Reply on RC1

The processes involved in the exchange of water, energy and carbon in terrestrial ecosystems are strongly intertwined. To accurately represent the terrestrial biosphere in land surface models (LSM), the intrinsic coupling between these processes is required. Soil moisture and leaf area index are two key variables at the nexus of water, energy and vegetation. Here, we evaluated three LSM (ISBA, ORCHIDEE and a diagnostic model, based on the LSA SAF algorithms) in their ability to simulate the latent heat flux (LE) and gross primary production (GPP) coherently, and their interactions through leaf area index (LAI) and soil moisture. The models were validated using in situ eddy covariance observations, soil moisture measurements and remote sensed LAI. It was found that the diagnostic model performed consistently well, regardless land cover, whereas important shortcomings of the prognostic models were revealed for in herbaceous/dry sites. Despite their different architecture and parametrization, ISBA and ORCHIDEE shared some key weaknesses. In both models, LE and GPP were found to be oversensitive to drought stress. Though the simulated soil water dynamics could be improved, this was not the main cause of errors in the surface fluxes. Instead, these errors were strongly correlated to errors in LAI. The simulated phenological cycle in ISBA and ORCHIDEE was delayed compared to observations, and failed to capture the observed seasonal variability. The feedback mechanism between GPP and LAI (i.e. the biomass allocation scheme) was identified as a key element to improve the intricate coupling between energy, water and vegetation in LSM.

Carbon, water and energy exchange between the land and atmosphere controls how ecosystems either accelerate or ameliorate the effect of climate change. However, evaluating improvements to processes controlling carbon cycling, water use and energy exchange in global land surface models (LSMs) remains challenging in part because of persistent model errors in estimating leaf area. Here we evaluate the changes in global carbon, water and energy exchange brought about when a LSM prognostic estimates of leaf area are made consistent with estimates from satellites. This approach achieves two aims; first to quantify the effect of ignoring errors in leaf area index (LAI) on land‐atmosphere fluxes and second, to evaluate how closely this LSM replicates fluxes with and without an LAI constraint. We implemented an ensemble Kalman filter with spatiotemporal adaptive inflation to more closely match community land model (CLM5.0) estimates of leaf area to those from the Global Inventory Modeling and Mapping Studies leaf area index (LAI3g) product. We then evaluate the model's estimates of gross primary productivity (GPP) and latent heat flux (LE) against well established global estimates of these fluxes. We find that the model is biased high by 27% relative to the LAI3g product. Moreover, the effect of bias in LAI is substantial for GPP (18%) and LE (6%) and likely to confound efforts to refine processes controlling these fluxes. This data assimilation approach serves as a method to evaluate the efficacy of refinements to flux processes until the processes controlling the dynamics of LAI are better resolved in LSMs.

Land surface-atmosphere interactions are strongly influenced by vegetation, since the latter drives the exchange of energy, water and carbon at canopy level via transpiration and photosynthesis. These biochemical processes are related to both the stomatal response to meteorological variations (linking the canopy to the deepest soil layers), and the allocation of carbon in different parts of the plant such as roots and leaves.  In recent years, the characterization of these processes has gained increasing attention in land surface models (LSMs), which are powerful tools that reproduce the soil-plant-atmosphere continuum and the mutual feedback of its components. Vegetation in LSMs is described either statically -- based on a prescribed vegetation climatology or cover -- or dynamically, that is, evolving in time its characteristics such as leaf area index and vegetation cover fraction, among the others. However, the dynamic simulation of vegetation is often simplified in LSMs with respect to state-of-the-art bio-geophysical and forest models.  In the Noah Multi-Parametrization (Noah-MP, v. 4.0.1) LSM, multiple parametrizations are available for each individual sub-process scheme such as dynamic vegetation, runoff partitioning, groundwater recharge and radiative transfer through the canopy, among others. It is thus important to identify the land cover type, soil and climate characteristics of the specific study site and tailor the parametrization to find the “optimal” combination of sub-process schemes, i.e. the one which best reproduces in-situ observations.  In this study, we evaluate point-scale simulations generated using different parametrizations of dynamic vegetation schemes within Noah-MP, run in offline mode within the NASA’s Land Information System (LIS). We compare the LSM results of gross primary productivity, soil moisture and evapotranspiration over several years between 2000 and 2023 to both ground-based estimates and remote sensing datasets derived from multiple observations and platforms such as MODIS, OCO-2, MSG and FLUXCOM. The study focuses on sites along the Italian peninsula, mostly forests, with croplands and grasslands as well, some of which are equipped with Eddy-covariance stations for carbon and water fluxes measurements and are included in the FLUXNET network.  The sites are all natural, rain-fed ecosystems mostly located in drought-prone, Mediterranean regions. This study is meant to reveal previously neglected uncertainties in dynamic vegetation simulations, especially in dry regions, and to fine-tune the combination of sub-processes schemes in Noah-MP for future data assimilation experiments.

