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Land surface models (LSMs), which form the land component of earth system models, rely on numerous processes for describing carbon, water and energy budgets, often associated with highly uncertain parameters. Data assimilation (DA) is a useful approach for optimising the most critical parameters in order to improve model accuracy and refine future climate predictions. In this study, we compare two different DA methods for optimising the parameters of seven plant functional types (PFTs) of the ORCHIDEE LSM using daily averaged eddy-covariance observations of net ecosystem exchange and latent heat flux at 78 sites across the globe. We perform a technical investigation of two classes of minimisation methods – local gradient-based (the L-BFGS-B algorithm, limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints) and global random search (the genetic algorithm) – by evaluating their relative performance in terms of the model–data fit and the difference in retrieved parameter values. We examine the performance of each method for two cases: when optimising parameters at each site independently ("single-site" approach) and when simultaneously optimising the model at all sites for a given PFT using a common set of parameters ("multi-site" approach). We find that for the single site case the random search algorithm results in lower values of the cost function (i.e. lower model–data root mean square differences) than the gradient-based method; the difference between the two methods is smaller for the multi-site optimisation due to a smoothing of the cost function shape with a greater number of observations. The spread of the cost function, when performing the same tests with 16 random first-guess parameters, is much larger with the gradient-based method, due to the higher likelihood of being trapped in local minima. When using pseudo-observation tests, the genetic algorithm results in a closer approximation of the true posterior parameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challenges of different DA techniques and provide some advice on using it for the LSM parameter optimisation. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs), used within earth system models, rely on numerous processes for describing carbon, water and energy budgets, often associated with highly uncertain parameters. Data assimilation (DA) is a useful approach for optimising the most critical parameters in order to improve model accuracy and refine future climate predictions. In this study, we compare two different DA methods for optimising the parameters of seven plant functional types (PFTs) of the ORCHIDEE land surface model using daily averaged eddy-covariance observations of net ecosystem exchange and latent heat flux at 78 sites across the globe. We perform a technical investigation of two classes of minimisation methods - local gradient-based (the L-BFGS-B algorithm) and global random search (the genetic algorithm) - by evaluating their relative performance in terms of the model-data fit and the difference in retrieved parameter values. We examine the performance of each method for two cases: when optimising parameters at each site independently ("single-site" approach) and when simultaneously optimising the model at all sites for a given PFT using a common set of parameters ("multi-site" approach). We find that for the single site case the random search algorithm results in lower values of the cost function (i.e. lower model - data root mean square differences) than the gradient-based method; the difference between the two methods is smaller for the multi-site optimisation due to a smoothing of the cost function shape with a greater number of observations. The spread of the cost function, when performing the same tests with 16 random first guess parameters, is much larger with the gradient based method, due to the higher likelihood of being trap in local minima. When using pseudo-observations tests the genetic algorithm results in a closer approximation of the true posterior parameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challenges of different DA techniques and provide some advice on using it for the LSM parameters optimisation. [ABSTRACT FROM AUTHOR]

Canopy structure is one of the most important vegetation characteristics for land-atmosphere interactions, as it determines the energy and scalar exchanges between the land surface and the overlying air mass. In this study we evaluated the performance of a newly developed multi-layer energy budget in the land surface model ORCHIDEE-CAN (Organising Carbon and Hydrology In Dynamic Ecosystems - CANopy), which simulates canopy structure and can be coupled to an atmospheric model using an implicit coupling procedure. We aim to provide a set of acceptable parameter values for a range of forest types. Top-canopy and sub-canopy flux observations from eight sites were collected in order to conduct this evaluation. The sites crossed climate zones from temperate to boreal and the vegetation types included deciduous, evergreen broad leaved and evergreen needle leaved forest with a maximum LAI (all-sided) ranging from 3.5 to 7.0. The parametrization approach proposed in this study was based on three selected physical processes - namely the diffusion, advection and turbulent mixing within the canopy. Short-term sub-canopy observations and long-term surface fluxes were used to calibrate the parameters in the sub-canopy radiation, turbulence and resistances modules with an automatic tuning process. The multi-layer model was found to capture the dynamics of sub-canopy turbulence, temperature and energy fluxes. The performance of the new multi-layer model was further compared against the existing single-layer model. Although, the multi-layer model simulation results showed little or no improvements to both the nighttime energy balance and energy partitioning during winter compared with a single-layer model simulation, the increased model complexity does provide a more detailed description of the canopy micrometeorology of various forest types. The multi-layer model links to potential future environmental and ecological studies such as the assessment of in-canopy species vulnerability to climate change, the climate effects of disturbance intensities and frequencies, and the consequences of biogenic volatile organic compounds (BVOC) emissions from the terrestrial ecosystem. [ABSTRACT FROM AUTHOR]

