# 6 SDBM

For the calculation of GPP with the Simple Diagnostic Biosphere Model (SDBM), ULUND and iLab use the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) to constrain photosynthetic uptake of CO₂, instead of SIF as described in the previous sections. This option of using FAPAR instead of SIF has been explained before (e.g. in the WP3 section of the DMP, D9.5, as well as in the 2nd Periodic Report in form of a technical review report).

The reasons for doing so are the following:

* FAPAR products are more mature and the use of FAPAR within our modelling system directly yields a GPP estimate.

* FAPAR characterises radiation available for photosynthesis, in contrast to SIF, which quantifies the excess photon flux not used for photosynthesis.

* FAPAR has a clear model-independent physical definition as opposed to LAI, which in the model serves as a parameter, which does not necessarily coincide with a physical definition of LAI as leaf surface area.

* There are three EO data streams (VOD, SIF and LAI) listed in the CoCO₂ Description of Action. While VOD and SIF are used by other partners, to explore the constraint of all three EO data types (and hence the spread in GPP), we decided to use FAPAR as a substitute for LAI (see previous point).

We do not believe that this has a negative impacts on this task and the results, but rather the opposite, because within task 3.3 we can explore a larger observational database to constrain GPP (last point from the above list). The novelty then lies in the comparison of the GPP products derived from different EO datasets against the data-driven products and *in situ* measured fluxes.

## 6.1 Methods

The SDBM version used here is conceptually similar to the one used by Kaminski et al. (2017) and is based on Knorr and Heimann (1995). The spatial resolution of the model is 0.5° times 0.5° globally. The model classifies global terrestrial ecosystems into 8 biome types based on the MODIS land cover classification (Friedl et al., 2010): (1) cropland/urban/natural vegetation mosaic, (2) needleleaf forest, (3) broadleaf forest, (4) mixed forest, (5) shrubland, (6) savanna or grassland, (7) tundra, (8) barren or sparsely vegetated.

The terrestrial uptake of CO₂ by photosynthesis (gross primary productivity, GPP) is calculated using a light-use efficiency approach:

$$GPP(x,t) = \varepsilon \cdot \alpha(x,t) \cdot FAPAR(x,t) \cdot Srad(x,t) \qquad (6.1)$$

where $\varepsilon$ denotes the biome-specific light use efficiency parameter, $\alpha$ a plant water stress factor, and $Srad$ the incident solar radiation driving photosynthesis. Autotrophic respiration ($R_a$) is calculated from

$$R_a(x,t) = r \cdot GPP(x,t), \qquad (6.2)$$

with $r$ being a constant with a value of $r = 1 - 1/2.13$ (Waring et al., 1998). Ecosystem heterotrophic respiration ($R_h$) is calculated following a $Q_{10}$ functional relationship with temperature $T$ and is, as the photosynthesis calculation, modulated by the water stress factor:

$$R_h(x,t) = n(x) \cdot \alpha(x,t) \cdot Q_{10}^{T/10} \qquad (6.3)$$

The biome-specific parameter $Q_{10}$ expresses the ratio of respiration at ambient temperature $T + 10$ to that at $T$, with $T$ given in °C. The spatially varying normalisation factor $n(x)$ is the ratio of the temporal integrals of NPP(x,t) = GPP(x,t) - $R_a(x,t)$ and $R_h(x,t)$ computed for prior
parameter values over the entire model simulation period. It thus ensures a balanced prior biosphere over the model simulation period but allows for non-zero posterior net flux.

We explore two ways of deriving the drought stress factor $\alpha$. For the first version we use results by Kaminski et al. (2017), where

$$ \alpha(x, t) = AET(x, t)/PET(x, t) \qquad (6.4) $$

with actual (AET) and potential (PET) evapotranspiration taken from a simulation of the BETHY model (Knorr, 2000) run with meteorological input from the WATCH/ERA Interim data set (WFDEI, Weedon et al., 2014).

As our ambition in CoCO2 is to provide a setup that can be integrated into the IFS, in addition we derived a drought stress factor from the IFS output, more specifically from ERA5 (Hersbach et al., 2020). However, an attempt to use Eq. (6.4) with AET and PET taken directly from ERA5 failed for the following reasons: (1) AET frequently exceeded PET and (2) pronounced and unexplained spatial variations in PET, in particular values close to zero in desert areas (Sahara) or evergreen tropical forests (Amazon). Instead, we computed AET from surface latent heat flux directly from ERA5 divided by latent heat of evaporation. We further calculated PET as equilibrium evapotranspiration (Monteith, 1965) from the following ERA5 single levels fields:

* surface net thermal radiation flux,

* surface net solar radiation flux,

* surface latent heat flux,

* 2m temperature,

* surface pressure.

Using the atmospheric transport model TM3 (Heimann and Körner, 2003) in the form described by Kaminski et al. (2022), the process parameters $\epsilon$ and $Q_{10}$ as well as the normalisation factor $n$ and an intial atmospheric concentration were calibrated against remotely sensed atmospheric CO$_2$ concentrations for the year 2021 (Reuter et al., 2018; for more details on the data see next section). The calibration used a gradient descent method and relied on efficient derivative information provided by automatic differentiation (Hascoët and Pascual, 2013) of the processing chain.

## 6.2 Data

For performing the SDBM experiments we used three different types of data: 1) model input data, 2) data for model calibration and 3) data for model evaluation.

Meteorological data from the fifth generation of ECMWF atmospheric reanalyses (ERA5) of the global climate (Hersbach et al., 2020) for the years 2017 to 2021 were used as model input data. The meteorological fields were obtained on a regular 0.5° times 0.5° grid. In particular, we used the following monthly mean fields to run SDBM:

* surface solar radiation downwards,

* 2m temperature,

* water stress factor computed either by BETHY, or ERA5 (using additional fields as described in the previous section),

* Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) by green vegetation.

For FAPAR, we used the product derived by the Joint Research Centre-Two-stream Inversion Package (JRC-TIP) from satellites (Pinty et al., 2011) and made available by the Joint Research Centre of the European Commission via https://fapar.jrc.ec.europa.eu. For an extensive evaluation of the FAPAR data see Pinty et al. (2007, 2008, and 2011) and also the assessment by Mota et al. (2021) and references therein. The FAPAR data have a 10-daily
parameter values over the entire model simulation period. It thus ensures a balanced prior biosphere over the model simulation period but allows for non-zero posterior net flux.

We explore two ways of deriving the drought stress factor $\alpha$. For the first version we use results by Kaminski et al. (2017), where

$$ \alpha(x, t) = AET(x, t)/PET(x, t) \qquad (6.4) $$

with actual (AET) and potential (PET) evapotranspiration taken from a simulation of the BETHY model (Knorr, 2000) run with meteorological input from the WATCH/ERA Interim data set (WFDEI, Weedon et al., 2014).

As our ambition in CoCO2 is to provide a setup that can be integrated into the IFS, in addition we derived a drought stress factor from the IFS output, more specifically from ERA5 (Hersbach et al., 2020). However, an attempt to use Eq. (6.4) with AET and PET taken directly from ERA5 failed for the following reasons: (1) AET frequently exceeded PET and (2) pronounced and unexplained spatial variations in PET, in particular values close to zero in desert areas (Sahara) or evergreen tropical forests (Amazon). Instead, we computed AET from surface latent heat flux directly from ERA5 divided by latent heat of evaporation. We further calculated PET as equilibrium evapotranspiration (Monteith, 1965) from the following ERA5 single levels fields:

* surface net thermal radiation flux,

* surface net solar radiation flux,

* surface latent heat flux,

* 2m temperature,

* surface pressure.

Using the atmospheric transport model TM3 (Heimann and Körner, 2003) in the form described by Kaminski et al. (2022), the process parameters $\varepsilon$ and $Q_{10}$ as well as the normalisation factor $n$ and an intial atmospheric concentration were calibrated against remotely sensed atmospheric CO$_2$ concentrations for the year 2021 (Reuter et al., 2018; for more details on the data see next section). The calibration used a gradient descent method and relied on efficient derivative information provided by automatic differentiation (Hascoët and Pascual, 2013) of the processing chain.

## 6.2 Data

For performing the SDBM experiments we used three different types of data: 1) model input data, 2) data for model calibration and 3) data for model evaluation.

Meteorological data from the fifth generation of ECMWF atmospheric reanalyses (ERA5) of the global climate (Hersbach et al., 2020) for the years 2017 to 2021 were used as model input data. The meteorological fields were obtained on a regular 0.5° times 0.5° grid. In particular, we used the following monthly mean fields to run SDBM:

* surface solar radiation downwards,

* 2m temperature,

* water stress factor computed either by BETHY, or ERA5 (using additional fields as described in the previous section),

* Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) by green vegetation.

For FAPAR, we used the product derived by the Joint Research Centre-Two-stream Inversion Package (JRC-TIP) from satellites (Pinty et al., 2011) and made available by the Joint Research Centre of the European Commission via https://fapar.jrc.ec.europa.eu. For an extensive evaluation of the FAPAR data see Pinty et al. (2007, 2008, and 2011) and also the assessment by Mota et al. (2021) and references therein. The FAPAR data have a 10-daily
temporal resolution and were spatially aggregated to a regular 0.5° times 0.5° spatial resolution.

