon budget compatible with the Paris Agreement. The full exploitation of these projects would therefore generate substantial additional warming. As high-impact climate extremes intensify with each increment of warming, a central challenge is to quantify how emissions from individual projects translate into concrete physical and societal impacts across current and future generations. Within the Source2Suffering project, we develop a modelling framework that links project-level CO₂ and CH₄ emissions to lifetime exposure to six categories of high-impact climate extremes, including heatwaves, droughts, and floods, using a storyline-based approach. The framework also quantifies each project’s contribution to committed glacier mass loss and multi-century sea-level rise. By explicitly representing uncertainties, it provides probabilistic estimates of how warming increments induced by individual fossil fuel projects propagate through physical processes to generate compound and cascading risks. The results reveal marked spatial and intergenerational inequalities in exposure. These arise from (i) physical mechanisms that amplify extreme hazards in many regions of the Global South, and (ii) demographic trends that concentrate most of the world’s present and future population in these highly affected areas. By establishing a tractable link between specific emission sources, the physical drivers of high-impact extremes, and their long-term societal consequences, this framework contributes to the development of scientifically grounded information to support climate mitigation efforts.

Climate variability gives rise to different extreme events, including crop failure, drought, flood, heatwave, tropical cyclone, and wildfire. The temporal and spatial characteristics of these events have already begun to shift, and are expected to change further under continued global warming. In studies of climate change impacts, different types of events are often studied in isolation. However, locations exposed to extreme events are typically not only affected by a single extreme event category. We therefore quantify exposure and mean recovery times for six different extreme event types on a 0.5°×0.5° global grid under historical and projected future climates. We use harmonized ensembles of global biophysical impact simulations driven with (i) bias-adjusted climate data, to assess current conditions, and (ii) climate model projections, to quantify future changes in recovery times. While we observe a decrease in global mean recovery time as a function of warming level across all event types, the decrease rates differ strongly between event types ranging from constant to flattening rates. Being able to quantify the temporal evolution of recovery times is crucial for disaster planning and adaptation or relocation efforts.

Global gridded crop models (GGCMs) are crucial for projecting the impacts of climate change, yet their performance under climatic extremes remains poorly understood. Using historical subnational yield data, we evaluate 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) across major crops. Models largely capture the signal of heat and drought extremes but consistently underestimate the magnitude of losses. For extreme wet conditions, they often miss both the signal and magnitude (losses underestimated in 83–95\% of cases, best to worst model). However, underestimation is reduced for compound heat–drought extremes (38–77\%). Notably, the model ensemble median, widely used to reduce uncertainty, performs poorly under extremes, systematically underestimating risks. We identify model characteristics that influence underestimation under extremes, such as the representation of radiation or soil nutrient dynamics. Improving key process representations and input data quality is essential to strengthen climate risk assessments for global agriculture and food security.

Groundwater serves as a crucial freshwater resource for people and ecosystems, playing a vital role in adapting to climate change. Yet, its availability and dynamics are affected by climate variations, changes in land use, and abstraction. Despite its importance, our understanding of how global change will influence groundwater in the future remains limited. Multi-model ensembles are powerful tools for impact assessments; compared to single-model studies, they provide a more comprehensive understanding of uncertainties and enhance the robustness of projections by capturing a range of possible outcomes. However, to date, no ensemble of groundwater models has been available to assess the impacts of global change. Here, we present the new Groundwater sector within ISIMIP, which combines multiple global, continental, and regional-scale groundwater models. We describe the rationale for the sector, the sectoral output variables that underpinned the modeling protocol, and showcase current model differences and possible future analysis. Currently, eight models are participating in this sector, ranging from gradient-based groundwater models to specialized karst recharge models, each producing up to 19 out of 23 modeling protocol-defined output variables. To showcase the benefits of a joint sector, we utilize available model outputs of the participating models to show the substantial differences in estimating water table depth (global arithmetic mean 6–127 m) and groundwater recharge (global arithmetic mean 78–228 mm yr⁻¹), which is consistent with recent studies on the uncertainty of groundwater models, but with distinct spatial patterns. We further outline synergies with 13 of the 17 existing ISIMIP sectors and specifically discuss those with the global water and water quality sectors. Finally, this paper outlines a vision for ensemble-based groundwater studies that can contribute to a better understanding of the impacts of climate change, land use change, environmental change, and socio-economic change on the world's largest accessible freshwater store – groundwater.

Spatio-temporal patterns of extreme climate events have been extensively studied, yet two questions remain underexplored: Do such events occur regularly, and how do regularity patterns change under global warming? We address these questions by investigating dominant periods in crop failure, heatwave, and wildfire data. Here, we show that under pre-industrial conditions dominant periods emerge in 28% of cropland exposed to crop failure and 10% of wildfire-affected areas, likely related to climatic oscillations such as the El Niño-Southern Oscillation, while heatwaves occur irregularly. The number of dominant periods increases by 2–13% during the transition from the pre-industrial era to the anthropocene. In the anthropocene, the occurrence of extreme events shifts towards monotonic growth, replacing previous natural regularity patterns. Linearly de-trended projections reveal an additional shift towards smaller dominant periods due to climate change. These shifts in regularity are crucial for adaptation planning, and our method offers an additional approach for studying extreme events.

We present a new international migration model that combines stochastic sampling techniques with dynamic accounting of flows by means of evolution equations. Migration flows are sampled from paramaterized probability distributions based on reported migration flow data that is partitioned by socio-economic covariates. This method allows for non-trivial time evolution that goes beyond extrapolation, while requiring minimal prior knowledge about the elusive processes driving migration flows. It thus combines the advantages of different existing modeling approaches. In hindcasts our model compares well with bilateral migrant stock data in many world regions and country income groups. Moreover, we observe a significant difference between the full model and its deterministic formulation, which highlights the non-Gaussian and interdependent nature of migration flow distributions and corroborates the use of a stochastic dynamic approach. Our model can be flexibly extended with additional information, e.g. regional migration policies, which are expected to further improve the agreement with data.