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While it is well accepted that climate change and growing water needs affect long-term sustainable water resources management, performing accurate simulations of water cycle and energy balance dynamics at regional scale remains a challenging task.Traditional Soil-Vegetation-Atmosphere-Transfer (SVAT) models are used for numerical surface water and energy simulations. These models, by conception, do not account for the groundwater lower boundary that permits a full hydrosystem representation. Conversely, while addressing important features such as subsurface heterogeneity and river–aquifer exchanges, groundwater models often integrate overly simplified upper boundary conditions ignoring soil heating and the impacts of vegetation processes on radiation fluxes and root-zone uptakes. In this paper, one of the first attempts to jointly model water and energy fluxes with a special focus on both surface and groundwater at the regional scale is proposed on the Seine hydrosystem (78,650 km$^{2}$), which overlays one of the main multi-aquifer systems of Europe.This study couples the SVAT model ORCHIDEE and the process-based hydrological–hydrogeological model CaWaQS, which describes water fluxes, via a one-way coupling approach from ORCHIDEE toward CaWaQS based on the blueprint published by [de Marsily et al., 1978]. An original transport library based on the resolution of the diffusion/advection transport equation was developed in order to simulate heat transfer in both 1D-river networks and pseudo-3D aquifer systems. In addition, an analytical solution is used to simulate heat transport through aquitards and streambeds. Simulated ORCHIDEE surface water and energy fluxes feed fast surface runoff and slow recharge respectively and then is used as CaWaQS forcings to compute river discharges, hydraulic heads and temperature dynamics through space and time, within each of the hydrosystem compartments. The tool makes it possible to establish a fully consistent water and energy budget over a period of 17 years. It also simulates temperature evolution in each aquifer and evaluates that river thermal regulation mostly relies by order of importance on short wave radiations (109.3 W${\cdot }$m$^{-2}$), groundwater fluxes (48.1 W${\cdot }$m$^{-2}$) and surface runoff (22.7 W${\cdot }$m$^{-2}$).

Southeast Asia is a region known for active land-use changes (LUC) over the past 60 years; yet, how trends in net CO2 uptake and release resulting from LUC activities (net LUC flux) have changed through past decades remains uncertain. The level of uncertainty in net LUC flux from process-based models is so high that it cannot be concluded that newer estimates are necessarily more reliable than older ones. Here, we examined net LUC flux estimates of Southeast Asia for the 1980s−2010s from older and newer sets of Dynamic Global Vegetation Model simulations (TRENDY v2 and v7, respectively), and forcing data used for running those simulations, along with two book-keeping estimates (H&N and BLUE). These estimates yielded two contrasting historical LUC transitions, such that TRENDY v2 and H&N showed a transition from increased emissions from the 1980s to 1990s to declining emissions in the 2000s, while TRENDY v7 and BLUE showed the opposite transition. We found that these contrasting transitions originated in the update of LUC forcing data, which reduced the loss of forest area during the 1990s. Further evaluation of remote sensing studies, atmospheric inversions, and the history of forestry and environmental policies in Southeast Asia supported the occurrence of peak emissions in the 1990s and declining thereafter. However, whether LUC emissions continue to decline in Southeast Asia remains uncertain as key processes in recent years, such as conversion of peat forest to oil-palm plantation, are yet to be represented in the forcing data, suggesting a need for further revision.

With the growing complexity of land surface models used to represent the terrestrial part of wider Earth system models, the need for sophisticated and robust parameter optimisation techniques is paramount. Quantifying parameter uncertainty is essential for both model development and more accurate projections. In this study, we assess the power of history matching by comparing results to the variational data assimilation approach commonly used in land surface models for parameter estimation. Although both approaches have different setups and goals, we can extract posterior parameter distributions from both methods and test the model–data fit of ensembles sampled from these distributions. Using a twin experiment, we test whether we can recover known parameter values. Through variational data assimilation, we closely match the observations. However, the known parameter values are not always contained in the posterior parameter distribution, highlighting the equifinality of the parameter space. In contrast, while more conservative, history matching still gives a reasonably good fit and provides more information about the model structure by allowing for non-Gaussian parameter distributions. Furthermore, the true parameters are contained in the posterior distributions. We then consider history matching's ability to ingest different metrics targeting different physical parts of the model, thus helping to reduce the parameter space further and improve the model–data fit. We find the best results when history matching is used with multiple metrics; not only is the model–data fit improved, but we also gain a deeper understanding of the model and how the different parameters constrain different parts of the seasonal cycle. We conclude by discussing the potential of history matching in future studies. [ABSTRACT FROM AUTHOR]

