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In response to escalating global climate change and its increasing impacts worldwide, this study investigates the consequences of extreme weather events, focusing on the unprecedented 2022 monsoon season in the Swat River basin of Pakistan. Record-breaking rainfall, exceeding the 1960–2021 averages by 7–8 %, triggered catastrophic debris flows and floods, aggravating low-income communities' challenges. The resulting financial instability severely affected millions, causing extensive damage to homes, crops, and transportation. The study employs a multidisciplinary approach, combining field investigations, remote sensing data interpretation, and numerical simulations to identify the factors contributing to debris flow incidents. Analysis of land cover changes reveals a decrease in grasslands and an increase in barren land, indicating the adverse effects of deforestation on the region. Topography and gully morphology are crucial in initiating debris flows, with steep gradients and shallow slope failures predominant. Numerical simulations show that debris flows reached high velocities of 18 m/s and depths of 40 m within 45 minutes. Two debris flows resulted in the formation of dams along the Swat River, intensifying subsequent floods. The study emphasizes the interplay of extreme rainfall and deforestation during the rainy season, rendering the region susceptible to debris flows and hindering restoration efforts. The findings underscore the urgent need for comprehensive disaster mitigation strategies. Recommendations include climate change mitigation, reforestation initiatives, and discouraging construction activities in flood-prone and debris flow-prone regions. The study advocates for enhanced early warning systems and rigorous land use planning to protect the environment and local communities, highlighting the imperative of proactive measures in the face of escalating climate challenges. Additionally, the study investigates the spatial distribution of various events and their consequences, including potential hydro-meteorological triggers, and how such events initiate processes that change mountain landscapes. It also assesses the degree to which the 2022 monsoon can be classified as abnormal. The combination of empirical evidence and practical insights presented in this study highlights research gaps and proposes routes toward attaining a comprehensive comprehension of monsoon-triggered geological hazards and consequences. [ABSTRACT FROM AUTHOR]

The impact of climate change on crops and agricultural yield is an actual threat while being a challenging issue due to the high complexity of factors that intervene at the local scale of the crop. Assessing it, requires the use of coupled models climate-phenology, meanwhile methods to identify management and genotypes suitable for local future conditions, in order to sustain adaptation strategies. We present the implementation and use of a new integrated climate-phenology adaptation support modeling system based on regional CORDEX climate models and the CERES Maize model from DSSAT platform, with new modules for optimal management and genotype identification using a hybrid method: deterministic modeling and -ML/ genetic algorithms. It was run as a regional pilot over Romania, operating in real-time in interaction with users, performing agro-climate projections (combination of fertilization, sowing date, soil) and providing best crop management simulated under climate change projections. Multi-model ensemble simulations for two climate scenarios RCP4.5 and RCP8.5 and twelve management scenarios show new results for the region. For the actual genotype we find a projected mean decrease in yield in both climate scenarios for all sowing dates and fertilization levels tested, response shown to be sensitive to initial soil parameters. This response was linked to two factors: a shorter growing season by up to 10 % and a loss of fertilization efficiency in a warmer climate. A warning points to results showing a narrowing of agro-management opportunities for crop yield but in opposite it is shown a significant role of optimal genotype-range identification that may provide crop solutions under climate change even in extreme years. Identifying best genotype under warmer climate along sets of six cross-parameter simulations show systematic lower values of the maximal yields, but emphasizes genotype windows of increases in the intermediate yield values in scenarios compared to actual climate. The highest harvest sensitivity to genotype is shown to be to changes in the thermal time to juvenil respectively to maturity stage under warmer climate. The results sustain using a deterministic coupled modeling system combined with data-driven modeling for identifying optimal adaptation including fertilization paths that contribute to climate change mitigation. [ABSTRACT FROM AUTHOR]

Biochar (BC) application to croplands aims to sequester carbon and improve soil quality, but its impact on soil organic carbon (SOC) dynamics is not represented in most land models used for assessing land-based climate change mitigation; therefore, we are unable to quantify the effects of biochar application under different climate or land management conditions. Here, to fill this gap, we implement a submodel to represent biochar in a microbial decomposition model named MIMICS (MIcrobial-MIneral Carbon Stabilization). We first calibrate and validate MIMICS with new representations of the density-dependent microbial turnover rate, adsorption of available organic carbon on mineral soil particles, and soil moisture effects on decomposition using global field-measured cropland SOC at 285 sites. We further integrate biochar in MIMICS by accounting for its effect on microbial decomposition and SOC sorption/desorption and optimize two biochar-related parameters in these processes using 134 paired SOC measurements with and without biochar addition. The MIMICS-biochar version can generally reproduce the short-term (≤ 6 years) and long-term (8 years) SOC changes after adding (mean addition rate of 25.6 t ha -1) biochar (R2= 0.79 and 0.97, respectively) with a low root-mean-square error (RMSE = 3.73 and 6.08 g kg -1 , respectively). Our study incorporates sorption and soil moisture processes into MIMICS and extends its capacity to simulate biochar decomposition, providing a useful tool to couple with dynamic land models to evaluate the effectiveness of biochar application with respect to removing CO 2 from the atmosphere. [ABSTRACT FROM AUTHOR]

Salt marshes are crucial eco-geomorphic features of tidal environments as they provide important ecological functions and deliver a wide range of ecosystem services. Being controlled by the interplay between hydrodynamics, geomorphology, and vegetation, the contribution of both organic matter (OM) and inorganic sediments drives salt marsh vertical accretion. This allows marshes to keep pace with relative sea level rise and likewise capture and store carbon, making them valuable allies in climate mitigation strategies. Thus, soil organic matter (SOM), i.e. the organic component of the soil, plays a key role within salt marsh environments, directly contributing to soil formation and supporting carbon storage. This study aims at inspecting spatial patterns of OM in surface salt marsh soils (top 20 cm), providing further insights into the physical and biological factors driving OM dynamics that affect salt marsh survival and carbon sink potential. Our results reveal two scales of variations in SOM content in marsh environments. At the marsh scale, OM variability is influenced by the interplay between surface elevation and changes in sediment supply linked with the distance from the marsh edge. At the system scale, OM content distribution is dominated by the gradient generated by marine and fluvial influence. The observed variations in SOM are explained by the combination of inorganic and organic input, preservation conditions, and sediment grain size. Our results highlight the importance of marshes as carbon sink environments, further emphasising that environmental conditions within a tidal system may generate strongly variable and site-specific carbon accumulation patterns, enhancing blue carbon assessment complexity. [ABSTRACT FROM AUTHOR]

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Emissions and atmospheric concentrations of CH4 continue to increase, maintaining CH4 as the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the factors explaining the well-observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in-situ and greenhouse gas observing satellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land-surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full datasets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this edition benefits from important progress in estimating inland freshwater emissions, with better accounting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double accounting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double accounting that still exists (average of 23 Tg CH4 yr-1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr-1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr-1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr-1 or ~65 % are attributed to direct anthropogenic sources in the fossil, agriculture and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr-1or 63–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr-1 (range 9–40). Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr-1) larger global emissions (669 Tg CH4 yr-1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr-1 in Saunois et al. (2016, 2020), respectively), and for the first time uncertainty in bottom-up and top-down budgets overlap. The latitudinal distribution from atmospheric inversion-based emissions indicates a predominance of tropical and southern hemisphere emissions (~65 % of the global budget, <30° N) compared to mid (30° N–60° N, ~30 % of emissions) and high-northern latitudes (60° N–90° N, ~4 % of global emissions). This latitudinal distribution is similar in the bottom-up budget though the bottom-up budget estimates slightly larger contributions for the mid and high-northern latitudes, and slightly smaller contributions from the tropics and southern hemisphere than the inversions. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. We identify five major priorities for improving the CH4 budget: i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; ii) further development of process-based models for inland-water emissions; iii) intensification of CH4 observations at local (e.g., FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; iv) improvements of transport models and the representation of photochemical sinks in top-down inversions, and v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024). [ABSTRACT FROM AUTHOR]

To mitigate the impact of greenhouse gas (GHG) and air pollutant emissions, it is of utmost importance to understand where emissions occur. In the real world, atmospheric pollutants are produced by various human activities from point sources (e.g. power plants and industrial facilities) but also from diffuse sources (e.g. residential activities and agriculture). However, as tracking all these single sources of emissions is practically impossible, emission inventories are typically compiled using national-level statistics by sector, which are then downscaled at the grid-cell level using spatial information. In this work, we develop high-spatial-resolution proxies for use in downscaling the national emission totals for all world countries provided by the Emissions Database for Global Atmospheric Research (EDGAR). In particular, in this paper, we present the latest EDGAR v8.0 GHG, which provides readily available emission data at different levels of spatial granularity, obtained from a consistently developed GHG emission database. This has been achieved through the improvement and development of high-resolution spatial proxies that allow for a more precise allocation of emissions over the globe. A key novelty of this work is the potential to analyse subnational GHG emissions over the European territory and also over the United States, China, India, and other high-emitting countries. These data not only meet the needs of atmospheric modellers but can also inform policymakers working in the field of climate change mitigation. For example, the EDGAR GHG emissions at the NUTS 2 level (Nomenclature of Territorial Units for Statistics level 2) over Europe contribute to the development of EU cohesion policies, identifying the progress of each region towards achieving the carbon neutrality target and providing insights into the highest-emitting sectors. The data can be accessed at 10.2905/b54d8149-2864-4fb9-96b9-5fd3a020c224 specifically for EDGAR v8.0 (Crippa et al., 2023a) and 10.2905/D67EEDA8-C03E-4421-95D0-0ADC460B9658 for the subnational dataset (Crippa et al., 2023b). [ABSTRACT FROM AUTHOR]