As soil moisture and vegetation water content both affect the emissivity from the land surface, each of them can be derived from satellite-based passive microwave measurements. In this study, we use soil moisture retrievals from the 36 km SMAP L2 product and X-band vegetation optical depth (VOD) from AMSR2 LPRM version 6. VOD is a proxy for vegetation water content, linked to the leaf area index (LAI). We developed a machine learning-based observation operator to map LAI to VOD.We assimilate the SMAP and AMSR2 products into the Noah-MP land surface model (LSM) with dynamic vegetation. This is done by means of a one-dimensional Ensemble Kalman Filter (EnKF) within the NASA Land Information System (LIS). SMAP soil moisture retrievals update soil moisture in each of the four soil layers of the LSM, while AMSR2 VOD retrievals update the LAI. A cumulative distribution function (CDF) matching approach rescales the soil moisture retrievals to the model climatology. Model LAI is mapped to VOD by means of the above-mentioned observation operator. The resulting data assimilation (DA) system produces consistent estimates of all land surface variables on a quarter-degree regular grid over the European continent from 1 April 2015 through 31 March 2022.This joint SMAP and AMSR2 DA system is validated by assessing a number of geophysical variables. The surface and root-zone soil moisture estimates are evaluated using in situ observations from the ISMN. Gross primary production (GPP) and evapotranspiration are evaluated using FLUXNET data. Estimates for LAI are compared with optical satellite data from MODIS. The results are compared with open loop (model-only), and SMAP- and AMSR2-only DA experiments.SMAP-only DA primarily improves soil moisture estimates, while AMSR2-only DA mainly improves estimates of GPP and ET. Preliminary results indicate that the joint DA has the potential to combine the improvements of both individual assimilation systems.

Vegetation plays a crucial role in the land surface water and energy balance modulating the interactions and feedback with climate at the regional to global scale. The availability of unprecedented Earth observation products covering recent decades (and extended up to real-time) are therefore of paramount importance to better represent the vegetation and its time evolution in the land surface models (LSMs) used for offline analysis/initialization and for the seasonal-to-decadal predictions. Here, we integrate realistic vegetation Leaf Area Index (LAI) variability from latest generation satellite campaigns, available through Copernicus Land Monitoring Service (CLMS), in three different LSMs that conducted the same coordinated set of offline land-only simulations forced by hourly atmospheric fields derived from the ERA5 atmospheric reanalysis. The experiment implementing realistic interannually-varying LAI (SENS) is compared with simulations utilizing a climatological LAI (CTRL) to quantify the vegetation feedback and the effects on the simulation of near-surface soil moisture.The results show that the inter-annually varying LAI considerably affects the simulation of near-surface soil moisture anomalies in all three models and over the same water-limited regions, but surprisingly the effects diverge among models: compared with ESA-CCI observations, the near-surface soil moisture anomalies significantly improve in  one of the three LSMs (HTESSEL-LPJGuess) while the other two (ECLand and ISBA-CTRIP) display opposite effects with significant worsening of the anomaly correlation coefficients. It is found that the enhanced simulation of near-surface soil moisture is enabled by the positive feedback that is activated by the effective vegetation cover (EVC) parameterization, implemented only in HTESSEL-LPJGuess. The EVC parameterization works such that the effective fraction of the bare soil being covered by vegetation does vary with LAI following an exponential function constrained by available satellite observations. The increased (reduced) soil-moisture limitation during dry (wet) periods produces negative (positive) LAI and therefore EVC anomalies, which in turn generate a dominating positive feedback on the near-surface soil moisture of HTESSEL-LPJGuess by exposing more (less) bare soil to direct evaporation from the sub-surface layer. On the other hand, in the EC-Land and ISBA-CTRIP models, EVC is fixed in time as it cannot vary with LAI and so the positive feedback described cannot be activated. The only feedback on near-surface soil moisture anomalies that operates  in these two models is negative and comes from the reduced (increased) transpiration related to the negative (positive) LAI anomalies.Simply prescribing observed vegetation data into LSMs does not guarantee the introduction of the correct coupling and feedback on climate. In this respect, this multi-model comparison experiment demonstrates the fundamental role of the inclusion of the underlying vegetation processes in LSMs. Ignoring the proper representation of the vegetation processes could lead to unrealistic (and even the opposite effects compared with observations) behaviour in reanalysis and climate predictions.

