Determination of the life cycle climate change impacts of land use and albedo change in algal biofuel production
Not Available
Fortier, Marie-Odile P.↗
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Projecting environmental suitable areas for malaria transmission in China under climate change scenarios
Highlights: • Climate change may form new area suitable for malaria but little is known in China. • A set of environmental factors was used in MaxEnt model to identify suitable areas. • A slightly wider areas extended to the northeast was projected for P. falciparum. • Suitable areas are projected to increase more under RCP8.5 scenarios. • Most imported cases occurred in areas suitable for malaria transmission. The proportion of imported malaria cases in China has increased over recent years, and has presented challenges for the malaria elimination program in China. However, little is known about the geographic distribution and environmental suitability for malaria transmission under projected climate change scenarios.
54 ENVIRONMENTAL SCIENCES↗
Quantifying Food Security and Mitigation Risks Consequential to Climate Change Impacts on Crop Yields
Climate change is expected to impact crop yields globally, with some regions benefiting from favorable conditions and CO2 fertilization, while others face adverse effects from altered precipitation and higher temperatures. Changes in crop yields can destabilize the global food system and pose challenges to food security. Moreover, crop production is crucial, as biofuels are becoming increasingly important contributors to climate change mitigation measures aimed at limiting global warming. This study uses the Integrated Model to Assess the Global Environment integrated assessment model framework to analyze different indicators related to food security and climate change mitigation under varying climate change impacts on crop yields. Twelve spatially explicit crop productivity projections were taken from the full archive of the Global Gridded Crop Model Intercomparison of 120 climate-crop model combinations, forced by CMIP6-based climate scenarios. The selection includes two average-performing climate-crop model combinations, two pessimistic combinations that perform one standard deviation below the mean, and two optimistic model combinations that perform one standard deviation above the mean. To single out the effect of climate change on productivity changes, we drew samples from two representative concentration pathways (RCP2.6 and RCP8.5). These productivity projections were applied within an otherwise uniform scenario (SSP2) and analyzed for their effect on total calorie demand, crop prices, and number of people at risk of undernourishment to quantify food security. Risks to climate change mitigation targets were explored by modeling the total bioenergy supply, emissions, and global mean temperature. The results revealed significant differences in the risk of food security and mitigation potential between different regions and climate change scenarios. Across scenarios, the crop area extent can vary up to 2 million km2 due to changing crop yields. The projected change in global hunger ranges from 60 to 160 million undernourished people, indicating uncertainty between climate and crop model combinations. Low-income regions are especially impacted because of their high sensitivity to changes in food prices. Global climate change mitigation ambitions can also deviate by the latter part of the 21st century, as changes in yields will impact biofuel production as well as agriculture, forestry and other land use emissions. The quantitative insights generated by this study highlight the need for global policy efforts to make the agricultural system more adaptive to climate change to handle potential negative impacts.
crop yields↗
Overview of different aspects of climate change effects on soils
Climate change [i.e., high atmospheric carbon dioxide (CO 2 ) concentrations (≥400 ppm); increasing air temperatures (2-4°C or greater); significant and/or abrupt changes in daily, seasonal, and inter-annual temperature; changes in the wet/dry cycles; intensive rainfall and/or heavy storms; extended periods of drought; extreme frost; heat waves and increased fire frequency] is and will significantly affect soil properties and fertility, water resources, food quantity and quality, and environmental quality. Biotic processes that consume atmospheric CO 2 and create organic carbon (C) that is either reprocessed to CO 2 or stored in soils, are the subject of active current investigations with great concern over the influence of climate change. In addition, abiotic C cycling and its influence on the inorganic C pool in soils is a fundamental global process in which acidic atmospheric CO 2 participates in the weathering of carbonate and silicate minerals, ultimately delivering bicarbonate and Ca 2 + or other cations that precipitate in the form of carbonates in soils or are transported to the rivers, lakes, and oceans. Soil responses to climate change will be complex, and there are many uncertainties and unresolved issues. The objective of the review is to initiate and further stimulate a discussion about some important and challenging aspects of climate-change effects on soils, such as accelerated weathering of soil minerals and resulting C and elemental fluxes in and out of soils, soil/geo-engineering methods used to increase C sequestration in soils, soil organic matter (SOM) protection, transformation and mineralization, and SOM temperature sensitivity. This review reports recent discoveries and identifies key research needs required to understand the effects of climate change on soils.