Ocean alkalinity enhancement (OAE) is a negative emissions technology (NET) that shows significant potential for climate change mitigation. By increasing the bicarbonate ion concentration in ocean water, OAE could enhance long-term carbon storage and mitigate ocean acidification. However, the side effects and/or potential co-benefits of OAE on natural planktonic communities remain poorly understood. To address this knowledge gap, a mesocosm experiment was conducted in the oligotrophic waters of Gran Canaria. A CO2 -equilibrated total alkalinity (TA) gradient was employed in increments of 300 µ mol L -1 , ranging from ∼ 2400 to ∼ 4800 µ mol L -1. This study represents the first attempt to evaluate the potential impacts of OAE on planktonic communities under natural conditions. The results show that net community production (NCP), gross production (GP), community respiration (CR) rates, and the metabolic balance (GP:CR) did not exhibit a linear response to the whole alkalinity gradient. Instead, significant polynomial and linear regression models were observed for all rates up to Δ TA 1800 µ mol L -1 , in relation to the dissolved inorganic carbon (DIC) concentrations. Notably, the Δ TA 1500 and 1800 µ mol L -1 treatments showed peaks in NCP shifting from a heterotrophic to an autotrophic state, with NCP values of 4 and 8 µ mol O 2 kg -1 d -1 , respectively. These peaks and the optimum curve were also reflected in the nanoplankton abundance, size-fractionated chlorophyll a , and 14C uptake data. Furthermore, abiotic precipitation occurred in the highest treatment after day 21, but no impact on the measured parameters was detected. Overall, a damaging effect of CO2 -equilibrated OAE in the range applied here on phytoplankton primary production, community metabolism, and composition could not be inferred. In fact, a potential co-benefit to OAE was observed in the form of the positive curvilinear response to the DIC gradient up to the Δ TA 1800 treatment. Further experimental research at this scale is key to gain a better understanding of the short- and long-term effects of OAE on planktonic communities. [ABSTRACT FROM AUTHOR]

Achieving climate targets requires mitigation against climate change but also understanding of the response of land and ocean carbon systems. In this context, global soil carbon stocks and their response to environmental changes are key. This paper quantifies the global soil carbon feedbacks due to changes in atmospheric CO 2 , and the associated climate changes, for Earth system models (ESMs) in CMIP6. A standard approach is used to calculate carbon cycle feedbacks, defined here as soil carbon-concentration (βs) and carbon-climate (γs) feedback parameters, which are also broken down into processes which drive soil carbon change. The sensitivity to CO 2 is shown to dominate soil carbon changes at least up to a doubling of atmospheric CO 2. However, the sensitivity of soil carbon to climate change is found to become an increasingly important source of uncertainty under higher atmospheric CO 2 concentrations. [ABSTRACT FROM AUTHOR]

Identifying the climate-induced variability in the condition of vegetation is particularly important in the context of recent climate change and plants' impact on the mitigation of climate change. In this paper, we present the coherence and time lags in the spectral response of three individual vegetation types in the European temperate zone to the influencing meteorological factors in the period 2002–2022. Vegetation condition in broadleaved forest, coniferous forest and pastures was measured with monthly anomalies of two spectral indices – normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI). As meteorological elements we used monthly anomalies of temperature (T), precipitation (P), vapour pressure deficit (VPD), evapotranspiration (ETo), and the teleconnection indices North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP). Periodicity in the time series was assessed using the wavelet transform, but no significant intra- or interannual cycles were detected in both vegetation (NDVI and EVI) and meteorological variables. In turn, coherence between NDVI and EVI and meteorological elements was described using the methods of wavelet coherence and Pearson's linear correlation with time lag. In the European temperate zone analysed in this study, NAO produces strong coherence mostly for forests in a circa 1-year band and a weaker coherence in a circa 3-year band. For pastures these interannual patterns are hardly recognisable. The strongest relationships occur between conditions of the vegetation and T and ETo – they show high coherence in both forests and pastures. There is a significant cohesion with the 8–16-month (ca. 1-year) and 20–32-month (ca. 2-year) bands. More time-lagged significant correlations between vegetation indices and T occur for forests than for pastures, suggesting a significant lag in the forests' response to the changes in T. [ABSTRACT FROM AUTHOR]

Greenhouse gases monitoring is important to ensure climate goals are being achieved. This study unveils the potential of using atmospheric tall towers in direct flux measurements, bridging the gap between atmospheric and ecosystem monitoring networks. The ICOS Cities (PAUL) project aims to monitor CO2 emissions in urban areas, where concentrated emissions make them key targets for climate change mitigation. This study explores synergy between ICOS atmospheric and ecosystem networks by utilizing slow-response analysers (~2 sec) on tall atmospheric towers for ecosystem studies using the Eddy Covariance method. A standard setup with an ultrasonic anemometer and an infrared (IR) fast-response CO2 analyser was installed and compared with measurements from an existing cavity ring down spectroscopy (CRDS) analyser measuring CO2, CO, and CH4. Deployed on the 100 m Saclay tower near Paris, covering a 43.9 km² 80 % footprint with heavy traffic roads, a nearby heating plant, and a forest, the setup addressed technical challenges and height-induced complexities. Corrections for flux attenuation by high frequency losses were limited to <20 % on average for all stabilities, around 11 % for unstable conditions. Wavelet-based eddy covariance allowed 18–34 % more data exploitation than standard EC enabling the analysis of non-stationary fluxes, particularly from a point source such was the case of a heating plant. The estimated storage term produced by atmospheric profiling measurements reported an expected increase at night, destocking during the first half of the day. Storage term represented at times more than half of the surface flux. Elevated mean fluxes for CO2 (10 μmolm−2s−1) and CH4 (200 nmolm−2s−1) were observed from the heating plant wind direction during December and January. Conversely, the forest direction exhibited the strongest sink among all wind directions, with −4 μmolm−2s−1 during July and August. These results demonstrate the feasibility and versatility of utilizing atmospheric towers for urban emission monitoring, offering valuable insights for emission monitoring strategies worldwide. [ABSTRACT FROM AUTHOR]

The swift and ongoing rise of global temperatures over the past decades led to an increasing number of climate variables showing statistically significant changes compared to their pre-industrial state. Determining when these climate signals emerge from the noise of internal climate variability (i.e., estimating the time of emergence, ToE) is crucial for climate risk assessments and adaptation planning. However, robustly disentangling the climate signal from internal variability represents a challenging task. While climate projections are communicated increasingly frequently through global warming levels (GWLs), the ToE is usually still expressed in terms of time horizons. Here, we present a framework to robustly derive global warming levels of emergence (GWLoE) using five single-model initial-condition large ensembles (SMILEs) and apply it to four selected temperature and precipitation indices. We show that the concept of GWLoE is particularly promising to constrain temperature projections and that it proves a viable tool to communicate scientific results. We find that > 85 % of the global population is exposed to emerged signals of nighttime temperatures at a GWL of 1.5 °C, increasing to > 95 % at 2.0 °C. Daily maximum temperature follows a similar yet less pronounced path. Emerged signals for mean and extreme precipitation start appearing at current GWLs and increase steadily with further warming (∼ 10 % population exposed at 2.0 °C). Related probability ratios for the occurrence of extremes indicate a strong increase with widespread saturation of temperature extremes (extremes relative to historical conditions occur every year) reached below 2.5 °C warming particularly in (sub)tropical regions. These results indicate that we are in a critical period for climate action as every fraction of additional warming substantially increases the adverse effects on human wellbeing. [ABSTRACT FROM AUTHOR]

Quantification and mapping of surficial seabed sediment organic carbon have wide-scale relevance for marine ecology, geology and environmental resource management, with carbon densities and accumulation rates being a major indicator of geological history, ecological function and ecosystem service provisioning, including the potential to contribute to nature-based climate change mitigation. While global analyses can appear to provide a definitive understanding of the spatial distribution of sediment carbon, regional maps may be constructed at finer resolutions and can utilise targeted data syntheses and refined spatial data products and therefore have the potential to improve these estimates. Here, we report a national systematic review of data on organic carbon content in seabed sediments across Canada and combine this with a synthesis and unification of the best available data on sediment composition, seafloor morphology, hydrology, chemistry and geographic settings within a machine learning mapping framework. Predictive quantitative maps of mud content, dry bulk density, organic carbon content and organic carbon density were produced along with cell-specific estimates of their uncertainty at 200 m resolution across 4 489 235 km 2 of the Canadian continental margin (92.6 % of the seafloor area above 2500 m) (10.5683/SP3/ICHVVA, Epstein et al., 2024). Fine-scale variation in carbon stocks was identified across the Canadian continental margin, particularly in the Pacific Ocean and Atlantic Ocean regions. Overall, we estimate the standing stock of organic carbon in the top 30 cm of surficial seabed sediments across the Canadian shelf and slope to be 10.9 Gt (7.0–16.0 Gt). Increased empirical sediment data collection and higher precision in spatial environmental data layers could significantly reduce uncertainty and increase accuracy in these products over time. [ABSTRACT FROM AUTHOR]

Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly in land surface and earth system models. As a first attempt to fill this gap, we propose a generic formula that directly connects SOC concentration with DOC concentration in headwater streams, where a single parameter, the transformation rate from SOC in the soil to DOC leaching flux, Pr , accounts for the overall processes governing SOC conversion to DOC and leaching from soils (along with runoff) into headwater streams. We then derive a high-resolution Pr map over the contiguous U.S. (CONUS) in five major steps: 1) selecting 2595 headwater catchments where observed riverine DOC data are available with reasonable quality; 2) estimating catchment-average SOC for the 2595 catchments based on high-resolution SOC data; 3) estimating the Pr values for these catchments based on the generic formula and catchment-average SOC; 4) developing a predictive model of Pr with machine learning (ML) techniques and catchment-scale climate, hydrology, geology, and other attributes; and 5) deriving a national map of Pr , based on the ML model. For evaluation, we compare the DOC concentration derived using the Pr map and the observed DOC concentration values at another 3210 headwater gauges. The resulting mean absolute scaled error and coefficient of determination are 0.73 and 0.47, respectively, suggesting the effectiveness of the overall methodology. Efforts to constrain uncertainty and evaluate the sensitivity of Pr to different factors are discussed. To illustrate the use of such a map, we derive a riverine DOC concentration reanalysis dataset for more than two million small catchments over CONUS. The map, robustly derived and empirically validated, lays a critical cornerstone for better simulating the terrestrial carbon cycle in land surface and earth system models. Our findings not only set a foundation for improving our predictive understanding of the terrestrial carbon cycle at the regional and global scales but also hold promises for informing policy decisions related to decarbonization and climate change mitigation. [ABSTRACT FROM AUTHOR]

Understanding the intricate hydrological interactions between lakes and their surrounding watersheds is pivotal for advancing hydrological research, optimizing water resource management, and informing climate change mitigation strategies. Yet, these complex dynamics are often insufficiently captured in existing hydrological models, such as the bi-direction surface and subsurface flow. To bridge this gap, we introduce a novel lake–watershed coupled model, an enhancement of the Simulator of Hydrological Unstructured Domains. This high-resolution, distributed model employs unstructured triangles as its fundamental hydrological computing units (HCUs), offering a physical approach to hydrological modeling. We validated our model using data from Qinghai Lake in China, spanning from 1979 to 2018. Remarkably, the model not only successfully simulated the streamflow of the Buha River, a key river within the Qinghai Lake basin, achieving a Nash–Sutcliffe efficiency (NSE) of 0.62 and 0.76 for daily and monthly streamflow, respectively, but also accurately reproduced the decrease–increase U-shaped curve of lake level change over the past 40 years, with an NSE of 0.71. Our model uniquely distinguishes the contributions of various components to the lake's long-term water balance, including river runoff, surface direct runoff, lateral groundwater contribution, direct evaporation, and precipitation. This work underscores the potential of our coupled model as a powerful tool for understanding and predicting hydrological processes in lake basins, thereby contributing to more effective water resource management and climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

The AR6 Scenario Database is a vital repository of climate change mitigation pathways used in the latest IPCC assessment cycle. In its current version, several scenarios in the database lack information about the level of gross carbon removal on land, as net and gross removals on land are not always separated and consistently reported across models. This makes scenario analyses focusing on carbon removals challenging. We test and compare the performance of different regression models to impute missing data on land carbon sequestration from available data on net CO2 emissions in agriculture, forestry, and other land use. We find that a gradient boosting regression performs best among the tested regression models and provide a publicly available imputation dataset [ https://doi.org/10.5281/zenodo.10696654 ] (Prütz et al., 2024) on carbon removal on land for 404 incomplete scenarios in the AR6 Scenario Database. We discuss the limitations of our approach, its use cases, and how this approach compares to other recent AR6 data re-analyses. [ABSTRACT FROM AUTHOR]

The integrated assessment model (IAM) MESSAGEix-GLOBIOM developed by IIASA is widely used to analyze global change and socioeconomic development scenarios within energy and land systems across different scales. However, to date, the representation of impacts from climate effects and water systems in the IAM has been limited. We present a new nexus module for MESSAGEix-GLOBIOM that improves the representation of climate impacts and enables the analysis of interactions between population, economic growth, energy, land, and water resources in a dynamic system. The module uses a spatially resolved representation of water systems to retain hydrological information without compromising computational feasibility. It maps simplified water availability and key infrastructure assumptions with the energy and land systems. The results of this study inform on the transformation pathways required under climate change impacts and mitigation scenarios. The pathways include multi-sectoral indicators highlighting the importance of water as a constraint in energy and land-use decisions and the implications of global responses to limited water availability from different sources, suggesting possible shifts in the energy and land sectors. [ABSTRACT FROM AUTHOR]

Methane (CH4) is a significant greenhouse gas in exacerbating climate change. Approximately 25 % of CH4 is emitted from storage tanks. It is crucial to spatially explore the CH4 emission patterns from storage tanks for efficient strategy proposals to mitigate climate change. However, due to the lack of publicly accessible storage tank locations and distributions, it is difficult to ascertain the CH4 emission spatial pattern over a large-scale area. To address this problem, we generated a storage tank dataset (STD) by implementing a deep learning model with manual refinement based on high spatial resolution images (1–2m) from the GaoFen-1, GaoFen-2, GaoFen-6, and Ziyuan-3 satellites over cities in China with officially reported numerous storage tanks in 2021. STD is the first storage tank dataset over 92 typical cities in China. The dataset can be accessed at https://zenodo.org/records/10514151 (Chen et al., 2024). It provides a detailed georeferenced inventory of 14,461 storage tanks, wherein each storage tank is validated and assigned the construction year (2000–2021) by visual interpretation referring to the collected high spatial resolution images, historical high spatial resolution images of Google Earth, and field survey. The inventory comprises storage tanks having various distribution patterns in different cities. Spatial consistency analysis with CH4 emission product shows good agreement with storage tank distributions. The intensive construction of storage tanks significantly induces CH4 emissions from 2005 to 2020, underscoring the need for more robust measures to curb CH4 release and aid in climate change mitigation efforts. Our proposed dataset STD will foster the accurate estimation of CH4 released from storage tanks for CH4 control and reduction and ensure more efficient treatment strategies are proposed to better understand the impact of storage tanks on the environment, ecology, and human settlements. [ABSTRACT FROM AUTHOR]

Today, city planners design urban futures by considering environmental degradation and climate mitigation. Here, we studied the greenhouse gas fluxes of urban lawns and meadows and linked the observations with plant functional types and soil properties. In eight lawns and eight meadows in the Helsinki metropolitan area, Finland, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes were measured using manual chambers, and plant functional types were recorded. Four of these sites, i.e. an irrigated lawn, an old mesic meadow, a non-irrigated lawn and a young dry meadow, were more intensively studied in 2021–2022. The process-based ecosystem model JSBACH was utilized together with the momentary observations collected approximately every second week on CO2 exchange to quantify the annual carbon (C) balance of these sites. On the remaining sites, we studied the initial dynamics of conversion from lawns to meadows by transforming parts of lawns to meadows in late 2020 and conducting measurements from 2020 to 2022. The mean photosynthetic production (GPP) of the irrigated lawn and mesic meadow was the highest in this study, whereas the dry meadow had the lowest GPP. The studied lawns were stronger C sinks compared to the meadows. However, the net exchange values were uncertain as the soils were not in equilibrium with the vegetation at all sites, which is common for urban habitats, and modelling the heterotrophic emissions was therefore challenging. The conversion from a lawn to a meadow did not affect the fluxes of CH4 and N2O. Moreover, the mesic meadow was more resistant to drought events than the non-irrigated lawn. Lastly, the proportion of herbaceous flowering plants other than grasses was higher in meadows than in lawns. Even though social and economic aspects also steer urban development, these results can guide planning when considering environmentally friendlier green spaces and carbon smartness. [ABSTRACT FROM AUTHOR]

While various root-associated fungi could facilitate soil carbon (C) storage and therefore aid climate change mitigation, so far research in this area has largely focused on mycorrhizal fungi, and potential impacts and mechanisms for other fungi are largely unknown. Here, with the aim of identifying novel organisms that could be introduced to crop plants to promote C sequestration, we assessed the soil C storage potential of 12 root-associated, non-mycorrhizal fungal isolates (spanning nine genera and selected from a wide pool based on traits potentially linked to soil C accrual) and investigated fungal, plant and microbial mediators. We grew wheat plants inoculated with individual isolates in chambers allowing continuous 13 C labelling. After harvest, we quantified C storage potential by measuring pools of different origin (plant vs. soil) and different stability with long-term soil incubations and size/density fractionation. We assessed plant and microbial community responses as well as fungal physiological and morphological traits in a parallel in vitro study. While inoculation with 3 of the 12 isolates resulted in significant total soil C increases, soil C stability improved under inoculation with most isolates – as a result of increases in resistant C pools and decreases in labile pools and respired C. Further, these increases in soil C stability were positively associated with various fungal traits and plant growth responses, including greater fungal hyphal density and plant biomass, indicating multiple direct and indirect mechanisms for fungal impacts on soil C storage. We found more evidence for metabolic inhibition of microbial decomposition than for physical limitation under the fungal treatments. Our study provides the first direct experimental evidence in plant–soil systems that inoculation with specific non-mycorrhizal fungal strains can improve soil C storage, primarily by stabilising existing C. By identifying specific fungi and traits that hold promise for enhancing soil C storage, our study highlights the potential of non-mycorrhizal fungi in C sequestration and the need to study the mechanisms underpinning it. [ABSTRACT FROM AUTHOR]

Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0.1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km 2) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68 –2.38 gC m -2 , R2=0.87 –0.77; biomass: RMSE =11.34 g m -2 , R2=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias =-47 g m -2 , - 39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes. [ABSTRACT FROM AUTHOR]

Continental shelf sediments contain some of the largest stocks of organic carbon (OC) on Earth and play a vital role in influencing the global carbon cycle. Quantifying how much OC is stored in shelf sediments and determining its residence time is key to assessing how human activities can accelerate the process of OC remineralization into carbon dioxide. Spatial variations in terrestrial carbon stocks are well studied and mapped at high resolution, but our knowledge of the distribution of marine OC in different seafloor settings is still very limited, particularly in the highly dynamic and spatially variable shelf environments. The lack of knowledge reduces our ability to understand and predict how much and for how long oceans sequester CO2. In this study, we use high-resolution multibeam echosounder (MBES) data from the Eastern Shore Islands offshore Nova Scotia (Canada), combined with OC measurements from discrete samples, to assess the distribution of OC content in seafloor sediments. We derive three different spatial estimates of organic carbon: i) assuming a homogenous seafloor the carbon stock estimates were scaled to the entire study region; ii) using a high-resolution substrate map, the estimates were scaled to the areas of soft substrate only, and, finally, iii) using Empirical Bayesian Regression Kriging (EBRK) regression prediction within the area of soft substrate, carbon stock estimates in areas of soft substrate were refined to account for spatial variability in the concentration of OC. These three distinct spatial models yielded dramatically different estimates of average standing stock of OC in our study area, 1275, 259 and 203 Mt of OC respectively. Our study demonstrates that high-resolution mapping is critically important for improved estimates of OC stocks on continental shelves, and to the identification of carbon hotspots that need to be considered in seabed management and climate mitigation strategies. [ABSTRACT FROM AUTHOR]

The interaction between forest and climate exhibits regional differences due to a variety of biophysical mechanisms. Observational and modeling studies have investigated the impacts of forested and non-forested areas on a single climate variable, but the influences of forest cover change on a combination of temperature and precipitation (e.g., drought) have not been explored, owing to the complex relationship between drought conditions and forests. In this study, we use historical forest and climate datasets to explore the relationship between forest cover fraction and drought from 1992–2018. A set of linear models and an analysis of variance approach are utilized to investigate the effect of forest cover change, precipitation and temperature on droughts across different timescales and climate zones. Our findings reveal that precipitation is the dominant factor (among the three factors) leading to drought in the equatorial, temperate and snow regions, while temperature controls drought in the arid region. The impact of forest cover changes on droughts varies under different precipitation and temperature quantiles. Precipitation modulates forest cover's impact on long-term drought in the arid region, while temperature modulates the impact of forest cover changes on both short- and long-term drought in the arid region as well as only on long-term drought in the temperate region. Forest cover can also modulate the impacts of precipitation and temperature on drought. High forest cover leads to a combined effect of precipitation and temperature on long-term drought in arid and snow regions, while precipitation is the only dominant factor in low forest cover conditions. In contrast, low forest cover triggers a strong combined effect of precipitation and temperature on drought in the temperate region. Our findings improve the understanding of the interaction between land cover change and the climate system and further assist decision-makers to modulate land management strategies in different regions in light of climate change mitigation and adaptation. [ABSTRACT FROM AUTHOR]

Forests are invaluable for maintaining biodiversity, watersheds, rainfall levels, bioclimatic stability, carbon sequestration and climate change mitigation, and the sustainability of large-scale climate regimes. In other words, forests provide a wide range of ecosystem services and livelihoods for the people and play a critical role in influencing global atmospheric cycles. Providing sustainable, reliable, and accurate information on forest cover change is essential for an holistic forest management, efficient use of resources, neutralizing the effects of global warming and better monitoring of deforestation activities. Within the scope of this study, it is aimed to perform semantic segmentation of 5 different tree species (larch, red pine, yellow pine, oak, spruce) from Sentinel-2 satellite images. For this purpose, the regions where these tree species are densely populated in Turkey (Marmara, Aegean, Eastern Black Sea) were selected as pilot regions. A unique data set was created using the data of the selected pilot regions. As a result of the study, it was possible to determine the forest types temporally for the selected classes with more than 90% Intersection over Union score for all classes. The developed deep learning model with the created forest data set can be implemented to the other forests areas with same species in other parts of the world. [ABSTRACT FROM AUTHOR]

Identifying the climate-induced variability in the condition of vegetation is particularly important in the context of the recent climate change, and plants' impact on mitigation of the climate change. In this paper, we present the coherence and time lags in the spectral response of three individual vegetation types in European temperate zone to the influencing meteorological factors, in the period 2002-2022. Vegetation condition in broadleaved forest, coniferous forest and pastures was measured with monthly anomalies of two spectral indices - NDVI and EVI. As meteorological elements we used monthly anomalies of temperature (T), precipitation (P), vapour pressure deficit (VPD), evapotranspiration (ETo), and teleconnection indices North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP). Periodicity in the time series was assessed using the Wavelet Transform, but no significant intra-or interannual cycles were detected in both vegetation (NDVI and EVI) and meteorological variables. In turn, coherence between NDVI/EVI and meteorological elements was described using the methods of Wavelet Coherence and Pearson's linear correlation with time lag. In European temperate zone analysed in this study, NAO produces strong coherence mostly for forests, with circa 1 year delay and - a weaker coherence - with circa 3 year delay. For pastures these interannual patterns are hardly recognizable. The strongest relationships occur between condition of the vegetation and T and ETo - they show high coherence in both forests and pastures. There is a significant cohesion with 8-16 month (ca. 1 year) delay and 20-32 month (ca. 2 year) delay. More time lagged significant correlations between vegetation indices and T occur for forests than for pastures, suggesting a significant lag in the forests' response to the changes in T. [ABSTRACT FROM AUTHOR]

Salt marshes are crucial eco-geomorphic features of tidal environments as they provide numerous important ecological functions and deliver a wide range of ecosystem services that contribute to human well-being. Being controlled by the interplay between hydrodynamics, geomorphology and vegetation, the deposition of both organic matter (OM) and inorganic sediments drives salt-marsh vertical accretion. This allows marshes to keep pace with relative sea-level rise, and likewise capture and store carbon, making them valuable allies in climate mitigation strategies. Thus, Soil Organic Matter (SOM), i.e. the organic component of the soil, plays a key role within salt-marsh environments, directly contributing to soil formation and supporting carbon storage. This study aims at inspecting spatial patterns of OM in surface salt-marsh soils, providing further insights into the physical and biological factors driving OM dynamics, affecting salt-marsh survival and carbon sink potential. Our results reveal two scales of variations in sedimentary OM content in salt-marsh soils. At the marsh scale OM variability is influenced by the interplay between surface elevation and changes in sediment supply linked with the distance from tidal channels. At the system scale, OM content distribution is dominated by the gradient generated by marine and fluvial influence. Variations in inorganic and organic inputs, both autochthonous and allochthonous, sediment grain size, and preservation conditions may explain the observed variations in SOM. Our results highlight marsh importance as carbon sink environments, furthermore emphasizing that environmental conditions within a tidal system may generate strongly variable and site-specific carbon accumulation patterns, enhancing blue carbon assessment complexity. [ABSTRACT FROM AUTHOR]

Wetland systems are among the largest stores of carbon on the planet, the most biologically diverse of all ecosystems, and dominant controls on the hydrologic cycle. However, their representation in land surface models (LSMs), which are the terrestrial lower boundary of Earth system models (ESMs) that inform climate actions, is limited. Here, we explore different possible parameterizations to represent wetland–groundwater–upland interactions with varying levels of system and computational complexity. We perform a series of numerical experiments informed by field observations from a particular type of wetland, called a fen, at the well-instrumented White Gull Creek in Saskatchewan, in the boreal region of North America. In this study, we focus on how modifying the modelling connection between the upland and the wetland affects the system's outcome. We demonstrate that the typical representation of groundwater-dependent wetlands in LSMs, which ignores interactions with groundwater and uplands, can be inadequate. We show that the optimal level of model complexity depends on the land cover, soil type, and the ultimate modelling purpose, being nowcasting and prediction, scenario analysis, or diagnostic learning. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are projected to increase in intensity globally, impacting human lives, infrastructure, and important agricultural activities, such as banana production. Banana production is already impacted by TCs in several parts of the world, leading to price volatility and impacted livelihoods of banana producers. While many potential impacts on banana production have already been quantified on a local scale, it remains unclear how bananas could be impacted by TCs across the globe under future climate conditions. To address this, we first looked at the documented impacts of cyclones on banana production from different places all around the world. Using spatially explicit data on banana producing regions and future TC occurrence and magnitude, we then identify the spatial distribution and extent of areas where TCs could impact banana production. Our results suggest that considerable portions of global banana production are at risk to be impacted by TCs under future climate conditions, and we show this for different return periods (RP). Globally, at the 100-year return period, 24.3 % of all bananas producing areas will be majorly or completely damaged under present climate conditions and 26.5 % under future climate conditions. When looking at production, we see that 30.1 % of global production is projected to be majorly or completely damaged at the 100-year RP. The regions predominantly affected by future TCs are Asia and the Caribbean, experiencing substantial disruption in banana production. Our results therefore indicate, that considerable efforts in climate change mitigation and adaptation need to be made in order to ensure the stability of global banana supply chains. [ABSTRACT FROM AUTHOR]

