Vladislav Bastrikov - Academia.edu (original) (raw)

Papers by Vladislav Bastrikov

Environmental Research Letters

. In the global methane budget, the largest natural source is attributed to wetlands that encompa... more . 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).

Journal of Hydrometeorology

The rate at which land surface soils dry following rain events is an important feature of terrest... more 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 o...

. Most land surface models can either calculate the vegetation distribution and dynamics internal... more . 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.

Journal of Advances in Modeling Earth Systems

Journal of Advances in Modeling Earth Systems

The implementation of boundary conditions is a key aspect of climate simulations. We describe her... more 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.

Environmental Research Letters

Environmental Research Letters

<p>Australia plays an important role in the global terrestrial carbon cycle on inte... more <p>Australia plays an important role in the global terrestrial carbon cycle on inter-annual timescales. While the Australian continent is included in global assessments of the carbon cycle, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome productivity (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, leading to marked differences in carbon accumulation in the vegetation on decadal to centennial timescales. We showed that the spread in carbon storage resulted from differences in simulated carbon residence time rather than differences in net carbon uptake. Differences in simulated long-term accumulated NBP between models were mostly due to model responses to land-use change. The DGVMs also simulated different sensitivities to atmospheric CO<sub>2</sub> concentration. Notably, models with nutrient cycles did not simulate the smallest response. While our results suggested that changes in the climate forcing do not have a large impact on the carbon cycle on long timescales, the inter-annual variability in precipitation drives the year-to-year variability in NBP. We analysed the impact of key modes of climate variability, including the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). While the DGVMs agreed on sign of the response of NBP to El Niño and La Niña, and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differs strongly between models. In addition, we identified simulated phenology and fire as associated with high model uncertainty, indicating differences in simulated vegetation composition and process representation. Model disagreement in simulated vegetation carbon, phenology and carbon residence time imply different types of vegetation cover across Australia between models, whether prescribed or resulting from model assumptions. Our study highlights the need to evaluate parameter assumptions and key processes that drive vegetation dynamics, such as phenology, mortality and fire, in an Australian context to reduce uncertainty across models.</p>

Atmospheric Chemistry and Physics

Observations from the Orbiting Carbon Observatory 2 (OCO-2) satellite have been used to estimate ... more Observations from the Orbiting Carbon Observatory 2 (OCO-2) satellite have been used to estimate CO 2 fluxes in many regions of the globe and provide new insight into the global carbon cycle. The objective of this study is to infer the relationships between patterns in OCO-2 observations and environmental drivers (e.g., temperature, precipitation) and therefore inform a process understanding of carbon fluxes using OCO-2. We use a multiple regression and inverse model, and the regression coefficients quantify the relationships between observations from OCO-2 and environmental driver datasets within individual years for 2015-2018 and within seven global biomes. We subsequently compare these inferences to the relationships estimated from 15 terrestrial biosphere models (TBMs) that participated in the TRENDY model inter-comparison. Using OCO-2, we are able to quantify only a limited number of relationships between patterns in atmospheric CO 2 observations and patterns in environmental driver datasets (i.e., 10 out of the 42 relationships examined). We further find that the ensemble of TBMs exhibits a large spread in the relationships with these key environmental driver datasets. The largest uncertainty in the models is in the relationship with precipitation, particularly in the tropics, with smaller uncertainties for temperature and photosynthetically active radiation (PAR). Using observations from OCO-2, we find that precipitation is associated with increased CO 2 uptake in all tropical biomes, a result that agrees with half of the TBMs. By contrast, the relationships that we infer from OCO-2 for temperature and PAR are similar to the ensemble mean of the TBMs, though the results differ from many individual TBMs. These results point to the limitations of current space-based observations for inferring environmental relationships but also indicate the potential to help inform key relationships that are very uncertain in stateof-the-art TBMs.

Journal of Advances in Modeling Earth Systems

<p>... more <p>The rate at which land surface soils are drying following rain events is an important feature of terrestrial models since it determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, soil moisture (SM) “drydowns”, i.e. the SM temporal dynamic following a significant rainfall event, are of particular interest when evaluating and calibrating land-surface models. By investigating drydowns, characterized by an exponential decay time scale metric τ, we aim to improve the representation of soil moisture in the ORCHIDEE global land-surface model. In this presentation, we consider τ calculated over a number of ISMN (International Soil Moisture Network) sites found within the footprint of FLUXNET towers. These in-situ sites cover a range of vegetation types and climates. Using the ORCHIDEE land-surface model, we first compare τ from the modelled SM timeseries to the same values computed from the in-situ SM measurements. We then assess the potential of using τ as a data assimilation metric to constrain some parameters of the ORCHIDEE model through a standard Bayesian optimisation procedure; we first select a number of key of water, carbon, and energy parameters through a sensitivity analysis. The optimised soil moisture timeseries are evaluated using the FLUXNET evapotranspiration and GPP data. We conclude by considering the potential of  global satellite products like SMOS or the ESA-CCI surface SM satellite data in order to scale up the experiment to a global scale optimisation.</p>