Abstract. The performance of the joint assimilation in a land surface model of a Soil Wetness Index (SWI) product provided by an exponential filter together with Leaf Area Index (LAI) is investigated. The data assimilation is evaluated with different setups using the SURFEX modeling platform, for a period of seven years (2001–2007), at the SMOSREX grassland site in southwestern France. The results obtained with a Simplified Extended Kalman Filter demonstrate the effectiveness of a joint data assimilation scheme when both SWI and Leaf Area Index are merged into the ISBA-A-gs land surface model. The assimilation of a retrieved Soil Wetness Index product presents several challenges that are investigated in this study. A significant improvement of around 13 % of the root-zone soil water content is obtained by assimilating dimensionless root-zone SWI data. For comparison, the assimilation of in situ surface soil moisture is considered as well. A lower impact on the root zone is noticed. Under specific conditions, the transfer of the information from the surface to the root zone was found not accurate. Also, our results indicate that the assimilation of in situ LAI data may correct a number of deficiencies in the model, such as low LAI values in the senescence phase by using a seasonal-dependent error definition for background and observations. In order to verify the specification of the errors for SWI and LAI products, a posteriori diagnostics are employed. This approach highlights the importance of the assimilation design on the quality of the analysis. The impact of data assimilation scheme on CO2 fluxes is also quantified by using measurements of net CO2 fluxes gathered at the SMOSREX site from 2005 to 2007. An improvement of about 5 % in terms of rms error is obtained.

Ecosystem process modeling is vital for a broad range of applications. It requires a key metric to characterize vegetation canopies: the leaf area index (LAI). Many land surface models utilize satellite-derived LAI for modeling ecosystem processes. The use of satellite-derived LAI constrains the models for predicting ecosystem processes when observational data are unavailable. Enriching prognostic models for predicting the LAI is favorable to land surface studies for forecasting vegetation-related processes. An attention-enhanced long and short memory (AELSTM) model was developed to predict the vegetation LAI time series based on climatic data. The AELSTM outperformed comparative machine learning models when assessed using satellite and flux tower data. The LAI predicted by AELSTM are temporally and spatially consistent with satellite-derived LAI. The R2 values attained 0.86–0.93 across biomes. AELSTM can be coupled with the land surface models to predict gross primary productivity. Our proposed model was demonstrated to be effective in predicting the LAI time series under different Shared Socioeconomic Pathways in CMIP6. Our study demonstrated that deep learning approaches are capable of modeling and characterizing the spatial and temporal patterns of key vegetation variables such as LAI. Moreover, the study has provided references for vegetation processes in land surface studies.

A numerical analysis of aggregation error in evapotranspiration estimates due to heterogeneity of soil moisture and leaf area index

<p>Vegetation greening in the recent three decades significantly alters the carbon and water cycles over China. The response of terrestrial ecosystem productivity to flash droughts could be influenced by vegetation conditions and characteristics of flash droughts. However, it is still unclear that how the sensitivity of vegetation to flash drought varies with increasing leaf area index (LAI) across China. We use a land surface model and multiple satellite LAI products to assess the response of gross primary productivity (GPP) to flash droughts. Evapotranspiration is increased with increasing LAI and soil moisture is correspondingly decreased. Thus, the frequency, duration, and severity of flash droughts are all intensified from a water-budget perspective. The increasing LAI is contributed to the enhanced terrestrial carbon sink through increasing water use efficiency (WUE). The resistance and resilience of GPP to flash drought are also enhanced due to the increased LAI across various climates and vegetation types. These results refine the sensitivity of GPP to flash droughts in greening China and constrain the prognostic models to simulate the response of vegetation to droughts in changing environments.</p>

Abstract. LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0.25∘ spatial resolution over 2010–2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration and river discharge are employed for the validation. Secondly, the global analysis is used to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. This is performed by computing anomalies of the land surface states. They display strong negative values for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS_HRES, 0.10∘ spatial resolution) over 2017–2018. Monitoring capacities are studied by comparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS_HRES forecasts of the LSVs with the LDAS_HRES assimilation run compared to the open-loop experiment. The positive impact of initialization from an analysis in forecast mode is particularly visible for LAI that evolves at a slower pace than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8 d lead time. This highlights the impact of initial conditions on LSV forecasts and the value of jointly analysing soil moisture and vegetation states.