The implementation of boundary conditions is a key aspect of climate simulations. We describe here how the Climate Model Intercomparison Project Phase 6 (CMIP6) forcing data sets have been processed and implemented in Version 6 of the Institut Pierre‐Simon Laplace (IPSL) climate model (IPSL‐CM6A‐LR) as used for CMIP6. Details peculiar to some of the Model Intercomparison Projects are also described. IPSL‐CM6A‐LR is run without interactive chemistry; thus, tropospheric and stratospheric aerosols as well as ozone have to be prescribed. We improved the aerosol interpolation procedure and highlight a new methodology to adjust the ozone vertical profile in a way that is consistent with the model dynamical state at the time step level. The corresponding instantaneous and effective radiative forcings have been estimated and are being presented where possible. Plain Language Summary: Climate Model Intercomparison Project Phase 6 is an international project to compare the results from climate model simulations performed according to a common protocol. Such simulations require boundary conditions (called "climate forcings"), which are fed to the models in order to represent, for example, long‐lived greenhouse gases, ozone, atmospheric aerosols, or land surface properties. The same forcing data sets are used by the different modeling groups who carry out the Climate Model Intercomparison Project Phase 6 simulations; however, their implementation may differ as it depends on the model structure. This article gives details of how these forcing data were implemented in the IPSL‐CM6A‐LR model. Some of the forcing data are common to all types all simulations, whereas others depend on the runs considered. Radiative forcings, as estimated in the model, are presented for some of the forcing mechanisms. Key Points: We present how the CMIP6 forcing data were implemented in the IPSL‐CM6A‐LR climate model for the realization of the CMIP6 set of climate simulationsAn improved conservative interpolation procedure for emissions is detailed and illustrated to compute tropospheric aerosolsWe present a new methodology to adjust the prescribed ozone vertical profile to match the model atmospheric dynamical state around the tropopause [ABSTRACT FROM AUTHOR]

Quantification of land surface–atmosphere fluxes of carbon dioxide (CO 2) and their trends and uncertainties is essential for monitoring progress of the EU27 + UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO 2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO 2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27 + UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27 + UK NGHGI were evaluated using the standard deviation reported by the EU member states following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO 2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO 2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO 2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27 + UK accounts for 926 ± 13 Tg C yr -1 , while eight other BU sources report a mean value of 948 [ 937,961 ] Tg C yr -1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875 Tg C in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO 2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for - 91 ± 32 Tg C yr -1 , while six other BU approaches reported a mean sink of - 62 [ -117,-49 ] Tg C yr -1 , and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported - 69 [ -152,-5 ] Tg C yr -1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of - 73 Tg C yr -1 has a slightly smaller spread (0th–100th percentiles of [ -135,+45 ] Tg C yr -1), and it was calculated after removing net land–atmosphere CO 2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO 2 fossil and net CO 2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27 + UK. We conclude that CO 2 emissions from fossil sources have decreased over the past 30 years in the EU27 + UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (- 0.61) Tg C yr -2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000 Tg C yr -1 between CO 2 fossil emissions and net CO 2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO 2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at 10.5281/zenodo.8148461 (McGrath et al., 2023). [ABSTRACT FROM AUTHOR]

New (a)biotic conditions, resulting from climate change, are expected to change disturbance dynamics, e.g., wind throw, forest fires and insect outbreaks, and their interactions. Unprecedented natural disturbance 30 dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases in the atmosphere, even leading to the risk that forests transform from sinks into sources of CO2. This study aims to enhance the capability of the ORCHIDEE land surface model to study the impacts of climate change on bark beetle dynamics and subsequent effects on forest functioning. The bark beetle outbreak model is based on previous work by Temperli et al. 2013 for the LandClim landscape model. The new implementation of this model in ORCHIDEE r7791 35 accounts for the following differences between ORCHIDEE and LandClim: (1) the coarser spatial resolution of ORCHIDEE, (2) the higher temporal resolution of ORCHIDEE, and (3) the pre-existing process representation of wind throw, drought, and forest structure in ORCHIDEE. Qualitative evaluation demonstrated the model's ability to simulate a wide range of observed post-disturbance forest dynamics: (1) resistance to bark beetle infestation even in the presence of windthrow events; (2) slow transition (3-7 years) from an endemic into an epidemic bark beetle population following medium intensity window events at cold locations; and (3) fast transition (1-3 years) from endemic to epidemic triggered by strong windthrow events. Although all simulated sites eventually recovered from disturbances, the time needed to recover varied from 5 to 10 years depending on the disturbance dynamics. In addition to enhancing the functionality of the ORCHIDEE model, the new bark beetle model represents a fundamental change in the way mortality is simulated as it replaces a framework in which mortality is conceived as a continuous process by one in which mortality is represented by abrupt events. Changing the mortality framework provided new insights into carbon balance estimates, showing the risk of overestimating the short-term sequestration potential under the commonly used continuous mortality framework. [ABSTRACT FROM AUTHOR]