Accurate assessment of anthropogenic carbon dioxide (CO 2) emissions and their redistribution amo... more Accurate assessment of anthropogenic carbon dioxide (CO 2) 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 CO 2 emissions (E FF) are based on energy statistics and cement production data, while emissions from land use change (E LUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly and its growth rate (G ATM) is computed from the annual changes in concentration. The ocean CO 2 sink (S OCEAN) and terrestrial CO 2 sink (S LAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B IM), 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), E FF was 9.5±0.5 GtC yr −1 , E LUC 1.5±0.7 GtC yr −1 , G ATM 4.9±0.02 GtC yr −1 (2.3±0.01 ppm yr −1), S OCEAN 2.5±0.6 GtC yr −1 , and S LAND 3.2±0.6 GtC yr −1 , with a budget imbalance B IM of 0.4 GtC yr −1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E FF 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, E LUC was 1.5 ± 0.7 GtC yr −1 , for total anthropogenic CO 2 emissions of 11.5 ± 0.9 GtC yr −1 (42.5 ± 3.3 GtCO 2). Also for 2018, G ATM was 5.1 ± 0.2 GtC yr −1 (2.4 ± 0.1 ppm yr −1), S OCEAN was 2.6 ± 0.6 GtC yr −1 , and S LAND was 3.5±0.7 GtC yr −1 , with a B IM of 0.3 GtC. The global atmospheric CO 2 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 E FF 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 CO 2 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 CO 2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO 2 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.

Geoscientific Model Development Discussions

Land surface models (LSMs), used within earth system models, rely on numerous processes for descr... more 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 methodslocal 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.

Hydrology and Earth System Sciences

River discharge plays an important role in earth's water cycle, but it is difficult to estimate d... more River discharge plays an important role in earth's water cycle, but it is difficult to estimate due to un-gauged rivers, human activities and measurement errors. One approach is based on the observed flux and a simple annual water balance model (ignoring human processes) for un-gauged rivers, but it only provides annual mean values which is insufficient for oceanic modelings. Another way is by forcing a land surface model (LSM) with atmospheric conditions. It provides daily values but with uncertainties associated with the models. We use data assimilation techniques by merging the modeled river discharges by the ORCHIDEE (without human processes currently) LSM and the observations from the Global Runoff Data Centre (GRDC) to obtain optimized discharges over the entire basin. The "model systematic errors" and "human impacts" (dam operation, irrigation, etc.) are taken into account by an optimization parameter x (with annual variation), which is applied to correct model intermediate variable runoff and drainage over each sub-watershed. The method is illustrated over the Iberian Peninsula with 27 GRDC stations over the period 1979-1989. ORCHIDEE represents a realistic discharge over the north of the Iberian Peninsula with small model systematic errors, while the model overestimates discharges by 30-150 % over the south and northeast regions where the blue water footprint is large. The normalized bias has been significantly reduced to less than 30 % after assimilation, and the assimilation result is not sensitive to assimilation strategies. This method also corrects the discharge bias for the basins without observations assimilated by extrapolating the correction from adjacent basins. The "correction" increases the interannual variability in river discharge because of the fluctuation of water usage. The E (P − E) of GLEAM (Global Land Evaporation Amsterdam Model, v3.1a) is lower (higher) than the bias-corrected value, which could be due to the different P forcing and probably the missing processes in the GLEAM model.

Geoscientific Model Development

* TropEBF-tropical evergreen broadleaf forest; TempENF-temperate evergreen needleleaf forest; Tem... more * TropEBF-tropical evergreen broadleaf forest; TempENF-temperate evergreen needleleaf forest; TempEBF-temperate evergreen broadleaf forest; TempDBF-temperate deciduous broadleaf forest; BorENF-boreal evergreen needleleaf forest; BorDBF-boreal deciduous broadleaf forest; C 3 grass-C 3 grassland.

Geoscientific Model Development

Current land surface models (LSMs) typically represent soils in a very simplistic way, assuming s... more 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 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 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 Published by Copernicus Publications on behalf of the European Geosciences Union. 938 M. Camino-Serrano et al.: ORCHIDEE-SOM 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.