<strong class="journal-contentHeaderColor">Abstract.</strong> Plant and microbial nitrogen (N) dynamics and N availability regulate the photosynthetic capacity and capture, allocation, and turnover of carbon (C) in terrestrial ecosystems. Studies have shown that a wide divergence in representations of N dynamics in terrestrial surface processes leads to large uncertainty in climate simulations and the projections of future trajectories. In this study, a plant C-N interface coupling framework was developed and implemented in a coupled biophysical-ecosystem-biogeochemical model (SSiB5/TRIFFID/DayCent-SOM). The main concept and structure of this plant C-N framework and its coupling methodology are presented. Different from many current approaches, this framework not only involves soil organic matter cycling, but uniquely takes into account plant N metabolism first, such as plant resistance and self-adjustment, which are represented by dynamic C / N ratios for each plant functional type (PFT). Then, when available N is less than plant N demand, N restricts plant growth, reducing gross primary productivity (GPP), and modulating plant respiration rates and phenology. All these considerations ensure a full incorporation of N regulations to plant growth and C cycling. This new approach has been tested to assess the effects of this coupling framework and N limitation on the terrestrial carbon cycle. Measurements from flux tower sites with different PFTs from 1996&ndash;2013 and global satellite-derived observations from 1948&ndash;2007 are used as references to assess the effect of the C-N coupling process on the long-term mean vegetation distribution and terrestrial C cycling using the offline SSiB5/TRIFFID/DayCent-SOM model, which use observed meteorological forcing to drive the model. The sensitivity of the terrestrial C cycle to different components in the framework is also assessed. The results show a general improvement with the new plant C-N coupling framework, with more consistent emergent properties, such as GPP, leaf area index (LAI), and respiration, compared to observations. The main improvements occur in tropical Africa and boreal regions, accompanied by a decrease of the bias in global GPP and LAI by 16.3 % and 27.1 %, respectively.

Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We used an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The terrestrial biosphere model that we used is the integrated model – LPJ-PM coupled from the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the global Land and Ground satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a Proper Orthogonal Decomposition-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 of 0.91 and 0.81 and an RMSD reduced by 50.4 % and 38.4 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The assimilated GPP and ET demonstrated a better performance in the arid and semiarid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS had a slight difference, and the ET that assimilated SMAP soil moisture data was more improved than that assimilated SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm month−1; estimated standard deviation of μ: 1.91 mm month−1). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

To improve the prediction of crop yields at an aggregate scale, we developed a data assimilation-crop modeling framework that incorporates remotely sensed soil moisture and leaf area index (LAI) into a crop model using sequential data assimilation. The core of the framework is an Ensemble Kalman Filter (EnKF) used to control crop model runs, assimilate remote sensing (RS) data and update model state variables. We modified the Decision Support System for Agro-technology Transfer – Cropping System Model (DSSAT-CSM)-Maize model (Jones et al., 2003) to be able to stop and start simulations at any given time in the growing season, such that the EnKF can update model state variables as RS data become available. The data assimilation-crop modeling framework was evaluated against 2003–2009 maize yields in Story County, Iowa, USA, assimilating AMSR-E soil moisture and MODIS-LAI data independently and simultaneously. Assimilating LAI or soil moisture independently slightly improved the correlation of observed and simulated yields (R=0.51 and 0.50) compared to no data assimilation (open-loop; R=0.47) but prediction errors improved with reductions in MBE and RMSE by 0.5 and 0.5Mgha−1 respectively for LAI assimilation while these were reduced by 1.8 and 1.1Mgha−1 for soil moisture assimilation. Yield correlation improved more when both soil moisture and LAI were assimilated (R=0.65) suggesting a cause–effect interaction between soil moisture and LAI, prediction errors (MBE and RMSE) were also reduced by 1.7 and 1.8Mgha−1 with respect to open-loop simulations. Results suggest that assimilation of LAI independently might be preferable when conditions are extremely wet while assimilation of soil moisture+LAI might be more suitable when conditions are more nominal. AMSR-E soil moisture tends to be more biased under the presence of high vegetation (i.e., when crops are fully developed) and that updating rootzone soil moisture by near-surface soil moisture assimilation under very wet conditions could increase the modeled percolation causing excessive nitrogen (N) leaching hence reducing crop yields even with water stress reduced at a minimum due to soil moisture assimilation. However, applying the data assimilation-crop modeling framework strategically by considering a-priori information on climate condition expected during the growing season may improve yield prediction performance substantially, in our case with higher correlation (R=0.80) and more reductions in MBE and RMSE (2.5 and 3.3Mgha−1) compared to when there is no data assimilation. Scaling AMSR-E soil moisture to the climatology of the model did not improve our data assimilation results because the model is also biased. Better soil moisture products e.g., from Soil Moisture Active Passive (SMAP) mission, may solve the soil moisture data issue in the near future.

