Increasing instability of the ocean, ice and atmosphere threatens farming, finance and society. We all need to wake up to that fact. Increasing instability of the ocean, ice and atmosphere threatens farming, finance and society. We all need to wake up to that fact.

Abstract. The concentration of carbon dioxide in the atmosphere changes our climate and its variability. It impacts all parts of nature and society. Consequently, there is an ongoing societal discussion about speeding up the transition to net-zero carbon emissions. The faster emissions are reduced, the less carbon dioxide will accumulate in the atmosphere where it largely remains influencing climate for hundreds of years. What has not yet been broadly studied, is the question whether the rate of the emissions themselves in addition to the resulting concentration has significant impact on climate and weather. To address this question, we run simulations with the Geophysical Fluid Dynamics Laboratory Atmospheric Model version 2 (GFDL-AM2), with different CO2 forcing rates. In order to investigate mainly the atmospheric role, the oceanic boundary condition is supplied by a slab ocean. We find that for the the same warming level (2 °C) but different warming rates (e.g. annual rates of 1 % CO2 increase compared to 4 %), the differences in the annual average temperature and precipitation and day-to-day variability patterns are of the same order of magnitude between different simulations with the same rate and between different simulations with different rates. Thus, we find that without a significant influence of ocean circulation changes, the fast mixing times within the atmosphere and thereby the lack of an atmospheric memory, inhibits a significant role of the rate of CO2 emissions for weather variability. This result is not unexpected, but needed confirmation. In summary, the atmospheric dynamics alone do not allow for an influence of the rate of carbon emissions on the annual average and day-to-day variability in temperature and precipitation.

Monsoon systems transport water and energy across the globe, making them a central component of the global circulation system. Each monsoon system has its own regional characteristics ranging from particular continental shapes to dynamic vegetation patterns and the influence of mountain ranges. This individuality makes it difficult to access the common core meridional monsoon dynamics by only using observations or realistic simulations. Idealized frameworks have proven to be useful approaches to study monsoon systems with regard to their commonalties. Here, we present the latest insight of our work on the Monsoon Planet – an aquaplanet setup with an idealized circumglobal land stripe.

The last decade has witnessed a surging occurrence of extreme heat worldwide. This can directly dampen local production capacity and also induce indirect repercussions through the global supply network. Yet, the cascading effect of region-specific extreme heat may differ greatly, which is by far poorly understood. By combining temperature observations with Acclimate—a dynamic agent-based model, we identify the region-specific temperature threshold for dampening local production and investigate the response of the global supply network to extreme heat in a region-by-region manner. Economic agents with significant repercussions on the globe are identified and indirect benefits along the global supply network from local heat adaptation are revealed. The outcome of this study supports common but differentiated adaptation strategies towards extreme heat.

Within Earth's climate system, the ocean, cryosphere, and vegetation exhibit hysteresis behavior such that their state depends on their past and not merely on their current boundary conditions. The atmosphere's fast mixing time scales were thought to inhibit the necessary memory effect for such multistability. Here, we show that moisture accumulation within the atmospheric column generates hysteresis in monsoon circulation independent of oceanic heat storage and yields two stable atmospheric states for the same solar insolation. The dynamics of monsoon rainfall is thus that of a seasonal transition between two stable states. The resulting hysteresis is shown in observational data and reproduced in a general circulation model where it increases with decreasing oceanic memory and exhibits the two distinct states that persist for more than 60 y. They are stabilized by moisture accumulation within the atmospheric column that carries information across time scales much longer than those typical for mixing. The possibility of abrupt shifts between these two states has implications for the future evolution of global monsoon rainfall that is crucial for the agricultural productivity currently feeding more than two billion people.

The East Asian Monsoon (EAM) dominates the climate over the densely populated region of eastern China and adjacent regions and therefore influences a fifth of the world's population. Thus, it is highly relevant to assess the changes in the central characteristics of the East Asian Summer Monsoon (EASM) under future warming in the latest generation of coupled climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). We apply a set of selection criteria to 34 CMIP6 models to identify the six best performing (TOP6) models that best capture the EASM in the reference period 1995–2014. All of these models project an increase in June–August rainfall independent of the underlying emission scenario. The multi-model mean increase is 16.5 % under SSP5-8.5, 11.8 % under SSP3-7.0, 12.7 % under SSP2-4.5 and 9.3 % under SSP1-2.6 in the period 2081–2100 compared to 1995–2014. For China, the projected monsoon increase is slightly higher (12.6 % under SSP1-2.6 and 18.1 % under SSP5-8.5). The EASM rainfall will particularly intensify in southeastern China, Taiwan and North Korea. The multi-model mean indicates a linear relationship of the EASM rainfall depending on the global mean temperature that is relatively independent of the underlying scenario: per degree of global warming, the rainfall is projected to increase by 0.17 mm d−1, which refers to 3.1 % of rainfall in the reference period. It is thus predominately showing a “wet regions get wetter” pattern. The changes in the wind fields in the region are relatively small indicating the minor importance of dynamic factors, while pointing towards thermodynamic factors as responsible for the rainfall increase. The interannual variability is also robustly projected to increase between 17.6 % under SSP1-2.6 and 23.8 % under SSP5-8.5 in the multi-model mean between 2051–2100 and 1965–2014. Comparing the same periods, extremely wet seasons are projected to occur 7 times more often under SSP5-8.5.