For the model calibration we used atmospheric CO₂ concentrations derived by the ensemble median algorithm EMMA operated by University of Bremen (Reuter et al., 2013) using measurements provided by the Greenhouse Gases Observing Satellites 1 and 2 and by the Orbiting Carbon Observatory 2. We use column integrated data for the year 2021 for the calibration. In addition to the observations for the calibration we also need additional data to account for contributions from surface fluxes to the atmospheric CO₂ concentrations that are not explicitly simulated by the SDBM (i.e., ocean-atmosphere exchange fluxes, emissions from fossil fuel and biomass burning). These data are aggregated to the TM3 model resolutions and then added as prior information for the transport modelling, particularly we use:

* ocean atmosphere exchange fluxes (Rödenbeck et al., 2022),

* emissions from fossil fuel burning were derived from the Fossil Fuel Data Assimilation System (Asefi-Najafabady et al., 2014) for the spatial pattern and from the IEA GHG emissions from fuel combustion dataset (IEA, 2022) for the annual global totals,

* emissions from biomass burning were derived from the GFED4 data base (van der Werf et al., 2017).

For the evaluation of SDBM fluxes at the global scale, we use CO₂ in situ observations provided by the ESRL network (Lan et al., 2022), in addition to the FLUXCOM v2 product. For local-scale evaluation, we compare SDBM modelled GPP against flux estimates from eddy covariance measurements at two sites: Sodankylä in Finland representing boreal needleleaf forest and Las Majadas de Tietar in Spain representing a savanna biome type. These are the two sites from the European Space Agency's Land surface Carbon Constellation project (https://lcc.inversion-lab.com), which demonstrated the synergistic exploitation of a range of observations (both remotely sensed and in situ) around these sites in combination with a numerical land surface model to improve understanding of the terrestrial carbon and water cycles.

## 6.3 Results and evaluation

We show first spatial maps of the two GPP products (one using the ERA5-derived water stress factor, SDBM-ERA, and one using the BETHY-derived water stress factor, SDBM-BETHY) as annual averages over the whole simulation period (2017-2021) and compare these against the annual average GPP estimates from FLUXCOM v2 (see Figure 30). While the spatial pattern of annual GPP from the SDBM-BETHY simulation generally agrees well with the one from the FLUXCOM v2 product (albeit has somewhat lower totals, see below), there is a distinct difference in the spatial pattern of annual GPP from the SDBM-ERA simulation. The most prominent difference is a much smaller GPP in the SDBM-ERA simulation compared to FLUXCOM v2 for the central tropics. This is true for all three land regions in the tropics: Amazonia, Central Africa (e.g. Congo) as well as the maritime continent (e.g. Malaysia, Indonesia and Papua New Guinea). Also for the Northern high latitudes (>60°N), the FLUXCOM v2 GPP has larger values than SDBM-ERA GPP. In contrast, for the interior of Australia SDBM-ERA simulates larger GPP than FLUXCOM v2.
temporal resolution and were spatially aggregated to a regular 0.5° times 0.5° spatial
resolution.

For the model calibration we used atmospheric CO₂ concentrations derived by the ensemble median algorithm EMMA operated by University of Bremen (Reuter et al., 2013) using measurements provided by the Greenhouse Gases Observing Satellites 1 and 2 and by the Orbiting Carbon Observatory 2. We use column integrated data for the year 2021 for the calibration. In addition to the observations for the calibration we also need additional data to account for contributions from surface fluxes to the atmospheric CO₂ concentrations that are not explicitly simulated by the SDBM (i.e., ocean-atmosphere exchange fluxes, emissions from fossil fuel and biomass burning). These data are aggregated to the TM3 model resolutions and then added as prior information for the transport modelling, particularly we use:

* ocean atmosphere exchange fluxes (Rödenbeck et al., 2022),

* emissions from fossil fuel burning were derived from the Fossil Fuel Data Assimilation System (Asefi-Najafabady et al., 2014) for the spatial pattern and from the IEA GHG emissions from fuel combustion dataset (IEA, 2022) for the annual global totals,

* emissions from biomass burning were derived from the GFED4 data base (van der Werf et al., 2017).

For the evaluation of SDBM fluxes at the global scale, we use CO₂ in situ observations
provided by the ESRL network (Lan et al., 2022), in addition to the FLUXCOM v2 product. For
local- scale evaluation, we compare SDBM modelled GPP against flux estimates from eddy
covariance measurements at two sites: Sodankylä in Finland representing boreal needleleaf
forest and Las Majadas de Tietar in Spain representing a savanna biome type. These are the
two sites from the European Space Agency's Land surface Carbon Constellation project
(https://lcc.inversion-lab.com), which demonstrated the synergistic exploitation of a range of
observations (both remotely sensed and in situ) around these sites in combination with a
numerical land surface model to improve understanding of the terrestrial carbon and water
cycles.

6.3 Results and evaluation

We show first spatial maps of the two GPP products (one using the ERA5-derived water stress
factor, SDBM-ERA, and one using the BETHY-derived water stress factor, SDBM-BETHY) as
annual averages over the whole simulation period (2017-2021) and compare these against
the annual average GPP estimates from FLUXCOM v2 (see Figure 30). While the spatial
pattern of annual GPP from the SDBM-BETHY simulation generally agrees well with the one
from the FLUXCOM v2 product (albeit has somewhat lower totals, see below), there is a
distinct difference in the spatial pattern of annual GPP from the SDBM-ERA simulation. The
most prominent difference is a much smaller GPP in the SDBM-ERA simulation compared to
FLUXCOM v2 for the central tropics. This is true for all three land regions in the tropics:
Amazonia, Central Africa (e.g. Congo) as well as the maritime continent (e.g. Malaysia,
Indonesia and Papua New Guinea). Also for the Northern high latitudes (>60°N), the
FLUXCOM v2 GPP has larger values than SDBM-ERA GPP. In contrast, for the interior of
Australia SDBM-ERA simulates larger GPP than FLUXCOM v2.
<figure><img src="image_96.png" /><figcaption>Figure 30: Maps of annual GPP from SDBM for the year 2021 using the ERA5-derived water stress factor (top) and the BETHY-derived water stress factor (middle) compared to the annual GPP from FLUXCOM v2.</figcaption></figure>
<figure><img src="image_97.png" /><figcaption>Figure 30: Maps of annual GPP from SDBM for the year 2021 using the ERA5-derived water stress factor (top) and the BETHY-derived water stress factor (middle) compared to the annual GPP from FLUXCOM v2.</figcaption></figure>
Figure 31 shows time series of the two globally aggregated monthly simulated GPP products from SDBM using the ERA5-derived water stress factor (left) and the BETHY-derived water stress factor together with globally aggregated monthly FLUXCOM v2 GPP product.

<figure><img src="image_97.png" /><figcaption>Figure 31: Time series of globally aggregated, monthly modelled GPP from SDBM (blue line) using the ERA5-derived water stress factor (left) and the BETHY-derived water stress factor (right) compared against the globally aggregated FLUXCOM v2 GPP product (red line).</figcaption></figure>

The simulated GPP from SDBM-BETHY is in clearly better agreement with the FLUXCOM GPP than the GPP from SDBM-ERA when aggregated to global totals: an average root mean square error (RMSE) of 1.57 GtC/month with FLUXCOM v2 GPP for SDBM-BETHY versus 2.45 GtC/month for SDBM-ERA. However, in both cases the simulated global GPP from the two SDBM versions have lower values during Northern hemisphere winter than the FLUXCOM v2 GPP and do not show the plateauing of winter values as visible in the FLUXCOM GPP. In all three cases there is very little interannual variability in the global annual total GPP (see Table 7 for the values), with the FLUXCOM v2 product showing the largest variability (standard deviation of 0.75 GtC/yr compared to 0.63 GtC/yr and 0.48 GtC/yr for the SDBM-ERA and SDBM-BETHY GPP, respectively).

<table><caption>Table 7: Annual SDBM GPP in GtC/yr</caption><thead><tr><td>year</td><td>global GPP (α from BETHY)</td><td>global GPP (α from ERA5)</td><td>FLUXCOM v2</td></tr></thead><tbody><tr><td>2017</td><td>122.8</td><td>116.3</td><td>136.8</td></tr><tr><td>2018</td><td>122.3</td><td>115.6</td><td>137.1</td></tr><tr><td>2019</td><td>122.2</td><td>115.3</td><td>137.3</td></tr><tr><td>2020</td><td>122.8</td><td>116.3</td><td>138.5</td></tr><tr><td>2021</td><td>123.4</td><td>116.9</td><td>138.3</td></tr></tbody></table>

There is also a mean difference of 6.6 GtC/yr between the two SDBM products. The difference at total global annual scale between the simulated SDBM GPP and the FLUXCOM v2 product is, however, even larger with 21.2 GtC/yr and 14.6 GtC/yr for the SDBM-ERA and SDBM-BETHY GPP respectively (see Figure 30). Next, we compare the simulated atmospheric CO₂ concentrations from TM3 using the SDBM flux estimates against observed atmospheric CO₂ concentrations at selected sites

Figure 32).
Figure 31 shows time series of the two globally aggregated monthly simulated GPP products from SDBM using the ERA5-derived water stress factor (left) and the BETHY-derived water stress factor together with globally aggregated monthly FLUXCOM v2 GPP product.