Greenland ice sheet mass loss continues to accelerate as global temperatures increase. The surface albedo of the ice sheet determines the amount of absorbed solar energy, which is a key factor in driving surface snow and ice melting. Satellite-retrieved snow albedo allows us to compare and optimise modelled albedo over the entirety of the ice sheet. We optimise the parameters of the albedo scheme in the ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) land surface model for 3 random years taken over the 2000–2017 period and validate over the remaining years. In particular, we want to improve the albedo at the edges of the ice sheet, since they correspond to ablation areas and show the greatest variations in runoff and surface mass balance. By giving a larger weight to points at the ice sheet's edge, we improve the model–data fit by reducing the root-mean-square deviation by over 25 % for the whole ice sheet for the summer months. This improvement is consistent for all years, even those not used in the calibration step. We also show the optimisation successfully improves the model–data fit at 87.5 % of in situ sites from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) network. We conclude by showing which additional model outputs are impacted by changes to the albedo parameters, encouraging future work using multiple data streams when optimising these parameters. [ABSTRACT FROM AUTHOR]

Global change ecology nowadays embraces ever‐growing large observational datasets (big‐data) and complex mathematical models that track hundreds of ecological processes (big‐model). The rapid advancement of the big‐data‐big‐model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time‐scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine‐learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource‐consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin‐up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin‐up, we show that an unoptimized MLA reduced the computation demand by 77%–80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA‐derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one‐order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade‐off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin‐up acceleration procedures, and opens the door to a wide variety of future applications, with complex non‐linear models benefit most from the computational efficiency. [ABSTRACT FROM AUTHOR]

The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992–2020) of annual land cover maps at 300 m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992–2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300 m resolution indicate the percentage cover (0 %–100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models – Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) – to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at 10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023). [ABSTRACT FROM AUTHOR]

Canopy structure is one of the most important vegetation characteristics for land-atmosphere interactions, as it determines the energy and scalar exchanges between the land surface and the overlying air mass. In this study we evaluated the performance of a newly developed multilayer energy budget in the ORCHIDEE-CAN v1.0 land surface model (Organising Carbon and Hydrology In Dynamic Ecosystems - CANopy), which simulates canopy structure and can be coupled to an atmospheric model using an implicit coupling procedure. We aim to provide a set of acceptable parameter values for a range of forest types. Top-canopy and sub-canopy flux observations from eight sites were collected in order to conduct this evaluation. The sites crossed climate zones from temperate to boreal and the vegetation types included deciduous, evergreen broad-leaved and evergreen needle-leaved forest with a maximum leaf area index (LAI; all-sided) ranging from 3.5 to 7.0. The parametrization approach proposed in this study was based on three selected physical processes - namely the diffusion, advection, and turbulent mixing within the canopy. Short-term sub-canopy observations and long-term surface fluxes were used to calibrate the parameters in the sub-canopy radiation, turbulence, and resistance modules with an automatic tuning process. The multi-layer model was found to capture the dynamics of sub-canopy turbulence, temperature, and energy fluxes. The performance of the new multi-layer model was further compared against the existing single-layer model. Although the multi-layer model simulation results showed few or no improvements to both the nighttime energy balance and energy partitioning during winter compared with a single-layer model simulation, the increased model complexity does provide a more detailed description of the canopy micrometeorology of various forest types. The multi-layer model links to potential future environmental and ecological studies such as the assessment of in-canopy species vulnerability to climate change, the climate effects of disturbance intensities and frequencies, and the consequences of biogenic volatile organic compound (BVOC) emissions from the terrestrial ecosystem. [ABSTRACT FROM AUTHOR]

Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) fluxes and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021b). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven sectoral model results, and inverse modeling estimates, extending the previous period 1990-2018 to the year 2020 to the extent possible. BU and TD products are compared with European National Greenhouse Gas Inventories (NGHGIs) reported by Parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. [ABSTRACT FROM AUTHOR]

The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992-2020) of annual land cover maps at 300-m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need 2D parameters rather than 2D land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992-2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300-m resolution indicate the percentage cover (0-100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g., re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models - ORCHIDEE and JULES - to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. [ABSTRACT FROM AUTHOR]

In the global methane budget, the largest natural source is attributed to wetlands, which encompass all ecosystems composed of waterlogged or inundated ground, capable of methane production. Among them, northern peatlands that store large amounts of soil organic carbon have been functioning, since the end of the last glaciation period, as long-term sources of methane (CH 4) and are one of the most significant methane sources among wetlands. To reduce uncertainty of quantifying methane flux in the global methane budget, it is of significance to understand the underlying processes for methane production and fluxes in northern peatlands. A methane model that features methane production and transport by plants, ebullition process and diffusion in soil, oxidation to CO 2 , and CH 4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model that includes an explicit representation of northern peatlands. ORCHIDEE-PCH 4 was calibrated and evaluated on 14 peatland sites distributed on both the Eurasian and American continents in the northern boreal and temperate regions. Data assimilation approaches were employed to optimized parameters at each site and at all sites simultaneously. Results show that methanogenesis is sensitive to temperature and substrate availability over the top 75 cm of soil depth. Methane emissions estimated using single site optimization (SSO) of model parameters are underestimated by 9 g CH 4 m -2 yr -1 on average (i.e., 50 % higher than the site average of yearly methane emissions). While using the multi-site optimization (MSO), methane emissions are overestimated by 5 g CH 4 m -2 yr -1 on average across all investigated sites (i.e., 37 % lower than the site average of yearly methane emissions). [ABSTRACT FROM AUTHOR]

