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Greater accessibility to China’s vast archive of satellite Earth observations could enhance scientific progress, disaster preparedness, and international cooperation.

European heatwaves have raised interest in the impact of land-cover conditions on temperature extremes. Analyses of observations from an extensive network of flux towers in Europe reveal a difference in the response of forests and grassland to extreme or long-lasting heat.

Eddy-covariance observations suggest that rain pulses over global drylands drive substantial soil carbon emissions, which are underestimated in current measurement and modelling approaches.

Earth system models often categorize plants to just a few functional types, and plant characteristics are defined per type, neglecting their diversity. The authors show how the use of plant traits can improve the modeling of global carbon, water, and energy fluxes

Three key axes of variation of ecosystem functional changes and their underlying causes are identified from a dataset of surface gas exchange measurements across major terrestrial biomes and climate zones.

It is unclear whether trait trade-offs and optimality principles observed at the individual level scale up to the ecosystem level. Here, the authors show that plant trait coordination principles also predict patterns between community-level traits and ecosystem-scale processes.

Actionable research recommendations are outlined to improve the monitoring and modelling of forest resources and their carbon sink, and to better inform forest management decisions and the European Green Deal.

Groundwater supplies about 59% of global river flow, suggesting a larger contribution of groundwater to the global water cycle than currently appreciated, according to an analysis integrating estimates from models and observations.

Climate change is expected to intensify the global hydrological cycle and to alter evapotranspiration, but direct observational constraints are lacking at the global scale. Now a data-driven, machine-learning technique and a suite of process-based models have been used to show that from 1982 to 1997 global evapotranspiration increased by about 7.1 millimetres per year per decade. But since 1998 this increase has ceased, probably because of moisture limitation in the Southern Hemisphere.

Past continental dryness trends are difficult to assess. A comprehensive analysis of hundreds of combinations of data sets suggests that only 24.6% of the global land area have been exposed to robust dryness changes since 1948.

Without new models, better metrics and more investment, cascades of extreme events could derail the United Nations Sustainable Development Goals.

Water availability is a major control of vegetation dynamics and terrestrial carbon cycling. Here, the authors show that vegetation sensitivity to soil moisture has been increasing in the last 36 years, especially in (semi)arid areas, and that state-of-the-art land surface models fail to capture this trend.

The authors investigate the broad-scale climatological and soil properties that co-vary with major axes of plant functional traits. They find that variation in plant size is attributed to latitudinal gradients in water or energy limitation, while variation in leaf economics traits is attributed to both climate and soil fertility including their interaction.

Changes in air temperature are usually considered for quantifying changes in temperature extremes such as heatwaves. This study shows that the incidence of heat extremes in soils is increasing faster than air temperature in some regions, with implications for hydrological and biogeochemical processes.

Vegetation resilience to drought is underlain by plant physiological responses. Here, the authors combine remote sensing data, explainable machine learning and model simulations to map global vegetation responses to drought linked to physiological processes such as stomatal regulation and light use efficiency.

In the era of climate change, human societies face growing exposure to disasters and complex climate risks. This perspective explores the transformative potential of integrated Artificial Intelligence in developing multi-hazard Early Warning Systems for all.

Analyses of the temperature sensitivity of terrestrial carbon turnover suggest that hydrometeorology and temperature control the spatial variability in carbon turnover times globally.

The photosynthetic capacity of forest ecosystems is an important variable in the global carbon cycle. Here, it is shown that older, more diverse forests have less fluctuation between years in photosynthetic capacity.

Dynamic interactions between chemical and biological controls govern the stability of soil organic carbon and drive complex, emergent patterns in soil carbon persistence.

Climate change is expected to impact moisture supply, which is critical for production of food and carbon uptake by terrestrial ecosystems. A shift from ecosystem energy to water limitation is predicted between 1980 and 2100, with implications for ecosystem function under climate change.

Evidence is growing on the impacts of climate change on human and natural systems. A two-step attribution approach—machine-learning-assisted literature review coupled with grid-cell-level temperature and precipitation—allows comprehensive mapping of the evidence on impacts and tentative attribution to anthropogenic influence.

Factorial climate model simulations show that 90% of the inter-annual variability in global land carbon uptake is driven by soil moisture and its atmospheric feedback on temperature and air humidity.

The authors show increased negative extremes in gross primary productivity in northern midlatitude ecosystems, particularly over grasslands and croplands, attributed to impacts of warm droughts. This highlights the vulnerability of terrestrial carbon sinks and food security to increasing extreme events.

Recent evidence suggests that, on a global scale, terrestrial ecosystems will provide a positive feedback in a warming world, albeit of uncertain magnitude.

Leaf respiration may be inhibited by light, but how this affects ecosystem-level processes is unclear. Analysing globally distributed eddy-covariance observations, Keenan et al. show that this inhibition is widespread and follows consistent seasonal patterns within ecosystem types.

Soil’s role in Earth’s carbon budget is uncertain. A new model links soil temperature and moisture to global soil respiration. Heterotrophic respiration has risen by 2% per decade since the 1980s, with a projected 40% increase by century end.

A deep learning and data-driven modelling study finds that microbial carbon use efficiency is a major determinant of soil organic carbon storage and its spatial variation across the globe.

The mechanisms driving soil carbon storage, one of the largest stores of terrestrial carbon, remain poorly understood. Here, the authors present data from the long-term Jena Experiment on grassland biodiversity, showing that elevated carbon storage at high plant diversity is a direct function of increased soil microbial activity.

Earth system models disagree on the fate of soil organic carbon under climate change. Reproducing spatial patterns of the climatological temperature sensitivity of soil carbon is a necessary condition for trustworthy simulations of the carbon-cycle–climate feedback.

Artificial Intelligence is transforming the study of extreme climate events like floods, droughts, and wildfires, helping to overcome challenges such as limited data and real-time integration. This review article highlights the need for transparent, reliable AI models to improve disaster response, risk communication and stakeholder trust.

A multiscale Earth system modelling approach that integrates machine learning could pave the way for improved climate projections and support actionable climate science.

The effects of climate extremes such as droughts or storms on the carbon cycle of ecosystems are investigated; such extremes can decrease regional carbon stocks.

Extreme weather and climate events could increase ecosystem disturbances and, potentially, destabilize ecosystems, but different feedbacks between climate and ecosystems are often not accounted for. This Perspective proposes a framework to characterize ecoclimatic events and understand the role of human activities in driving them.

A study of how temperature and water availability fluctuations affect the carbon balance of land ecosystems reveals different controls on local and global scales, implying that spatial climate covariation drives the global carbon cycle response.

A global, observation-based assessment of whole-ecosystem carbon turnover times shows that the overall mean global carbon turnover time is about 23 years and that locally its spatial variability depends on precipitation at least as strongly as on temperature.

The COVID-19 pandemic has caused substantial global impact. This Perspective provides insight into the environmental effects of the pandemic, documenting how it offers an opportunity to better understand the Earth System.

Complex Earth system challenges can be addressed by incorporating spatial and temporal context into machine learning, especially via deep learning, and further by combining with physical models into hybrid models.

Questions of causality are ubiquitous in Earth system sciences and beyond, yet correlation techniques still prevail. This Perspective provides an overview of causal inference methods, identifies promising applications and methodological challenges, and initiates a causality benchmark platform.