In Part I, the authors succeeded in coupling the spectral atmospheric model (SAMIL_R42L9) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG/IAP/CAS) with the land surface model, Atmosphere-Vegetation-Interaction-Model (AVIM) and analyzed the climate basic state and land surface physical fluxes simulated by R42_AVIM. In this Part II, we further evaluate the simulated results of the biological processes, including leaf area index (LAI), biomass and net primary productivity (NPP) etc. Results indicate that R42_AVIM can simulate the global distribution of LAI and has good consistency with the monthly mean LAI provided by Max Planck Institute for Meteorology. The simulated biomass corresponds reasonably to the vegetation classifications. In addition, the simulated annual mean NPP has a consistent distribution with the data provided by IGBP and MODIS, and compares well with the work in literature. This land-atmosphere coupled model will offer a new experiment tool for the research on the two-way interaction between climate and biosphere, and the global terrestrial ecosystem carbon cycle.

&amp;lt;p&amp;gt;Plants take up water from the soil via roots and release it into the atmosphere through stomata; uptake of CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; from the atmosphere also proceeds through the stomata, implying tight coupling of transpiration and photosynthesis. We distinguish leaf-level (biochemical and stomatal) responses to external stimuli on different timescales: fast responses taking place over seconds to hours, and longer-term (acclimation) responses taking place over weeks to months. Typically, land-surface models (LSMs) have focused on the fast responses, and have not accounted for acclimation responses, although these can be different in magnitude and even in sign. We have developed a method that explicitly separates these two timescales in order to implement an existing optimality-based model, the P model, with a sub-daily timestep; and, thereby, to include acclimated responses within an LSM framework. The resulting model, compared to flux-tower gross primary production (GPP) data in five &amp;amp;#8220;well-watered&amp;amp;#8221; biomes from boreal to tropical, correctly reproduces diurnal cycles of GPP throughout the growing season. No changes of parameters are required between biomes, because optimality ensures that current parameter values are always adapted to the local environment. This is a clear practical advantage because it eliminates the need to specify different parameter values for different plant functional types. However, in areas with large seasonal variations in moisture variability, the model does not perform well. Here we address the issue of soil-moisture controls on GPP, which is a challenging issue for LSMs in general. We note two problems: an error in magnitude, and an error in shape. The model tends to overestimate GPP in dry areas because it does not consider the effect of low soil moisture (as opposed to atmospheric dryness) on photosynthesis; and it does not simulate the &amp;amp;#8216;midday depression&amp;amp;#8217; that is observed under very high vapour pressure deficits. Moving beyond commonly used (empirical) water-stress formulations, we have incorporated soil moisture limitation on photosynthesis in the sub-daily P model. The main idea is to control GPP via hydraulic limitation. The revised model firstly assesses the &amp;amp;#8220;demand&amp;amp;#8221;&amp;amp;#8212;the transpiration that would take place under well-watered conditions&amp;amp;#8212;then constrains the actual transpiration at a rate that does not exceed the canopy&amp;amp;#8217;s estimated hydraulic capacity. This transpiration rate is then used to obtain revised rates of stomatal conductance and GPP, &amp;amp;#8220;corrected&amp;amp;#8221; for water stress. Preliminary results evaluating the revised model&amp;amp;#8217;s performances against flux tower measurements at dry sites are encouraging, suggesting a route towards a parameter-sparse and globally applicable LSM.&amp;lt;/p&amp;gt;