Most land surface models can, depending on the simulation experiment, calculate the vegetation distribution and dynamics internally by making use of biogeographical principles or use vegetation maps to prescribe spatial and temporal changes in vegetation distribution. Irrespective of whether vegetation dynamics are simulated or prescribed, it is not practical to represent vegetation across the globe at the species level because of its daunting diversity. This issue can be circumvented by making use of 5 to 20 plant functional types (PFTs) by assuming that all species within a single functional type show identical land–atmosphere interactions irrespective of their geographical location. In this study, we hypothesize that remote-sensing-based assessments of aboveground biomass can be used to constrain the process in which real-world vegetation is discretized in PFT maps. Remotely sensed biomass estimates for Africa were used in a Bayesian framework to estimate the probability density distributions of woody, herbaceous and bare soil fractions for the 15 land cover classes, according to the United Nations Land Cover Classification System (UN-LCCS) typology, present in Africa. Subsequently, the 2.5th and 97.5th percentiles of the probability density distributions were used to create 2.5 % and 97.5 % credible interval PFT maps. Finally, the original and constrained PFT maps were used to drive biomass and albedo simulations with the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) model. This study demonstrates that remotely sensed biomass data can be used to better constrain the share of dense forest PFTs but that additional information on bare soil fraction is required to constrain the share of herbaceous PFTs. Even though considerable uncertainties remain, using remotely sensed biomass data enhances the objectivity and reproducibility of the process by reducing the dependency on expert knowledge and allows assessing and reporting the credible interval of the PFT maps which could be used to benchmark future developments. [ABSTRACT FROM AUTHOR]

Abstract: The main sources of uncertainties for grab sampling, short-term (charcoal canisters) and long term (track detectors) measurements are: systematic bias of reference equipment; random Poisson and non-Poisson errors during calibration; random Poisson and non-Poisson errors during measurements. The origins of non-Poisson random errors during calibration are different for different kinds of instrumental measurements. The main sources of uncertainties for retrospective measurements conducted by surface traps techniques can be divided in two groups: errors of surface 210Pb (210Po) activity measurements and uncertainties of transfer from 210Pb surface activity in glass objects to average radon concentration during this object exposure. It''s shown that total measurement error of surface trap retrospective technique can be decreased to 35%. [Copyright &y& Elsevier]

In the global methane budget, the largest natural source is attributed to wetlands that encompass all ecosystems composed of waterlogged or inundated ground, capable of methane production. Among them, northern peatlands that store large amounts of soil organic carbon have been functioning, since the end of the last glaciation period, as long-term sources of methane (CH4) and are one of the most significant methane sources among wetlands. To reduce global methane budget uncertainties, it is of significance to understand processes driving methane production and fluxes in northern peatlands. A methane model that features methane production and transport by plants, ebullition process and diffusion in soil, oxidation to CO2 and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4 was calibrated and evaluated on 14 peatland sites distributed on both Eurasian and American continents in the northern boreal and temperate regions. Data assimilation approaches were employed to optimized parameters at each site and at all sites simultaneously. Results show that, in ORCHIDEE-PCH4, methanogenesis is sensitive to temperature and substrate availability over the top 75 cm of soil depth. Methane emissions estimated using single site optimization (SSO) of model parameters are underestimated by 9 g CH4 m-2 year-1 on average (i.e. 50% higher than the site average of yearly methane emissions). While using the multi-sites optimization (MSO), methane emissions are overestimated by 5 g CH4 m-2 year-1 on average across all investigated sites (i.e. 37% lower than the site average of yearly methane emissions). [ABSTRACT FROM AUTHOR]

Most land surface models can either calculate the vegetation distribution and dynamics internally by making use of biogeographical principles or use vegetation maps to prescribe spatial and temporal changes in vegetation distribution. Irrespective of whether vegetation dynamics are simulated or prescribed, it is not practical to represent vegetation across the globe at the species level because of its daunting diversity. This issue can be circumvented by making use of 5 to 20 plant functional types (PFT) by assuming that all species within a single functional type show identical land-atmosphere interactions irrespective of their geographical location. In this study, we hypothesize that remote-sensing based assessments of above-ground biomass can be used to refine discretizing real-world vegetation in PFT maps. Remotely sensed biomass estimates for Africa were used in a Bayesian framework to estimate the probability density distributions of woody, herbaceous, and bare soil fractions for the 15 land cover classes, according to the UN- LCCS typology, present in Africa. Subsequently, the 2.5 and 97.5 percentile of the probability density distributions were used to create 2.5 % and 97.5 % confidence interval PFT maps. Finally the original and refined PFT maps were used to drive biomass and albedo simulations with the ORCHIDEE model. This study demonstrates that remotely sensed biomass data can be used to better constrain PFT maps. Among the advantages of using remotely sensed biomass data were the reduced dependency on expert knowledge and the ability to report the confident interval of the PFT maps. Applying this approach at the global scale, would increase confidence in the PFT maps underlying assessments of present day biomass stocks. [ABSTRACT FROM AUTHOR]

