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This volume discusses the adverse effects of climatic changes on our planet. It examines AI-based tools and technologies and how they can assist in identifying energy emission reductions, CO2 removal, and support the development of greener transportation networks, monitoring deforestation, and forecast extreme weather events.

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses.

Simulations of water fluxes at high spatial resolution that consistently cover historical observations, seasonal forecasts, and future climate projections are key to providing climate services aimed at supporting operational and strategic planning, and developing mitigation and adaptation policies. The End-to-end Demonstrator for improved decision-making in the water sector in Europe (EDgE) is a proof-of-concept project funded by the Copernicus Climate Change Service program that addresses these requirements by combining a multimodel ensemble of state-of-the-art climate model outputs and hydrological models to deliver sectoral climate impact indicators (SCIIs) codesigned with private and public water sector stakeholders from three contrasting European countries. The final product of EDgE is a water-oriented information system implemented through a web application. Here, we present the underlying structure of the EDgE modeling chain, which is composed of four phases: 1) climate data processing, 2) hydrological modeling, 3) stakeholder codesign and SCII estimation, and 4) uncertainty and skill assessments. Daily temperature and precipitation from observational datasets, four climate models for seasonal forecasts, and five climate models under two emission scenarios are consistently downscaled to 5-km spatial resolution to ensure locally relevant simulations based on four hydrological models. The consis­tency of the hydrological models is guaranteed by using identical input data for land surface parameterizations. The multimodel outputs are composed of 65 years of historical observations, a 19-yr ensemble of seasonal hindcasts, and a century-long ensemble of climate impact projections. These unique, high-resolution hydroclimatic simulations and SCIIs provide an unprecedented information system for decision-making over Europe and can serve as a template for water-related climate services in other regions.

Soil Organic Carbon (SOC) is considered a proxy of soil health, contributing to food production, mitigation, and adaptation to climate change and other ecosystem services. Implementing Recommended Management Practices (RMPs) may increase SOC stocks, contributing to achieve the United Nations Framework Convention on Climate Change 21st Conference of the Parties agreements reached in Paris, France. In this framework, the “4 per 1000” initiative invites partners implementing practical actions to reach a SOC stock annual growth of 4‰. For the first time, we assessed the achievement of 4‰ objective in Mediterranean agricultural soils, aiming at (i) analyzing a representative data collection assessing edaphoclimatic variables and SOC stocks from field experiments under different managements in arable and woody crops, (ii) providing evidence on SOC storage potential, (iii) identifying the biophysical and management variables associated with SOC storage, and (iv) recommending a set of mitigation strategies for global change. Average storage rates amounted to 15 and 80 Mg C ha−1 year−1 × 1000 in arable and woody crops, respectively. Results show that application of organic amendments led to significantly higher SOC storage rates than conventional management, with average values about 1.5 times higher in woody than in arable crops (93 vs. 63 Mg C ha−1 year−1 × 1000). Results were influenced by the initial SOC content, experiment duration, soil texture, and climate regime. The relatively lower levels of SOC in Mediterranean soils, and the high surface covered by woody crops, may reflect the high potential of these regions to achieving significant increases in SOC storage at the global scale.

Ocean observational gridded products are vital for climate monitoring, ocean and climate research, model evaluation, and supporting climate mitigation and adaptation measures. This paper describes the 4th version of the Institute of Atmospheric Physics (IAPv4) ocean temperature and ocean heat content (OHC) objective analysis product. It accounts for recent developments in quality control (QC) procedures, climatology, bias correction, vertical and horizontal interpolation, and mapping and is available for the upper 6000 m (119 levels) since 1940 (more reliable after ∼ 1957) for monthly and 1°×1° temporal and spatial resolutions. IAPv4 is compared with the previous version, IAPv3, and with the other data products, sea surface temperatures (SSTs), and satellite observations. It has a slightly stronger long-term upper 2000 m OHC increase than IAPv3 for 1955–2023, mainly because of newly developed bias corrections. The IAPv4 0–2000 m OHC trend is also higher during 2005–2023 than IAPv3, mainly because of the QC process update. The uppermost level of IAPv4 is consistent with independent SST datasets. The month-to-month OHC variability for IAPv4 is desirably less than IAPv3 and the other OHC products investigated in this study, the trend of ocean warming rate (i.e., warming acceleration) is more consistent with the net energy imbalance at the top of the atmosphere than IAPv3, and the sea level budget can be closed within uncertainty. The gridded product is freely accessible at https://doi.org/10.12157/IOCAS.20240117.002 for temperature data (Cheng et al., 2024a) and at https://doi.org/10.12157/IOCAS.20240117.001 for ocean heat content data (Cheng et al., 2024b).