Monsoon systems are transporting water vapour and energy across the globe, making them a central component of the global circulation system. Changes in different forcing parameters have the potential to fundamentally change the monsoon characteristics as indicated in various paleoclimatic records. Here, we use the Atmosphere Model version 2 developed at the Geophysical Fluid Dynamics Laboratory (GFDL-AM2) and couple it with a slab ocean to analyse the monsoon's sensitivity to changes in different forcing parameters on a planet with idealized topography. This Monsoon Planet concept of an Aquaplanet with a broad zonal land stripe allows to reduce the influence of topography and to access the relevant meridional monsoon dynamics. In the simulations that enable monsoon dynamics, a bimodal rainfall distribution develops during the monsoon months with one maximum over the tropical ocean and the other one over land. The intensity and expansion of the land monsoon depends on the relative height of a local maximum in the surface pressure field that is acting as a barrier and determines the landward moisture transport. This dynamic is emerging during the course of one year, but also occurs when varying different parameters in a sensitivity analysis (slab ocean depth, sulfate aerosols, carbon dioxide, solar constant, land albedo). This structure of a bimodal rainfall distribution and a pressure-barrier located between the two maxima is also present in the Westafrican monsoon.

Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model dis-crepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multimodel ensemble to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global tempera-ture change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Last, we compare these scaling rates with those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation ex-tremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution.

Historically, economic growth has been closely coupled to carbon emissions responsible for climate change, but to stabilize global mean temperature, net-zero carbon emissions are necessary. Some economies have begun to reduce emissions while continuing to grow, but this decoupling is not fast enough to achieve global climate targets. Subnational climate actions seem to be crucial for the achievement of these targets. Here, we uncover the effectiveness of subnational efforts by estimating decoupling rates and CO2 emission intensities over the last three decades for over 1,500 subnational regions, encompassing 85% of global emissions, using global data on reported economic output and gridded production-based emissions. Thirty percent of regions with available data have fully decoupled, with higher-income and historically carbon-intensive regions exhibiting higher rates of decoupling and declining emission intensity. Countries of the Organization for Economic Co-operation and Development with greater spending on subnational climate actions show higher decoupling rates, as do subnational regions in EU countries where climate policies have been implemented, highlighting the effectiveness of subnational policies. Moreover, subnational analysis reveals greater variance of decoupling rates within national boundaries than between them and that countries with weaker governance typically show higher variance of decoupling within their borders. If recent rates of production-based carbon decoupling continue, less than half of subnational regions would reach net-zero before 2050, even when accounting for observed acceleration via socioeconomic development and assuming no interregional carbon leakage.

Abstract. Ice rises or ice rumples act as ice-shelf pinning points that can have an important role in regulating the ice discharge of marine outlet glaciers. As an example, the observed recent gradual ungrounding of the ice shelf of West Antarctica's Thwaites Glacier from its last pinning points likely diminished the buttressing effect of the ice shelf and thus contributed to the destabilization of the outlet. Here we use an idealized experimental setting to simulate the response of an Antarctic-type, instability-prone marine outlet glacier to a successive ungrounding of its ice shelf from a topographic high and a subsequent re-grounding. We show that the glacier retreat down the landward down-sloping (retrograde) bed, induced by the loss in pinning-point buttressing, can be unstable and irreversible given a relatively deep subglacial bed depression. In this case, glacier retreat and re-advance show a hysteretic behavior and if the bed depression is sufficiently deep, the glacier does not recover from but remains locked in its collapsed state. Conversely, reversibility requires a sufficiently shallow bed depression. Based on a simple flux balance analysis, we argue that the combination of a deep bed depression and limited ice-shelf buttressing hampers grounding-line re-advance due to the dominant and highly non-linear influence of the bed depth on the ice discharge across the grounding line. We conclude that outlets that rest on a deep bed depression and are weakly buttressed, such as Thwaites Glacier, are more susceptible to abrupt and irreversible retreat than stronger buttressed glaciers on more moderate retrograde slope, such as Pine Island Glacier. In particular, our results suggest that the wide and deep marine bed depression in the interior of Thwaites Glacier's drainage basin might promote potential future unstable retreat and also represent a strong limitation for a possible re-advance of the glacier in case it would collapse.