The rate at which land surface soils dry following rain events is an important feature of terrestrial models. It determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, surface soil moisture (SSM) "drydowns," i.e., the SSM temporal dynamics following a significant rainfall event, are of particular interest when evaluating and calibrating land surface models (LSMs). By investigating drydowns, characterized by an exponential decay time scale τ, we aim to improve the representation of SSM in the ORCHIDEE global LSM. We consider τ calculated over 18 International Soil Moisture Network sites found within the footprint of FLUXNET towers, covering different vegetation types and climates. Using the ORCHIDEE LSM, we compare τ from the modeled SSM time series to values computed from in situ SSM measurements. We then assess the potential of using τ observations to constrain some water, carbon, and energy parameters of ORCHIDEE, selected using a sensitivity analysis, through a standard Bayesian optimization procedure. The impact of the SSM optimization is evaluated using FLUXNET evapotranspiration and gross primary production (GPP) data. We find that the relative drydowns of SSM can be well calibrated using observation-based τ estimates, when there is no need to match the absolute observed and modeled SSM values. When evaluated using independent data, τ-calibration parameters were able to improve drydowns for 73% of the sites. Furthermore, the fit of the model to independent fluxes was only minutely changed. We conclude by considering the potential of global satellite products to scale up the experiment to a global-scale optimization. [ABSTRACT FROM AUTHOR]

This work is motivated by the identification of the land‐atmosphere interactions as one of the key sources of uncertainty in climate change simulations. It documents new developments in related processes, namely, boundary layer/convection/clouds parameterizations and land surface parameterization in the Earth System Model of the Institut Pierre Simon Laplace (IPSL). Simulations forced by prescribed oceanic conditions are produced with different combinations of atmospheric and land surface parameterizations. They are used to explore the sensitivity to the atmospheric physics and/or soil physics of major biases in the near surface variables over continents,the energy and moisture coupling established at the soil/atmosphere interface in not too wet (energy limited) and not too dry (moisture limited) soil moisture regions also known as transition or "hot‐spot" regions,the river runoff at the outlet of major rivers. The package implemented in the IPSL‐Climate Model for the Phase 6 of the Coupled Models Intercomparison Project (CMIP6) allows us to reduce several biases in the surface albedo, the snow cover, and the continental surface air temperature in summer as well as in the temperature profile in the surface layer of the polar regions. The interactions between soil moisture and atmosphere in hotspot regions are in better agreement with the observations. Rainfall is also significantly improved in volume and seasonality in several major river basins leading to an overall improvement in river discharge. However, the lack of consideration of floodplains and human influences in the model, for example, dams and irrigation, impacts the realism of simulated discharge. Plain Language Summary: Land surface‐atmosphere interactions play an essential role in the climate system. They strongly modulate the regional climates and have impacts on the global scale for instance through freshwater release into the oceans. Climate hazards (heat waves, droughts) and their impacts on populations also strongly depend on interactions between land and atmosphere and on their evolution with climate change. Climate models are precious tools to investigate how the Earth climate behaves. The sixth phase of the Climate Model Intercomparison Project (CMIP6) provides important tools to measure the progress and address the remaining open questions regarding the continental climate modeling. The representation of the land‐atmosphere coupled system by the IPSL‐Climate Model involved in CMIP6 is thoroughly evaluated against observations and compared with simulations using the CMIP5 version. Several biases concerning the temperature over land and over the ice sheets and with the snow cover are significantly reduced. Numerous improvements were made developping advanced parameterizations and tuning of the radiation and of the turbulent mixing in the atmospheric model. The realism of the seasonal cycle of hydrological variables such as the precipitation or the river discharge is also improved over many regions. The new treatment of hydrology paves the way for future developments on water resource aspects in the climate model. Key Points: The representation of the land‐atmosphere coupled system by the IPSL model is thoroughly evaluatedImprovements with respect to previous versions are documented in the context of the Coupled Model Intercomparison Project, CMIPAdvanced parameterization of land and atmospheric processes, tuning of the radiation, and the turbulent mixing yielded many improvements [ABSTRACT FROM AUTHOR]