<figure><img src="image_98.png" /><figcaption>Figure 31: Time series of globally aggregated, monthly modelled GPP from SDBM (blue line) using the ERA5-derived water stress factor (left) and the BETHY-derived water stress factor (right) compared against the globally aggregated FLUXCOM v2 GPP product (red line).</figcaption></figure>

The simulated GPP from SDBM-BETHY is in clearly better agreement with the FLUXCOM GPP than the GPP from SDBM-ERA when aggregated to global totals: an average root mean square error (RMSE) of 1.57 GtC/month with FLUXCOM v2 GPP for SDBM-BETHY versus 2.45 GtC/month for SDBM-ERA. However, in both cases the simulated global GPP from the two SDBM versions have lower values during Northern hemisphere winter than the FLUXCOM v2 GPP and do not show the plateauing of winter values as visible in the FLUXCOM GPP. In all three cases there is very little interannual variability in the global annual total GPP (see Table 7 for the values), with the FLUXCOM v2 product showing the largest variability (standard deviation of 0.75 GtC/yr compared to 0.63 GtC/yr and 0.48 GtC/yr for the SDBM-ERA and SDBM-BETHY GPP, respectively).

<table><caption>Table 7: Annual SDBM GPP in GtC/yr</caption><thead><tr><td>year</td><td>global GPP (α from BETHY)</td><td>global GPP (α from ERA5)</td><td>FLUXCOM v2</td></tr></thead><tbody><tr><td>2017</td><td>122.8</td><td>116.3</td><td>136.8</td></tr><tr><td>2018</td><td>122.3</td><td>115.6</td><td>137.1</td></tr><tr><td>2019</td><td>122.2</td><td>115.3</td><td>137.3</td></tr><tr><td>2020</td><td>122.8</td><td>116.3</td><td>138.5</td></tr><tr><td>2021</td><td>123.4</td><td>116.9</td><td>138.3</td></tr></tbody></table>

There is also a mean difference of 6.6 GtC/yr between the two SDBM products. The difference at total global annual scale between the simulated SDBM GPP and the FLUXCOM v2 product is, however, even larger with 21.2 GtC/yr and 14.6 GtC/yr for the SDBM-ERA and SDBM-BETHY GPP respectively (see Figure 30). Next, we compare the simulated atmospheric CO₂ concentrations from TM3 using the SDBM flux estimates against observed atmospheric CO₂ concentrations at selected sites

Figure 32).
<figure><img src="image_98.png" /></figure>

**Figure 32:** Evaluation of the SDBM modelled GPP against atmospheric CO₂ concentrations at selected sites (Azores, top; Mauna Loa, middle; South Pole, bottom) for the version using the ERA5-derived water stress factor (left) and the BETHY-derived water stress factor (right). Shown are the modelled concentrations for the year 2021 for the model (black line) and the observations (red line). Note that the observations are not used in the assimilation.
<figure><img src="image_99.png" /></figure>

**Figure 32:** Evaluation of the SDBM modelled GPP against atmospheric CO₂ concentrations at selected sites (Azores, top; Mauna Loa, middle; South Pole, bottom) for the version using the ERA5-derived water stress factor (left) and the BETHY-derived water stress factor (right). Shown are the modelled concentrations for the year 2021 for the model (black line) and the observations (red line). Note that the observations are not used in the assimilation.
Here again, we show results from both model versions using the ERA5-derived water stress factor (SDBM-ERA) as well as the one using the BETHY-derived water stress factor (SDBM-BETHY).

The comparison against the measured atmospheric CO₂ concentrations shows for both cases that the simulations using the GPP estimates from the calibrated model (red lines) fit the observations in general reasonably well (green lines). Here, we show this exemplarily for the year 2021 for only three measurement station: Azores (AZR), Mauna Loa (MLO) and South Pole (SPO). At the stations that are somewhat more effected by the terrestrial CO₂ exchange fluxes (AZR and MLO) we also see that the fit against the observations is better in the case for the SDBM-BETHY model, especially for the Northern hemisphere summer months where the vegetation is most active and influencing atmospheric CO₂ concentrations in these regions.

Finally, we show in Figure 33 a comparison of the simulated GPP against GPP derived from eddy-covariance net flux measurements for two sites representing contrasting ecosystem types: a savanna type in Majadas de Tietar, Spain, and an evergreen forest type in Sodankylä, Finland.

<figure><img src="image_99.png" /></figure>

**Figure 33:** Comparison of monthly modelled GPP from SDBM (blue line) using the ERA5-derived water stress factor (left) and the BETHY-derived water stress factor (right) compared against the GPP derived from eddy covariance net CO₂ flux measurements at the Majadas de Tietar, Spain (top panel) and Sodankylä, Finland (bottom panel) sites (eddy covariance data are provided by M. Aurela, FMI, for Sodankylä, and T. El-Madany, MPI-BGC, for Majadas de Tietar).
Here again, we show results from both model versions using the ERA5-derived water stress factor (SDBM-ERA) as well as the one using the BETHY-derived water stress factor (SDBM-BETHY).

The comparison against the measured atmospheric CO₂ concentrations shows for both cases that the simulations using the GPP estimates from the calibrated model (red lines) fit the observations in general reasonably well (green lines). Here, we show this exemplarily for the year 2021 for only three measurement station: Azores (AZR), Mauna Loa (MLO) and South Pole (SPO). At the stations that are somewhat more effected by the terrestrial CO₂ exchange fluxes (AZR and MLO) we also see that the fit against the observations is better in the case for the SDBM-BETHY model, especially for the Northern hemisphere summer months where the vegetation is most active and influencing atmospheric CO₂ concentrations in these regions.

Finally, we show in Figure 33 a comparison of the simulated GPP against GPP derived from eddy-covariance net flux measurements for two sites representing contrasting ecosystem types: a savanna type in Majadas de Tietar, Spain, and an evergreen forest type in Sodankylä, Finland.

<figure><img src="image_100.png" /></figure>

**Figure 33:** Comparison of monthly modelled GPP from SDBM (blue line) using the ERA5-derived water stress factor (left) and the BETHY-derived water stress factor (right) compared against the GPP derived from eddy covariance net CO₂ flux measurements at the Majadas de Tietar, Spain (top panel) and Sodankylä, Finland (bottom panel) sites (eddy covariance data are provided by M. Aurela, FMI, for Sodankylä, and T. El-Madany, MPI-BGC, for Majadas de Tietar).
In SDBM the Majadas site is modelled as composition of 80% savanna and 20% broadleaf forest biome and the Sodankylä site as 67% needleleaf forest and 33% shrubland biome with an overall fractional vegetation cover of 70%. For both sites we show results from the 0.5° times 0.5° grid cells that include the location of the sites. At the Majadas site we see an improvement in the comparison against the locally obtained GPP fluxes when using the BETHY-derived water stress factor compared to the ERA5-derived water stress factor (RMSE of 30.48 gC/m²/month versus 39.74 gC/m²/month). But for both SDBM GPP versions we see a very good agreement in the seasonality of the fluxes. Most apparent is the overestimation of GPP in the SDBM-ERA case compared to the site GPP observations during the end of the summer/early autumn. This effect is heavily reduced or even absent when using the BETHY-derived water stress factor. Also, in the case of the SDBM-BETHY GPP the peak value of the amplitude agrees rather well with the site observations. We also note here, that *in situ* GPP observations are not directly measured but derived from measurements of the net CO₂ exchange flux through modelling of the ecosystem respiration component.

The comparison of the SDBM modelled GPP against the GPP *in situ* observations at the Sodankylä site shows also a very good agreement in the seasonality of GPP, and for the SDBM-BETHY GPP also in the amplitude of the seasonal cycle. While the seasonal amplitude in GPP from the SDBM-ERA simulations is clearly underestimated compared to the *in situ* data, there is a very good (albeit in some years, i.e. 2018/19, there is slight overestimation) agreement of the seasonal amplitude of the SDBM-BETHY GPP with the *in situ* GPP (RMSE of 38.39 gC/m²/month for SDBM-ERA and 22.52 gC/m²/month for SDBM-BETHY).

## 6.4 Summary

We present two simulations of GPP by SDBM using different water stress factors and compared these against a range of observations where the version using the BETHY-derived water stress factor shows a better agreement with the independent observations. However, in both cases there is rather little interannual variation in the globally aggregated GPP.

In general, the strength of the SDBM approach lies in its efficiency and conceptual simplicity. A disadvantage may be the diagnostic nature of the model, which would complicate its use in a forecasting system. The approach, however, demands so little computational effort that SDBM can be maintained at minimal cost alongside a prognostic model. Furthermore, the envisaged MVS is not a forecasting system but an analysis system. Here, the SDBM is ideally positioned to provide a most realistic natural flux component. In fact, in intercomparison studies, it outperformed prognostic models (Nemry et al., 1999; Heimann et al., 1998). A particular advantage of the setup demonstrated here is that the model can be directly driven with IFS output.

Here, we list potential further activities around the integration of the SDBM into the MVS:

* Use of *in situ* CO₂ observations for calibration of the SDBM parameters rather than satellite data. The MVS will be based on satellite data, thus, using the independent *in situ* data stream for calibration would maximise independence.

* Extend the set of data used for calibration by eddy covariance flux measurements. This would ensure a most realistic simulation of the diurnal cycle.

* Analyse why the computation of the drought stress factor α based on ERA yields GPP that is in less agreement with the independent observations (atmospheric CO₂ and GPP derived from eddy covariance measurements) than the GPP computation using the drought stress factor from BETHY based on the WATCH/ERA Interim data set (WFDEI, Weedon et al., 2014), and improve the ERA-based computation or include an explicit representation of the water cycle in SDBM.

