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Forests can store large amounts of carbon and provide essential ecosystem services. Massive tree planting is thus sometimes portrayed as a panacea to mitigate climate change and related impacts. Recent controversies about the potential benefits and drawbacks of forestation have centered on the carbon storage potential of forests and the local or global thermodynamic impacts. Here we discuss how global-scale forestation and deforestation change the Earth’s energy balance, thereby affect the global atmospheric circulation and even have profound effects on the ocean circulation. We perform multicentury coupled climate model simulations in which preindustrial vegetation cover is either completely forested or deforested and carbon dioxide mixing ratio is kept constant. We show that global-scale forestation leads to a weakening and poleward shift of the Northern mid-latitude circulation, slows-down the Atlantic meridional overturning circulation, and affects the strength of the Hadley cell, whereas deforestation leads to reversed changes. Consequently, both land surface changes substantially affect regional precipitation, temperature, clouds, and surface wind patterns across the globe. The design process of large-scale forestation projects thus needs to take into account global circulation adjustments and their influence on remote climate. Based on coupled climate model simulations the authors show that changes to the Earth’s surface energy balance following global-scale forestation and deforestation may change the strength of the jet stream, the Hadley cell, and the ocean circulation, which alters remote climate patterns across the globe

Weather extremes are often associated with atmospheric blocking, but how the underlying physical processes leading to blocking respond to climate change is not yet fully understood. Here we track blocks as upper-level negative potential vorticity (PV) anomalies and apply a Lagrangian analysis to 100 years of present-day (∼2000) and future (∼2100, under the RCP8.5 scenario) climate simulations restarted from the Community Earth System Model–Large Ensemble Project runs (CESM-LENS) to identify different physical processes and quantify how their relative importance changes in a warmer and more humid climate. The trajectories reveal two contrasting airstreams that both contribute to the formation and maintenance of blocking: latent heating in strongly ascending airstreams (moist processes) and quasi-adiabatic flow near the tropopause with weak radiative cooling (dry processes). Both are reproduced remarkably well when compared against ERA-Interim reanalysis, and their relative importance varies regionally and seasonally. The response of blocks to climate change is complex and differs regionally, with a general increase in the importance of moist processes due to stronger latent heating (+1 K in the median over the Northern Hemisphere) and a larger fraction (+15%) of strongly heated warm conveyor belt air masses, most pronounced over the storm tracks. Future blocks become larger (+7%) and their negative PV anomaly slightly intensifies (+0.8%). Using a Theil–Sen regression model, we propose that the increase in size and intensity is related to the increase in latent heating, resulting in stronger cross-isentropic transport of air with low PV into the blocking anticyclones. Our findings provide evidence that moist processes become more important for the large-scale atmospheric circulation in the midlatitudes, with the potential for larger and more intense blocks.

The Paris Agreement1 aims to ‘pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels.’ However, it has been suggested that temperature targets alone are insufficient to limit the risks associated with anthropogenic emissions2,3. Here, using an ensemble of model simulations, we show that atmospheric CO2 increase—an even more predictable consequence of emissions than global temperature increase—has a significant direct impact on Northern Hemisphere summer temperature, heat stress, and tropical precipitation extremes. Hence in an iterative climate mitigation regime aiming solely for a specific temperature goal, an unexpectedly low climate response may have corresponding ‘dangerous’ changes in extreme events. The direct impact of higher CO2 concentrations on climate extremes therefore substantially reduces the upper bound of the carbon budget, and highlights the need to explicitly limit atmospheric CO2 concentration when formulating allowable emissions. Thus, complementing global mean temperature goals with explicit limits on atmospheric CO2 concentrations in future climate policy would limit the adverse effects of high-impact weather extremes.

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5°C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0°C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios.Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006-2015), the second two being estimates of a similar decade but under 1.5 and 2°C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5°C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2°C scenario.

In July 2021, a cut-off low-pressure system brought extreme rainfall to Western Europe, leading to flooding that caused loss of life and infrastructure damage. Here, we use ensemble boosting to investigate alternative storylines of the event, given the observed dynamical situation. Using a fully coupled free-running climate model, we identify atmospheric flow analogues of the 2021 event in an initial-condition large ensemble. These analogues are re-initialised with slightly perturbed atmospheric initial conditions to generate physically plausible alternative storylines. The storylines are used to investigate how a potentially worse event could have unfolded given the same large-scale dynamics. We identify rainfall events with longer persistence and larger extent, yet the observed event appears to be towards the upper end of what is plausible in the current climate. Such storylines can be used to prepare for possible future events, helping society to imagine dangerous, but plausible, scenarios. The July 2021 extreme rainfall event in Western Europe appears near the upper bound of plausibility in the current climate, though alternative storylines revealing the potential for even more severe outcomes in terms of rainfall persistence, spatial extent, and location – given the large-scale dynamics of the event, according to a boosted ensemble.