The subalpine forests in the Qinghai-Tibet Plateau (QTP) act as carbon sinks in the context of climate change and ecosystem dynamics. In this study, we investigated the carbon sequestration function using the in-situ observations from an eddy covariance system for the subalpine forests. With two-year contiguous observations, the factors driving the seasonal variations in carbon sequestration potential were quantified. We first revealed the seasonal characteristics of carbon dynamics in the subalpine forests during the growing and dormant seasons, respectively. The diurnal carbon exchange exhibited significant fluctuations, as high as 10.78 μmol CO2 s-1 m-2 (12:30, autumn). The period from summer to autumn was identified as the peak in carbon sequestration rate in the subalpine forests. Subsequently, we explored the climatic factors influencing the carbon sequestration function. Photosynthetically active radiation (PAR) was found to be a major climatic factor driving the net ecosystem exchange (NEE) within the same season, significantly influencing forest growth and carbon absorption. Increasing altitude negatively impacts carbon absorption at the regional scale and the rising annual temperature significantly enhances carbon uptake, while the average annual precipitation shows a minor effect on NEE. At the annual scale, the observations at the subalpine forests demonstrated a strong carbon sequestration capability, with an average NEE of 389.03 g C m-2. Furthermore, we roughly assessed the carbon sequestration status of subalpine forests in the QTP. Despite challenges caused by climate change, these forests possess enormous carbon sequestration potential. Currently, they represent the most robust carbon sequestration ecosystem in the QTP. We conclude that enhancing the protection and management of subalpine forests under future climate change scenarios will positively impact global carbon cycling and contribute to climate change mitigation. Moreover, this study provides essential insights for understanding the carbon cycling mechanism in plateau ecosystems and global carbon balance. [ABSTRACT FROM AUTHOR]

Achieving climate targets requires mitigation against climate change, but also understanding of the response of land and ocean carbon systems. In this context, global soil carbon stocks and its response to environmental changes is key. This paper quantifies the global soil carbon feedback to changes in atmospheric CO2, and associated climate changes, for Earth system models (ESMs) in CMIP6. A standard approach is used to calculate carbon cycle feedbacks, defined here as soil specific carbon-concentration (βs) and carbon-climate (γs) feedback parameters. The sensitivity to CO2 is shown to dominate soil carbon changes at least up to a doubling of atmospheric CO2. However, the sensitivity of soil carbon to climate change is found to become an increasingly important source of uncertainty under higher atmospheric CO2 concentrations. [ABSTRACT FROM AUTHOR]

Earth system models suggest that anthropogenic climate change will influence marine phytoplankton over the coming century with light-limited regions becoming more productive and nutrient-limited regions less productive. Anthropogenic climate change can influence not only the mean state but also the internal variability around the mean state, yet little is known about how internal variability in marine phytoplankton will change with time. Here, we quantify the influence of anthropogenic climate change on internal variability in marine phytoplankton biomass from 1920 to 2100 using the Community Earth System Model 1 Large Ensemble (CESM1-LE). We find a significant decrease in the internal variability of global phytoplankton carbon biomass under a high emission (RCP8.5) scenario and heterogeneous regional trends. Decreasing internal variability in biomass is most apparent in the subpolar North Atlantic and North Pacific. In these high-latitude regions, bottom-up controls (e.g., nutrient supply, temperature) influence changes in biomass internal variability. In the biogeochemically critical regions of the Southern Ocean and the equatorial Pacific, bottom-up controls (e.g., light, nutrients) and top-down controls (e.g., grazer biomass) affect changes in phytoplankton carbon internal variability, respectively. Our results suggest that climate mitigation and adaptation efforts that account for marine phytoplankton changes (e.g., fisheries, marine carbon cycling) should also consider changes in phytoplankton internal variability driven by anthropogenic warming, particularly on regional scales. [ABSTRACT FROM AUTHOR]

Ocean Alkalinity Enhancement (OAE) is a Negative Emissions Technology (NET) that shows significant potential for climate change mitigation. By increasing the bicarbonate ion concentration in ocean water, OAE could enhance long-term carbon storage and mitigate ocean acidification. However, the side effects and/or potential co-benefits of OAE on natural planktonic communities remain poorly understood. To address this knowledge gap, a mesocosm experiment was conducted in the oligotrophic waters of Gran Canaria. A CO2-equilibrated Total Alkalinity (TA) gradient was employed in increments of 300 µmol·L-1, ranging from ~2400 to ~4800 µmol·L-1. This study represents the first attempt to evaluate the potential impacts of OAE on planktonic communities under natural conditions. The results show that Net Community Production (NCP), Gross Production (GP), Community Respiration (CR) rates, as well as the metabolic balance (GP:CR), did not exhibit a linear response to the whole alkalinity gradient. Instead, significant polynomial and linear regression models were observed for all rates up to ΔTA1800 µmol·L-1, in relation to the Dissolved Inorganic Carbon (DIC) concentrations. Notably, the ΔTA1500 and 1800 µmol·L-1 treatments showed peaks in NCP shifting from a heterotrophic to an autotrophic state, with NCP values of 4 and 8 µmol O2 kg-1 d-1, respectively. These peaks and the optimum curve were also reflected in the phytoplankton abundance, size-fractionated chlorophyll α and 14C uptake data. Furthermore, abiotic precipitation occurred in the highest treatment after day 21 but no impact on the measured parameters was detected. Overall, a damaging effect of CO2-equilibrated OAE in the range applied here, on phytoplankton primary production, community metabolism and composition could not be inferred. In fact, a potential cobenefit to OAE was observed in the form of the positive curvilinear response to the DIC gradient up to the ΔTA1800 treatment. Further experimental research at this scale is key to gain a better understanding of the short and long-term effects of OAE on planktonic communities. [ABSTRACT FROM AUTHOR]

The quantification and mapping of surficial seabed sediment organic carbon has wide-scale relevance across marine ecology, geology and environmental resource management, with carbon densities and accumulation rates being a major indicator of geological history, ecological function, and ecosystem service provisioning, including the potential to contribute to nature-based climate change mitigation. While global mapping products can appear to provide a definitive understanding of the spatial distribution of sediment carbon, there is inherently high uncertainty when making estimates at this scale. Finer resolution national maps which utilise targeted data syntheses and refined spatial data products are therefore vital to improve these estimates. Here, we report a national systematic review of data on organic carbon content in seabed sediments across Canada and combine this with a synthesis and unification of best available data on sediment composition, seafloor morphology, hydrology, chemistry, geographic setting and sediment mass accumulation rates within a machine learning mapping framework. Predictive quantitative maps of mud content, sediment dry bulk density, and organic carbon content, density and accumulation, were each produced along with cell specific estimates of their 95 % confidence interval (CI) bounds at 200 m resolution across 4,489,235 km² of the Canadian continental margin (92.6 % of the seafloor area above 2,500 m). Fine-scale variation in carbon stocks was identified across the Canadian continental margin, particularly in the Pacific and Atlantic Ocean regions. Carbon accumulation was predicted to be concentrated in coastal areas, with the highest rates in the Gulf of St Lawrence and Bay of Fundy. Overall, we estimate the standing stock of organic carbon in the top 30 cm of surficial seabed sediments across the Canadian shelf and slope to be 10.7 Gt (95 % CI 6.6 – 16.0 Gt), and accumulation at 4.9 Mt per year (95 % CI 2.6 – 9.3 Mt y-1). Increased in-situ sediment data collection and higher precision in spatial environmental data-layers could significantly reduce uncertainty and increase accuracy in these products over time. [ABSTRACT FROM AUTHOR]