* Increase spatial and temporal resolution of SDBM, for example to 0.01° (~1km at equator) and hourly.
In SDBM the Majadas site is modelled as composition of 80% savanna and 20% broadleaf forest biome and the Sodankylä site as 67% needleleaf forest and 33% shrubland biome with an overall fractional vegetation cover of 70%. For both sites we show results from the 0.5° times 0.5° grid cells that include the location of the sites. At the Majadas site we see an improvement in the comparison against the locally obtained GPP fluxes when using the BETHY-derived water stress factor compared to the ERA5-derived water stress factor (RMSE of 30.48 gC/m²/month versus 39.74 gC/m²/month). But for both SDBM GPP versions we see a very good agreement in the seasonality of the fluxes. Most apparent is the overestimation of GPP in the SDBM-ERA case compared to the site GPP observations during the end of the summer/early autumn. This effect is heavily reduced or even absent when using the BETHY-derived water stress factor. Also, in the case of the SDBM-BETHY GPP the peak value of the amplitude agrees rather well with the site observations. We also note here, that *in situ* GPP observations are not directly measured but derived from measurements of the net CO₂ exchange flux through modelling of the ecosystem respiration component.

The comparison of the SDBM modelled GPP against the GPP *in situ* observations at the Sodankylä site shows also a very good agreement in the seasonality of GPP, and for the SDBM-BETHY GPP also in the amplitude of the seasonal cycle. While the seasonal amplitude in GPP from the SDBM-ERA simulations is clearly underestimated compared to the *in situ* data, there is a very good (albeit in some years, i.e. 2018/19, there is slight overestimation) agreement of the seasonal amplitude of the SDBM-BETHY GPP with the *in situ* GPP (RMSE of 38.39 gC/m²/month for SDBM-ERA and 22.52 gC/m²/month for SDBM-BETHY).

## 6.4 Summary

We present two simulations of GPP by SDBM using different water stress factors and compared these against a range of observations where the version using the BETHY-derived water stress factor shows a better agreement with the independent observations. However, in both cases there is rather little interannual variation in the globally aggregated GPP.

In general, the strength of the SDBM approach lies in its efficiency and conceptual simplicity. A disadvantage may be the diagnostic nature of the model, which would complicate its use in a forecasting system. The approach, however, demands so little computational effort that SDBM can be maintained at minimal cost alongside a prognostic model. Furthermore, the envisaged MVS is not a forecasting system but an analysis system. Here, the SDBM is ideally positioned to provide a most realistic natural flux component. In fact, in intercomparison studies, it outperformed prognostic models (Nemry et al., 1999; Heimann et al., 1998). A particular advantage of the setup demonstrated here is that the model can be directly driven with IFS output.

Here, we list potential further activities around the integration of the SDBM into the MVS:

* Use of *in situ* CO₂ observations for calibration of the SDBM parameters rather than satellite data. The MVS will be based on satellite data, thus, using the independent *in situ* data stream for calibration would maximise independence.

* Extend the set of data used for calibration by eddy covariance flux measurements. This would ensure a most realistic simulation of the diurnal cycle.

* Analyse why the computation of the drought stress factor $\alpha$ based on ERA yields GPP that is in less agreement with the independent observations (atmospheric CO₂ and GPP derived from eddy covariance measurements) than the GPP computation using the drought stress factor from BETHY based on the WATCH/ERA Interim data set (WFDEI, Weedon et al., 2014), and improve the ERA-based computation or include an explicit representation of the water cycle in SDBM.

* Increase spatial and temporal resolution of SDBM, for example to 0.01° (~1km at equator) and hourly.
We further recommend to use an ensemble of natural flux models for the MVS, in order to derive uncertainty ranges on the natural flux products. A further role of the data-driven approach as employed by SDBM could be to guide the development of the prognostic members of the ensemble.

# 7 Synthesis of results

We examined global annual GPP estimates. Table 8 and Figure 34 show a synthetic comparison of simulated global annual GPP with FLUXCOM-V2, for ISBA and ORCHIDEE, and for all models, respectively.

<table><caption>Table 8: Global terrestrial GPP trend and standard deviation (SD) of de-trended annual values over the period 2002-2019. Mean bias and squared correlation coefficient ($R^2$) of ISBA and ORCHIDEE simulations vs. FLUXCOM-V2. $R^2$ is also given for de-trended time series. The F-test p-value is given (in brackets). Best mean bias and $R^2$ score values are in bold.</caption><thead><tr><th>GPP source</th><th>Trend (PgC/yr)</th><th>SD of de-trended (PgC/yr)</th><th>Mean bias (PgC/yr)</th><th>R<sup>2</sup> (p-value)</th><th>R<sup>2</sup> of de-trended (p-value)</th></tr></thead><tbody><tr><td>FLUXCOM-V2</td><td>0.36</td><td>0.62</td><td>0</td><td>1 (0)</td><td>1 (0)</td></tr><tr><td>ISBA_OL</td><td>0.58</td><td>1.28</td><td>-9.4</td><td>0.90 (0)</td><td>0.37 (0.008)</td></tr><tr><td>ISBA_SEKF</td><td>0.57</td><td>1.20</td><td>-13.8</td><td><strong>0.93 (0)</strong></td><td><strong>0.50 (0.001)</strong></td></tr><tr><td>ORCHIDEE_prior</td><td>0.58</td><td>1.39</td><td>35.0</td><td>0.91 (0)</td><td>0.44 (0.003)</td></tr><tr><td>ORCHIDEE_posterior</td><td>0.55</td><td>0.96</td><td><strong>0.3</strong></td><td><strong>0.93 (0)</strong></td><td>0.39 (0.005)</td></tr></tbody></table>

For the common years 2018-2019, ISBA_SEKF, ECLAND_DA, and SDBM_BETHY give similar estimates of about 122 PgC/yr (+/- 2 PgC/yr). ORCHIDEE_posterior gives 138 PgG/yr for this period, which is similar to FLUXCOM-V2 (137 PgG/yr).

As the ECLand and SDBM simulations cover a limited time period (3 and 5 years, respectively), it is difficult to assess their ability to represent the interannual variability of the global GPP. On the other hand, ISBA and ORCHIDEE have produced 18 years of global GPP and a comparison with FLUXCOM V2 is shown in Table 8.

It is interesting to note that ISBA_OL and ORCHIDEE_prior have similar trends of 0.58 PgC/yr, much larger than FLUXCOM-V2 (0.36 PgC/yr), and that in both cases the assimilation tends to slightly reduce the trend. The squared correlation coefficient ($R^2$) between the ISBA and ORCHIDEE simulations with FLUXCOM-V2 can be used to assess the consistency of the interannual variability. Assimilation slightly increases the $R^2$, for both ISBA and ORCHIDEE. As all GPP estimates show a strong trend, the $R^2$ values are sensitive to the trend, in addition to the interannual variability. Differences between simulations in the representation of interannual variability are more visible using de-trended time series (de-trended $R^2$). In all simulations the interannual variability is well represented, with F-test p-values less than 0.01.
We further recommend to use an ensemble of natural flux models for the MVS, in order to derive uncertainty ranges on the natural flux products. A further role of the data-driven approach as employed by SDBM could be to guide the development of the prognostic members of the ensemble.

# 7 Synthesis of results

We examined global annual GPP estimates. Table 8 and Figure 34 show a synthetic comparison of simulated global annual GPP with FLUXCOM-V2, for ISBA and ORCHIDEE, and for all models, respectively.

<table><caption>Table 8: Global terrestrial GPP trend and standard deviation (SD) of de-trended annual values over the period 2002-2019. Mean bias and squared correlation coefficient ($R^2$) of ISBA and ORCHIDEE simulations vs. FLUXCOM-V2. $R^2$ is also given for de-trended time series. The F-test p-value is given (in brackets). Best mean bias and $R^2$ score values are in bold.</caption><thead><tr><th>GPP source</th><th>Trend (PgC/yr)</th><th>SD of de-trended (PgC/yr)</th><th>Mean bias (PgC/yr)</th><th>R<sup>2</sup> (p-value)</th><th>R<sup>2</sup> of de-trended (p-value)</th></tr></thead><tbody><tr><td>FLUXCOM-V2</td><td>0.36</td><td>0.62</td><td>0</td><td>1 (0)</td><td>1 (0)</td></tr><tr><td>ISBA_OL</td><td>0.58</td><td>1.28</td><td>-9.4</td><td>0.90 (0)</td><td>0.37 (0.008)</td></tr><tr><td>ISBA_SEKF</td><td>0.57</td><td>1.20</td><td>-13.8</td><td><strong>0.93 (0)</strong></td><td><strong>0.50 (0.001)</strong></td></tr><tr><td>ORCHIDEE_prior</td><td>0.58</td><td>1.39</td><td>35.0</td><td>0.91 (0)</td><td>0.44 (0.003)</td></tr><tr><td>ORCHIDEE_posterior</td><td>0.55</td><td>0.96</td><td><strong>0.3</strong></td><td><strong>0.93 (0)</strong></td><td>0.39 (0.005)</td></tr></tbody></table>

For the common years 2018-2019, ISBA_SEKF, ECLAND_DA, and SDBM_BETHY give similar estimates of about 122 PgC/yr (+/- 2 PgC/yr). ORCHIDEE_posterior gives 138 PgG/yr for this period, which is similar to FLUXCOM-V2 (137 PgG/yr).