In September 2023, a spatially compounding event affected Europe, with an intense heatwave over northern France and nearly simultaneous extreme floods over the Iberian Peninsula and Greece. These extremes were accompanied by a low-high-low pressure system known as Omega pattern. However, the capability of climate models to reproduce such complex extremes and related atmospheric circulations is still unclear. By introducing a novel index, here we show that the 2023 event was not unique yet most extreme in recent decades. Using ensemble boosting, the fully-coupled Community Earth System Model 2 can reproduce such co-occurring extremes with remarkably similar configurations of the large-scale flow and even higher heatwave/rainfall than observed. This suggests that similar compounding events with even stronger intensity could, in principle, occur. However, such spatially compounding events are too rare in the model relative to reanalysis, suggesting that the model may underestimate their future occurrence in a warming climate. Several flood-heat-flood events associated with Omega atmospheric blocking have occurred since 1979 in Europe, including the most extreme September 2023 event, but a fully coupled Earth system model cannot generate spatial analogues as often as in reanalyses, according to an ensemble boosting analysis

Grassland landscapes are important ecosystems in East Africa, providing habitat and grazing grounds for wildlife and livestock and supporting pastoralism, an essential part of the agricultural sector. Since future grassland availability directly affects the future mobility needs of pastoralists and wildlife, we aim to model changes in the distribution of key grassland species under climate change. Here we combine a global and regional climate model with a machine learning-based species distribution model to understand the impact of regional climate change on different key grass species. The application of a dynamical downscaling step allows us to capture the fine-scale effects of the region’s complex climate, its variability and future changes. We show that the co-occurrence of the analysed grass species is reduced in large parts of eastern Africa, and particularly in the Turkana region, under the high-emission RCP8.5 scenario for the last 30 years of the 21 st century. Our results suggest that future climate change will alter the natural resource base, with potentially negative impacts on pastoralism and wildlife in East Africa. In East Africa, the distribution and co-occurrence of seven grass species are projected to change noticeably under the high-emission scenario for the last 30 years of the 21st century, according to an analysis that combines a regional climate model with machine-learning-based grass species data.

Heat waves such as the one in Europe 2003 have severe consequences for the economy, society, and ecosystems. It is unclear whether temperatures could have exceeded these anomalies even without further climate change. Developing storylines and quantifying the highest possible temperature levels is challenging given the lack of a long homogeneous time series and methodological framework to assess them. Here, we address this challenge by analyzing summer temperatures in a nearly 5000-yr preindustrial climate model simulation, performed with the Community Earth System Model CESM1. To assess how anomalous temperatures could get, we compare storylines generated by three different methods: 1) a return-level estimate, deduced from a generalized extreme value distribution; 2) a regression model, based on dynamic and thermodynamic heat wave drivers; and 3) a novel ensemble boosting method, generating large samples of reinitialized extreme heat waves in the long climate simulation. All methods provide consistent temperature estimates, suggesting that historical exceptional heat waves such as those in Chicago in 1995, Europe in 2003, and Russia in 2010 could have been substantially exceeded even in the absence of further global warming. These estimated unseen heat waves are caused by the same drivers as moderate observed events, but with more anomalous patterns. Moreover, altered contributions of circulation and soil moisture to temperature anomalies include amplified feedbacks in the surface energy budget. The methodological framework of combining different storyline approaches of heat waves with magnitudes beyond the observational record may ultimately contribute to adaptation and to the stress testing of ecosystems or socioeconomic systems to increase resilience to extreme climate stressors.

Heat waves such as the one in Europe 2003 have severe consequences for the economy, society, and ecosystems. It is unclear whether temperatures could have exceeded these anomalies even without further climate change. Developing storylines and quantifying highest possible temperature levels is challenging given the lack of long homogeneous time series and methodological framework to assess them. Here, we address this challenge by analysing summer temperatures in a nearly 5000-year pre-industrial climate model simulation, performed with the Community Earth System Model CESM1. To assess how anomalous temperatures could get, we compare storylines, generated by three different methods: (1) a return-level estimate, deduced from a generalized extreme value distribution, (2) a regression model, based on dynamic and thermodynamic heat wave drivers, and (3) a novel ensemble boosting method, generating large samples of re-initialized extreme heat waves in the long climate simulation. All methods provide consistent temperature estimates, suggesting that historical exceptional heat waves as in Chicago 1995, Europe 2003 and Russia 2010 could have been substantially exceeded even in the absence of further global warming. These estimated unseen heat waves are caused by the same drivers as moderate observed events, but with more anomalous patterns. Moreover, altered contributions of circulation and soil moisture to temperature anomalies include amplified feedbacks in the surface energy budget. The methodological framework of combining different storyline approaches of heat waves with magnitudes beyond the observational record may ultimately contribute to adaptation and to the stress testing of ecosystems or socio-economic systems to increase resilience to extreme climate stressors.

We present a model intercomparison project, LongRunMIP, the first collection of millennial-length (1,000+ years) simulations of complex coupled climate models with a representation of ocean, atmosphere, sea ice, and land surface, and their interactions. Standard model simulations are generally only a few hundred years long. However, modeling the long-term equilibration in response to radiative forcing perturbation is important for understanding many climate phenomena, such as the evolution of ocean circulation, time- and temperature-dependent feedbacks, and the differentiation of forced signal and internal variability. The aim of LongRunMIP is to facilitate research into these questions by serving as an archive for simulations that capture as much of this equilibration as possible. The only requirement to participate in LongRunMIP is to contribute a simulation with elevated, constant CO₂ forcing that lasts at least 1,000 years. LongRunMIP is an MIP of opportunity in that the simulations were mostly performed prior to the conception of the archive without an agreed-upon set of experiments. For most models, the archive contains a preindustrial control simulation and simulations with an idealized (typically abrupt) CO₂ forcing. We collect 2D surface and top-of-atmosphere fields and 3D ocean temperature and salinity fields. Here, we document the collection of simulations and discuss initial results, including the evolution of surface and deep ocean temperature and cloud radiative effects. As of October 2019, the collection includes 50 simulations of 15 models by 10 modeling centers. The data of LongRunMIP are publicly available. We encourage submissions of more simulations in the future.