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Fresh submarine groundwater discharge (FSGD) can deliver significant fluxes of water and solutes from land to sea. In the Arctic, which accounts for ∼34% of coastlines globally, direct observations and knowledge of FSGD are scarce. Through integration of observations and process‐based models, we found that regardless of ice‐bonded permafrost depth at the shore, summer SGD flow dynamics along portions of the Beaufort Sea coast of Alaska are similar to those in lower latitudes. Calculated summer FSGD fluxes in the Arctic are generally higher relative to low latitudes. The FSGD organic carbon and nitrogen fluxes are likely larger than summer riverine input. The FSGD also has very high CO2 making it a potentially significant source of inorganic carbon. Thus, the biogeochemistry of Arctic coastal waters is potentially influenced by groundwater inputs during summer. These water and solute fluxes will likely increase as coastal permafrost across the Arctic thaws. Plain Language Summary Groundwater flows from land to sea, transporting freshwater, organic matter, nutrients, and other solutes that impact coastal ecosystems. However, along coasts of the rapidly‐warming Arctic, there is limited knowledge regarding how much fresh groundwater enters the ocean. Using field observations and numerical models, we show that groundwater flowing from tundra in northern coastal Alaska carries large amounts of freshwater, organic matter, and carbon dioxide to the Arctic lagoons during summer. These inputs are likely significant to coastal biogeochemical cycling and marine food webs. Groundwater discharge and the associated transport of dissolved materials are expected to increase due to longer periods of above‐zero temperatures that thaw frozen soils below the tundra. Key Points Summer fresh submarine groundwater discharge (FSGD) to the Alaskan Beaufort Sea is only 3%–7% of rivers but carries as much organic matter Summer FSGD delivers a median of 116 (interquartile range: 35–405) and 6 (2–21) kg/d per km dissolved organic carbon and nitrogen Fresh groundwater at the beach of Simpson Lagoon (SL) has a median PCO2 of ∼33,000 μatm implying substantial CO2 flux

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one‐third of anthropogenic CO2 emissions during the 1959–2019 period. This sink‐estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation‐based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter‐model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties. Plain Language Summary Earth's natural vegetation absorbs about one‐third of CO2 emissions caused by human activities. This value is produced by a group of models rather than through direct observations. Our study assesses how well models reproduce the processes that drive the CO2 exchange between land and atmosphere using a wide range of data sets that are mainly derived from field measurements and satellite images. These reference data sets are prone to errors that are not quantified in a consistent manner. To account for such errors, we first compare different reference data sets against each other. We then compare model output against reference data and assess whether the differences are comparable to the differences among the reference data sets. We conclude that the performance of models is encouraging given how uncertain reference data are, but that ample potential for improvements remains. Key Points Differences between model and observations are often similar compared to differences between independently derived observation‐based data We quantify differences between independently derived observations to disentangle model deficiencies from observational uncertainties Future work should address biases in soil organic carbon, leaf area index, and the large spread of gross primary productivity among models

The latitudinal range of modern shallow‐water tropical corals is controlled by temperature, and presently limited to waters warmer than 16–18°C year‐round. However, even during Cenozoic climates with such temperatures in polar regions, coral reefs are not found beyond >50° latitude. Here, we test the hypothesis that daily available solar radiation limited poleward expansion of coral reefs during warm climates, using a new box model of shallow marine coral calcification. Our results show that calcification rates start to decline beyond 40° latitude and drop severely beyond 50° latitude, due to decreasing winter light intensity and day length, irrespective of aragonite saturation. This suggests that light ultimately prohibits further poleward expansion in warm climates. In addition, fossil coral reef distribution is not a robust proxy for water temperatures and poleward expansion of reefs beyond 50° latitude is not an expected carbon cycle feedback of climate warming. Plain Language Summary Modern tropical coral reefs are found approximately between 30° north and south of the equator, where waters are warmer than 16°C year‐round. It is therefore often assumed that water temperature has the most important control on how far poleward we find tropical coral reefs. In the geological past, the earth has seen periods when global temperatures were much higher than they are today and oceans had tropical temperatures all the way to polar regions. However, tropical coral fossils were never encountered at these high latitudes. Rather, their distribution was restricted to latitudes below 50°N. Another important condition for tropical coral reefs to survive, is light availability. Toward the poles, winter day length and light intensity decreases significantly. We think that low winter day length limited coral reef growth and survival at high latitudes during these past warm climates. We test this with a computer model of coral growth and find that decreasing winter day length and light intensity cause coral growth to drop severely beyond 50°N. This result suggests that low light availability during winter months prohibited coral reefs from occurring at high latitudes during past warm climates. Key Points The uppermost latitudinal limit of tropical coral reefs was limited by winter daily available radiation during past warm climates Fossil coral reef distribution is not a robust proxy for water temperatures For the correct interpretation of coral reef records, coral‐light relationships need to be taken into consideration