For the past decade, observations of carbonyl sulfide (OCS or COS) have been investigated as a proxy for carbon uptake by plants. OCS is destroyed by enzymes that interact with CO2 during photosynthesis, namely carbonic anhydrase (CA) and RuBisCO, where CA is the more important one. The majority of sources of OCS to the atmosphere are geographically separated from this large plant sink, whereas the sources and sinks of CO2 are co-located in ecosystems. The drawdown of OCS can therefore be related to the uptake of CO2 without the added complication of co-located emissions comparable in magnitude. Here we review the state of our understanding of the global OCS cycle and its applications to ecosystem carbon cycle science. OCS uptake is correlated well to plant carbon uptake, especially at the regional scale. OCS can be used in conjunction with other independent measures of ecosystem function, like solar-induced fluorescence and carbon and water isotope studies. More work needs to be done to generate global coverage for OCS observations and to link this powerful atmospheric tracer to systems where fundamental questions concerning the carbon and water cycle remain.

Abstract. For the past decade, observations of carbonyl sulfide (OCS or COS) have been investigated as a proxy for carbon uptake by plants. OCS is destroyed by enzymes that interact with CO2 during photosynthesis, namely carbonic anhydrase (CA) and RuBisCO, where CA is the more important. The majority of sources of OCS to the atmosphere are geographically separated from this large plant sink, whereas the sources and sinks of CO2 are co-located in ecosystems. The drawdown of OCS can therefore be related to the uptake of CO2 without the added complication of co-located emissions comparable in magnitude. Here we review the state of our understanding of the global OCS cycle and its applications to ecosystem carbon cycle science. OCS uptake is correlated well to plant carbon uptake, especially at the regional scale. OCS can be used in conjunction with other independent measures of ecosystem function, like solar-induced fluorescence and carbon and water isotope studies. More work needs to be done to generate global coverage for OCS observations and to link this powerful atmospheric tracer to systems where fundamental questions concerning the carbon and water cycle remain.

Current climate models disagree on how much carbon dioxide land ecosystems take up for photosynthesis. Tracking the stronger carbonyl sulfide signal could help.

Land surface temperature (LST) plays an essential role in water and energy exchanges between Earth's surface and atmosphere. Recent advancements in high-quality satellite-derived LST data and land data assimilation systems present a unique opportunity to bridge the gap between global observational data and land surface models (LSMs) to better constrain the water/energy budgets in a changing climate. In this vein, this study focuses on the assimilation of the ESA-CCI LST product into the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) with the aim of optimizing key parameters to improve the simulations of LST and surface energy fluxes. We use the land data assimilation system for the ORCHIDEE model (ORCHIDAS) to conduct a series of synthetic twin data assimilation experiments accounting for real data availability and uncertainty from ESA-CCI LST to find an optimal strategy for assimilating LST. Here, we test different strategies of assimilation, notably investigating: i) two optimization methods (random-search and gradient-based) and ii) different ways to assimilate LST using the only raw data and/or different characteristics of the LST diurnal cycle (e.g. mean daily, daily amplitude, maximum and minimum temperatures, morning and afternoon gradients). Upon identifying the optimal approach, we use it to assimilate ESA-CCI LST data across 34 sites in Europe provided by the WarmWinter database. Our results demonstrate the effectiveness of assimilating 3-hourly CCI-LST data over a single year in 2018, improving the accuracy of simulated LST and fluxes. This improvement, assessed against CCI-LST and in situ observations, reaches up to 60 % reduction in root mean square deviation, with a median reduction of 20 % over the entire validation period (2009–2020). Furthermore, we evaluate the effectiveness of optimized parameters for application at larger scales by using the median of optimized parameters per vegetation type across sites. Notably, the performances for both LST and fluxes exhibit not only consistent stability over the years, comparable to using site-specific parameters, but also indicate a significant improvement in the modeled fluxes. Future works will be focused on refining the utilization of the observation uncertainties provided by the ESA-CCI LST product (e.g. decomposed uncertainties and spatio-temporal variability) in the assimilation process. [ABSTRACT FROM AUTHOR]

