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Canada has major sources of atmospheric methane (CH4), with the world's second-largest boreal wetland and the world's fourth-largest natural gas production. However, Canada's CH4 emissions remain uncertain among estimates. Better quantification and characterization of Canada's CH4 emissions are critical for climate mitigation strategies. To improve our understanding of Canada's CH4 emissions, we performed an ensemble regional inversion for 2007–2017 constrained with the Environment and Climate Change Canada (ECCC) surface measurement network. The decadal CH4 estimates show no significant trend, unlike some studies that reported long-term trends. The total CH4 estimate is 17.4 (15.3–19.5) TgCH4yr-1 , partitioned into natural and anthropogenic sources at 10.8 (7.5–13.2) and 6.6 (6.2–7.8) TgCH4yr-1 , respectively. The estimated anthropogenic emission is higher than inventories, mainly in western Canada (with the fossil fuel industry). Furthermore, the results reveal notable spatiotemporal characteristics. First, the modelled differences in atmospheric CH4 among the sites show improvement after inversion when compared to observations, implying the CH4 observation differences could help in verifying the inversion results. Second, the seasonal variations show slow onset and a late-summer maximum, indicating wetland CH4 flux has hysteretic dependence on air temperature. Third, the boreal winter natural CH4 emissions, usually treated as negligible, appear quantifiable (≥ 20 % of annual emissions). Understanding winter emission is important for climate prediction, as the winter in Canada is warming faster than the summer. Fourth, the inter-annual variability in estimated CH4 emissions is positively correlated with summer air temperature anomalies. This could enhance Canada's natural CH4 emission in the warming climate. [ABSTRACT FROM AUTHOR]

Large-scale reforestation, afforestation, and forest restoration schemes have gained global support as climate change mitigation strategies due to their significant carbon dioxide removal (CDR) potential. However, there has been limited research into the unintended consequences of forestation from a biophysical perspective. In the Community Earth System Model version 2 (CESM2), we apply a global forestation scenario, within a Paris Agreement-compatible warming scenario, to investigate the land surface and hydroclimate response. Compared to a control scenario where land use is fixed to present-day levels, the forestation scenario is up to 2 °C cooler at low latitudes by 2100, driven by a 10 % increase in evaporative cooling in forested areas. However, afforested areas where grassland or shrubland are replaced lead to a doubling of plant water demand in some tropical regions, causing significant decreases in soil moisture (∼ 5 % globally, 5 %–10 % regionally) and water availability (∼ 10 % globally, 10 %–15 % regionally) in regions with increased forest cover. While there are some increases in low cloud and seasonal precipitation over the expanded tropical forests, with enhanced negative cloud radiative forcing, the impacts on large-scale precipitation and atmospheric circulation are limited. This contrasts with the precipitation response to simulated large-scale deforestation found in previous studies. The forestation scenario demonstrates local cooling benefits without major disruption to global hydrodynamics beyond those already projected to result from climate change, in addition to the cooling associated with CDR. However, the water demands of extensive forestation, especially afforestation, have implications for its viability, given the uncertainty in future precipitation changes. [ABSTRACT FROM AUTHOR]

Dissolved organic carbon (DOC) is the most active carbon pool in soils, which plays critical roles in soil carbon cycling, plant productivity, and global climate change. An accurate assessment of the quantity of DOC in the soil is essential for the detailed elucidation of ecosystem functions and services. Nevertheless, the global driving factors and distribution of soil DOC remain inadequately quantified due to the scarcity of large-scale data. Here, a comprehensive global database of 12807 soil DOC concentrations derived from 975 target papers in the literature was compiled. Detailed geographic locations, climate, and soil properties were also recorded as predictors of soil DOC. Machine learning techniques were employed to assess the relative importance of various predictors in the determination of soil DOC concentrations, which were subsequently extended for their prediction on a global scale. The worldwide soil DOC concentration spanned a wide range (0.04 to 7859 mg kg-1), averaging 222.78 mg kg-1. The 12 selected variables (including soil properties, month, climate, and ecosystem) explained 65 % of the variance in soil DOC concentrations. Elevation, soil clay, and soil organic carbon were three of the most important predictors. Global soil DOC concentration increased from the equator to the poles. The soil DOC stocks in the topsoil layer (0–30 cm) amounted to 12.17 Pg, with significant variations observed across different continents. These results are instrumental for informing strategies on soil management practices, ecosystem services, and the mitigation of climate change. Furthermore, our database can be combined with other carbon pools to explore the total soil carbon turnover and constrain Earth carbon models. The dataset is publicly available at https://doi.org/10.6084/m9.figshare.26379898 (Ren and Cai, 2024). [ABSTRACT FROM AUTHOR]

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]