Salt marshes lie between many human communities and the coast and have been presumed to protect these communities from coastal hazards by providing important ecosystem services. However, previous characterizations of these ecosystem services have typically been based on a small number of historical studies, and the consistency and extent to which marshes provide these services has not been investigated. Here, we review the current evidence for the specific processes of wave attenuation, shoreline stabilization and floodwater attenuation to determine if and under what conditions salt marshes offer these coastal protection services. We conducted a thorough search and synthesis of the literature with reference to these processes. Seventy-five publications met our selection criteria, and we conducted meta-analyses for publications with sufficient data available for quantitative analysis. We found that combined across all studies (n = 7), salt marsh vegetation had a significant positive effect on wave attenuation as measured by reductions in wave height per unit distance across marsh vegetation. Salt marsh vegetation also had a significant positive effect on shoreline stabilization as measured by accretion, lateral erosion reduction, and marsh surface elevation change (n = 30). Salt marsh characteristics that were positively correlated to both wave attenuation and shoreline stabilization were vegetation density, biomass production, and marsh size. Although we could not find studies quantitatively evaluating floodwater attenuation within salt marshes, there are several studies noting the negative effects of wetland alteration on water quantity regulation within coastal areas. Our results show that salt marshes have value for coastal hazard mitigation and climate change adaptation. Because we do not yet fully understand the magnitude of this value, we propose that decision makers employ natural systems to maximize the benefits and ecosystem services provided by salt marshes and exercise caution when making decisions that erode these services.

Policies to mitigate climate change by reducing greenhouse gas (GHG) emissions can yield public health benefits by also reducing emissions of hazardous co-pollutants, such as air toxics and particulate matter. Socioeconomically disadvantaged communities are typically disproportionately exposed to air pollutants, and therefore climate policy could also potentially reduce these environmental inequities. We sought to explore potential social disparities in GHG and co-pollutant emissions under an existing carbon trading program-the dominant approach to GHG regulation in the US and globally. We examined the relationship between multiple measures of neighborhood disadvantage and the location of GHG and co-pollutant emissions from facilities regulated under California's cap-and-trade program-the world's fourth largest operational carbon trading program. We examined temporal patterns in annual average emissions of GHGs, particulate matter (PM2.5), nitrogen oxides, sulfur oxides, volatile organic compounds, and air toxics before (January 1, 2011-December 31, 2012) and after (January 1, 2013-December 31, 2015) the initiation of carbon trading. We found that facilities regulated under California's cap-and-trade program are disproportionately located in economically disadvantaged neighborhoods with higher proportions of residents of color, and that the quantities of co-pollutant emissions from these facilities were correlated with GHG emissions through time. Moreover, the majority (52%) of regulated facilities reported higher annual average local (in-state) GHG emissions since the initiation of trading. Neighborhoods that experienced increases in annual average GHG and co-pollutant emissions from regulated facilities nearby after trading began had higher proportions of people of color and poor, less educated, and linguistically isolated residents, compared to neighborhoods that experienced decreases in GHGs. These study results reflect preliminary emissions and social equity patterns of the first 3 years of California's cap-and-trade program for which data are available. Due to data limitations, this analysis did not assess the emissions and equity implications of GHG reductions from transportation-related emission sources. Future emission patterns may shift, due to changes in industrial production decisions and policy initiatives that further incentivize local GHG and co-pollutant reductions in disadvantaged communities. To our knowledge, this is the first study to examine social disparities in GHG and co-pollutant emissions under an existing carbon trading program. Our results indicate that, thus far, California's cap-and-trade program has not yielded improvements in environmental equity with respect to health-damaging co-pollutant emissions. This could change, however, as the cap on GHG emissions is gradually lowered in the future. The incorporation of additional policy and regulatory elements that incentivize more local emission reductions in disadvantaged communities could enhance the local air quality and environmental equity benefits of California's climate change mitigation efforts.

Africa faces an urgent need to boost food production to satisfy the growing demand. Targeted investments in integrated agriculture and water management systems are essential to address this challenge. However, there is a lack of comprehensive information on the potential applications of climate-smart agriculture (CSA) and its perspectives. This paper reviews current crop modeling technologies and their applications in the context of climate change and the CSA framework in Africa. It assesses research trends in various crop simulation models, selected practices, and appropriate crops to improve crop productivity in a sustainable African environment. A total of 379 relevant papers were considered. Results showed that 55% of studies used process-based models, with Maize being the most studied crop. However, many priority crops in Africa have been overlooked in modeling studies. Additionally, more than 52% of studies have contributed to climate change adaptation strategies and reducing yield gaps. Only 12% of the modeling work focused on CSA practices and on integrated climate change mitigation and adaptation measures. Current simulation modeling studies in Africa have contributed to shaping CSA practices, but their impact is constrained by limited data quality, inadequate model calibration, and regional climate variability. Crop models can indeed help tailor CSA practices by identifying region-specific strategies under changing climate conditions, but they also need to integrate socio-economic factors like access to resources and farmer decision-making. Additionally, strengthening local research capacity and improving data infrastructure will be crucial for maximizing the effectiveness of these models in supporting future CSA adoption. To meet food security goals through sustainable agricultural practices in Africa, it is essential to adopt CSA frameworks with a robust climate change mitigation component. The research outcomes will offer a comprehensive evaluation of the prominent models used across Africa, providing a roadmap for future investigations to select adequate approaches and crops based on available data when implementing crop models for specific areas in Africa.

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called \"out-of-bag\"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.