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The study validates a WRF model with 15 km grid spacing for weather forecasting and hydroclimate monitoring in the La Plata Basin. Testing against observations from August 2012 to July 2014 showed high prediction performance for a 7-day lead-time, with approximately 70% of rain and no-rain days correctly predicted in the basin and temperature correlations exceeding 0.80. The model successfully reproduced observed conditions during a drought in southern Brazil and a high-precipitation season in northeastern Argentina, demonstrating its utility for monitoring soil moisture, evapotranspiration, and runoff.

The study evaluates a parsimonious geospatial tool for mapping monthly water budgets, groundwater recharge, baseflow, and quickflow in Peru and Myanmar. In Peru, the tool's spatial distribution of baseflow contributions showed a strong correlation with an established model (r2 = 0.81). In Myanmar, the model performed satisfactorily in representing month-to-month variation (NSE 0.6–0.8), though it exhibited scale-dependent errors in large basins. The findings suggest that while trade-offs exist between model complexity and accuracy, such simple models can support watershed management in resource-limited settings.

The analysis concludes that human-induced climate change has made the June 2019 heatwave in France more likely and more intense. Observational data suggests that a heatwave of this intensity is occurring at least 10 times more frequently today than a century ago, with temperatures approximately 4C higher. While climate models qualitatively agree on the strong human influence, they show smaller trends and fewer severe heatwaves than observations, simulating a probability increase of between a factor of 2 and 20. The study emphasizes that early-season heatwaves in June have more severe health and organizational impacts than mid-summer events.

The study found that four existing ozone damage functions used in terrestrial biosphere models failed to accurately reproduce observed ozone-induced biomass reductions in young European trees. However, the authors achieved a good agreement between observed and modelled damage by developing linear, plant-functional-type-specific dose-response relationships through the re-parameterisation of photosynthetically based injury functions.

The report assesses climate variability and future projections across central, eastern, western, and southern Africa, highlighting a critical gap in observational data and a lack of consensus among climate models. It details regional risks—such as the 'east African climate change paradox' and the vulnerability of the Congo rainforest—while providing country-specific analysis for Malawi, Rwanda, Senegal, Tanzania, Uganda, and Zambia. The document emphasizes the need for improved high-resolution modelling and better communication of scientific uncertainty to support adaptation in agriculture, water, energy, and urban infrastructure.

The document provides a framework for the Bureau of Land Management (BLM) to select a power sector model for its programmatic environmental impact statement (PEIS), specifically evaluating the Bureau of Ocean Energy Management’s MarketSim, the U.S. Energy Information Administration’s National Energy Modeling System (NEMS), and ICF International’s Integrated Planning Model (IPM). It emphasizes the necessity of balancing model complexity against transparency in the selection process.

The study compared CFSR climate data against conventional weather data using the Soil and Water Assessment Tool to simulate streamflow in the Lake Tana basin. While conventional data performed better overall, CFSR data is identified as a valuable alternative for hydrological predictions in remote, data-scarce areas where gauges are unavailable.

The report argues that mitigation and adaptation are complementary rather than substitutable, as adaptation has physical and societal limits and cannot offset the risks of catastrophic climate change. It critiques most Integrated Assessment Models (IAMs) for oversimplifying adaptation as an optimal, linear process and underestimating damage costs. The authors highlight the PAGE09 model as a more robust tool due to its uncertainty analysis. The document also reviews sector-specific physical models for water, agriculture, and coasts, noting that while they provide more detail, they often lack a dynamic representation of the adaptation process. It concludes by proposing a conceptual framework that combines different levels of mitigation and adaptation effort under various socioeconomic pathways to analyze residual risks.

The paper details a new soil moisture module for the DO3SE model, designed to address a limitation in regional risk assessments where soil water deficits were previously assumed not to limit ozone flux. The module uses the Penman-Monteith energy balance method and empirical data on pre-dawn leaf water status to estimate the biological control of transpiration, testing four different methods ranging from simple soil water content relations to complex models incorporating hydraulic resistance and plant capacitance.

The analysis reveals that the FUND model, used by the U.S. government to calculate the social cost of carbon, relies on outdated research from 1996 or earlier and contains significant modeling errors in its temperature-yield equations. The model projects a net benefit for agriculture due to CO2 fertilization, though this is offset by other climate damages.

The authors analyze the FUND model, which provided a significantly lower Social Cost of Carbon (SCC) estimate of $6 compared to other models like DICE ($28) and PAGE ($30). By disaggregating the model, they find that the low SCC is primarily driven by a large estimated net benefit in agriculture, largely due to CO2 fertilization. The paper identifies three technical issues in FUND's agricultural modeling: a risk of division by zero in the optimum temperature equation, the use of physically implausible temperature ranges in Monte Carlo analysis, and the use of truncated normal distributions that inflate the carbon fertilization effect. The authors argue that updating FUND with recent empirical research—which suggests smaller CO2 fertilization benefits and non-quadratic temperature impacts—would significantly increase the SCC estimate.

The report examines the gap between global water scarcity models and the local realities of migration. It argues that while global indicators identify high-stress regions, they fail to account for local coping mechanisms, social vulnerability, and the diverse reasons people move. The authors contend that water scarcity is rarely the sole driver of large-scale refugee movements; instead, it interacts with political and economic factors. The document advocates for a livelihoods-based approach to policy that enhances community resilience and recognizes migration as a potential adaptive strategy rather than merely a crisis of 'environmental refugees'.

The 2022 UK heatwave, which saw temperatures reach 40.3°C in Coningsby, was made at least 10 times more likely by human-caused climate change. Observational analysis indicates such an event would have been about 4°C cooler in pre-industrial times, though climate models systematically underestimate this trend. The event had significant impacts on human health, with projected excess mortality of over 840 people in England and Wales, and exposed critical vulnerabilities in UK infrastructure and urban planning.

The study concludes that the June 2019 heat wave in France was made at least five times more likely due to human-induced climate change. Observational data suggests a much higher increase in probability and a temperature rise of approximately 4ºC compared to a century ago, though climate models generally show lower trends and systematic biases in representing heat wave variability.