54 ENVIRONMENTAL SCIENCES↗
Assessing the observed impact of anthropogenic climate change
Impacts of recent regional changes in climate on natural and human systems are documented across the globe, yet studies explicitly linking these observations to anthropogenic forcing of the climate are scarce. Here in this work, we provide a systematic assessment of the role of anthropogenic climate change for the range of impacts of regional climate trends reported in the IPCC’s Fifth Assessment Report. We find that almost two-thirds of the impacts related to atmospheric and ocean temperature can be confidently attributed to anthropogenic forcing. In contrast, evidence connecting changes in precipitation and their respective impacts to human influence is still weak. Moreover, anthropogenic climate change has been a major influence for approximately three-quarters of the impacts observed on continental scales. Finally, hence the effects of anthropogenic emissions can now be discerned not only globally, but also at more regional and local scales for a variety of natural and human systems.
54 ENVIRONMENTAL SCIENCES↗
Delays in Reducing Waterborne and Water-related Infectious Diseases in China under Climate Change
Despite China’s rapid progress improving water, sanitation and hygiene (WSH) infrastructure and access, in 2011, 471 million people lacked access to improved sanitation, and 401 million people lacked access to household piped water. Infectious diseases are sensitive to changes in climate, particularly temperature, and WSH conditions. To explore possible impacts of climate change on these diseases in China in 2020 and 2030, we coupled estimates of the temperature sensitivity of diarrheal disease and three vector-borne diseases, temperature projections from global climate models using four emissions pathways, WSH-infrastructure development scenarios and projected demographic changes. By 2030, the projected impacts would delay China’s historically rapid progress toward reducing the burden of WSH-attributable infectious disease by 8-85 months. This developmental delay provides a key summary measure of the impact of climate change in China, and in other societies undergoing rapid social, economic, and environmental change.
54 ENVIRONMENTAL SCIENCES↗
Artificial Intelligence for Climate Change Mitigation Roadmap (Second Edition)
The first ICEF Artificial Intelligence for Climate Change Mitigation Roadmap was released in December 2023. Since that time, attention to artificial intelligence (AI) has continued to grow at a rapid pace. Tens of billions of dollars have poured into AI projects, policymakers around the world have considered new AI policies, and OpenAI reports that each month more than 200 million people now use ChatGPT. Signs of a changing climate continue to grow as well. Based on global average temperatures, July 22, 2024 was the warmest day ever recorded; 2023 was the warmest year ever recorded; and the 10 warmest years on record are the past 10 years. Yet global emissions of greenhouse gases continue to climb. Can AI help cut emissions of greenhouse gases? This Roadmap explores that question. In this second edition of the Artificial Intelligence for Climate Change Mitigation Roadmap, a team of 25 co-authors builds on last year’s roadmap—comprehensively updating all old chapters, adding six new chapters and offering 5–10 specific, actionable recommendations in each chapter. Our goal is to provide a useful resource for experts and non-experts alike. In Part I of this Roadmap, we provide brief introductions to both AI and climate change. In Part II, we explore eight sectors in which AI is helping respond to climate change and could do much more. In Part III, we explore crosscutting issues. We close with findings and recommendations.
54 ENVIRONMENTAL SCIENCES↗
"Intelligent Ensemble" Projections of Precipitation and Surface Radiation in Support of Agricultural Climate Change Adaptation
Earth's climate is changing and will continue to change into the foreseeable future. Expected changes in the climatological distribution of precipitation, surface temperature, and surface solar radiation will significantly impact agriculture. Adaptation strategies are, therefore, required to reduce the agricultural impacts of climate change. Climate change projections of precipitation, surface temperature, and surface solar radiation distributions are necessary input for adaption planning studies. These projections are conventionally constructed from an ensemble of climate model simulations (e.g., the Coupled Model Intercomparison Project 5 (CMIP5)) as an equal weighted average, one model one vote. Each climate model, however, represents the array of climate-relevant physical processes with varying degrees of fidelity influencing the projection of individual climate variables differently. Presented here is a new approach, termed the "Intelligent Ensemble, that constructs climate variable projections by weighting each model according to its ability to represent key physical processes, e.g., precipitation probability distribution. This approach provides added value over the equal weighted average method. Physical process metrics applied in the "Intelligent Ensemble" method are created using a combination of NASA and NOAA satellite and surface-based cloud, radiation, temperature, and precipitation data sets. The "Intelligent Ensemble" method is applied to the RCP4.5 and RCP8.5 anthropogenic climate forcing simulations within the CMIP5 archive to develop a set of climate change scenarios for precipitation, temperature, and surface solar radiation in each USDA Farm Resource Region for use in climate change adaptation studies.