Forestation can contribute to climate change mitigation. However, increasing frequency and intensity of climate extremes are posed to have profound impact on forests and consequently on the mitigation potential of forestation efforts. In this perspective, we critically assess forestation-reliant climate mitigation scenarios from five different integrated assessment models (IAMs) by showcasing the spatially explicit exposure of forests to fire weather and the simulated increase in global annual burned area. We provide a detailed description of the feedback from climate change to forest carbon uptake in IAMs. Few IAMs are currently accounting for feedback mechanisms like loss from fire disturbance. Consequently, many forestation areas proposed by IAM scenarios will be exposed to fire-promoting weather conditions and without costly prevention measures might be object to frequent burning. We conclude that the actual climate mitigation portfolio in IAM scenarios is subject to substantial uncertainty and that the risk of overly optimistic estimates of negative emission potential of forestation should be avoided. As a way forward we propose how to integrate more detailed climate information when modeling climate mitigation pathways heavily relying on forestation. [ABSTRACT FROM AUTHOR]

Methane (CH 4) is a significant greenhouse gas in exacerbating climate change. Approximately 25 % of CH 4 is emitted from storage tanks. It is crucial to spatially explore the CH 4 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 CH 4 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 4403 high-spatial-resolution images (1–2 m) from the Gaofen-1, Gaofen-2, Gaofen-6, and Ziyuan-3 satellites over city regions in China with officially reported numerous storage tanks in 2021. STD is the first storage tank dataset for over 92 typical city regions in China. The dataset can be accessed at 10.5281/zenodo.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 of the collected high-spatial-resolution images, historical high-spatial-resolution images of Google Earth, and field survey. The inventory comprises storage tanks with various distribution patterns in different city regions. Spatial consistency analysis with the CH 4 emission product shows good agreement with storage tank distributions. The intensive construction of storage tanks significantly induces CH 4 emissions from 2005 to 2020, underscoring the need for more robust measures to curb CH 4 release and aid in climate change mitigation efforts. Our proposed dataset, STD, will foster the accurate estimation of CH 4 released from storage tanks for CH 4 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]

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]

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]

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]

To reach climate mitigation goals, the share of wind power in the electricity production is going to increase substantially in France. In winter, low wind days are challenging for the electricity system if compounded with cold days that are associated with peak electricity demand. The scope of this study is to characterize the evolution of compound low wind and cold events in winter over the 1950–2022 period in France. Compound events are identified at the daily scale using a bottom-up approach based on two indices that are relevant to the French energy sector, derived from temperature and wind observations. The frequency of compound events shows high interannual variability, with some winters having no event and others having up to 13, and a decrease over the 1950–2022 period. Based on a k-means unsupervised classification technique, four weather types are identified, highlighting the diversity of synoptic situations leading to the occurrence of compound events. The weather type associated with the highest frequency of compound events presents pronounced positive sea-level pressure anomalies over Iceland and negative anomalies west of Portugal, limiting the entrance of the westerlies and inducing a north-easterly flow bringing cold air over France and Europe generally. We further show that the atmospheric circulation and its internal variability are likely to play a role in the observed reduction in cold days, suggesting that this negative trend may not be entirely be driven by anthropogenic forcings. Despite this suggested role for cold days, the observed decrease in compound events does not seem to be strongly influenced by the regional atmospheric circulation. [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]

Flood hazard and risk assessments (FHRAs), and the underlying models, form the basis of decisions regarding flood mitigation and climate adaptation measures and are thus imperative for safeguarding communities from the devastating consequences of flood events. In this perspective paper, we discuss how FHRAs should be validated to be fit-for-purpose in order to optimally support decision-making. We argue that current validation approaches focus on technical issues, with insufficient consideration of the context in which decisions are made. To address this issue, we propose a novel validation framework for FHRAs, structured in a three-level hierarchy: process-based, outcome-based, and impact-based. Our framework adds crucial dimensions to current validation approaches, such as the need to understand the possible impacts on society when the assessment has large errors. It further emphasizes the essential role of stakeholder participation, objectivity, and verifiability when assessing flood hazard and risk. Using the example of flood emergency management, we discuss how the proposed framework can be implemented. Although we have developed the framework for flooding, our ideas are also applicable to assessing risk caused by other types of natural hazards. [ABSTRACT FROM AUTHOR]