As the ECLand and SDBM simulations cover a limited time period (3 and 5 years, respectively), it is difficult to assess their ability to represent the interannual variability of the global GPP. On the other hand, ISBA and ORCHIDEE have produced 18 years of global GPP and a comparison with FLUXCOM V2 is shown in Table 8.

It is interesting to note that ISBA_OL and ORCHIDEE_prior have similar trends of 0.58 PgC/yr, much larger than FLUXCOM-V2 (0.36 PgC/yr), and that in both cases the assimilation tends to slightly reduce the trend. The squared correlation coefficient ($R^2$) between the ISBA and ORCHIDEE simulations with FLUXCOM-V2 can be used to assess the consistency of the interannual variability. Assimilation slightly increases the $R^2$, for both ISBA and ORCHIDEE. As all GPP estimates show a strong trend, the $R^2$ values are sensitive to the trend, in addition to the interannual variability. Differences between simulations in the representation of interannual variability are more visible using de-trended time series (de-trended $R^2$). In all simulations the interannual variability is well represented, with F-test p-values less than 0.01.
<figure><img src="image_100.png" /><figcaption>Figure 34: Comparison of simulated global annual GPP estimates with FLUXCOM-V2.</figcaption></figure>

Table 8 shows that for ISBA, the assimilation significantly improves the de-trended *R*², which increases from 0.37 to 0.50, but worsens the mean bias. For ORCHIDEE, the assimilation is quite efficient in reducing the bias with FLUXCOM-V2, which drops from 35.0 PgC/yr to 0.3 PgC/yr, but it worsens the de-trended *R*², which drops from 0.44 to 0.39. Note that the ORCHIDEE_prior GPP values are larger than 160 PgC/yr, which is outside the 80-160 PgC/yr range of possible values given by Zhang and Ye (2022). For both ISBA and ORCHIDEE, the assimilation reduces the standard deviation of the de-trended GPP time series (SD) but the simulation SD remains larger than the FLUXCOM-V2 SD (0.62 PgC/yr).
<figure><img src="image_101.png" /><figcaption>Figure 34: Comparison of simulated global annual GPP estimates with FLUXCOM-V2.</figcaption></figure>

Table 8 shows that for ISBA, the assimilation significantly improves the de-trended *R*², which increases from 0.37 to 0.50, but worsens the mean bias. For ORCHIDEE, the assimilation is quite efficient in reducing the bias with FLUXCOM-V2, which drops from 35.0 PgC/yr to 0.3 PgC/yr, but it worsens the de-trended *R*², which drops from 0.44 to 0.39. Note that the ORCHIDEE_prior GPP values are larger than 160 PgC/yr, which is outside the 80-160 PgC/yr range of possible values given by Zhang and Ye (2022). For both ISBA and ORCHIDEE, the assimilation reduces the standard deviation of the de-trended GPP time series (SD) but the simulation SD remains larger than the FLUXCOM-V2 SD (0.62 PgC/yr).
Pros and cons of data and modelling approaches are presented in Tables 9 and 10, respectively.

<table><caption>Table 9: Pros and cons of satellite products for global applications</caption><thead><tr><td>Data / tool</td><td>Pros</td><td>Cons</td></tr></thead><tbody><tr><td>LAI data</td><td><ul><li>A key variable for water and carbon fluxes</li><li>Straightforward observation operator (can be assimilated as is)</li><li>CGLS near-real time production</li></ul></td><td><ul><li>Definition can vary from one product to another (e.g. effective vs. true LAI)</li><li>Reduced availability caused by cloud coverage</li></ul></td></tr><tr><td>VOD data</td><td><ul><li>Independent from other satellite vegetation products</li><li>Good sampling time (1-3 days)</li></ul></td><td><ul><li>Not simulated by models</li><li>Must be rescaled as a proxy to LAI</li><li>Affected by radio-frequency interferences (RFI)</li></ul></td></tr><tr><td>SIF data</td><td><ul><li>Independent from other satellite vegetation products</li></ul></td><td><ul><li>Canopy-level values do not always correlate well with GPP</li><li>Reduced availability caused by cloud coverage</li></ul></td></tr><tr><td>FAPAR data</td><td><ul><li>Well understood model-independent physical definition, canopy radiative transfer modelling similar as for SIF or LAI</li><li>Well established and tested observation operator available (Sellers, 1985)</li><li>Can be used as a forcing for data-driven models such as SDBM</li></ul></td><td><ul><li>A radiative flux for which building a physically-based observation operator is not trivial for some land surface models</li><li>Reduced availability caused by cloud coverage</li></ul></td></tr></tbody></table>

<table><caption>Table 10: Pros and cons of modelling approaches for global applications</caption><thead><tr><td>Data / tool</td><td>Pros</td><td>Cons</td></tr></thead><tbody><tr><td>ISBA model</td><td><ul><li>Physically-based</li><li>Key processes are represented including plant growth</li><li>Uncalibrated, parsimonious approach</li><li>Advanced representation of soil-plant response to drought</li></ul></td><td><ul><li>Computing time can be a limitation at high spatial resolution</li><li>No mass conservation when sequential assimilation is activated</li></ul></td></tr></tbody></table>
Pros and cons of data and modelling approaches are presented in Tables 9 and 10, respectively.

<table><caption>Table 9: Pros and cons of satellite products for global applications</caption><thead><tr><td>Data / tool</td><td>Pros</td><td>Cons</td></tr></thead><tbody><tr><td>LAI data</td><td><ul><li>A key variable for water and carbon fluxes</li><li>Straightforward observation operator (can be assimilated as is)</li><li>CGLS near-real time production</li></ul></td><td><ul><li>Definition can vary from one product to another (e.g. effective vs. true LAI)</li><li>Reduced availability caused by cloud coverage</li></ul></td></tr><tr><td>VOD data</td><td><ul><li>Independent from other satellite vegetation products</li><li>Good sampling time (1-3 days)</li></ul></td><td><ul><li>Not simulated by models</li><li>Must be rescaled as a proxy to LAI</li><li>Affected by radio-frequency interferences (RFI)</li></ul></td></tr><tr><td>SIF data</td><td><ul><li>Independent from other satellite vegetation products</li></ul></td><td><ul><li>Canopy-level values do not always correlate well with GPP</li><li>Reduced availability caused by cloud coverage</li></ul></td></tr><tr><td>FAPAR data</td><td><ul><li>Well understood model-independent physical definition, canopy radiative transfer modelling similar as for SIF or LAI</li><li>Well established and tested observation operator available (Sellers, 1985)</li><li>Can be used as a forcing for data-driven models such as SDBM</li></ul></td><td><ul><li>A radiative flux for which building a physically-based observation operator is not trivial for some land surface models</li><li>Reduced availability caused by cloud coverage</li></ul></td></tr></tbody></table>

<table><caption>Table 10: Pros and cons of modelling approaches for global applications</caption><thead><tr><td>Data / tool</td><td>Pros</td><td>Cons</td></tr></thead><tbody><tr><td>ISBA model</td><td><ul><li>Physically-based</li><li>Key processes are represented including plant growth</li><li>Uncalibrated, parsimonious approach</li><li>Advanced representation of soil-plant response to drought</li></ul></td><td><ul><li>Computing time can be a limitation at high spatial resolution</li><li>No mass conservation when sequential assimilation is activated</li></ul></td></tr></tbody></table>
<table><tr><td></td><td><ul><li>Daily sequential assimilation of satellite data is possible</li><li>Continuous analysis of root zone soil moisture</li></ul></td><td></td></tr><tr><td>ECLand model</td><td><ul><li>Physically-based</li><li>Within the IFS</li><li>Daily sequential assimilation of satellite data is possible</li><li>Continuous analysis of root zone soil moisture</li></ul></td><td><ul><li>Computing time can be a limitation at high spatial resolution</li><li>Plant growth is not represented</li></ul></td></tr><tr><td>ORCHIDEE model</td><td><ul><li>Physically-based</li><li>Mass is conserved</li><li>Key processes are represented including plant growth</li></ul></td><td><ul><li>Computing time can be a limitation at high spatial resolution</li><li>Possible risk of circularity (in situ flux data are used, as in FLUXCOM)</li></ul></td></tr><tr><td>SDBM model</td><td><ul><li>Data driven</li><li>Excellent performance in model intercomparisons against prognostic models (Heimann et al. 1998, Nemry et al. 1999)</li><li>Input data can be derived directly from IFS/ERA5, hence integration into IFS is straightforward</li></ul>Computing cost efficient</td><td><ul><li>Not all processes are represented</li></ul></td></tr></table>

# 8 Conclusion

Numerical experiments performed by 4 modelling approaches (ISBA, ECLand, ORCHIDEE, SDBM) were compared.

The ISBA land surface model sequentially assimilated LAI using the Meteo-France Land Data Assimilation System (LDAS), with and without surface soil moisture. ISBA and FLUXCOM-V2 GPP were found to correlate well ($R > 0.8$) at high latitudes. Assimilation generally improved the temporal correlation between ISBA and FLUXCOM-V2 GPP, except for sparse vegetation areas. The temporal correlation between TROPOMI SIF and the LAI analysed by ISBA was very good at mid-latitudes (e.g. from France to Ukraine) and a strong increase of the correlation due to assimilation was observed. SIF and GPP were in good agreement during the growing season, but a discrepancy appeared during the senescence. The assimilation of microwave VOD was considered over the USA. The assimilation of VOD improved the representation of evapotranspiration and GPP, and the improvements were almost entirely due to the more frequent observations of VOD compared to LAI.

