Earth Island Institute

<strong class="journal-contentHeaderColor">Abstract.</strong> This article, the ninth in the series, presents kinetic and photochemical data sheets evaluated by the IUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation. It covers an extension of the gas phase and photochemical reactions of halogenated alkanes, alkenes, and oxygenated organic compounds implemented on the IUPAC website since 2008. The article consists of a summary table of the recommended kinetic parameters for the evaluated reactions, and a supplement containing the data sheets providing information upon which the recommendations are made.

<strong class="journal-contentHeaderColor">Abstract.</strong> This article, the ninth in the series, presents kinetic and photochemical data sheets evaluated by the IUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation. It covers an extension of the gas phase and photochemical reactions of halogenated alkanes, alkenes, and oxygenated organic compounds implemented on the IUPAC website since 2008. The article consists of a summary table of the recommended kinetic parameters for the evaluated reactions, and a supplement containing the data sheets providing information upon which the recommendations are made.

The subsidence of surface is considered within the framework of model of bending layer, lying on the pliable bed. The coefficient of bed and the transverse load on the cover layer are expressed through the parameters of layers forming the deposit and pressure drop.

Abstract Effective water demand management is critical for adaptation during drought periods. However, drought planning is challenged by the lack of rigorous water demand‐integrated measures and the limited number of drought event samples based on instrumental data. We propose a Normalized Reservoir Drought Index (NRDI) and associated drought event metrics that can inform reservoir operations. Using NRDI, we analyzed the effectiveness of demand management and paleo streamflow data in assessing drought risks—the exposure related to meeting water demands during certain drought events. We explored reservoir drought risk analysis using data from seven reservoirs in the western United States. The NRDI was derived from inflow and estimated water demand and subsequently compared to observed reservoir storage. The NRDI and event metrics demonstrated responsiveness to observed storage. Comparative analysis of drought risk using paleo and instrumental inflow highlighted the importance of paleo inflow, providing larger drought event samples and broader extreme ranges. Sensitivity of drought risks was quantified under varying demand conditions. As expected, the sensitivity was more significant in extreme events than moderate events. This paper provides an effective and robust measure for reservoir managers to manage water releases during drought periods and a valuable tool to assess reservoir risk to drought by considering natural variability more broadly, and providing a deeper understanding on the sensitivity of drought risk to demand alterations.

Abstract. We developed WULFFSS, a new stochastic monthly gridded forest-fire model for the western United States (US). Operating at 12-km resolution, WULFFSS calculates monthly probabilities of forest fires ≥100 ha and area burned per fire. The model is forced by variables related to vegetation, topographic, anthropogenic, and climate factors, organized into three indices representing spatial, annual-cycle, and lower frequency temporal domains. These indices can interact, so variables promoting fire in one domain amplify fire-promoting effects in another. Fire probability and size models use multiple logistic and linear regression, respectively, and can be easily updated as new data or ideas emerge. During its training period of 1985–2024, WULFFSS captures 72 % and 83 % of observed interannual variability in western US forest-fire frequency and area, respectively. It reproduces regional differences in seasonal timing, frequencies, and sizes of fires, and performs well in cross-validation exercises that test the model’s accuracy in years or regions not considered during model training. While lacking fine-scale fire dynamics, WULFFSS’ use of classic statistics promotes interpretability and efficient ensemble generation. Designed to run within a vegetation ecosystem model, bidirectional feedbacks between vegetation and fire can identify how ecosystem changes have altered or will alter fire-climate relationships across the western US. The model's predictive power should improve with increasingly accurate and extensive observational data, and its approach can be extended to other regions. Here we provide a thorough description of the WULFFSS model, including the motivation underlying its development, caveats to our approach, and areas for future improvement.

Abstract The greenhouse gas “endangerment finding” of the U.S. Environmental Protection Agency (EPA), established in 2009 after a 2006 U.S. Supreme Court case (Massachusetts vs. EPA) in which we participated as amicus curiae (friends of the court), has become the basis for U.S. regulation of greenhouse gases in the years since. The current Administration of President Donald Trump is now seeking its repeal. Here, we review the role climate science played in that 2006 case, and how the scientific evidence that undergirds the endangerment finding has gotten stronger in the 16 years since. Finally, we consider what will be the fate of the endangerment finding—and indeed that of role of science in contributing to policy—in light of the current challenging environment for science in the U.S.

<strong class="journal-contentHeaderColor">Abstract.</strong> In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets (published here <a href="https://doi.org/10.5281/zenodo.15327155" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.15327155</a> Smith et al., 2025a) to produce updated estimates for key indicators of the state of the climate system: net emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. This year, we additionally include indicators for sea-level rise and land precipitation change. We follow methods as closely as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. The indicators show that human activities are increasing the Earth&rsquo;s energy imbalance and driving faster sea-level rise compared to the AR6 assessment. For the 2015&ndash;2024 decade average, observed warming relative to 1850&ndash;1900 was 1.24 [1.11 to 1.35] &deg;C, of which 1.23 [1.0 to 1.5]&thinsp;&deg;C was human-induced. The 2024 observed record in global surface temperature (1.52&deg;C best estimate) is well above the best estimate of human-caused warming (1.36&deg;C). However, the 2024 observed warming can still be regarded as a typical year, considering the human induced warming level and the state of internal variability associated with the phase of El Ni&ntilde;o and Atlantic variability. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.27 [0.2&ndash;0.4] &deg;C per decade over 2015&ndash;2024. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 53.6&thinsp;&plusmn;&thinsp;5.2&thinsp;GtCO₂e per year over the last decade (2014&ndash;2023), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO₂ emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track decreases or increases in the rate of the climatic changes presented here.

<strong class="journal-contentHeaderColor">Abstract.</strong> Climate change is anticipated to alter the global water and carbon cycles, but the spatiotemporal effects of these climate-induced shifts remain poorly understood. Of particular relevance are the variations in rainfall intensity and frequency affecting the carbon and water cycles from daily to interannual time scales. Yet, the current models fail to reproduce these processes as capturing the complex interactions and interrelated dependencies at different timescales (daily to seasonal) requires the simultaneous estimation of multiple interconnected ecological processes. To address this challenge, here, we introduce initial version of our ecosystem process modelling using Long Short-Term Memory approach (EcoPro-LSTM_𝑣0) which uses a temporal multitask deep learning model designed to predict ecosystem responses, focusing on critical terrestrial variables, including ecosystem respiration (RECO), gross primary productivity (GPP), evapotranspiration (ET), and surface soil water content (SWC). Our approach leverages the capabilities of LSTM networks to capture the interdependencies of those processes across time scales. LSTMs excel at time-series prediction because they can learn long-term relationships and patterns in data. We trained and tested our model using long-term data from FLUXNET2015 Mediterranean sites (at hourly and daily time-steps), mainly in the USA and Europe, known for their ecological diversity and significance. We demonstrate our model's outperforming against state-of-the-art data products and test the robustness of our model and findings through k-fold cross-validation. We also showcase the model's interpretability in revealing how short- and long-term atmospheric drivers, like precipitation, influence GPP in Mediterranean climates. This model and accompanying insights can help better understand and manage ecosystems under climate change, especially in response to changing extreme events.