Predicting the responses of terrestrial ecosystem carbon to future global change strongly relies on our ability to model accurately the underlying processes at a global scale. However, terrestrial biosphere models representing the carbon and nitrogen cycles and their interactions remain subject to large uncertainties, partly because of unknown or poorly constrained parameters. Parameter estimation is a powerful tool that can be used to optimise these parameters by confronting the model with observations. In this paper, we identify sensitive model parameters from a recent version of the ORgainzing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model that includes the nitrogen cycle. These sensitive parameters include ones involved in parameterisations controlling the impact of the nitrogen cycle on the carbon cycle and, in particular, the limitation of photosynthesis due to leaf nitrogen availability. We optimise these ORCHIDEE parameters against carbon flux data collected on sites from the FLUXNET network. However, optimising against present-day observations does not automatically give us confidence in future projections of the model, given that environmental conditions are likely to shift compared to the present day. Manipulation experiments give us a unique look into how the ecosystem may respond to future environmental changes. One such type of manipulation experiment, the Free Air CO 2 Enrichment (FACE) experiment, provides a unique opportunity to assess vegetation response to increasing CO 2 by providing data under ambient and elevated CO 2 conditions. Therefore, to better capture the ecosystem response to increased CO 2 , we add the data from two FACE sites to our optimisations, in addition to the FLUXNET data. We use data from both CO 2 conditions of FACE, which allows us to gain extra confidence in the model simulations using this set of parameters. We find that we are able to improve the magnitude of modelled productivity. Although we are unable to correct the interannual variability fully, we start to simulate possible progressive nitrogen limitation at one of the sites. Using an idealised simulation experiment based on increasing atmospheric CO 2 by 1 % yr -1 over 100 years, we find that optimising against only FLUXNET data tends to imply a large fertilisation effect, whereas optimising against FLUXNET and FACE data (with information about nutrient limitation and acclimation of plants) decreases it significantly. [ABSTRACT FROM AUTHOR]

A key goal of the Paris Agreement (PA) is to reach net-zero greenhouse gas (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa's rapidly growing population and gross domestic product (GDP) make this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2 O) in Africa over 3 decades (1990–2018). We compare bottom-up (BU) approaches, including United Nations Convention Framework on Climate Change (UNFCCC) national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO 2 fluxes in the land use, land use change and forestry (LULUCF) sector and global atmospheric inversions. For inversions, we applied different methods to separate anthropogenic CH 4 emissions. The BU inventories show that, over the decade 2010–2018, fewer than 10 countries represented more than 75 % of African fossil CO 2 emissions. With a mean of 1373 Mt CO 2 yr -1 , total African fossil CO 2 emissions over 2010–2018 represent only 4 % of global fossil emissions. However, these emissions grew by +34% from 1990–1999 to 2000–2009 and by +31% from 2000–2009 to 2010–2018, which represents more than a doubling in 30 years. This growth rate is more than 2 times faster than the global growth rate of fossil CO 2 emissions. The anthropogenic emissions of CH 4 grew by 5 % from 1990–1999 to 2000–2009 and by 14.8 % from 2000–2009 to 2010–2018. The N 2 O emissions grew by 19.5 % from 1990–1999 to 2000–2009 and by 20.8 % from 2000–2009 to 2010–2018. When using the mean of the estimates from UNFCCC reports (including the land use sector) with corrections from outliers, Africa was a mean source of greenhouse gases of 262221863239 Mt CO 2 eq. yr -1 from all BU estimates (the subscript and superscript indicate min–max range uncertainties) and of +263717615873 Mt CO 2 eq. yr -1 from top-down (TD) methods during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of TD estimates is larger than the one of the BU estimates, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty in BU estimates. The main source of uncertainty comes from CO 2 fluxes in the LULUCF sector, for which the spread across inversions is larger than 50 %, especially in central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022). The medians of CH 4 emissions from inversions based on satellite retrievals and surface station networks are consistent with each other within 2 % at the continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH 4 emissions with BU inventories such as PRIMAP-hist. For N 2 O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N 2 O sources cannot be separated accurately from anthropogenic ones in inversions or because BU estimates ignore indirect emissions and underestimate emission factors. Future improvements can be expected thanks to a denser network of monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows us to compute budgets thanks to recombinations of those data products. Our results allow us to understand uncertainty and trends in emissions and removals in a region of the world where few observations exist and where most inventories are based on default IPCC guideline values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to the figures are available at 10.5281/zenodo.7347077 (Mostefaoui et al., 2022). [ABSTRACT FROM AUTHOR]

Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its Global Stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023) and provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU) and seven additional countries with large anthropogenic and/or natural emissions (USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of Congo (DR Congo)). The work utilizes updated National GHG Inventories (NGHGIs) reported by Annex I Parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available Biennial Update Reports (BURs) reported by non-Annex I Parties. The NGHGIs are considered in an integrated analysis that also relies on independent flux estimates from global inventory datasets, process-based models, inverse modeling and, when available, respective uncertainties. Whenever possible, it extends the period to 2021. Comparing NGHGIs with other approaches reveals that differences in the emission sources that are included in the estimate is a key source of divergence between approaches. A key system boundary difference is whether both anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are grouped/partitioned. Additionally, the natural fluxes are sensitive to the prior geospatial distribution of emissions in atmospheric inversions. Over the studied period, the total CH4 emissions in the EU, USA, and Russia show a steady decreasing trend since 1990, while for the non-EU emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. In the EU, the anthropogenic BU approaches are reporting relatively similar mean emissions over 2015 to 2020 of 18.5 ± 2.7 Tg CH4 yr-1 for EDGAR v7.0, 16 Tg CH4 yr-1 for GAINS and 19 Tg CH4 yr-1 for FAOSTAT, with the NGHGI estimates of 15 ± 1.8 Tg CH4 yr-1. Inversions give higher emission estimates as they include natural emissions. Over the same period, the three high-resolution regional inversions report a mean emission of 21 (19-25) Tg CH4 yr-1, while the mean of six coarser-resolution global inversions results in emission estimates of 24 (23-25) Tg CH4 yr-1. The magnitude of BU natural emissions (peatland and mineral soils, lakes and reservoirs, geological and biomass burning) accounts for 6.6 Tg CH4 yr-1 (Petrescu et al., 2023a) and explains the differences between the TD inversions and the BU estimates of anthropogenic emissions (including NGHGIs). For the other Annex I Parties in this study (USA and Russia), over 2015 to 2020, the mean of the four anthropogenic BU approaches reports 18.5 (13-27.9) Tg CH4 yr-1 for Russia and 29.1 (23.5- Tg CH4 yr-1 for the USA, against total TD mean estimates of 37 (30-43) Tg CH4 yr-1 and 43.4 (42-48) Tg CH4 yr-1, respectively. The averaged BU and TD natural emissions account for 16.2 Tg CH4 yr-1 for Russia and 14.6 Tg CH4 yr-1 for the USA, partly explaining the gap between the BU anthropogenic and total TD emissions. For the non-Annex I Parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences with the other global inventory-based estimates and even more with atmospheric-based ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the Global Stocktake, not only from the availability of data but also its accuracy. By systematically comparing the BU with TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more Parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in both BU and TD approaches and to better quantify remaining uncertainty. Consequently, TD methods may emerge as a powerful tool for verifying emission inventories for CH4, and other GHGs and informing international climate policy. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.10276087 (Petrescu et al., 2023b). [ABSTRACT FROM AUTHOR]

Abstract. Earth System Models (ESM) represent the time evolution of the biophysical (energy, water cycles) and biogeochemical (carbon cycle) components of the Earth. When used for near-future projections in the context of the Coupled Model Intercomparison Project (CMIP), they use as forcings the evolution of greenhouse gas and other pollutant concentrations and land-use changes simulated by an ensemble of Integrated Assessment Models (IAMs) for a combination of socio-economic pathways and mitigation targets (SSPs). More precisely, only one IAM output is used as representative of a single SSP while the inter-IAM spread is large for ammonia emissions and land-use changes, for instance. This makes the comparison of key ESM diagnostics among SSPs significantly noisy, without the capacity of disentangling SSP-driven and IAM-driven factors. In this paper, we quantify the projected change in land carbon store (CLCS) for the different SSPs with an advanced version of a land surface model embedded into IPSL-CM6 ESM. Through a set of land-only factorial simulations, we specifically aim at estimating the CLCS uncertainties associated with land-use change and nitrogen deposition trajectories. We showed that the spread of the simulated change in global land carbon store induced by the uncertainty on land-use changes is slightly larger than the one associated with the uncertainty on atmospheric CO2. Globally, uncertainty associated with N depositions is responsible for a spread in CLCS lower by a factor three, than the one driven by atmospheric CO2 or land-use changes. Our study calls for making available additional IAM scenarios for each SSP to be used in the next CMIP exercise, in order to specifically assess the IAM-related uncertainty impacts on the carbon cycle and the climate system. [ABSTRACT FROM AUTHOR]

Inverse estimation of carbon dioxide (CO2) sources and sinks uses atmospheric CO2 observations, mostly made near the Earth's surface. However, transport models used in such studies lack perfect representation of atmospheric dynamics and thus often fail to produce unbiased forward simulations. The error is generally larger for observations over the land than those over the remote/marine locations. The range of this error is estimated by using multiple transport models (16 are used here). We have estimated the remaining differences in CO2 fluxes due to the use of ocean-only versus all-sites (i.e., over ocean and land) observations of CO2 in a time-independent inverse modeling framework. The fluxes estimated using the ocean-only networks are more robust compared to those obtained using all-sites networks. This makes the global, hemispheric, and regional flux determination less dependent on the selection of transport model and observation network.