A daily LAI analysis was produced by integrating microwave VOD into ECLand using the ECMWF LDAS. This analysis was fed into the IFS NWP system in place of the monthly
<table><tr><td></td><td><ul><li>Daily sequential assimilation of satellite data is possible</li><li>Continuous analysis of root zone soil moisture</li></ul></td><td></td></tr><tr><td>ECLand model</td><td><ul><li>Physically-based</li><li>Within the IFS</li><li>Daily sequential assimilation of satellite data is possible</li><li>Continuous analysis of root zone soil moisture</li></ul></td><td><ul><li>Computing time can be a limitation at high spatial resolution</li><li>Plant growth is not represented</li></ul></td></tr><tr><td>ORCHIDEE model</td><td><ul><li>Physically-based</li><li>Mass is conserved</li><li>Key processes are represented including plant growth</li></ul></td><td><ul><li>Computing time can be a limitation at high spatial resolution</li><li>Possible risk of circularity (in situ flux data are used, as in FLUXCOM)</li></ul></td></tr><tr><td>SDBM model</td><td><ul><li>Data driven</li><li>Excellent performance in model intercomparisons against prognostic models (Heimann et al. 1998, Nemry et al. 1999)</li><li>Input data can be derived directly from IFS/ERA5, hence integration into IFS is straightforward</li></ul></td><td><ul><li>Not all processes are represented</li></ul></td></tr></table>

# 8 Conclusion

Numerical experiments performed by 4 modelling approaches (ISBA, ECLand, ORCHIDEE, SDBM) were compared.

The ISBA land surface model sequentially assimilated LAI using the Meteo-France Land Data Assimilation System (LDAS), with and without surface soil moisture. ISBA and FLUXCOM-V2 GPP were found to correlate well ($R > 0.8$) at high latitudes. Assimilation generally improved the temporal correlation between ISBA and FLUXCOM-V2 GPP, except for sparse vegetation areas. The temporal correlation between TROPOMI SIF and the LAI analysed by ISBA was very good at mid-latitudes (e.g. from France to Ukraine) and a strong increase of the correlation due to assimilation was observed. SIF and GPP were in good agreement during the growing season, but a discrepancy appeared during the senescence. The assimilation of microwave VOD was considered over the USA. The assimilation of VOD improved the representation of evapotranspiration and GPP, and the improvements were almost entirely due to the more frequent observations of VOD compared to LAI.

A daily LAI analysis was produced by integrating microwave VOD into ECLand using the ECMWF LDAS. This analysis was fed into the IFS NWP system in place of the monthly
climatology. The impact on the NWP of assimilating satellite data to dynamically updated vegetation showed promising signs on 2 m temperature forecasts and on forecast errors for near-surface relative humidity. This showed that microwave radiances can have an impact on NWP via improved analysis of vegetation parameters. While the dynamically updated LAI improved the meteorological results, the carbon fluxes appeared to be degraded when checked against observations.

The ORCHIDEE model parameters were tuned by assimilation of TROPOMI SIF. In situ GPP data were used to stabilise the optimisation procedure and to avoid overfitting of the SIF data and potential degradation of the GPP estimates. Parameter optimisation with TROPOMI SIF data and in situ GPP data resulted in a large reduction of simulated GPP by ORCHIDEE.

Two simulations of GPP by SDBM were performed using different water stress factors. The version using the BETHY-derived water stress factor showed a better agreement with the independent observations. However, in both cases there was rather little interannual variation in the globally aggregated GPP.

The simulated global annual GPP values were compared with the FLUXCOM-V2 estimates. For the common years 2018-2019, ISBA_SEKF, ECLAND_DA, and SDBM_BETHY gave similar estimates of about 122 PgC/yr (+/- 2 PgC/yr). ORCHIDEE_posterior gave 138 PgG/yr for this period, which was similar to FLUXCOM-V2 (137 PgG/yr). For ISBA, the assimilation significantly improved interannual variability of the GPP, but worsened the mean bias. For ORCHIDEE, the assimilation was quite efficient in reducing the bias with FLUXCOM-V2, but it worsened the interannual variability. For both ISBA and ORCHIDEE, the assimilation reduced the standard deviation of the de-trended GPP time series but it remained larger than the FLUXCOM-V2 value.

For future work, it is recommended to work on assimilating radiances and to continue using the multi-model approach implemented in COCO2. The former requires the development of observation operators, for which machine learning techniques could be explored. The latter could take several forms but regular intercomparisons such as the one carried out in this study are needed.

9 References

Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Le Moigne, P., Decharme, B., Mahfouf, J.-F., and Calvet, J.-C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017.

Albergel, C., Dutra, E., Bonan, B., Zheng, Y., Munier, S., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., and Calvet, J.-C.: Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation, Remote Sens., 11, 520, https://doi.org/10.3390/rs11050520, 2019.

Albergel, C., Zheng, Y., Bonan, B., Dutra, E., Rodríguez-Fernández, N., Munier, S., Draper, C., de Rosnay, P., Muñoz-Sabater, J., Balsamo, G., Fairbairn, D., Meurey, C., and Calvet, J.-C.: Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces, Hydrol. Earth Syst. Sci., 24, 4291–4316, https://doi.org/10.5194/hess-24-4291-2020, 2020.

Alessandri, A., and van Oorschot, F. Improved vegetation variability. CONFESS report. 2022. Available online: https://confessh2020.files.wordpress.com/2023/03/confess_d1-2-v3.0_final.pdf
climatology. The impact on the NWP of assimilating satellite data to dynamically updated vegetation showed promising signs on 2 m temperature forecasts and on forecast errors for near-surface relative humidity. This showed that microwave radiances can have an impact on NWP via improved analysis of vegetation parameters. While the dynamically updated LAI improved the meteorological results, the carbon fluxes appeared to be degraded when checked against observations.

The ORCHIDEE model parameters were tuned by assimilation of TROPOMI SIF. In situ GPP data were used to stabilise the optimisation procedure and to avoid overfitting of the SIF data and potential degradation of the GPP estimates. Parameter optimisation with TROPOMI SIF data and in situ GPP data resulted in a large reduction of simulated GPP by ORCHIDEE.

Two simulations of GPP by SDBM were performed using different water stress factors. The version using the BETHY-derived water stress factor showed a better agreement with the independent observations. However, in both cases there was rather little interannual variation in the globally aggregated GPP.

The simulated global annual GPP values were compared with the FLUXCOM-V2 estimates. For the common years 2018-2019, ISBA_SEKF, ECLAND_DA, and SDBM_BETHY gave similar estimates of about 122 PgC/yr (+/- 2 PgC/yr). ORCHIDEE_posterior gave 138 PgG/yr for this period, which was similar to FLUXCOM-V2 (137 PgG/yr). For ISBA, the assimilation significantly improved interannual variability of the GPP, but worsened the mean bias. For ORCHIDEE, the assimilation was quite efficient in reducing the bias with FLUXCOM-V2, but it worsened the interannual variability. For both ISBA and ORCHIDEE, the assimilation reduced the standard deviation of the de-trended GPP time series but it remained larger than the FLUXCOM-V2 value.

For future work, it is recommended to work on assimilating radiances and to continue using the multi-model approach implemented in COCO2. The former requires the development of observation operators, for which machine learning techniques could be explored. The latter could take several forms but regular intercomparisons such as the one carried out in this study are needed.

9 References

Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Le Moigne, P., Decharme, B., Mahfouf, J.-F., and Calvet, J.-C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017.

Albergel, C., Dutra, E., Bonan, B., Zheng, Y., Munier, S., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., and Calvet, J.-C.: Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation, Remote Sens., 11, 520, https://doi.org/10.3390/rs11050520, 2019.

Albergel, C., Zheng, Y., Bonan, B., Dutra, E., Rodríguez-Fernández, N., Munier, S., Draper, C., de Rosnay, P., Muñoz-Sabater, J., Balsamo, G., Fairbairn, D., Meurey, C., and Calvet, J.-C.: Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces, Hydrol. Earth Syst. Sci., 24, 4291–4316, https://doi.org/10.5194/hess-24-4291-2020, 2020.

Alessandri, A., and van Oorschot, F. Improved vegetation variability. CONFESS report. 2022. Available online: https://confessh2020.files.wordpress.com/2023/03/confess_d1-2-v3.0_final.pdf
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Balsamo, G., P. Viterbo, A. Beljaars, B. van den Hurk, M. Hirsch, A. Betts, K. Scipal A revised hydrology for the ECMWF model: verification from field site to terrestrial water storage and impact in the Integrated Forecast System J. Hydrometeorol., 10 (2009), pp. 623-643, 2009.

Barbu, A. L., Calvet, J.-C., Mahfouf, J.-F., and Lafont, S.: Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France, Hydrol. Earth Syst. Sci., 18, 173–192, https://doi.org/10.5194/hess-18-173-2014, 2014.

Bonan, B., Albergel, C., Zheng, Y., Barbu, A. L., Fairbairn, D., Munier, S., and Calvet, J.-C.: An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region, Hydrol. Earth Syst. Sci., 24, 325–347, https://doi.org/10.5194/hess-24-325-2020, 2020.