This study presents the global climate model IPSL‐CM6A‐LR developed at Institut Pierre‐Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Niño–Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL‐CM5A‐LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850–2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed. Plain Language Summary: Climate models are unique tools to investigate the characteristics and behavior of the climate system. While climate models and their components are developed gradually over the years, the sixth phase of the Coupled Model Intercomparison Project (CMIP6) has been the opportunity for the Institut Pierre‐Simon Laplace to develop, test, and evaluate a new configuration of its climate model called IPSL‐CM6A‐LR. The characteristics and emerging properties of this new model are presented in this study. The model climatology, as assessed from a range of metrics, is strongly improved, although a number of biases common to many models do persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL‐CM5A‐LR used in CMIP5. Key Points: The IPSL‐CM6A‐LR model climatology is much improved over the previous version, although some systematic biases and shortcomings persistA long preindustrial control and a large number of historical and scenario simulations have been performed as part of CMIP6The effective climate sensitivity of the IPSL model increases from 4.1 to 4.8 K between IPSL‐CM5A‐LR and IPSL‐CM6A‐LR [ABSTRACT FROM AUTHOR]

Nitrogen is an essential element controlling ecosystem carbon (C) productivity and its response to climate change and atmospheric [ CO2 ] increase. This study presents the evaluation – focussing on gross primary production (GPP) – of a new version of the ORCHIDEE model that gathers the representation of the nitrogen cycle and of its interactions with the carbon cycle from the OCN model and the most recent developments from the ORCHIDEE trunk version. We quantify the model skills at 78 FLUXNET sites by simulating the observed mean seasonal cycle, daily mean flux variations, and annual mean average GPP flux for grasslands and forests. Accounting for carbon–nitrogen interactions does not substantially change the main skills of ORCHIDEE, except for the site-to-site annual mean GPP variations, for which the version with carbon–nitrogen interactions is in better agreement with observations. However, the simulated GPP response to idealised [ CO2 ] enrichment simulations is highly sensitive to whether or not carbon–nitrogen interactions are accounted for. Doubling of the atmospheric [ CO2 ] induces an increase in the GPP, but the site-averaged GPP response to a CO2 increase projected by the model version with carbon–nitrogen interactions is half of the increase projected by the version without carbon–nitrogen interactions. This model's differentiated response has important consequences for the transpiration rate, which is on average 50 mm yr -1 lower with the version with carbon–nitrogen interactions. Simulated annual GPP for northern, tropical and southern latitudes shows good agreement with the observation-based MTE-GPP (model tree ensemble gross primary production) product for present-day conditions. An attribution experiment making use of this new version of ORCHIDEE for the time period 1860–2016 suggests that global GPP has increased by 50 %, the main driver being the enrichment of land in reactive nitrogen (through deposition and fertilisation), followed by the [ CO2 ] increase. Based on our factorial experiment and sensitivity analysis, we conclude that if carbon–nitrogen interactions are accounted for, the functional responses of ORCHIDEE r4999 better agree with the current understanding of photosynthesis than when the carbon–nitrogen interactions are not accounted for and that carbon–nitrogen interactions are essential in understanding global terrestrial ecosystem productivity. [ABSTRACT FROM AUTHOR]

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the "global carbon budget" – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr -1 , ELUC 1.5±0.7 GtC yr -1 , GATM 4.9±0.02 GtC yr -1 (2.3±0.01 ppm yr -1), SOCEAN 2.5±0.6 GtC yr -1 , and SLAND 3.2±0.6 GtC yr -1 , with a budget imbalance BIM of 0.4 GtC yr -1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr -1 , reaching 10 GtC yr -1 for the first time in history, ELUC was 1.5±0.7 GtC yr -1 , for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr -1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr -1 (2.4±0.1 ppm yr -1), SOCEAN was 2.6±0.6 GtC yr -1 , and SLAND was 3.5±0.7 GtC yr -1 , with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of -0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr -1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at 10.18160/gcp-2019 (Friedlingstein et al., 2019). [ABSTRACT FROM AUTHOR]

Nitrogen is an essential element controlling ecosystem carbon (C) productivity and its response to climate change and atmospheric [CO2] increase. This study presents the evaluation - focussing on gross primary production (GPP) - of a new version of the ORCHIDEE model that gathers the representation of the nitrogen cycle and of its interactions with the carbon cycle from the OCN model and the most recent developments from the ORCHIDEE trunk version. We quantify the model skills at 78 Fluxnet sites by simulating the observed mean seasonal cycle, daily mean flux variations, and annual mean average GPP flux for grasslands and forests. Accounting for carbon-nitrogen interactions does not substantially change the main skills of ORCHIDEE, except for the site-to-site annual mean GPP variations, for which the version with carbon-nitrogen interactions is in better agreement to observations. However, the simulated GPP response to idealized [CO2] enrichment simulations is highly sensitive to whether or not carbon-nitrogen interactions are accounted for. Doubling of the atmospheric [CO2] induces an increase of the GPP, but the site-averaged GPP response to CO2 increase projected by the model version with carbon-nitrogen interactions is half of the increase projected by the version without carbon-nitrogen interactions. This model's differentiated response has important consequences for the transpiration rate, which is on average 50 mm yr-1 lower with the version with carbon-nitrogen interactions. Simulated annual GPP for northern, tropical and southern latitudes shows good agreement with the observation-based MTE-GPP product for present-day conditions. An attribution experiment making use of this new version of ORCHIDEE for the time period 1860-2016 suggests that global GPP has increased by 50%, the main driver being the enrichment of land in reactive nitrogen (through deposition and fertilization), followed by the [CO2] increase. Based on our factorial experiment and sensitivity analysis, we conclude that if carbon-nitrogen interactions are accounted for, the functional responses of ORCHIDEE r4999 better agrees with current understanding of photosynthesis than when the carbon-nitrogen interactions are not accounted for, and that carbon-nitrogen interactions are essential in understanding global terrestrial ecosystem productivity. [ABSTRACT FROM AUTHOR]