Ice rises or ice rumples act as ice-shelf pinning points that can play an important role in regulating the ice discharge of marine outlet glaciers. As an example, the observed recent gradual ungrounding of the ice shelf of West Antarctica's Thwaites Glacier from its last pinning points likely diminished the buttressing effect of the ice shelf and thus contributed to the destabilization of the outlet. Here we use an idealized experimental setting to simulate the response of a marine outlet glacier resting on a landward down-sloping (retrograde) bed to a stepwise ungrounding of its ice shelf from a topographic high and a subsequent re-grounding. We show that the glacier retreat down the retrograde bed, induced by the loss in pinning-point buttressing, can be unstable and irreversible given a relatively deep subglacial bed depression. In this case, glacier retreat and re-advance show a hysteretic behavior, and if the bed depression is sufficiently deep, the glacier does not recover but remains locked in its retreated state. Conversely, reversibility requires a sufficiently shallow bed depression. Based on a simple flux balance analysis, we argue that the combination of a deep bed depression and limited ice-shelf buttressing hampers grounding-line re-advance due to the dominant and highly non-linear influence of the bed depth on the ice discharge across the grounding line. We conclude that outlets that rest on a deep bed depression and are weakly buttressed, such as Thwaites Glacier, are more susceptible to abrupt and irreversible retreat than more strongly buttressed glaciers on more moderate retrograde slopes, such as Pine Island Glacier. Our findings further suggest that the (ir)reversibility of large-scale grounding-line retreat may be strongly affected by calving-front migration and associated changes in ice-shelf buttressing.

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long-time horizons. Here, we utilize recent empirical findings from more than 1600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation including daily variability and extremes. Using an empirical approach which provides a robust lower-bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years due to historical carbon emissions and socioeconomic inertia (relative to a baseline without climate impacts, likely range of 11-29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to two degrees by sixfold over this near-term timeframe, and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately fifty percent and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, where reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

Opinion formation within society follows complex dynamics. Towards its understanding, axiomatic theory can complement data analysis. To this end, we propose an axiomatic model of opinion formation that aims to capture the interaction of individual conviction with social influence in a minimalist fashion. Despite only representing that (1) agents have an initial conviction with respect to a topic and are (2) influenced by their neighbours, the model shows the emergence of opinion clusters from an initially unstructured state. Here, we show that increasing individual self-reliance makes agents more likely to align their socially influenced opinion with their inner conviction which concomitantly leads to increased polarisation. The opinion drift observed with increasing self-reliance may be a plausible analogue of polarisation trends in the real-world. Modelling the basic traits of striving for individual versus group identity, we find a trade-off between individual fulfilment and societal cohesion. This finding from fundamental assumptions can serve as a building block to explain opinion polarisation.

Temperature and precipitation variability and extremes impact production globally. These production disruptions will change with future warming, impacting consumers locally as well as remotely through supply chains. Due to a potentially nonlinear economic response, trade impacts are difficult to quantify; empirical assessments rather focus on the direct inequality impacts of weather extremes. Here, simulating global economic interactions of profit-maximizing firms and utility-optimizing consumers, we assess risks to consumption resulting from weather-induced production disruptions along supply chains. Across countries, risks are highest for middle-income countries due to unfavourable trade dependence and seasonal climate exposure. We also find that risks increase in most countries under future climate change. Global warming increases consumer risks locally and through supply chains. However, high-income consumers face the greatest risk increase. Overall, risks are heterogeneous regarding income within and between countries, such that targeted local and global resilience building may reduce them. It is hard to study weather-induced impacts on consumption as supply-chain effects cause a nonlinear economic response. Here consumption risks due to weather-induced production disruptions along supply chains are assessed considering income inequality within and across countries.

This repository provides data and code to reproduce the results of the publication "R. Middelanis, S. N. Willner, K. Kuhla, L. Quante, C. Otto, and A. Levermann (2023). Stressed economies respond more strongly to climate extremes. Environmental Research Letters." dependencies: a working environment is provided in environment.yml the Acclimate post-processing package can be downloaded from the respective GitHub repostory with git@github.com:acclimate/post-processing.git. Switch to the develop branch with git checkout develop and install the package with conda develop . from within the repository data: See ./data/README.md for the required data and sources for those data that are not included in this repository. Steps to reproduce the results: 1. Generate Acclimate input data Direct loss time series are obtained from "Kuhla et al. (2021). Ripple resonance amplifies economic welfare loss from weather extremes. Environmental Reserach Letters". Acclimate input data (cf. ./data/README.md) are generated with ./code/forcing.py The input data used in the pubilcation are available at ./data/acclimate_input 2. Run Acclimate the Acclimate model can be downloaded from the respective GitHub repository at https://github.com/acclimate/acclimate 3. Run the analyses Acclimate output files of the calibration runs and the scenario runs are aggregated with functions aggregate_calibration_ensemble and aggregate_ensembles in ./code/utils.py, respectively. Aggregated ready-to-use output data is located in ./data/acclimate_output All figures can be reproduced with ./code/plotting.py