Boussetta, S., Balsamo, G., Beljaars, A., Kral, T., & Jarlan, L. (2013). Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model. International Journal of Remote Sensing, 34(9–10), 3520–3542. https://doi.org/10.1080/01431161.2012.716543

Boussetta, S, and Balsamo, G. Vegetation dataset of Land Use/Land Cover and Leaf Area Index. 2021. CONFESS project report. https://confessh2020.files.wordpress.com/2021/08/confess-d1-1-v1-0-.pdf

Calvet, J.-C., Noilhan, J., Roujean, J.-L., Bessemoulin, P., Cabelguenne, M., Olioso, A., and Wigneron, J.-P.: An interactive vegetation SVAT model tested against data from six contrasting sites, Agr. Forest Meteorol., 92, 73–95, https://doi.org/10.1016/S0168-1923(98)00091-4, 1998.

Calvet, J.-C., Rivalland, V., Picon-Cochard, C., and Guehl, J.-M.: Modelling forest transpiration and CO2 fluxes—response to soil moisture stress, Agr. Forest Meteorol., 124, 143–156, https://doi.org/10.1016/j.agrformet.2004.01.007, 2004.

Delbeke, J., Runge-Metzger, A., Slingenberg, Y., & Werksman, J. (2019). The Paris agreement. Towards a Climate-Neutral Europe: Curbing the Trend, 24–45. https://doi.org/10.4324/9789276082569-2

de Rosnay, P., P. Browne, E. de Boisséson, D. Fairbairn, Y. Hirahara, K. Ochi, D. Schepers, P. Weston, H. Zuo, M. Alonso-Balmaseda, G.. Balsamo, M. Bonavita, N. Bormann, A. Brown, M. Chrust, M. Dahoui, G. De Chiara, S. English, A. Geer, S. Healy, H. Hersbach, P. Laloyaux, L. Magnusson, S. Massart, A.. McNally, F. Pappenberger, F. Rabier: "Coupled data assimilation at ECMWF: current status, challenges and future developments", QJRMS, 148(747), pp 2672-2702, 2022, doi: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.4330
Anderson, M. C., Hain, C., Wardlow, B., Pimstein, A., Mecikalski, J. R., and Kustas, W. P.: Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States, J. Climate, 24, 2025–2044, https://doi.org/10.1175/2010JCLI3812.1, 2011.

Asefi-Najafabady, S., Rayner, P. J., Gurney, K. R., McRobert, A., Song, Y., Coltin, K., Huang, J., Elvidge, C., and Baugh, K. (2014), A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of results, J. Geophys. Res. Atmos., 119, 10,213–10,231, https://doi.org/10.1002/2013JD021296

Balsamo, G., P. Viterbo, A. Beljaars, B. van den Hurk, M. Hirsch, A. Betts, K. Scipal A revised hydrology for the ECMWF model: verification from field site to terrestrial water storage and impact in the Integrated Forecast System J. Hydrometeorol., 10 (2009), pp. 623-643, 2009.

Barbu, A. L., Calvet, J.-C., Mahfouf, J.-F., and Lafont, S.: Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France, Hydrol. Earth Syst. Sci., 18, 173–192, https://doi.org/10.5194/hess-18-173-2014, 2014.

Bonan, B., Albergel, C., Zheng, Y., Barbu, A. L., Fairbairn, D., Munier, S., and Calvet, J.-C.: An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region, Hydrol. Earth Syst. Sci., 24, 325–347, https://doi.org/10.5194/hess-24-325-2020, 2020.

Boussetta, S., Balsamo, G., Beljaars, A., Kral, T., & Jarlan, L. (2013). Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model. International Journal of Remote Sensing, 34(9–10), 3520–3542. https://doi.org/10.1080/01431161.2012.716543

Boussetta, S, and Balsamo, G. Vegetation dataset of Land Use/Land Cover and Leaf Area Index. 2021. CONFESS project report. https://confessh2020.files.wordpress.com/2021/08/confess-d1-1-v1-0-.pdf

Calvet, J.-C., Noilhan, J., Roujean, J.-L., Bessemoulin, P., Cabelguenne, M., Olioso, A., and Wigneron, J.-P.: An interactive vegetation SVAT model tested against data from six contrasting sites, Agr. Forest Meteorol., 92, 73–95, https://doi.org/10.1016/S0168-1923(98)00091-4, 1998.

Calvet, J.-C., Rivalland, V., Picon-Cochard, C., and Guehl, J.-M.: Modelling forest transpiration and CO2 fluxes—response to soil moisture stress, Agr. Forest Meteorol., 124, 143–156, https://doi.org/10.1016/j.agrformet.2004.01.007, 2004.

Delbeke, J., Runge-Metzger, A., Slingenberg, Y., & Werksman, J. (2019). The Paris agreement. Towards a Climate-Neutral Europe: Curbing the Trend, 24–45. https://doi.org/10.4324/9789276082569-2

de Rosnay, P., P. Browne, E. de Boisséson, D. Fairbairn, Y. Hirahara, K. Ochi, D. Schepers, P. Weston, H. Zuo, M. Alonso-Balmaseda, G.. Balsamo, M. Bonavita, N. Bormann, A. Brown, M. Chrust, M. Dahoui, G. De Chiara, S. English, A. Geer, S. Healy, H. Hersbach, P. Laloyaux, L. Magnusson, S. Massart, A.. McNally, F. Pappenberger, F. Rabier: "Coupled data assimilation at ECMWF: current status, challenges and future developments", QJRMS, 148(747), pp 2672-2702, 2022, doi: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.4330
EU Commission. (2021). Tracking EU Citizens' Interest in EC Priorities Using Online Search Data: The European Green Deal. https://doi.org/10.2760/18216

Friedl, M., Strahler, A., Hodges, J., 2010. ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000-2001. https://doi.org/10.3334/ORNLDAAC/968.

Gibelin, A.-L., Calvet, J.-C., Roujean, J.-L., Jarlan, L., and Los, S. O.: Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products, J. Geophys. Res., 111, D18102, https://doi.org/10.1029/2005JD006691, 2006.

Guo, Z., Dirmeyer, P. A., & Delsole, T. (2011). Land surface impacts on subseasonal and seasonal predictability. Geophysical Research Letters, 38(24), L24812. https://doi.org/10.1029/2011GL049945

Hascoët, L., Pascual, V., 2013. The Tapenade Automatic Differentiation tool: principles, model, and specification. ACM Trans. Math. Softw. 39 (3).

Heimann, M., Körner, S., 2003. The global atmospheric tracer model TM3. Tech. Rep. 5. Max-Planck-Institut für Biogeochemie, Jena, Germany.

Heimann, M., G. Esser, A. Haxeltine, J. Kaduk, D.W. Kicklighter, W. Knorr, G.H. Kohlmaier, A.D. McGuire, J. Melillo, B. Moore III, R.D. Otto, I.C. Prentice, W. Sauf, A. Schloss, S. Sitch, U. Wittenberg, and Würth, G. Evaluation of terrestrial carbon cycle models through simulations of the seasonal cycle of atmospheric CO2: First results of a model intercomparison study. Global Biogeochemical Cycles, 12(1):1–24, 1998.

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Knorr, W., Heimann, M., 1995. Impact of drought stress and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model. Tellus, Ser. B 47 (4), 471–489.

Knorr, W., 2000. Annual and interannual CO2 exchanges of the terrestrial biosphere: Process-based simulations and uncertainties. Glob. Ecol. Biogeogr. 9 (3), 225–252.

Kaminski, T., Scholze, M., Rayner, P., Voßbeck, M., Buchwitz, M., Reuter, M., Knorr, W., Chen, H., Agustí-Panareda, A., Löscher, A., and Meijer, Y.: Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories, Environmental Research Letters, 17, 014015, https://doi.org/10.1088/1748-9326/ac3cea, URL https://doi.org/10.1088/1748-9326/ac3cea, 2022.
EU Commission. (2021). Tracking EU Citizens' Interest in EC Priorities Using Online Search Data: The European Green Deal. https://doi.org/10.2760/18216

Friedl, M., Strahler, A., Hodges, J., 2010. ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000-2001. https://doi.org/10.3334/ORNLDAAC/968.

Gibelin, A.-L., Calvet, J.-C., Roujean, J.-L., Jarlan, L., and Los, S. O.: Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products, J. Geophys. Res., 111, D18102, https://doi.org/10.1029/2005JD006691, 2006.

Guo, Z., Dirmeyer, P. A., & Delsole, T. (2011). Land surface impacts on subseasonal and seasonal predictability. Geophysical Research Letters, 38(24), L24812. https://doi.org/10.1029/2011GL049945

Hascoët, L., Pascual, V., 2013. The Tapenade Automatic Differentiation tool: principles, model, and specification. ACM Trans. Math. Softw. 39 (3).

Heimann, M., Körner, S., 2003. The global atmospheric tracer model TM3. Tech. Rep. 5. Max-Planck-Institut für Biogeochemie, Jena, Germany.

Heimann, M., G. Esser, A. Haxeltine, J. Kaduk, D.W. Kicklighter, W. Knorr, G.H. Kohlmaier, A.D. McGuire, J. Melillo, B. Moore III, R.D. Otto, I.C. Prentice, W. Sauf, A. Schloss, S. Sitch, U. Wittenberg, and Würth, G. Evaluation of terrestrial carbon cycle models through simulations of the seasonal cycle of atmospheric CO2: First results of a model intercomparison study. Global Biogeochemical Cycles, 12(1):1–24, 1998.

Hersbach, H, Bell, B, Berrisford, P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999–2049. https://doi.org/10.1002/qj.3803

IEA, 2022. Greenhouse Gas Emissions from Energy 2022: https://www.iea.org/data-and-statistics/data-product/co2-emissions-from-fuel-combustion-highlights

Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020.