Large-scale reforestation, afforestation, and forest restoration schemes have gained global support as climate change mitigation strategies due to their significant carbon dioxide removal (CDR) potential. However, there has been limited research into the unintended consequences of forestation from a biophysical perspective. In the Community Earth System Model version 2 (CESM2), we apply a global forestation scenario, within a Paris Agreement-compatible warming scenario to investigate the land surface and hydroclimate response. Compared to a control scenario where land use is fixed to present-day levels, the forestation scenario is up to 2 °C cooler at low latitudes by 2100, driven by a 10 % increase in evaporative cooling in forested areas. However, afforested areas where grassland or shrubland are replaced lead to a doubling of plant water demand in some tropical regions, causing significant decreases in soil moisture (~5 % globally, 5–10 % regionally) and water availability (~10 % globally, 10–15 % regionally) in regions with increased forest cover. While there are some increases in low cloud and seasonal precipitation over the expanded tropical forests, with enhanced negative cloud radiative forcing, the impacts on large-scale precipitation and atmospheric circulation are limited. This contrasts with the precipitation response to simulated large-scale deforestation found in previous studies. The forestation scenario demonstrates local cooling benefits without major disruption to global hydrodynamics beyond those already projected to result from climate change, in addition to the cooling associated with CDR. However, the water demands of extensive forestation, especially afforestation, have implications for its viability given uncertainty in future precipitation changes. [ABSTRACT FROM AUTHOR]

There is considerable academic interest in the potential for air quality improvement as a co-benefit of climate change mitigation. Few studies use regional air quality models for simulating future co-benefits, but many use global chemistry-climate model output. Using regional atmospheric chemistry could provide a better representation of air quality changes than global chemistry-climate models, especially by improving the representation of elevated urban concentrations. We use a detailed regional atmospheric chemistry model (WRF-Chem v 4.2) to model European air quality in 2050 compared to 2014 following three climate change mitigation scenarios. We represent different climate futures by using air pollutant emissions and chemical boundary conditions (from CESM2-WACCM output) for three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0; a high, medium and low mitigation pathway). We find that in 2050, following SSP1-2.6, mean population-weighted PM2.5 concentrations across European countries reduces by 52 % compared to 2014. Whilst under SSP2-4.5, this average reduction is 34 %. The smallest average reduction was 18 % by following SSP3-7.0. Maximum 6-monthly-mean daily-maximum 8 h (6mDM8h) ozone (O3) is reduced across Europe by 15 % following SSP1-2.6, and 3 % following SSP2-4.5, but increases by 13 % following SSP3-7.0. This demonstrates clear co-benefits of climate mitigation. The additional resolution allows us to analyse regional differences and identify key sectors. We find that mitigation of agricultural emissions will be key for attaining meaningful co-benefits of mitigation policies, evidenced by the importance of changes in NO3 aerosol mass to determining future PM2.5 air quality and changes in CH4 emissions to future O3 air quality. [ABSTRACT FROM AUTHOR]

Extracting and processing raw materials into products in industry is a substantial source of CO2 emissions, which currently lacks process detail in many integrated assessment models (IAMs). To broaden the space of climate change mitigation options and to include circular economy and material efficiency measures in IAM scenario analysis, we developed MESSAGEix-Materials module representing material flows and stocks within the MESSAGEix-GLOBIOM IAM framework. With the development of MESSAGEix-Materials, we provide a fully open-source model that can assess different industry decarbonization options under various climate targets for the most energy and emissions-intensive industries: Aluminium, iron and steel, cement, and petrochemicals. We illustrate the model's operation with a baseline and mitigation (2 degrees) scenario setup and validate base year results for 2020 against historical datasets. We also discuss the industry decarbonization pathways and material stocks of the electricity generation technologies resulting from the new model features. [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]

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]

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]

With the onset of climate change, adaptive action will have to occur at all scales, including locally. This implies a growing responsibility for the public and, therefore, a need to spread awareness and inspire climate action. Communication via adaptation games shows potential in achieving social learning and addressing the so-called knowledge-action gap. However, few research efforts so far give voice to participants engaging with collaborative games in organisational and community settings. This paper advances this field by presenting a systematic research reflection on a collaborative tabletop board game, Minions of Disruptions™. It addresses two research questions: first exploring how to design a collaborative adaptation game for the general public, and then determining how the intentions outlined by the game designers are perceived by the game participants. Ten core design intentions determined through a focus group interview with game designers and facilitators were contrasted against responses from the post-game survey administered to all game participants from 2019–2022. The results of this study indicate that the design intentions behind Minions of Disruptions were largely received by the intended audiences, demonstrating success as a communication tool for collaborative climate action. Moreover, important insights about designing adaptation games for the public are raised, which can aid in drafting guidelines for successful engagement. [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]