The Southern Ocean is rich in highly dynamic mesoscale eddies and substantially modulates global biogeochemical cycles. However, the overall surface and subsurface effects of eddies on the Southern Ocean biogeochemistry have not been quantified observationally at a large scale. Here, we co‐locate eddies, identified in the Meta3.2DT satellite altimeter‐based product, with biogeochemical Argo floats to determine the effects of eddies on the dissolved inorganic carbon (DIC), nitrate, and dissolved oxygen concentrations in the upper 1,500 m of the ice‐free Southern Ocean, as well as the eddy effects on the carbon fluxes in this region. DIC and nitrate concentrations are lower in anticyclonic eddies (AEs) and increased in cyclonic eddies (CEs), while dissolved oxygen anomalies switch signs above (CEs: positive, AEs: negative) and below the mixed layer (CEs: negative, AEs: positive). We attribute these anomalies primarily to eddy pumping (isopycnal heave), as well as eddy trapping for oxygen. Maximum anomalies in all tracers occur at greater depths in the subduction zone north of the Antarctic Circumpolar Current (ACC) compared to the upwelling region in the ACC, reflecting differences in background vertical structures. Eddy effects on air–sea CO2${\\text{CO}}_{2}$exchange have significant seasonal variability, with additional outgassing in CEs in fall (physical process) and additional oceanic uptake in AEs and CEs in spring (biological and physical process). Integrated over the Southern Ocean, AEs contribute ∼0.03±${\\sim} 0.03\\pm $0.01 Pg C yr−1${\\text{yr}}^{-1}$(7 ±2%$\\pm 2\\%$ ) to the Southern Ocean carbon uptake, and CEs offset this by ∼0.01±${\\sim} 0.01\\pm $ 0.01 Pg C yr−1${\\text{yr}}^{-1}$(2 ±2%$\\pm 2\\%$ ). These findings underscore the importance of considering eddy impacts in observing networks and climate models. Plain Language Summary Here, we explore the impacts of swirling currents called mesoscale eddies on various biogeochemical properties of the upper ice‐free Southern Ocean. We used data from drifting floats that measure those properties and combined them with data about eddies detected from satellites. We found that eddies changed the dissolved inorganic carbon (DIC), nitrate, and oxygen levels mostly due to a process called eddy pumping, where the eddies push water up or down. Cyclonic eddies, which swirl clockwise in the Southern Hemisphere, tended to bring up deep, nutrient‐rich water, increasing DIC and nitrate levels. Anticyclonic eddies, which swirl counterclockwise in the Southern Hemisphere, tended to push waters down, decreasing DIC and nitrate levels. The effects were stronger in certain regions and during specific seasons, with cyclonic eddies mostly causing more carbon dioxide (CO2${\\text{CO}}_{2}$ ) to be released into the atmosphere, while anticyclonic eddies usually led to more CO2${\\text{CO}}_{2}$being absorbed by the ocean. Eddy impacts on oxygen showed a more complex picture, with higher oxygen near the surface and lower oxygen at depth in cyclonic eddies, and vice versa for anticyclones. Our findings emphasize the importance of considering eddies in measurement strategies and climate models. Key Points Cyclonic eddies pump dissolved inorganic carbon and nitrate upward, leading to less oceanic carbon uptake (anticyclones opposite) Cyclonic eddies pump low‐oxygen water upward (anticyclones opposite), and eddy trapping leads to opposite anomalies at depth The net anomalous eddy‐induced Southern Ocean carbon uptake is ∼0.02 ± 0.02 Pg C yr−1, with larger seasonal and regional signals