Taylor, Patrick C.↗
Farmers’ Perception of Climate Change and Its Impacts on Agriculture
Climate change and climate variability drive rapid glacier melt and snowpack loss, extreme precipitation and temperature events, and alteration of water availability in the Himalayas. There is increasing observational evidence of climate change impacts on water resource availability and agricultural productivity in the central Himalayan region. Here, we assess the farmers’ perception of climate change and its impacts on agriculture in western Nepal. We interviewed 554 households and conducted eight focus group discussions to collect farmers’ perceptions of temperature and rainfall characteristics, water availability, onset and duration of different seasons, and the impacts of such changes on their lives and livelihoods. Our results indicate that the farmers’ perceptions of rising annual and summer temperatures are consistent with observations. Perception, however, contradicts observed trends in winter temperature, as well as annual, monsoon, and winter precipitation. In addition, farmers are increasingly facing incidences of extreme events, including rainfall, floods, landslides, and droughts. These hazards often impact agricultural production, reducing household income and exacerbating the economic impacts on subsistence farmers. Integrated assessment of farmers’ perceptions and hydrometeorological observations is crucial to improving climate change impact assessment and informing the design of mitigation and adaptation strategies.
54 ENVIRONMENTAL SCIENCES↗
Baltic Sea coastal sediment-bound eukaryotes have increased year-round activities under predicted climate change related warming
Climate change related warming is a serious environmental problem attributed to anthropogenic activities, causing ocean water temperatures to rise in the coastal marine ecosystem since the last century. This particularly affects benthic microbial communities, which are crucial for biogeochemical cycles. While bacterial communities have received considerable scientific attention, the benthic eukaryotic community response to climate change remains relatively overlooked. In this study, sediments were sampled from a heated (average 5°C increase over the whole year for over 50 years) and a control (contemporary conditions) Baltic Sea bay during four different seasons across a year. RNA transcript counts were then used to investigate eukaryotic community changes under long-term warming. The composition of active species in the heated and control bay sediment eukaryotic communities differed, which was mainly attributed to salinity and temperature. The family level RNA transcript alpha diversity in the heated bay was higher during May but lower in November, compared with the control bay, suggesting altered seasonal activity patterns and dynamics. In addition, structures of the active eukaryotic communities varied between the two bays during the same season. Hence, this study revealed that long-term warming can change seasonality in eukaryotic diversity patterns. Relative abundances and transcript expression comparisons between bays suggested that some taxa that now have lower mRNA transcripts numbers could be favored by future warming. Furthermore, long-term warming can lead to a more active metabolism in these communities throughout the year, such as higher transcript numbers associated with diatom energy production and protein synthesis in the heated bay during winter. In all, these data can help predict how future global warming will affect the ecology and metabolism of eukaryotic community in coastal sediments.
59 BASIC BIOLOGICAL SCIENCES↗
Harvest legacies and climate change interact to shape forest structure and biomass through time
Forest structural change driven by climate trends has been observed worldwide and is expected to increase in the future. Management of forest structure has been an important tool for mitigating the impacts of climate change but forest structure may shift independently of management goals as it interacts with climate change. Here, we investigated the long-term impacts of harvest-based management strategies on structure and resistance to climate-induced biomass loss using a process-based ecosystem model for a midwestern USA hardwood forest. We identified aboveground biomass loss events and compared the cumulative number of these events following a five-year period of active management under four management strategies and two climate change scenarios. Management legacy had the clearest impact on climate-driven biomass loss over the mid-term (∼25 years) with the shelterwood scenario experiencing no loss events during this period. However, by the end of the century legacy effects faded and climate change severity became the driver of differences as greater warming scenarios experienced twice the loss events, and end-of-century loss events were 3–10 times more frequent than mid-century events. We found that while structure was distinct among harvest scenarios through the mid-century, differences were negligible by the end-of-century; identical to when management correlated with loss event frequency. Here, we found that loss events were preceded by a drop in precipitation two years prior, while no specific forest structure preceded a loss event. However, the structures preceding a biomass loss event were distinct across different legacies, implying additional influences of past management.
54 ENVIRONMENTAL SCIENCES↗
Ecological acclimation: A framework to integrate fast and slow responses to climate change
Ecological responses to climate change occur across vastly different time-scales, from minutes for physiological plasticity to decades or centuries for community turnover and evolutionary adaptation. Accurately predicting the range of ecosystem trajectories will require models that incorporate both fast processes that may keep pace with climate change and slower ones likely to lag behind and generate disequilibrium dynamics. However, the knowledge necessary for this integration is currently fragmented across disciplines. We develop ‘ecological acclimation’ as a unifying framework to emphasize the similarity of dynamics driven by processes operating on dramatically different time-scales and levels of biological organization. The framework focuses on ecoclimate sensitivities, measured as the change in an ecological response variable per unit of climate change. Acclimation processes acting at different time-scales cause these sensitivities to shift in magnitude and even direction over time. We highlight shifting ecoclimate sensitivities in case studies from diverse ecosystems, including terrestrial plant communities, coral reefs and soil microbiomes. Models predicting future ecosystem states inevitably make assumptions about acclimation processes; these assumptions must be explicit for users to evaluate whether a model is appropriate for a given forecast horizon. Similarly, decision frameworks that clearly account for multiple acclimation processes and their distinct time-scales will help natural resource managers plan for ecological impacts of climate change from years to many decades into the future. We outline a synthetic research programme focused on the time-scales of ecological acclimation to reduce uncertainty in ecological forecasts.