Environmental Research Letters

. In the global methane budget, the largest natural source is attributed to wetlands that encompa... more . 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).

Journal of Hydrometeorology

The rate at which land surface soils dry following rain events is an important feature of terrest... more 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 o...

. Most land surface models can either calculate the vegetation distribution and dynamics internal... more . 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.

Journal of Advances in Modeling Earth Systems

Journal of Advances in Modeling Earth Systems

The implementation of boundary conditions is a key aspect of climate simulations. We describe her... more 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.

Environmental Research Letters

Environmental Research Letters

<p>Australia plays an important role in the global terrestrial carbon cycle on inte... more <p>Australia plays an important role in the global terrestrial carbon cycle on inter-annual timescales. While the Australian continent is included in global assessments of the carbon cycle, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome productivity (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, leading to marked differences in carbon accumulation in the vegetation on decadal to centennial timescales. We showed that the spread in carbon storage resulted from differences in simulated carbon residence time rather than differences in net carbon uptake. Differences in simulated long-term accumulated NBP between models were mostly due to model responses to land-use change. The DGVMs also simulated different sensitivities to atmospheric CO<sub>2</sub> concentration. Notably, models with nutrient cycles did not simulate the smallest response. While our results suggested that changes in the climate forcing do not have a large impact on the carbon cycle on long timescales, the inter-annual variability in precipitation drives the year-to-year variability in NBP. We analysed the impact of key modes of climate variability, including the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). While the DGVMs agreed on sign of the response of NBP to El Niño and La Niña, and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differs strongly between models. In addition, we identified simulated phenology and fire as associated with high model uncertainty, indicating differences in simulated vegetation composition and process representation. Model disagreement in simulated vegetation carbon, phenology and carbon residence time imply different types of vegetation cover across Australia between models, whether prescribed or resulting from model assumptions. Our study highlights the need to evaluate parameter assumptions and key processes that drive vegetation dynamics, such as phenology, mortality and fire, in an Australian context to reduce uncertainty across models.</p>

Atmospheric Chemistry and Physics

Observations from the Orbiting Carbon Observatory 2 (OCO-2) satellite have been used to estimate ... more Observations from the Orbiting Carbon Observatory 2 (OCO-2) satellite have been used to estimate CO 2 fluxes in many regions of the globe and provide new insight into the global carbon cycle. The objective of this study is to infer the relationships between patterns in OCO-2 observations and environmental drivers (e.g., temperature, precipitation) and therefore inform a process understanding of carbon fluxes using OCO-2. We use a multiple regression and inverse model, and the regression coefficients quantify the relationships between observations from OCO-2 and environmental driver datasets within individual years for 2015-2018 and within seven global biomes. We subsequently compare these inferences to the relationships estimated from 15 terrestrial biosphere models (TBMs) that participated in the TRENDY model inter-comparison. Using OCO-2, we are able to quantify only a limited number of relationships between patterns in atmospheric CO 2 observations and patterns in environmental driver datasets (i.e., 10 out of the 42 relationships examined). We further find that the ensemble of TBMs exhibits a large spread in the relationships with these key environmental driver datasets. The largest uncertainty in the models is in the relationship with precipitation, particularly in the tropics, with smaller uncertainties for temperature and photosynthetically active radiation (PAR). Using observations from OCO-2, we find that precipitation is associated with increased CO 2 uptake in all tropical biomes, a result that agrees with half of the TBMs. By contrast, the relationships that we infer from OCO-2 for temperature and PAR are similar to the ensemble mean of the TBMs, though the results differ from many individual TBMs. These results point to the limitations of current space-based observations for inferring environmental relationships but also indicate the potential to help inform key relationships that are very uncertain in stateof-the-art TBMs.

Journal of Advances in Modeling Earth Systems

<p>... more <p>The rate at which land surface soils are drying following rain events is an important feature of terrestrial models since it determines, for example, the water availability for vegetation, the occurrences of droughts, and the surface heat exchanges. As such, soil moisture (SM) “drydowns”, i.e. the SM temporal dynamic following a significant rainfall event, are of particular interest when evaluating and calibrating land-surface models. By investigating drydowns, characterized by an exponential decay time scale metric τ, we aim to improve the representation of soil moisture in the ORCHIDEE global land-surface model. In this presentation, we consider τ calculated over a number of ISMN (International Soil Moisture Network) sites found within the footprint of FLUXNET towers. These in-situ sites cover a range of vegetation types and climates. Using the ORCHIDEE land-surface model, we first compare τ from the modelled SM timeseries to the same values computed from the in-situ SM measurements. We then assess the potential of using τ as a data assimilation metric to constrain some parameters of the ORCHIDEE model through a standard Bayesian optimisation procedure; we first select a number of key of water, carbon, and energy parameters through a sensitivity analysis. The optimised soil moisture timeseries are evaluated using the FLUXNET evapotranspiration and GPP data. We conclude by considering the potential of  global satellite products like SMOS or the ESA-CCI surface SM satellite data in order to scale up the experiment to a global scale optimisation.</p>