Reactive Transport Models (RTMs) are essential tools for understanding and predicting intertwined ecohydrological and biogeochemical processes on land and in rivers. While traditional RTMs have focused primarily on subsurface processes, recent watershed‐scale RTMs have integrated ecohydrological and biogeochemical interactions between surface and subsurface. These emergent, watershed‐scale RTMs are often spatially explicit and require extensive data, computational power, and computational expertise. There is however a pressing need to create parsimonious models that require minimal data and are accessible to scientists with limited computational background. To that end, we have developed BioRT‐HBV 1.0, a watershed‐scale, hydro‐biogeochemical RTM that builds upon the widely used, bucket‐type HBV model known for its simplicity and minimal data requirements. BioRT‐HBV uses the conceptual structure and hydrology output of HBV to simulate processes including advective solute transport and biogeochemical reactions that depend on reaction thermodynamics and kinetics. These reactions include, for example, chemical weathering, soil respiration, and nutrient transformation. The model uses time series of weather (air temperature, precipitation, and potential evapotranspiration) and initial biogeochemical conditions of subsurface water, soils, and rocks as input, and output times series of reaction rates and solute concentrations in subsurface waters and rivers. This paper presents the model structure and governing equations and demonstrates its utility with examples simulating carbon and nitrogen processes in a headwater catchment. As shown in the examples, BioRT‐HBV can be used to illuminate the dynamics of biogeochemical reactions in the invisible, arduous‐to‐measure subsurface, and their influence on the observed stream or river chemistry and solute export. With its parsimonious structure and easy‐to‐use graphical user interface, BioRT‐HBV can be a useful research tool for users without in‐depth computational training. It can additionally serve as an educational tool that promotes pollination of ideas across disciplines and foster a diverse, equal, and inclusive user community. Plain Language Summary Reactive Transport models (RTMs) are essential tools to understand the movement of water, nutrients and other elements from land to rivers and their interactions with each other. Recent watershed scale RTMs, unlike earlier ones that primarily focus on the subsurface processes, have integrated belowground processes and above‐ground dynamics and characteristics including changing weather and vegetation cover. However, these models require large amount of data and are challenging for users with limited computational background. Here we developed BioRT‐HBV 1.0, a parsimonious, watershed‐scale RTM with a graphical user interface that is comparatively easy to learn and use and requires minimal data. BioRT‐HBV can simulate a wide variety of processes like chemical weathering, carbon and nutrient transformation, soil organic carbon decomposition, among others. Here, we introduce the model structure, its governing equations, and examples that demonstrate the use of model in simulating carbon and nitrogen processes. We put forward this model as a potential research and educational tool that can be used by students and researchers from diverse disciplines. Key Points We introduce BioRT‐HBV, a watershed scale reactive transport model that is parsimonious, flexible with reaction network, easy to use and requires minimal data BioRT‐HBV can simulate a variety of user‐defined biogeochemical processes, including carbon and nitrogen processes BioRT‐HBV is open source for any researchers interested in ecohydrological and biogeochemical reactive transport processes

Forest carbon use efficiency (CUE, the ratio of net to gross primary productivity) represents the fraction of photosynthesis that is not used for plant respiration. Although important, it is often neglected in climate change impact analyses. Here we assess the potential impact of thinning on projected carbon cycle dynamics and implications for forest CUE and its components (i.e., gross and net primary productivity and plant respiration), as well as on forest biomass production. Using a detailed process‐based forest ecosystem model forced by climate outputs of five Earth System Models under four representative climate scenarios, we investigate the sensitivity of the projected future changes in the autotrophic carbon budget of three representative European forests. We focus on changes in CUE and carbon stocks as a result of warming, rising atmospheric CO2 concentration, and forest thinning. Results show that autotrophic carbon sequestration decreases with forest development, and the decrease is faster with warming and in unthinned forests. This suggests that the combined impacts of climate change and changing CO2 concentrations lead the forests to grow faster, mature earlier, and also die younger. In addition, we show that under future climate conditions, forest thinning could mitigate the decrease in CUE, increase carbon allocation into more recalcitrant woody pools, and reduce physiological‐climate‐induced mortality risks. Altogether, our results show that thinning can improve the efficacy of forest‐based mitigation strategies and should be carefully considered within a portfolio of mitigation options. Key Points How will C‐fluxes, CUE, and C‐stocks of the major European forest types may respond to elevated atmospheric CO2, warming, and management in the future? Results show that managed forests left unthinned will reduce their CUE and their C‐stocks capability faster under climate change because of accelerated development Results show that thinning may have a large influence on C‐sequestration improving forest efficiency in stocking C as also in preventing risks of forest dieback