Most phosphorus (P) in soils is unavailable for direct biological uptake, as it is locked within primary or secondary mineral particles, adsorbed to mineral surfaces, or immobilized inside of organic material. Deciphering the composition of different P forms in soil is critical for understanding P bioavailability and its underlying dynamics. However, widely used global estimates of different soil P forms are based on a dataset containing few measurements in which many regions or soil types are unrepresented. This poses a major source of uncertainty in assessments that rely on these estimates to quantify soil P constraints on biological activity controlling global food production and terrestrial carbon balance. To address this issue, we consolidated a database of six major soil P "forms" containing 1857 entries from globally distributed (semi-)natural soils and 11 related environmental variables. These six different forms of P (labile inorganic P (Pi), labile organic P (Po), moderately labile Pi, moderately labile Po, primary mineral P, and occluded P) were measured using a sequential P fractionation method. As they do not represent precise forms of specific discrete P compounds in the soil but rather resemble operational pools, we will now refer to them as P pools. In order to quantify the relative importance of 11 soil-forming variables in predicting soil P pool concentrations and then make further predictions at the global scale, we trained random forest regression models for each of the P pools and captured observed variation with R2 higher than 60 %. We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. When expressed in relative values (proportion of total P), the model showed that soil pH is generally the most important predictor for proportions of all soil P pools, alongside the prominent influences of soil organic carbon, total P concentration, soil depth, and biome. These results suggest that, while concentration values of P pools logically strongly depend on soil total P concentration, the relative values of the different pools are modulated by other soil properties and the environmental context. Using the trained random forest models, we predicted soil P pools' distributions in natural systems at a resolution of 0.5∘×0.5∘. Our global maps of different P pools in soils as well as the pools' underlying drivers can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system. [ABSTRACT FROM AUTHOR]

Integrated assessment models (IAMs) are a central tool for the quantitative analysis of climate change mitigation strategies. However, due to their global, cross-sectoral and centennial scope, IAMs cannot explicitly represent the temporal and spatial details required to properly analyze the key role of variable renewable energy (VRE) in decarbonizing the power sector and enabling emission reductions through end-use electrification. In contrast, power sector models (PSMs) can incorporate high spatiotemporal resolutions but tend to have narrower sectoral and geographic scopes and shorter time horizons. To overcome these limitations, here we present a novel methodology: an iterative and fully automated soft-coupling framework that combines the strengths of a long-term IAM and a detailed PSM. The key innovation is that the framework uses the market values of power generations and the capture prices of demand flexibilities in the PSM as price signals that change the capacity and power mix of the IAM. Hence, both models make endogenous investment decisions, leading to a joint solution. We apply the method to Germany in a proof-of-concept study using the IAM REgional Model of INvestments and Development (REMIND) v3.0.0 and the PSM Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2 and confirm the theoretical prediction of almost-full convergence in terms of both decision variables and (shadow) prices. At the end of the iterative process, the absolute model difference between the generation shares of any generator type for any year is < 5 % for a simple configuration (no storage, no flexible demand) under a "proof-of-concept" baseline scenario and 6 %–7 % for a more realistic and detailed configuration (with storage and flexible demand). For the simple configuration, we mathematically show that this coupling scheme corresponds uniquely to an iterative mapping of the Lagrangians of two power sector optimization problems of different time resolutions, which can lead to a comprehensive model convergence of both decision variables and (shadow) prices. The remaining differences in the two models can be explained by a slight mismatch between the standing capacities in the real world and optimal modeling solutions based purely on cost competition. Since our approach is based on fundamental economic principles, it is also applicable to other IAM–PSM pairs. [ABSTRACT FROM AUTHOR]

Greenhouse gas stabilisation in the atmosphere is one of the most pressing challenges of this century. Sequestering carbon in the soil by changing land use and management is increasingly proposed as part of climate mitigation strategies, but our understanding of this is limited in quantitative terms. Here we collate a substantial national and regional data set (15790 soil cores), and analyse it in an advanced statistical modelling framework. This produced new estimates of the effects of land use on soil carbon stocks in the UK, different in magnitude and ranking order from the previous best estimates. Soil carbon stocks were highest in woodlands, followed by rough grazing and semi-natural grasslands, then improved grasslands, and lowest in croplands. Estimates were smaller than the previous estimates, partly because of new data, but mainly because the effect is more reliably characterised using a logarithmic transformation of the data. With the very large data set analysed here, the uncertainty in the differences among land uses was small enough to identify consistent mean effects. However, the variability in these effects was large, and this was similar across all surveys. This has important implications for agri-environment schemes, seeking to sequester carbon in the soil by altering land use, because the effect of a given intervention is very hard to verify. We examined the validity of the "space-for-time" substitution, and although the results were not unequivocal, we estimated that the effects are likely to be over-estimated by 5-33%, depending upon land use. [ABSTRACT FROM AUTHOR]

Globally, northern peatlands are major carbon deposits with important implications for the climate system. It is therefore crucial to understand their spatial occurrence, especially in the context of peatland degradation by land cover change and climate change. This study was aimed at mapping peatlands in the forested landscape of Sweden by modelling soil data against lidar-based terrain indices. Machine learning methods were used to produce nationwide raster maps at 10 m spatial resolution indicating the presence or not of peatlands. Four different definitions of peatlands were examined: 30, 40, 50 and 100 cm thickness of the organic horizon. Depending on peatland definition, testing with a hold-out dataset indicated an accuracy of 0.89–0.91 and Matthew's correlation coefficient of 0.79–0.81. The final maps showed a national forest peatland extent of 60 292–71 996 km2 , estimates which are in the range of previous studies employing traditional soil maps. In conclusion, these results emphasize the possibilities of mapping boreal peatlands with lidar-based terrain indices. The final peatland maps are publicly available at 10.17043/rimondini-2023-peatlands-2 (Rimondini et al., 2023) and may be employed for spatial planning, estimating carbon stocks and evaluating climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

Effective climate change mitigation necessitates swift societal transformations. Social tipping processes, where small triggers initiate qualitative systemic shifts, are potential key mechanisms instigating societal change. A necessary foundation for societal tipping processes is the creation of enabling conditions. Here we assess future sea-level rise estimates and social survey data within the framework of a social activation model to exemplify the enabling conditions for tipping processes. We find that in many countries, climate change concern is sufficient, the enabling conditions and opportunities for social activation already exist. Further, drawing upon the interrelation between climate change concern and anticipation of future sea level rise, we report three qualitative classes of tipping potential that are regionally clustered, with greatest potential for tipping in Western Pacific rim and East Asian countries. These findings propose a transformative pathway where climate change concern increases the social tipping potential, while extended anticipation time horizons can trigger the system towards an alternative trajectory of larger social activation for climate change mitigation. [ABSTRACT FROM AUTHOR]

We quantify weekly methane emissions at 0.25 ∘ × 0.3125 ∘ (≈25 × 25 km 2) resolution from the Permian Basin, the largest oil production basin in the US, by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) from May 2018 to October 2020. The mean oil and gas emission from the region (± standard deviation of weekly estimates) was 3.7 ± 0.9 Tg a -1 , higher than previous TROPOMI inversion estimates that may have used biased prior emissions or background assumptions. We find strong week-to-week variability in emissions superimposed on longer-term trends, and these are consistent with independent inferences of temporal emission variability from tower, aircraft, and multispectral satellite data. New well development and natural gas spot price were significant drivers of variability in emissions over our study period but the concurrent 50 % increase in oil and gas production was not. The methane intensity (methane emitted per unit of methane gas produced) averaged 4.6 % ± 1.3 % and steadily decreased from 5 %–6 % in 2018 to 3 %–4 % in 2020. While the decreasing trend suggests improvement in operator practices during the study period, methane emissions from the Permian Basin remained high, with methane intensity an order of magnitude above the industry target of <0.2 %. Our success in using TROPOMI satellite observations for weekly estimates of emissions from a major oil production basin shows promise for application to near-real-time monitoring in support of climate change mitigation efforts. [ABSTRACT FROM AUTHOR]

As aviation's contribution to anthropogenic climate change is increasing, industry aims at reducing the aviation climate effect. However, the large contribution of non-CO2 effects to the total climate effect of aviation and their large variability for each individual flight inhibit finding appropriate guidance. Here, we present a method for the simplified calculation of CO2 equivalent emissions, expressed using the physical climate metrics ATR100 or AGWP100, from CO2 and non-CO2 effects for a given flight, exclusively based on the aircraft seat category as well as the origin and destination airports. The simplified calculation method estimates non-CO2 climate effects of air traffic as precisely as possible, without detailed information on the actual flight route, actual fuel burn, and current weather situation. For this purpose, we evaluate a global data set containing detailed flight trajectories, flight emissions, and climate responses, and derive a set of regression formulas for climate effects, which we call climate effect functions, as well as regression formulas for fuel consumption and NOx emissions. Compared to previous studies, this method is available for a larger number of aircraft types, including most commercial airliners with seat capacities starting from 101 passengers, and delivers more specific results through a clustering approach. The climate effects calculated using the climate effect functions derived in this study exhibit a mean absolute relative error of 15.0 % and a root mean square error of 1.24 nK with respect to results from the climate response model AirClim. The climate effect functions are designed for climate footprint assessments, but would not create an incentive in an emission trading system, for which detailed information on the current weather as well as the actual flight route and profile would be required. [ABSTRACT FROM AUTHOR]