The TransCom 3 experiment was begun to explore the estimation of carbon sources and sinks via the inversion of simulated tracer transport. We build upon previous TransCom work by presenting the seasonal inverse results which provide estimates of carbon flux for 11 land and 11 ocean regions using 12 atmospheric transport models. The monthly fluxes represent the mean seasonal cycle for the 1992 to 1996 time period. The spread among the model results is larger than the average of their estimated flux uncertainty in the northern extratropics and vice versa in the tropical regions. In the northern land regions, the model spread is largest during the growing season. Compared to a seasonally balanced biosphere prior flux generated by the CASA model, we find significant changes to the carbon exchange in the European region with greater growing season net uptake which persists into the fall months. Both Boreal North America and Boreal Asia show lessened net uptake at the onset of the growing season with Boreal Asia also exhibiting greater peak growing season net uptake. Temperate Asia shows a dramatic springward shift in the peak timing of growing season net uptake relative to the neutral CASA flux while Temperate North America exhibits a broad flattening of the seasonal cycle. In most of the ocean regions, the inverse fluxes exhibit much greater seasonality than that implied by the ΔpCO2 derived fluxes though this may be due, in part, to misallocation of adjacent land flux. In the Southern Ocean, the austral spring and fall exhibits much less carbon uptake than implied by ΔpCO2 derived fluxes. Sensitivity testing indicates that the inverse estimates are not overly influenced by the prior flux choices. Considerable agreement exists between the model mean, annual mean results of this study and that of the previously published TransCom annual mean inversion. The differences that do exist are in poorly constrained regions and tend to exhibit compensatory fluxes in order to match the global mass constraint. The differences between the estimated fluxes and the prior model over the northern land regions could be due to the prior model respiration response to temperature. Significant phase differences, such as that in the Temperate Asia region, may be due to the limited observations for that region. Finally, differences in the boreal land regions between the prior model and the estimated fluxes may be a reflection of the timing of spring thaw and an imbalance in respiration versus photosynthesis

Spatial and temporal variations of atmospheric CO2 concentrations contain information about surface sources and sinks, which can be quantitatively interpreted through tracer transport inversion. Previous CO2 inversion calculations obtained differing results due to different data, methods and transport models used. To isolate the sources of uncertainty, we have conducted a set of annual mean inversion experiments in which 17 different transport models or model variants were used to calculate regional carbon sources and sinks from the same data with a standardized method. Simulated transport is a significant source of uncertainty in these calculations, particularly in the response to prescribed “background” fluxes due to fossil fuel combustion, a balanced terrestrial biosphere, and air–sea gas exchange. Individual model-estimated fluxes are often a direct reflection of their response to these background fluxes. Models that generate strong surface maxima near background exchange locations tend to require larger uptake near those locations. Models with weak surface maxima tend to have less uptake in those same regions but may infer small sources downwind. In some cases, individual model flux estimates cannot be analyzed through simple relationships to background flux responses but are likely due to local transport differences or particular responses at individual CO2 observing locations. The response to the background biosphere exchange generates the greatest variation in the estimated fluxes, particularly over land in the Northern Hemisphere. More observational data in the tropical regions may help in both lowering the uncertain tropical land flux uncertainties and constraining the northern land estimates because of compensation between these two broad regions in the inversion. More optimistically, examination of the model-mean retrieved fluxes indicates a general insensitivity to the prior fluxes and the prior flux uncertainties. Less uptake in the Southern Ocean than implied by oceanographic observations, and an evenly distributed northern land sink, remain in spite of changes in this aspect of the inversion setup.]

Information about regional carbon sources and sinks can be derived from variations in observed atmospheric CO2 concentrations via inverse modelling with atmospheric tracer transport models. A consensus has not yet been reached regarding the size and distribution of regional carbon fluxes obtained using this approach, partly owing to the use of several different atmospheric transport models. Here we report estimates of surface-atmosphere CO2 fluxes from an intercomparison of atmospheric CO2 inversion models (the TransCom 3 project), which includes 16 transport models and model variants. We find an uptake of CO2 in the southern extratropical ocean less than that estimated from ocean measurements, a result that is not sensitive to transport models or methodological approaches. We also find a northern land carbon sink that is distributed relatively evenly among the continents of the Northern Hemisphere, but these results show some sensitivity to transport differences among models, especially in how they respond to seasonal terrestrial exchange of CO2. Overall, carbon fluxes integrated over latitudinal zones are strongly constrained by observations in the middle to high latitudes. Further significant constraints to our understanding of regional carbon fluxes will therefore require improvements in transport models and expansion of the CO2 observation network within the tropics.