Biogeochemical (BGC) ocean models are simplified representations of complex coupled processes, usually resulting in a large number of parameters, that need to be calibrated. In general, these parameters are constrained relying on incomplete and very heterogeneous sets of data. In addition, as biogeochemical tracers strongly depend on ocean circulation, the spatio‐temporal uncertainties in the physical forcing can bias the circulation, which makes the calibration of ocean carbon models challenging. This study addresses the calibration of ocean biogeochemical models when dealing with imperfect physical forcings and sparse observations. We design a numerical testbed based on a simple BGC box model. It comprises different uncertainty scenarios for the physical forcing as well as different observation configurations of the considered nutrient, phytoplankton, zooplankton, detritus dynamics. We propose and benchmark a learning‐based scheme against a variational data assimilation (DA) approach. The former frames the calibration as learning a neural operator between observations and model parameters. The experiments revealed that the DA‐based calibration is highly sensitive to imperfect physical forcing and limited observations, often leading to significant estimation errors in BGC parameters. Conversely, the learning‐based approach demonstrated a greater robustness in parameter estimation and simulated BGC patterns. We discuss further how these results could transfer to more realistic BGC models and real observing systems. Plain Language Summary Ocean carbon models are essential tools for studying climate change, especially its role in the earth's carbon cycle. However, calibrating these models requires diverse data sources, including observational data sets that are often scarce and model‐based data sets that contain significant uncertainties. The quality of these data impacts the accuracy and validity of the models. This study explores the relationship between data quality and model validity using a simple ocean carbon model. Additionally, the study investigates emerging learning methods, as neural networks, that handle imperfect data to represent ocean processes. By comparing a traditional variational data assimilation method with a new learning‐based approach, the study evaluates their effectiveness in model calibration. The results show that the traditional method is highly sensitive to data quality, while the learning method is more robust. As these results are representative of an idealized framework with a simple carbon model, we conclude by discussing how this method could apply to more realistic models. Key Points Calibrating biogeochemical models through assimilation is sensitive to physical forcing uncertainties and to the observation configuration The calibration of the biogeochemical models can be stated as a learning problem The neural method shows a more robust calibration compared to data assimilation when dealing with imperfect forcings, sparse observations

Repeat observations along the meridional Atlantic section A16 from Iceland to 56°S show substantial changes in the total dissolved inorganic carbon (DIC) concentrations in the ocean between occupations from 1989 through 2005. The changes correspond to the expected increase in DIC driven by the uptake of anthropogenic CO2 from the atmosphere, but the ΔDIC is more varied and larger, in some locations, than can be explained solely by this process. Concomitant large changes in oxygen (O2) suggest that processes acting on the natural carbon cycle also contribute to ΔDIC. Precise partial pressure of CO2 measurements suggest small but systematic increases in the bottom waters. To isolate the anthropogenic CO2 component (ΔCanthro) from ΔDIC, an extended multilinear regression approach is applied along isopycnal surfaces. This yields an average depth‐integrated ΔCanthro of 0.53 ± 0.05 mol m−2 yr−1 with maximum values in the temperate zones of both hemispheres and a minimum in the tropical Atlantic. A higher decadal increase in the anthropogenic CO2 inventory is found for the South Atlantic compared to the North Atlantic. This anthropogenic CO2 accumulation pattern is opposite to that seen for the entire Anthropocene up to the 1990s. This change could perhaps be a consequence of the reduced downward transport of anthropogenic CO2 in the North Atlantic due to recent climate variability. Extrapolating the results for this section to the entire Atlantic basin (63°N to 56°S) yields an uptake of 5 ± 1 Pg C decade−1, which corresponds to about 25% of the annual global ocean uptake of anthropogenic CO2 during this period.