Canada has major sources of atmospheric methane (CH4), with the world second-largest boreal wetland and the world fourth-largest natural gas production. However, Canada's CH4 emissions remain uncertain among estimates. Better quantification and characterization of Canada's CH4 emissions are critical for climate mitigation strategies. To improve our understanding of Canada's CH4 emissions, we performed an ensemble regional inversion (2007–2017) constrained with the Environment and Climate Change Canada (ECCC) surface measurement network. The decadal CH4 estimates show no significant trend, unlike some studies that reported long-term trends. The total CH4 estimate is 17.4 (15.3–19.5) Tg CH4 year-1, partitioned into natural and anthropogenic sources, 10.8 (7.5–13.2) and 6.6 (6.2–7.8) Tg CH4 year-1, respectively. The estimated anthropogenic emission is higher than inventories, mainly in western Canada (with the fossil fuel industry). Furthermore, the results reveal notable spatiotemporal characteristics. First, the modelled gradients of atmospheric CH4 show improvement after inversion when compared to observations, implying the CH4 gradients could help verify the inversion results. Second, the seasonal variations show slow onset and late summer maximum, indicating wetland CH4 flux has hysteretic dependence on air temperature. Third, the boreal winter natural CH4 emissions, usually treated as negligible, appear quantifiable (≥ 20 % of annual emissions). Understanding winter emission is important for climate prediction, as the winter in Canada is warming faster than the summer. Fourth, the inter-annual variability in estimated CH4 emissions is positively correlated with summer air temperature anomalies. This could enhance Canada's natural CH4 emission in the warming climate. [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]

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]

As many large metropolitan areas have pledged for a rapid decrease of their greenhouse gas emissions through ambitious climate mitigation policies, the need for rapid and robust quantification methods became more pressing. At the global scale, the scarcity of satellite carbon dioxide (CO2) observations remains the major roadblock to producing independent city-scale CO2 emissions estimates from atmospheric data, except for a handful of cities benefiting from a dense network of ground-based CO2 sensors. In this study, we quantify the potential of assimilating indirect measurements from spaceborne sensors (here nitrogen dioxide – NO2) to constrain fossil fuel CO2 emissions, relying on the co-emission of these two species during combustion. We developed a modeling framework using a NOx-aerosol chemistry transport model (WRF-Chem) and performed simulations of NO2 and CO2 over the Middle-East, an area known for its large cities, its frequent clear sky conditions and a fairly constant albedo from its desertic land. We first demonstrate the importance of production/destruction processes impacting NO2 lifetime at short and long distances from the source, suggesting that simplified approaches may be impacted by large errors. In comparison to TROPOMI satellite observations, the simulated NO2 plumes from emissions inventories (EDGAR) revealed large misattribution of NO2 emissions at fine scales, hence an uncertain disaggregation of national emissions to single point sources in the industrial and energy sectors in the EDGAR inventory. We further studied the relationship between NO2 and CO2 during summer and winter seasons by simulating the enhancement ratios (δNO2:δXCO2) in plumes produced by cities and point sources We found that the enhancement ratios are consistent with the observed ratios derived from the ESA TROPOMI (NO2) and NASA OCO-3 missions (XCO2). We conclude here that the spatial misattribution of NO2 emissions parallels the misattribution of CO2 emissions, and that improved NO2 inventories could therefore be used to improve the monitoring of CO2 emissions at sub-national scales in current global inventories when a sufficiently-large amount of NO2 satellite measurements are available. [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]

China stands out as a major contributor to anthropogenic methane (CH4) emissions, with coal mine methane (CMM) playing a crucial role. To control and reduce CH4 emissions, China has made a dedicated commitment and formulated an ambitious mitigation plan. To verify the process made, the consistent acquisition of independent CH4 emission data is required. This paper aims to implement a wind-assigned anomaly method for the precise determination of regional-scale CMM emissions within the coal-rich Shanxi province. We use the TROPOspheric Monitoring Instrument (TROPOMI) CH4 observations from May 2018 to May 2023, coupled with ERA5 wind covering the Changzhi, Jincheng and Yangquan regions. The derived emission strengths are 8.4× 1026 ± 1.6 × 1025 molec. s-1 (0.706 ± 0.013 Tg yr-1), 1.4 × 1027 ± 1.9 × 1025 molec. s-1 (1.176 ± 0.016 Tg yr-1), and 4.9 × 1026± 1.8 × 1025 molec. s-1 (0.412 ± 0.015 Tg yr-1), respectively. Our results exhibit biases of -18 %, 8 %, and 14 % when compared to the bottom-up inventory. Larger discrepancies are found when comparing the estimates to the CAMS-GLOB-ANT and EDGARv7.0 inventories. This suggests that the two inventories may be overestimating the CH4 emissions in the Jincheng and Yangquan regions. Our estimates provide a comprehensive characterization of the regions within the Shanxi province, contribute to the validation of emission inventories, and help to develop climate mitigation strategies. [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]

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 ∆TA1800 µmol·L-1, in relation to the Dissolved Inorganic Carbon (DIC) concentrations. Notably, the ∆TA1500 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 nanophytoplankton 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 ∆TA1800 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]

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]

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, 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 13C labelling. After harvest, we quantified C persistence, and pools of different origin (plant vs soil) and of 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 three 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]

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]