The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.

The COVID-19 pandemic affected Europe unevenly, with surges in infections and deaths fluctuating across different regions and time periods. Hyper-localised hotspots and staggered timelines created intense, asynchronous waves of infections and deaths that distort country-level and cumulative data, obscuring the pandemic's spatiotemporal dynamics through aggregation. Despite extensive research comparing states and analysing subnational variance within individual countries, the detailed subnational and transnational dynamics of the COVID-19 pandemic across Europe as a whole have not been comprehensively described.

Securing the forest carbon sink for the European Union's climate ambition.

The European Union (EU) climate policies rely on a functioning forest carbon sink. Forests cover about 40% of the EU area and have absorbed about 436 Mt of carbon dioxide equivalent per year between 1990 and 2022, which is about 10% of the EU's anthropogenic emissions. However, the ability of forests to act as carbon sinks is rapidly declining owing to increasing natural and anthropogenic pressures, threatening the EU's climate goals and calling for prompt actions.

Artificial intelligence for modeling and understanding extreme weather and climate events.

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes and data with limited annotations. This paper reviews how AI is being used to analyze extreme climate events (like floods, droughts, wildfires, and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models.

DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts.

With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models.

Vegetation-climate feedbacks across scales.

Vegetation is often viewed as a consequence of long-term climate conditions. However, vegetation itself plays a fundamental role in shaping Earth's climate by regulating the energy, water, and biogeochemical cycles across terrestrial landscapes. It exerts influence by consuming water resources through transpiration and interception, lowering atmospheric CO concentration, altering surface roughness, and controlling net radiation and its partitioning into sensible and latent heat fluxes.

Enhanced global carbon cycle sensitivity to tropical temperature linked to internal climate variability.

The sensitivity of atmospheric CO growth rate to tropical temperature (γ) has almost doubled between 1959 and 2011, a trend that has been linked to increasing drought in the tropics. However, γ has declined since then. Understanding whether these variations in γ reflect forced changes or internal climate variability in the carbon cycle is crucial for future climate projections.

Reclassifying historical disasters: From single to multi-hazards.

Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is challenging, which is attributable to the complex nature of natural hazard interactions and the limited availability of multi-hazard observations.

Lexcube: Interactive Visualization of Large Earth System Data Cubes.

Many subsystems of Earth are constantly monitored in space and time and undergo continuous anthropogenic interventions. However, research into this transformation remains largely inaccessible to the public due to the complexity of the Big Data generated by models and Earth observation. To overcome this barrier, we present the Leipzig Explorer of Earth Data Cubes (lexcube.

Global variations in critical drought thresholds that impact vegetation.

Identifying the thresholds of drought that, if crossed, suppress vegetation functioning is vital for accurate quantification of how land ecosystems respond to climate variability and change. We present a globally applicable framework to identify drought thresholds for vegetation responses to different levels of known soil-moisture deficits using four remotely sensed vegetation proxies spanning 2001-2018. The thresholds identified represent critical inflection points for changing vegetation responses from highly resistant to highly vulnerable in response to drought stress, and as a warning signal for substantial vegetation impacts.

A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research.

Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes).

Citizen science plant observations encode global trait patterns.

Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportunistic citizen science data can help map plant functional traits globally.

Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation.

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land-climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential.

The three major axes of terrestrial ecosystem function.

The leaf economics spectrum and the global spectrum of plant forms and functions revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability.

A unified vegetation index for quantifying the terrestrial biosphere.

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters.

Correction: Kernel methods and their derivatives: Concept and perspectives for the earth system sciences.

[This corrects the article DOI: 10.1371/journal.pone.

Kernel methods and their derivatives: Concept and perspectives for the earth system sciences.

Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical foundation and exhibit excellent performance in practice. However, kernel machines are still considered black-box models as the kernel feature mapping cannot be accessed directly thus making the kernels difficult to interpret.

Inferring causation from time series in Earth system sciences.

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques.

Publisher Correction: Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity.

In the version of this Article originally published, the wrong Supplementary Information pdf was uploaded, in which the figures did not correspond with those mentioned in the main text and the R code was not presented properly. This has now been replaced.

Ecosystem functioning is enveloped by hydrometeorological variability.

Terrestrial ecosystem processes, and the associated vegetation carbon dynamics, respond differently to hydrometeorological variability across timescales, and so does our scientific understanding of the underlying mechanisms. Long-term variability of the terrestrial carbon cycle is not yet well constrained and the resulting climate-biosphere feedbacks are highly uncertain. Here we present a comprehensive overview of hydrometeorological and ecosystem variability from hourly to decadal timescales integrating multiple in situ and remote-sensing datasets characterizing extra-tropical forest sites.