Kaminski, T., Scholze, M., Vossbeck, M., Knorr, W., Buchwitz, M., Reuter, M., 2017. Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide. Remote Sens. Environ. 203, 109–124.

Knorr, W., Heimann, M., 1995. Impact of drought stress and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model. Tellus, Ser. B 47 (4), 471–489.

Knorr, W., 2000. Annual and interannual CO2 exchanges of the terrestrial biosphere: Process-based simulations and uncertainties. Glob. Ecol. Biogeogr. 9 (3), 225–252.

Kaminski, T., Scholze, M., Rayner, P., Voßbeck, M., Buchwitz, M., Reuter, M., Knorr, W., Chen, H., Agustí-Panareda, A., Löscher, A., and Meijer, Y.: Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories, Environmental Research Letters, 17, 014015, https://doi.org/10.1088/1748-9326/ac3cea, URL https://doi.org/10.1088/1748-9326/ac3cea, 2022.
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Leroux, D., J.-C. Calvet, S. Munier, and C. Albergel: Using satellite-derived vegetation products to evaluate LDAS-Monde over the Euro-Mediterranean area, Remote Sensing, 10, 1199, https://doi.org/10.3390/rs10081199, 2018.

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Mota B., Gobron N., Morgan O., Cappucci F., Lanconelli C., and Robustelli M. Cross-ECV consistency at global scale: LAI and FAPAR changes. Remote Sensing of Environment, 2021, 263. https://doi.org/10.1016/j.rse.2021.112561

Mucia, A., Bonan, B., Albergel, C., Zheng, Y., and Calvet, J.-C.: Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA, Biogeosciences, 19, 2557–2581, https://doi.org/10.5194/bg-19-2557-2022, 2022.

Nemry, B., L. François, J. C. Gérard, A. Bondeau, and M. Heimann, Comparing global models of terrestrial net primary productivity (NPP): Analysis of the seasonal atmospheric CO2 signal, Global Change Biol., 5, 65–76, 1999.

Noilhan, J. and Mahfouf, J.-F. F.: The ISBA land surface parameterisation scheme, Global Planet. Change, 13, 145–159, https://doi.org/10.1016/0921-8181(95)00043-7, 1996.

Orth, R., Dutra, E., & Pappenberger, F. (2016). Improving weather predictability by including land surface model parameter uncertainty. Monthly Weather Review, 144(4), 1551–1569. https://doi.org/10.1175/MWR-D-15-0283.1
Kumar, S. V., Holmes, T. R., Bindlish, R., de Jeu, R., and Peters-Lidard, C.: Assimilation of vegetation optical depth retrievals from passive microwave radiometry, Hydrol. Earth Syst. Sci., 24, 3431–3450, https://doi.org/10.5194/hess-24-3431-2020, 2020.

Lan, X., E.J. Dlugokencky, J.W. Mund, A.M. Crotwell, M.J. Crotwell, E. Moglia, M. Madronich, D. Neff and K.W. Thoning (2022), Atmospheric Carbon Dioxide Dry Air Mole Fractions from the NOAA GML Carbon Cycle Cooperative Global Air Sampling Network, 1968-2021, Version: 2022-11-21, https://doi.org/10.15138/wkgj-f215

Leroux, D., J.-C. Calvet, S. Munier, and C. Albergel: Using satellite-derived vegetation products to evaluate LDAS-Monde over the Euro-Mediterranean area, Remote Sensing, 10, 1199, https://doi.org/10.3390/rs10081199, 2018.

Loveland, T. R., Reed, B. C., Ohlen, D. O., Brown, J. F., Zhu, Z., Yang, L., & Merchant, J. W. (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6–7), 1303–1330. https://doi.org/10.1080/014311600210191

Mahfouf, J.-F.; Bergaoui, K.; Draper, C.; Bouyssel, F.; Taillefer, F.; Taseva, L. A comparison of two off-line soil analysis schemes for assimilation of screen level observations. J. Geophys. Res., 114, D08105, 2009. https://doi.org/10.1029/2008JD011077

Masson, V., P. Le Moigne, E. Martin, S. Faroux, A. Alias, R. Alkama, S. Belamari, A. Barbu, A. Boone, F. Bouyssel, P. Brousseau, E. Brun, J.-C. Calvet, D. Carrer, B. Decharme, C. Delire, S. Donier, K. Essaouini, A.-L. Gibelin, H. Giordani, F. Habets, M. Jidane, G. Kerdraon, E. Kourzeneva, M. Lafaysse, S. Lafont, C. Lebeaupin Brossier, A. Lemonsu, J.-F. Mahfouf, P. Marguinaud, M. Mokhtari, S. Morin, G. Pigeon, R. Salgado, Y. Seity, F. Taillefer, G. Tanguy, P. Tulet, B. Vincendon, V. Vionnet, and A. Voldoire: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013.

Moesinger, L., Dorigo, W., De Jeu, R., Van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave vegetation optical depth climate archive VODCA (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.2575599, 2019.

Monteith, J.L. (1965) Light distribution and photosynthesis in field crops. Annals of Botany 29, 17–37.

Mota B., Gobron N., Morgan O., Cappucci F., Lanconelli C., and Robustelli M. Cross-ECV consistency at global scale: LAI and FAPAR changes. Remote Sensing of Environment, 2021, 263. https://doi.org/10.1016/j.rse.2021.112561

Mucia, A., Bonan, B., Albergel, C., Zheng, Y., and Calvet, J.-C.: Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA, Biogeosciences, 19, 2557–2581, https://doi.org/10.5194/bg-19-2557-2022, 2022.

Nemry, B., L. François, J. C. Gérard, A. Bondeau, and M. Heimann, Comparing global models of terrestrial net primary productivity (NPP): Analysis of the seasonal atmospheric CO2 signal, Global Change Biol., 5, 65–76, 1999.

Noilhan, J. and Mahfouf, J.-F. F.: The ISBA land surface parameterisation scheme, Global Planet. Change, 13, 145–159, https://doi.org/10.1016/0921-8181(95)00043-7, 1996.

Orth, R., Dutra, E., & Pappenberger, F. (2016). Improving weather predictability by including land surface model parameter uncertainty. Monthly Weather Review, 144(4), 1551–1569. https://doi.org/10.1175/MWR-D-15-0283.1
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## Document History

<table><thead><tr><td>Version</td><td>Author(s)</td><td>Date</td><td>Changes</td></tr></thead><tbody><tr><td>0.1</td><td>JC Calvet (Meteo-France)</td><td>30/05/2023</td><td>Template</td></tr><tr><td>1.0</td><td>JC Calvet (Meteo-France)</td><td>06/09/2023</td><td>First draft</td></tr><tr><td>2.0</td><td>JC Calvet (Meteo-France)</td><td>18/09/2023</td><td>For review</td></tr><tr><td>2.1</td><td>JC Calvet (Meteo-France)</td><td>18/10/2023</td><td>Final</td></tr><tr><td></td><td></td><td></td><td></td></tr></tbody></table>

## Internal Review History

<table><thead><tr><td>Internal Reviewers</td><td>Date</td><td>Comments</td></tr></thead><tbody><tr><td>Paul Palmer (UEDIN)</td><td>16/10/2023</td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr></tbody></table>

## Estimated Effort Contribution per Partner

<table><thead><tr><td>Partner</td><td>Effort</td></tr></thead><tbody><tr><td>Meteo-France</td><td>0.1 pm</td></tr><tr><td>ECMWF</td><td>0.1 pm</td></tr><tr><td>CEA</td><td>0.1 pm</td></tr><tr><td>iLab</td><td>0.1 pm</td></tr><tr><td>ULUND</td><td>0.1 pm</td></tr><tr><td>Total</td><td>0.5 pm</td></tr></tbody></table>
## Document History

<table><thead><tr><td>Version</td><td>Author(s)</td><td>Date</td><td>Changes</td></tr></thead><tbody><tr><td>0.1</td><td>JC Calvet (Meteo-France)</td><td>30/05/2023</td><td>Template</td></tr><tr><td>1.0</td><td>JC Calvet (Meteo-France)</td><td>06/09/2023</td><td>First draft</td></tr><tr><td>2.0</td><td>JC Calvet (Meteo-France)</td><td>18/09/2023</td><td>For review</td></tr><tr><td>2.1</td><td>JC Calvet (Meteo-France)</td><td>18/10/2023</td><td>Final</td></tr><tr><td></td><td></td><td></td><td></td></tr></tbody></table>

## Internal Review History

<table><thead><tr><td>Internal Reviewers</td><td>Date</td><td>Comments</td></tr></thead><tbody><tr><td>Paul Palmer (UEDIN)</td><td>16/10/2023</td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td></tr></tbody></table>

## Estimated Effort Contribution per Partner

<table><thead><tr><td>Partner</td><td>Effort</td></tr></thead><tbody><tr><td>Meteo-France</td><td>0.1 pm</td></tr><tr><td>ECMWF</td><td>0.1 pm</td></tr><tr><td>CEA</td><td>0.1 pm</td></tr><tr><td>iLab</td><td>0.1 pm</td></tr><tr><td>ULUND</td><td>0.1 pm</td></tr><tr><td>Total</td><td>0.5 pm</td></tr></tbody></table>
This publication reflects the views only of the author, and the Commission cannot be held responsible for any use
which may be made of the information contained therein.
This publication reflects the views only of the author, and the Commission cannot be held responsible for any use
which may be made of the information contained therein.