Current land surface models (LSMs) typically represent soils in a very simplistic way, assuming soil organic carbon (SOC) as a bulk, and thus impeding a correct representation of deep soil carbon dynamics. Moreover, LSMs generally neglect the production and export of dissolved organic carbon (DOC) from soils to rivers, leading to overestimations of the potential carbon sequestration on land. This common oversimplified processing of SOC in LSMs is partly responsible for the large uncertainty in the predictions of the soil carbon response to climate change. In this study, we present a new soil carbon module called ORCHIDEESOM, embedded within the land surface model ORCHIDEE, which is able to reproduce the DOC and SOC dynamics in a vertically discretized soil to 2 m. The model includes processes of biological production and consumption of SOC and DOC, DOC adsorption on and desorption from soil minerals, diffusion of SOC and DOC, and DOC transport with water through and out of the soils to rivers. We evaluated ORCHIDEE-SOM against observations of DOC concentrations and SOC stocks from four European sites with different vegetation covers: a coniferous forest, a deciduous forest, a grassland, and a cropland. The model was able to reproduce the SOC stocks along their vertical profiles at the four sites and the DOC concentrations within the range of measurements, with the exception of the DOC concentrations in the upper soil horizon at the coniferous forest. However, the model was not able to fully capture the temporal dynamics of DOC concentrations. Further model improvements should focus on a plant- and depth-dependent parameterization of the new input model parameters, such as the turnover times of DOC and the microbial carbon use efficiency. We suggest that this new soil module, when parameterized for global simulations, will improve the representation of the global carbon cycle in LSMs, thus helping to constrain the predictions of the future SOC response to global warming. [ABSTRACT FROM AUTHOR]

Simulation of vegetation-climate feedbacks in high latitudes in the ORCHIDEE land surface model was improved by the addition of three new circumpolar plant functional types (PFTs), namely non-vascular plants representing bryophytes and lichens, Arctic shrubs and Arctic C3 grasses. Non-vascular plants are assigned no stomatal conductance, very shallow roots, and can desiccate during dry episodes and become active again during wet periods, which gives them a larger phenological plasticity (i.e. adaptability and resilience to severe climatic constraints) compared to grasses and shrubs. Shrubs have a specific carbon allocation scheme, and differ from trees by their larger survival rates in winter, due to protection by snow. Arctic C3 grasses have the same equations as in the original ORCHIDEE version, but different parameter values, optimised from in situ observations of biomass and net primary productivity (NPP) in Siberia. In situ observations of living biomass and productivity from Siberia were used to calibrate the parameters of the new PFTs using a Bayesian optimisation procedure. With the new PFTs, we obtain a lower NPP by 31% (from 55° N), as well as a lower roughness length (-41%), transpiration (-33%) and a higher winter albedo (by C3.6%) due to increased snow cover. A simulation of the water balance and runoff and drainage in the high northern latitudes using the new PFTs results in an increase of fresh water discharge in the Arctic ocean by 11% (+140 km³ yr-1), owing to less evapotranspiration. Future developments should focus on the competition between these three PFTs and boreal tree PFTs, in order to simulate their area changes in response to climate change, and the effect of carbon-nitrogen interactions. [ABSTRACT FROM AUTHOR]

Current Land Surface Models (LSMs) typically represent soils in a very simplistic way, assuming soil organic carbon (SOC) as a bulk, thus impeding a correct representation of deep soil carbon dynamics. Moreover, LSMs generally neglect the production and export of dissolved organic carbon (DOC) from soils to rivers, leading to overestimations of the potential carbon sequestration on land. These common oversimplified processing of SOC in LSMs is partly responsible for the large uncertainty in the predictions of the soil carbon response to climate change. In this study, we present a new soil carbon module called ORCHIDEE-SOM, embedded within the land surface model ORCHIDEE, which is able to reproduce the DOC and SOC dynamics in a vertically discretized soil to two meters. The model includes processes of biological production and consumption of SOC and DOC, DOC adsorption on- and desorption from soil minerals, diffusion of SOC and DOC and DOC transport with water through and out of the soils to rivers. We evaluated ORCHIDEE-SOM against observations of DOC concentrations and SOC stocks from four European sites with different vegetation covers: a coniferous forest, a deciduous forest, a grassland and a cropland. The model was able to reproduce the SOC stocks along their vertical profiles at the four sites and the DOC concentrations within the range of measurements, with the exception of the DOC concentrations in the upper soil horizon at the coniferous forest. However, the model was not able to fully capture the temporal dynamics of DOC concentrations. Further model improvements should focus on a plant- and depth- dependent parameterization of the new input model parameters, such as the decomposition times of DOC and the microbial carbon use efficiency. We suggest that this new soil module, when parameterized for global simulations, will improve the representation of the global carbon cycle in LSMs, thus helping to constrain the predictions of the future SOC response to global warming. [ABSTRACT FROM AUTHOR]