climate adaptation↗
The Role of Long-Lived Greenhouse Gases as Principal LW Control Knob that Governs the Global Surface Temperature for Past and Future Climate Change
The climate system of the Earth is endowed with a moderately strong greenhouse effect that is characterized by non-condensing greenhouse gases (GHGs) that provide the core radiative forcing. Of these, the most important is atmospheric CO2. There is a strong feedback contribution to the greenhouse effect by water vapor and clouds that is unique in the solar system, exceeding the core radiative forcing due to the non-condensing GHGs by a factor of three. The significance of the non-condensing GHGs is that once they have been injected into the atmosphere, they remain there virtually indefinitely because they do not condense and precipitate from the atmosphere, their chemical removal time ranging from decades to millennia. Water vapor and clouds have only a short lifespan, with their distribution determined by the locally prevailing meteorological conditions, subject to Clausius-Clapeyron constraint. Although solar irradiance is the ultimate energy source that powers the terrestrial greenhouse effect, there has been no discernible long-term trend in solar irradiance since precise monitoring began in the late 1970s. This leaves atmospheric CO2 as the effective control knob driving the current global warming trend. Over geological time scales, volcanoes are the principal source of atmospheric CO2, and the weathering of rocks is the principal sink, with the biosphere participating as both a source and a sink. The problem at hand is that human industrial activity is causing atmospheric CO2, to increase by 2 ppm per year, whereas the interglacial rate has been 0.005 ppm per year. This is a geologically unprecedented rate to turn the CO2 climate control knob. This is causing the global warming that threatens the global environment.
Greenhouse effect↗
Initial Exploratory Study of the Impacts of Climate Change on Air Traffic Operations
Climate change is already happening and is projected to accelerate in the future. It will persist for the foreseeable future, even with optimistic projections of technological advances and behavior changes. It will make aviation more and more challenging over time. Among other things, it is expected to cause increased severe weather, more extreme temperatures, and rising sea levels that constrain when, where, and how aircraft can fly. NASA has many prior, ongoing, and upcoming efforts towards mitigating the impacts of aviation on climate change. There is also a need to perform a comprehensive investigation of the reverse: that is, the impacts of climate change on aviation. This is an initial exploratory study on this topic utilizing the NAS Digital Twin—composed of the ATM TestBed simulation platform and NAS Digital Twin airspace management algorithms—to run NAS-wide simulations with Convective Weather Avoidance Model polygons at different avoidance thresholds to emulate the increase in severe weather that could manifest due to climate change. It was found that air traffic management may be able to limit the impact of climate change in terms of delay and fuel burn. However, increased severe weather due to climate change can make separation—particularly, aircraft-to-weather separation—more challenging by reducing available airspace and constraining the solution space. Challenging situations were identified and corresponding potential solutions were proposed towards ensuring air traffic operations continue to be robust and resilient to the impacts of increased severe weather due to climate change.
climate change, weather, air traffic operations↗
Response of the Intertropical Convergence Zone to Climate Change: Location, Width, and Strength
Not provided.
Meteorology & Atmospheric Sciences↗
Protocol for Projecting Allele Frequency Change under Future Climate Change at Adaptive-Associated Loci
Not Available
54 ENVIRONMENTAL SCIENCES↗
Competition and facilitation may lead to asymmetric range shift dynamics with climate change
Not provided.
Biodiversity & Conservation↗
Uncertainty in Simulating Wheat Yields Under Climate Change
Anticipating the impacts of climate change on crop yields is critical for assessing future food security. Process-based crop simulation models are the most commonly used tools in such assessments1,2. Analysis of uncertainties in future greenhouse gas emissions and their impacts on future climate change has been increasingly described in the literature3,4 while assessments of the uncertainty in crop responses to climate change are very rare. Systematic and objective comparisons across impact studies is difficult, and thus has not been fully realized5. Here we present the largest coordinated and standardized crop model intercomparison for climate change impacts on wheat production to date. We found that several individual crop models are able to reproduce measured grain yields under current diverse environments, particularly if sufficient details are provided to execute them. However, simulated climate change impacts can vary across models due to differences in model structures and algorithms. The crop-model component of uncertainty in climate change impact assessments was considerably larger than the climate-model component from Global Climate Models (GCMs). Model responses to high temperatures and temperature-by-CO2 interactions are identified as major sources of simulated impact uncertainties. Significant reductions in impact uncertainties through model improvements in these areas and improved quantification of uncertainty through multi-model ensembles are urgently needed for a more reliable translation of climate change scenarios into agricultural impacts in order to develop adaptation strategies and aid policymaking.
Asseng, S.↗