Wetland methane (CH4) emissions have a significant impact on the global climate system. However, the current estimation of wetland CH4 emissions at the global scale still has large uncertainties. Here we developed six distinct bottom‐up machine learning (ML) models using in situ CH4 fluxes from both chamber measurements and the Fluxnet‐CH4 network. To reduce uncertainties, we adopted a multi‐model ensemble (MME) approach to estimate CH4 emissions. Precipitation, air temperature, soil properties, wetland types, and climate types are considered in developing the models. The MME is then extrapolated to the global scale to estimate CH4 emissions from 1979 to 2099. We found that the annual wetland CH4 emissions are 146.6 ± 12.2 Tg CH4 yr−1 (1 Tg = 1012 g) from 1979 to 2022. Future emissions will reach 165.8 ± 11.6, 185.6 ± 15.0, and 193.6 ± 17.2 Tg CH4 yr−1 in the last two decades of the 21st century under SSP126, SSP370, and SSP585 scenarios, respectively. Northern Europe and near‐equatorial areas are the current emission hotspots. To further constrain the quantification uncertainty, research priorities should be directed to comprehensive CH4 measurements and better characterization of spatial dynamics of wetland areas. Our data‐driven ML‐based global wetland CH4 emission products for both the contemporary and the 21st century shall facilitate future global CH4 cycle studies. Plain Language Summary Wetland CH4 emissions have a significant impact on the global climate system. However, the current estimation of wetland emissions at the global scale still has large uncertainties. This study developed a multi‐model ensemble (MME) estimate of the emissions using six distinct machine‐learning models and in situ CH4 fluxes from both chamber measurements and the Fluxnet‐CH4 network to reduce uncertainties. Precipitation, air temperature, soil properties, wetland type, and climate type were considered in developing the six models. The MME was then extrapolated to the global scale to estimate emissions from 1979 to 2099. We found that the annual wetland emissions will increase significantly by the last two decades of the 21st century. Northern Europe and near‐equatorial areas are the emission hotspots. Global CH4 has high emissions in the summer while low emissions in the winter. The spatial and temporal variations can be mainly explained by temperature, elevation, soil organic matter, and surface downward solar radiation. The uncertainties mainly come from in situ data with uneven spatial distribution and unchanged assumptions of the wetland area data. Key Points A multi‐model ensemble is conducted to quantify the global wetland methane emissions Estimated global wetland methane emissions are 146.6 ± 12.2 Tg CH4 yr−1 during 1979–2022 and will increase by 13.1% (SSP126), 26.6% (SSP370), and 32.0% (SSP585) by 2080–2099 Temperature, elevation, soil organic matter, and surface downward solar radiation are the most important features in six machine learning wetland methane emission models

Global water erosion strongly affects the terrestrial carbon balance. However, this process is currently ignored by most global land surface models (LSMs) that are used to project the responses of terrestrial carbon storage to climate and land use changes. One of the main obstacles to implement erosion processes in LSMs is the high spatial resolution needed to accurately represent the effect of topography on soil erosion and sediment delivery to rivers. In this study, we present an upscaling scheme for including erosion‐induced lateral soil organic carbon (SOC) movements into the ORCHIDEE LSM. This upscaling scheme integrates information from high‐resolution (3″) topographic and soil erodibility data into a LSM forcing file at 0.5° spatial resolution. Evaluation of our model for the Rhine catchment indicates that it reproduces well the observed spatial and temporal (both seasonal and interannual) variations in river runoff and the sediment delivery from uplands to the river network. Although the average annual lateral SOC flux from uplands to the Rhine River network only amounts to 0.5% of the annual net primary production and 0.01% of the total SOC stock in the whole catchment, SOC loss caused by soil erosion over a long period (e.g., thousands of years) has the potential to cause a 12% reduction in the simulated equilibrium SOC stocks. Overall, this study presents a promising approach for including the erosion‐induced lateral carbon flux from the land to aquatic systems into LSMs and highlights the important role of erosion processes in the terrestrial carbon balance. Plain Language Summary Global land surface models (LSMs) are the main tools used to simulate the terrestrial carbon (C) cycle and to predict its response to climate and land cover changes. Currently, the processes of vertical C fluxes between soils, plants, and the atmosphere (e.g., photosynthesis, plant growth, and litter and soil organic matter decomposition) has been well represented in many LSMs; however, the lateral soil C delivery through the river network caused by water erosion is still missing in most LSMs. This study introduces a LSM approach which is suitable to simulate the large‐scale soil C delivery from upland soils to inland waters at high temporal resolution (daily) and accounting for the small‐scale (~90 m) spatial variability in topography and soil properties. Evaluation of our model in the Rhine catchment demonstrates a good performance with regard to reproducing the spatial and temporal (daily, seasonal, and interannual) variability of soil C delivery rate from uplands to river networks at large spatial scale and for exploring the impacts of changes in vegetation cover (land use change) and climate (e.g., changes in rainfall amounts and regimes) on the regional soil C balance. Key Points We presented an upscaling scheme for including erosion‐induced lateral soil and carbon transfers into a global land surface model Our model is a useful tool to estimate the impacts of climate and land cover changes on erosion‐induced soil carbon loss at large scale Model application for the Rhine basin demonstrates that erosion‐induced soil carbon losses substantially reduce soil carbon stocks