Fire, environmental and anthropogenic controls on pantropical tree cover.

Explaining tropical tree cover distribution in areas of intermediate rainfall is challenging, with fire's role in limiting tree cover particularly controversial. We use a novel Bayesian approach to provide observational constraints on the strength of the influence of humans, fire, rainfall seasonality, heat stress, and wind throw on tropical tree cover. Rainfall has the largest relative impact on tree cover (11.

Submesoscales are a significant turbulence source in global ocean surface boundary layer.

The turbulent ocean surface boundary layer is a key part of the climate system affecting both the energy and carbon cycles. Accurately simulating the boundary layer is critical in improving climate model performance, which deeply relies on our understanding of the turbulence in the boundary layer. Turbulent energy sources in the boundary layer are traditionally believed to be dominated by waves, winds and convection.

Artificial intelligence reveals past climate extremes by reconstructing historical records.

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century.

Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future.

Episodes of high near-surface ozone concentrations tend to cover large areas for several days. They are strongly dependent on meteorology, precursor emissions, and the ambient photochemical conditions. This study introduces a new pseudo-Lagrangian algorithm that identifies the spatiotemporal patterns of episodes, allowing for a good characterization of their areal extent and an assessment of their drivers.

Anthropogenic amplification of precipitation variability over the past century.

As the climate warms, the consequent moistening of the atmosphere increases extreme precipitation. Precipitation variability should also increase, producing larger wet-dry swings, but that is yet to be confirmed observationally. Here we show that precipitation variability has already grown globally (over 75% of land area) over the past century, as a result of accumulated anthropogenic warming.

Last Glacial Maximum pattern effects reduce climate sensitivity estimates.

Here, we show that the Last Glacial Maximum (LGM) provides a stronger constraint on equilibrium climate sensitivity (ECS), the global warming from increasing greenhouse gases, after accounting for temperature patterns. Feedbacks governing ECS depend on spatial patterns of surface temperature ("pattern effects"); hence, using the LGM to constrain future warming requires quantifying how temperature patterns produce different feedbacks during LGM cooling versus modern-day warming. Combining data assimilation reconstructions with atmospheric models, we show that the climate is more sensitive to LGM forcing because ice sheets amplify extratropical cooling where feedbacks are destabilizing.

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity.

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns.

A new conceptual model of global ocean heat uptake.

We formulate a new conceptual model, named "2", to describe global ocean heat uptake, as simulated by atmosphere-ocean general circulation models (AOGCMs) forced by increasing atmospheric CO, as a function of global-mean surface temperature change and the strength of the Atlantic meridional overturning circulation (AMOC, ). 2 has two routes whereby heat reaches the deep ocean. On the basis of circumstantial evidence, we hypothetically identify these routes as low- and high-latitude.

Emergent constraints on carbon budgets as a function of global warming.

Earth System Models (ESMs) continue to diagnose a wide range of carbon budgets for each level of global warming. Here, we present emergent constraints on the carbon budget as a function of global warming, which combine the available ESM historical simulations and future projections for a range of scenarios, with observational estimates of global warming and anthropogenic CO emissions to the present day. We estimate mean and likely ranges for cumulative carbon budgets for the Paris targets of 1.

Chemistry-albedo feedbacks offset up to a third of forestation's CO removal benefits.

Forestation is widely proposed for carbon dioxide (CO) removal, but its impact on climate through changes to atmospheric composition and surface albedo remains relatively unexplored. We assessed these responses using two Earth system models by comparing a scenario with extensive global forest expansion in suitable regions to other plausible futures. We found that forestation increased aerosol scattering and the greenhouse gases methane and ozone following increased biogenic organic emissions.

Critical transitions in the Amazon forest system.

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system.

Significantly wetter or drier future conditions for one to two thirds of the world's population.

Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios.

A theory of demographic optimality in forests.

Carbon uptake by the land is a key determinant of future climate change. Unfortunately, Dynamic Global Vegetation Models have many unknown internal parameters which leads to significant uncertainty in projections of the future land carbon sink. By contrast, observed forest inventories in both Amazonia and the USA show strikingly common tree-size distributions, pointing to a simpler modelling paradigm.

Contrasting internally and externally generated Atlantic Multidecadal Variability and the role for AMOC in CMIP6 historical simulations.

Atlantic multidecadal variability (AMV) has long been thought to be an expression of low-frequency variability in the Atlantic Meridional Overturning Circulation (AMOC). However, alternative hypotheses have been forwarded, including that AMV is primarily externally forced. Here, we review the current state of play by assessing historical simulations made for the sixth coupled model intercomparison project (CMIP6).

Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022.

Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts.

Impacts on South America moisture transport under Amazon deforestation and 2 °C global warming.

The increase in greenhouse gasses (GHG) anthropogenic emissions and deforestation over the last decades have led to many chemical and physical changes in the climate system, affecting the atmosphere's energy and water balance. A process that could be affected is the Amazonian moisture transport in the South American continent (including La Plata basin), which is crucial to the southeast Brazilian water regime. The focus of our research is on evaluating how local (i.

Adjusting 1.5 degree C climate change mitigation pathways in light of adverse new information.

Understanding how 1.5 °C pathways could adjust in light of new adverse information, such as a reduced 1.5 °C carbon budget, or slower-than-expected low-carbon technology deployment, is critical for planning resilient pathways.