Accurate assessment of anthropogenic carbon dioxide (CO 2) emissions and their redistribution amo... more Accurate assessment of anthropogenic carbon dioxide (CO 2) 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 CO 2 emissions (E FF) are based on energy statistics and cement production data, while emissions from land use change (E LUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly and its growth rate (G ATM) is computed from the annual changes in concentration. The ocean CO 2 sink (S OCEAN) and terrestrial CO 2 sink (S LAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B IM), 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), E FF was 9.5±0.5 GtC yr −1 , E LUC 1.5±0.7 GtC yr −1 , G ATM 4.9±0.02 GtC yr −1 (2.3±0.01 ppm yr −1), S OCEAN 2.5±0.6 GtC yr −1 , and S LAND 3.2±0.6 GtC yr −1 , with a budget imbalance B IM of 0.4 GtC yr −1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E FF 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, E LUC was 1.5 ± 0.7 GtC yr −1 , for total anthropogenic CO 2 emissions of 11.5 ± 0.9 GtC yr −1 (42.5 ± 3.3 GtCO 2). Also for 2018, G ATM was 5.1 ± 0.2 GtC yr −1 (2.4 ± 0.1 ppm yr −1), S OCEAN was 2.6 ± 0.6 GtC yr −1 , and S LAND was 3.5±0.7 GtC yr −1 , with a B IM of 0.3 GtC. The global atmospheric CO 2 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 E FF 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 CO 2 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 CO 2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO 2 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.

Geoscientific Model Development Discussions

Land surface models (LSMs), used within earth system models, rely on numerous processes for descr... more 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 methodslocal 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.

Hydrology and Earth System Sciences

River discharge plays an important role in earth's water cycle, but it is difficult to estimate d... more River discharge plays an important role in earth's water cycle, but it is difficult to estimate due to un-gauged rivers, human activities and measurement errors. One approach is based on the observed flux and a simple annual water balance model (ignoring human processes) for un-gauged rivers, but it only provides annual mean values which is insufficient for oceanic modelings. Another way is by forcing a land surface model (LSM) with atmospheric conditions. It provides daily values but with uncertainties associated with the models. We use data assimilation techniques by merging the modeled river discharges by the ORCHIDEE (without human processes currently) LSM and the observations from the Global Runoff Data Centre (GRDC) to obtain optimized discharges over the entire basin. The "model systematic errors" and "human impacts" (dam operation, irrigation, etc.) are taken into account by an optimization parameter x (with annual variation), which is applied to correct model intermediate variable runoff and drainage over each sub-watershed. The method is illustrated over the Iberian Peninsula with 27 GRDC stations over the period 1979-1989. ORCHIDEE represents a realistic discharge over the north of the Iberian Peninsula with small model systematic errors, while the model overestimates discharges by 30-150 % over the south and northeast regions where the blue water footprint is large. The normalized bias has been significantly reduced to less than 30 % after assimilation, and the assimilation result is not sensitive to assimilation strategies. This method also corrects the discharge bias for the basins without observations assimilated by extrapolating the correction from adjacent basins. The "correction" increases the interannual variability in river discharge because of the fluctuation of water usage. The E (P − E) of GLEAM (Global Land Evaporation Amsterdam Model, v3.1a) is lower (higher) than the bias-corrected value, which could be due to the different P forcing and probably the missing processes in the GLEAM model.

Geoscientific Model Development

* TropEBF-tropical evergreen broadleaf forest; TempENF-temperate evergreen needleleaf forest; Tem... more * TropEBF-tropical evergreen broadleaf forest; TempENF-temperate evergreen needleleaf forest; TempEBF-temperate evergreen broadleaf forest; TempDBF-temperate deciduous broadleaf forest; BorENF-boreal evergreen needleleaf forest; BorDBF-boreal deciduous broadleaf forest; C 3 grass-C 3 grassland.

Geoscientific Model Development

Current land surface models (LSMs) typically represent soils in a very simplistic way, assuming s... more 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 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 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 Published by Copernicus Publications on behalf of the European Geosciences Union. 938 M. Camino-Serrano et al.: ORCHIDEE-SOM 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.