Fire carbon emissions contribute to the accumulation of atmospheric CO2 and affect climate change. It is crucial to accurately monitor the dynamics of global fire emissions for fire management and climate change mitigation. However, there are large uncertainties in the existing satellite-based global fire emission products. This study analyzed the uncertainties of six state-of-the-art fire emission products and merged them using the three-corner hat method (TCH), producing a new global fire emission dataset, FiTCH. Our results revealed that satellite-based products such as the Global Fire Assimilation System (GFAS), the Quick Fire Emissions Dataset (QFED), and the Global Fire Emissions Database (GFED) had low uncertainties in fire emissions, while the Fire INventory from National Center for Atmospheric Research (NCAR) (FINN), the Fire Energetics and Emissions Research (FEER), and Xu et al. (2021) data had high uncertainties. The proposed FiTCH dataset presented the lowest uncertainties with a mean annual fire emission of 1978.47 Tg C in 2001–2021. Among biomes, tropical forests and tundra showed higher uncertainties than other biomes such as temperate forests and Mediterranean forests. In drought years, forests showed increased fire emissions, especially in boreal forests, while nonforest regions like grasslands displayed decreased emissions. By integrating the FiTCH data and historical fire emissions in the late 20th century, 1994 was identified as a break year, before which global fire emissions increased significantly and after which the emissions decreased. Global land temperatures and fire emissions have decoupled in the past two decades. However, climate change still causes threats to forest carbon sequestration, especially for boreal forests. This study highlights the importance of forest fire monitoring and management for effective climate mitigation and ecosystem conservation. [ABSTRACT FROM AUTHOR]

The substantial climate change mitigation potential of restoring peatlands through rewetting and intensifying agriculture to reduce greenhouse gas (GHG) emissions is largely recognized. The green deal in Denmark aims at restoring 100 000 ha of peatlands by 2030. This area corresponds to more than half of the Danish peatland, with an expected reduction in GHG emissions of almost half of the entire land use, land use change and forestry (LULUFC) emissions. Recent advances established the functional relationship between hydrological regimes, i.e., water table depth (WTD), and CO2 and CH4 emissions. This builds the basis for science-based tools to evaluate and prioritize peatland restoration projects. With this article, we lay the foundation of such a development by developing a high-resolution WTD map for Danish peatlands. Further, we define WTD response functions (CO2 and CH4) fitted to Danish flux data to derive a national GHG emission estimate for peat soils. We estimate the annual GHG emissions to be 2.6 MtCO2-eq , which is around 15 % lower than previous estimates. Lastly, we investigate alternative restoration scenarios and identify substantial differences in the GHG reduction potential depending on the prioritization of fields in the rewetting strategy. If wet fields are prioritized, which is not unlikely in a context of a voluntary bottom-up approach, the GHG reduction potential is just 30 % for the first 10 000 ha with respect to a scenario that prioritizes drained fields. This underpins the importance of the proposed framework linking WTD and GHG emissions to guide a spatially differentiated peatland restoration. The choice of model type used to fit the CO2 WTD response function, the applied global warming potentials and uncertainties related to the WTD map are investigated by means of a scenario analysis, which suggests that the estimated GHG emissions and the reduction potential are associated with coefficients of variation of 13 % and 22 %, respectively. [ABSTRACT FROM AUTHOR]

We use 2019 TROPOMI satellite observations of atmospheric methane in an analytical inversion to quantify methane emissions from the Middle East and North Africa at up to ∼25 km × 25 km resolution, using spatially allocated national United Nations Framework Convention on Climate Change (UNFCCC) reports as prior estimates for the fuel sector. Our resulting best estimate of anthropogenic emissions for the region is 35 % higher than the prior bottom-up inventories (+ 103 % for gas, + 53 % for waste, + 49 % for livestock, -14 % for oil) with large variability across countries. Oil and gas account for 38 % of total anthropogenic emissions in the region. TROPOMI observations can effectively optimize and separate national emissions by sector for most of the 23 countries in the region, with 6 countries accounting for most of total anthropogenic emissions including Iran (5.3 (5.0–5.5) Tg a -1 ; best estimate and uncertainty range), Turkmenistan (4.4 (2.8–5.1) Tg a -1) , Saudi Arabia (4.3 (2.4–6.0) Tg a -1) , Algeria (3.5 (2.4–4.4) Tg a -1) , Egypt (3.4 (2.5–4.0) Tg a -1) , and Turkey (3.0 (2.0–4.1) Tg a -1). Most oil–gas emissions are from the production (upstream) subsector, but Iran, Turkmenistan, and Saudi Arabia have large gas emissions from transmission and distribution subsectors. We identify a high number of annual oil–gas emission hotspots in Turkmenistan, Algeria, and Oman and offshore in the Persian Gulf. We show that oil–gas methane emissions for individual countries are not related to production, invalidating a basic premise in the construction of activity-based bottom-up inventories. Instead, local infrastructure and management practices appear to be key drivers of oil–gas emissions, emphasizing the need for including top-down information from atmospheric observations in the construction of oil–gas emission inventories. We examined the methane intensity, defined as the upstream oil–gas emission per unit of methane gas produced, as a measure of the potential for decreasing emissions from the oil–gas sector and using as reference the 0.2 % target set by the industry. We find that the methane intensity in most countries is considerably higher than this target, reflecting leaky infrastructure combined with deliberate venting or incomplete flaring of gas. However, we also find that Kuwait, Saudi Arabia, and Qatar meet the industry target and thus show that the target is achievable through the capture of associated gas, modern infrastructure, and the concentration of operations. Decreasing methane intensities across the Middle East and North Africa to 0.2 % would achieve a 90 % decrease in oil–gas upstream emissions and a 26 % decrease in total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge. [ABSTRACT FROM AUTHOR]

Afforestation and reduction of fossil fuel emissions are two major components of climate mitigation policies. However, their effects on the Earth's climate are different because a reduction of fossil fuel emissions directly alters the biogeochemical cycle of the climate system and modifies the physics of the atmosphere via its impact on radiation and the energy budget, while afforestation causes biophysical changes in addition to changes in the biogeochemical cycle. In this paper, we compare the climate and carbon cycle consequences of carbon removal by afforestation and an equivalent fossil fuel emission reduction using simulations from an intermediate complexity Earth system model. We performed two major sets of idealized simulations in which fossil fuel emissions follow extended Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5, 3-7.0, and 5-8.5), and equal amounts of carbon are removed by afforestation in one set and by a reduction in fossil fuel emissions in another set. Our simulations show that the climate is cooler by 0.36, 0.47, and 0.42 ∘C in the long term (2471–2500) in the case of reduced fossil fuel emissions compared to the case with afforestation when the emissions follow the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. The global mean surface temperature is cooler in the reduced fossil fuel emissions case compared to the afforestation case because the net biophysical effect of warming from afforestation partly offsets the biogeochemical cooling effect of afforestation. Thus, in terms of climate benefits, reducing fossil fuel emissions could be relatively more beneficial than afforestation for the same amount of carbon removed from the atmosphere. However, a robust understanding of the processes that govern the biophysical effects of afforestation should be improved before considering our results for climate policy. [ABSTRACT FROM AUTHOR]

Accurate quantification of regional terrestrial fluxes is essential for improving our knowledge of the carbon sequestration potential of ecosystems, ecosystem functioning, and emission reduction demand in the context of climate change mitigation. However, the quantification is challenging owing to methodological and observational constraints, especially for regions with severe gaps in the ground-based observational network, like India. This study examines the potential of recent satellite missions, such as TROPOMI and OCO-2 providing retrievals of Solar-Induced chlorophyll Fluorescence (SIF) to improve terrestrial biosphere CO2 flux estimates over India. Here, we present high-resolution estimates of Gross Primary Productivity (GPP) and Net Ecosystem Exchange (NEE) over India on a 0.1°×0.1° grid at a temporal resolution of 1 hour from 2012 to 2020. These products can be used for various applications such as those related to carbon cycle (e.g., inverse modelling of CO2), benchmarking terrestrial biosphere models over the region, and understanding ecosystem responses to climate change. We follow a satellite-based diagnostic data-driven approach using a biosphere model, namely the Vegetation Photosynthesis and Respiration Model (VPRM) simulating both GPP and NEE, based on light use efficiency and satellite observations of the near-infrared radiance of vegetation (NIRv). We calibrate the standard VPRM GPP estimates using SIF-GPP relationship and investigate the model performance by comparing the simulations with eddy-covariance flux tower measurements. Our best model predictions are with a mean bias error (MBE) = 2.4 µmol m-2 s-1, root mean squared error (RMSE) = 3.8 µmol m-2 s-1 and squared correlation coefficient (R²) = 0.56 when evaluating with observations at a monthly scale over the period from 2012 to 2018. The observed seasonal anomalies in NEE and GPP range from -4.9 to 8.0 µmol m-2 s-1 and -7.0 to 17.0 µmol m-2 s-1, respectively, and are well captured by our model. The model simulations are highly correlated with observations during 2018, the only common year when both EC and SIF observations are available, with R² values of 0.68 and 0.74 for NEE and GPP, respectively. Incorporating the SIF signals in the vegetation model improves model performance in capturing the seasonality and magnitudes of GPP, thereby improving the estimates of NEE. We show the influence of soil temperature and soil moisture on ecosystem respiration and refined the VPRM's ecosystem respiration calculation to better constrain the fluxes, resulting in simulations closer to the observations. Ecosystem respiration fluxes are less well constrained than ecosystem productivity fluxes due to the limited observations. Based on satellite observations and the refined model, the annual NEE and GPP estimates range from -0.38 Pg C yr-1 to -0.53 Pg C yr-1 (land C sink) and 3.39 Pg C yr-1 to 3.88 Pg C yr-1, respectively over India for the years from 2012 to 2020. The biospheric flux distribution over the region is found to be associated with ecosystem heterogeneity, and variations in precipitation, and soil characteristics at a regional scale. Overall, our results show that the satellite-based SIF data products can potentially inform the ecosystem-scale vegetation responses across biomes over India. Future improvements in the terrestrial biosphere CO2 flux estimates over India can be attained through the carbon cycle data assimilation with the availability of both flux and mixing ratio observations of CO2. [ABSTRACT FROM AUTHOR]