By offsetting about one fourth of carbon dioxide (CO2) emissions released by anthropogenic activities into the atmosphere, terrestrial ecosystems play a pivotal role for climate. They assimilate CO2 in leaf chloroplasts by photosynthesis, but most of the assimilated carbon is released back into the atmosphere through ecosystem respiration. Although the net carbon budget of terrestrial ecosystems is the main quantity of interest for climate studies, quantifying the gross fluxes is crucial in order to better understand the drivers of the net fluxes. As highlighted by the large spread in global land surface model (LSM) simulations relative to the spatio-temporal patterns of the terrestrial gross primary productivity (GPP), large uncertainties remain in our understanding of the carbon sequestration in land surfaces. Part of these uncertainties result from unknown or poorly calibrated parameters in LSMs. Over the past decade, the use of space-borne retrievals of solar-induced fluorescence (SIF) as a proxy of GPP at large temporal and spatial scales has grown in importance. Their use for constraining LSMs implies that these models are able to simulate SIF observables and represent the functional links between GPP and SIF. In this study, we present the development of a SIF observation operator in the ORCHIDEE LSM. It relies on i) the representation of the fluorescence yield of photosystem II (PSII) at the leaf scale to simulate the dynamic regulation of SIF depending on meteorological conditions (e.g. incoming PAR, temperature, relative humidity, concentration of CO2 at the leaf level), and on ii) the calculation of the total canopy fluorescence (PSI and PSII) based on a parametric modelling of the 1D radiative transfer model in SCOPE enabling fast computation of the radiative transfer. The calculation of the yield of PSII fluorescence depends on new parametric model to estimate the total Non Photochemical Quenching.We then apply a Bayesian assimilation framework to assimilate monthly OCO-2-SIF product at 0.5° over 2015-2016 and hence calibrate ORCHIDEE over an ensemble of pixels for all vegetation plant functional types (PFTs). Through the optimization of photosynthesis and phenological ORCHIDEE parameters, the feedback on the simulated GPP is considerable with a decrease of the global budget by 33 GtC.yr-1 over the 1990-2009 period (from 162 GtC.yr-1 with the standard model parameters to 129 GtC.yr-1 after assimilation). Although the optimized global GPP budget gets in close agreement with that of independent FLUXCOM GPP products (Tramontana et al., 2016) (121 GtC.yr-1 in average over the same period), the spatio-temporal distribution of the improvement of ORCHIDEE relative to both OCO-2-SIF observations and FLUXCOM-GPP estimates show contrasted results between ecosystems. Indeed, our assimilation framework results in opposite variations in the modeled SIF and GPP for some PFTs. This suggest a biome dependency of the SIF-GPP relationship, likely through differences in PSII fluorescence yield at the leaf scale or in the radiative transfer regime, that needs to be resolve to enhance the observational constraint brought by space-borne SIF products on vegetation productivity. We discuss the benefits of using SIF data to constrain LSMs with regards to other available observations. [ABSTRACT FROM AUTHOR]

Soil moisture plays a key role in water, carbon and energy exchanges between the land surface and the atmosphere. Therefore, a better representation of this variable in the Land-Surface Models (LSMs) used in climate modelling could significantly reduce the uncertainties associated with future climate predictions. In this study, the ESA-CCI soil moisture (SM) combined product (v4.2) has been confronted to the simulated top-first layers/cms of the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) for the years 2008-2016, to evaluate its potential to improve the model using data assimilation techniques. The ESA-CCI data are first rescaled to match the climatology of the model and the signal representative depth is selected. Results are found to be relatively consistent over the first 20 cm of the model. Strong correlations found between the model and the ESA-CCI product show that ORCHIDEE can adequately reproduce the observed SM dynamics. As well as considering two different atmospheric forcings to drive the model, we consider two different model parameterizations related to the soil resistance to evaporation. The correlation metric is shown to be more sensitive to the choice of meteorological forcing than to the choice of model parameterization. Therefore, the metric is not optimal in highlighting structural deficiencies in the model. In contrast, the temporal autocorrelation metric is shown to be more sensitive to this model parameterization, making the metric a potential candidate for future data assimilation experiments. [ABSTRACT FROM AUTHOR]