The assessment of cropland carbon and nitrogen (C & N) balances play a key role to identify cost effective mitigation measures to combat climate change and reduce environmental pollution. In this paper, a biogeochemical modelling approach is adopted to assess all C & N fluxes in a regional cropland ecosystem of Thessaly, Greece. Additionally, the estimation and quantification of the modelling uncertainty in the regional inventory are realized through the propagation of parameter distributions through the model leading to result distributions for modelling estimations. The model was applied on a regional dataset of approximately 1000 polygons deploying model initializations and crop rotations for the 5 major crop cultivations and for a timespan of 8 years. The full statistical analysis on modelling results yields for the C balance carbon input fluxes into the soil of 12.4 ± 1.4 tons C ha-1 yr-1 and output fluxes of 11.9 ± 1.3 tons C ha-1 yr-1, with a resulting average carbon sequestration of 0.5 ± 0.3 tons C ha-1 yr-1. The averaged N influx was 212.3 ± 9.1 kg N ha-1 yr-1 while outfluxes were estimated on average of 198.3 ± 11.2 kg N ha-1 yr-1. The net N accumulation into the soil nitrogen pools was estimated to 14.0 ± 2.1 kg N ha-1 yr-1. The N outflux consist of gaseous N fluxes composed by N2O emissions 2.6 ± 0.8 kg N2O-N ha-1 yr-1, NO emissions of 3.2 ± 1.5 kg NO-N ha-1 yr-1, N2 emissions 15.5 ± 7.0 kg N2-N ha-1 yr-1 and NH3 emissions of 34.0 ± 6.7 kg NH3-N ha-1 yr-1, as well as aquatic N fluxes (only nitrate leaching into surface waters) of 14.± 4.5 kg NO3-N ha-1 yr-1, N fluxes of N removed from the fields in yields, straw and feed of 128.8 ± 8.5 kg N ha-1 yr-1. [ABSTRACT FROM AUTHOR]

Methane (CH 4) is a strong greenhouse gas that has become the focus of climate mitigation policies in recent years. Ethane/methane ratios can be used to identify and partition the different sources of methane, especially in areas with natural gas mixed with biogenic methane emissions, such as cities. We assessed the precision, accuracy, and selectivity of three commercially available laser-based analyzers that have been marketed as measuring instantaneous dry-mole fractions of methane and ethane in ambient air. The Aerodyne SuperDUAL instrument performed the best out of the three instruments, but it is large and requires expertise to operate. The Aeris Mira Ultra LDS analyzer also performed well for the price point and small size, but it required characterization of the water vapor dependence of reported concentrations and careful set-up for use. The Picarro G2210-i precisely measured methane, but it did not detect the 10 ppbv (part-per-billion by volume) increases in ambient ethane detected by the other two instruments when sampling a plume of incompletely combusted natural gas. For long-term tower deployments or those with large mobile laboratories, the Aerodyne SuperDUAL provides the best precision for methane and ethane. The more compact Aeris MIRA can, with careful use, quantify thermogenic methane sources to sufficient precision for mobile and short-term deployments in urban or oil and gas areas. We weighed the advantages of each instrument, including size, power requirement, ease of use on mobile platforms, and expertise needed to operate the instrument. We recommend the Aerodyne SuperDUAL or the Aeris MIRA Ultra LDS depending on the situation. [ABSTRACT FROM AUTHOR]

As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO 2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO 2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO 2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO 2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO 2 yr -1) in global land-use CO 2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO 2 yr -1 for the period 2000–2020, and NGHGIs, which estimate a sink of -1.9 GtCO 2 yr -1 in the same period. Most of the gap is found on forest land (3.5 GtCO 2 yr -1), with differences also for deforestation (2.4 GtCO 2 yr -1), for fluxes from other land uses (1.0 GtCO 2 yr -1) and to a lesser extent for fluxes from organic soils (0.2 GtCO 2 yr -1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-6.4 GtCO 2 yr -1) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO 2 yr -1) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement. Data from this study are openly available online via the Zenodo portal (Grassi et al., 2023) at 10.5281/zenodo.7650360. [ABSTRACT FROM AUTHOR]

In the context of climate change, the increasing demand for energy-efficient buildings and sustainable urban development has become a pressing issue due to the significant proportion of global energy consumption and carbon dioxide (CO2) emissions attributable to the building sector. This requires a concerted effort to reduce its environmental impact, and Geographic Information System (GIS) applications are vital tools for achieving this by optimizing heat supply, calculating costs, analyzing profitability, and balancing CO2 emissions. This study aims to address the challenge of achieving energy efficiency and reducing CO2 emissions in the building sector, specifically at the district level. To this end, the research objective is to develop a QGIS plugin that can simulate urban energy demand at the district level by integrating 2D data with CityGML files and connecting QGIS to SimStadt software via API to visualize the simulated urban energy results in 3D on the Web Globe. The proposed plugin leverages the open-source QGIS tool QField to capture building conditions and connect 2D and 3D data on urban energy simulation. Supplementary to this, this plugin provides up-to-date information on energy demand, consumption, CO2 emissions, building component conditions via updating related tables in the database. Decision-makers can use this comprehensive and user-friendly tool to understand and act on the results, ultimately leading to a CO2-neutral district by 2045. The development of the QGIS plugin represents a significant step towards sustainable urban development and climate change mitigation by utilizing GIS applications for optimizing energy demand and reducing CO2 emissions in the built environment. [ABSTRACT FROM AUTHOR]

Most phosphorus (P) in soils is unavailable for direct biological uptake as it is locked within primary or secondary mineral particles, adsorbed to mineral surfaces, or immobilized inside of organic material. Deciphering the composition of different P pools in soil is critical for understanding P bioavailability and its underlying dynamics. However, widely used global estimates of different soil P pools are based on a dataset containing few measurements in which many regions or soil types are unrepresented. This poses a major source of uncertainty in assessments that rely on these estimates to quantify soil P constraints on biological activity controlling global food production and terrestrial carbon balance. To address this issue, we consolidated a database of six major soil P pools containing 1857 entries from globally distributed (semi-)natural soils and 11 related environmental variables. The P pools (labile inorganic P (Pi), labile organic P (Po), moderately labile Pi, moderately labile Po, primary mineral P, and occluded P) were measured using a sequential P fractionation method. Using the database, we trained random forest regression models for each of the P pools and captured observed variation with R2 higher than 60%. We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH. When expressed in relative concentrations (i.e., as a proportion of total P), the model showed that soil pH is the most important predictor for proportions of all soil P pools, except for labile Pi proportion, which is primarily controlled by soil depth. Using the trained random forest models, we predicted soil P pools’ distributions in natural systems at a resolution of 0.5° × 0.5°. Our global maps of different P pools in soils as well as the pools’ underlying drivers can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system. [ABSTRACT FROM AUTHOR]

The use of satellite instruments to detect and quantify methane emissions from fossil fuel production activities is highly beneficial to support climate change mitigation. Different hyperspectral and multispectral satellite sensors have recently shown potential to detect and quantify point-source emissions from space. The Sentinel-2 (S2) mission, despite its limited spectral design, supports the detection of large emissions with global coverage and high revisit frequency thanks to coarse spectral coverage of methane absorption lines in the shortwave infrared. Validation of S2 methane retrieval algorithms is instrumental in accelerating the development of a systematic and global monitoring system for methane point sources. Here, we develop a benchmarking framework for such validation. We first develop a methodology to generate simulated S2 datasets including methane point-source plumes. These benchmark datasets have been created for scenes in three oil and gas basins (Hassi Messaoud, Algeria; Korpeje, Turkmenistan; Permian Basin, USA) under different scene heterogeneity conditions and for simulated methane plumes with different spatial distributions. We use the simulated methane plumes to validate the retrieval for different flux rate levels and define a minimum detection threshold for each case study. The results suggest that for homogeneous and temporally invariant surfaces, the detection limit of the proposed S2 methane retrieval ranges from 1000 to 2000 kg h -1 , whereas for areas with large surface heterogeneity and temporal variations, the retrieval can only detect plumes in excess of 500 kg h -1. The different sources of uncertainty in the flux rate estimates have also been examined. Dominant quantification errors are either wind-related or plume mask-related, depending on the surface type. Uncertainty in wind speed, both in the 10 m wind (U10) and in mapping U10 to the effective wind (Ueff) driving plume transport, is the dominant source of error for quantifying individual plumes in homogeneous scenes. For heterogeneous and temporally variant scenes, the surface structure underlying the methane plume affects the plume masking and can become a dominant source of uncertainty. [ABSTRACT FROM AUTHOR]