The model SCOPE is a 1-D model based on radiative transfer, micrometeorology, and plant physiology (van der Tol et al., 2009b). Version 1.53 of SCOPE is used in this study with the default version of the biochemical code (referred as fluorescence model choice 0; van der Tol et al., 2014). SCOPE treats canopy radiative transfer in the visible and infrared and chlorophyll fluorescence, as well as the energy balance. The modules of SCOPE are executed in the following order:

A semi-empirical radiative transfer model for incident sun and sky radiation, based on the SAIL model (Verhoef and Bach, 2007). This module calculates the outgoing radiation spectrum (0.4 to 50 µm) at the top of the canopy (hereafter TOC), as well as the net radiation and absorbed photosynthetically active radiation (aPAR) per surface element.

In this study, SCOPE uses a canopy structure characterized by a spherical leaf angle distribution (parameters LIDFa and LIDFb in Table 1) as a function of LAI with 60 distributed elementary layers. The geometry of the vegetation is treated stochastically. SCOPE calculates the illumination of leaves with respect to their position and orientation in the canopy. The spectra of reflected and emitted radiation as observed above the canopy in the satellite observation direction are computed. It is worth noting that SCOPE permits variation only in the vertical dimension. Thus, it is valid for vegetation in which variations in the horizontal are smaller than in the vertical dimension. This is maybe a limitation for some natural canopies, especially when coupling to the CCDAS as performed in Sect. 2.1.2. However, the sensitivity of this limitation to the CCDAS results is beyond the scope of this study.

We briefly describe the fluorescence model at leaf level (more detail is given in van der Tol et al., 2009a, 2014) with focus on the variables and parameters relevant for the photosynthesis. The model of Faquahar et al. (1980) divides photosynthesis into two main processes: (1) regeneration of the ribulose bisphosphate (RuP2), which depends on the light and (2) the maximum carboxylation rate at RuP2 saturated conditions in the presence of sufficient light. The regeneration of RuP2 for two photosystems (PSII and PSI) gives the link between photosynthesis and fluorescence.

As already mentioned above, the fluorescence model in SCOPE is formulated such that the sum of the probabilities of an absorbed photon to result in fluorescence, photochemistry, and heat is unity. Following this, the fluorescence ΦFt from a single leaf is calculated over the spectrum window of 640–850 nm as follows: ΦFt=ΦFm1-Φp, where ΦFm is the fluorescence yield and computed as follows: ΦFm=KfKf+Kd+Kn. With Kn being the rate coefficient relative to nonphotochemical quenching (NPQ), a parameter obtained from Pulse amplitude modulated (PAM) fluorometry. PAM measures the photosynthetic efficiency of photosystem II (PSII). Kn is parametrized by using Flexas et al. (2002)'s data set as follows: Kn=6.2473×x-0.5944×x, where x stands for the degree of light saturation and defined as: x=1-ΦpΦp0. Φp and Φp0 (given by the following expressions) stand for the fractions of actual and dark photochemistry yields, respectively: Φp0=KpKf+Kd+Kp. Kf is the rate constant for fluorescence and sets to 0.05, Kp is the rate constant for photochemistry with a value of 4.0, Kd, with a value of 0.95, is the rate constant for thermal deactivation at ΦFm Φp=Φp0JaJe. Ja and Je stand for the actual and potential electron transport rates, respectively. Ja is the electron transport rate used for gross primary productivity (GPP). van der Tol et al. (2014) used Pulse-Amplitude fluorescence measurements to derive an empirical relation between the efficiencies of photochemistry and fluorescence. This relationship was derived after analysing the response of non-photochemical quenching (NPQ) in plants to light saturation. The formulations of GPP in SCOPE follow that of Collatz et al. (1991) and Collatz et al. (1992) for C3 and C4 plants, respectively. The potential electron transport rate Je is related to the rate of absorbed photons (or absorbed photosynthetically active radiation, i.e. aPAR), hence to the visible radiation. The fluorescence is linearly related to the short wave (visible) radiation, while it is related to Vcmax mainly when the gross primary productivity GPP is limited by the carboxylation enzyme Rubisco and the capacity for the export or the utilization of the products of photosynthesis.

The total top-of-canopy fluorescent radiance is obtained from the fluorescence flux (i.e., ΦFt in Eq. 1) and the spectral radiance of single leaves over all layers and orientations, taking into account the probabilities of viewing sunlit and shaded components. The model then calculates radiation transport in a multilayer canopy as a function of the solar zenith angle and leaf orientation to simulate fluorescence in the direction of satellite observation (van der Tol et al., 2009b).

Leaf biochemistry affects reflectance, transmittance, transpiration, photosynthesis, stomatal resistance, and chlorophyll fluorescence. Reflectance and transmittance coefficients, which are a function of Cab are calculated by following the PROSPECT model (Jacquemoud and Baret, 1990). Two excitation fluorescence matrices (EF-matrices) representing fluorescence from both sides of the leaf are computed. The matrices convert a spectrum of aPAR into a spectrum of fluorescence. Details on the radiative transfer model of the fluorescence at the TOC level are given in van der Tol et al. (2009b).

Within CCDAS we replace the canopy radiative transfer and photosynthesis schemes of BETHY with their corresponding schemes from SCOPE and add the fluorescence model of SCOPE. The spatial resolution, vegetation characteristics as well as the meteorological and phenological data of BETHY are used to force SCOPE. The spatial resolution is 2∘ × 2∘ with 3462 land grid points for the globe. CCDAS uses 13 plant functional types (PFT; see Table 2), which have been derived by a condensation (grouping different crop types into one crop PFT) of the original 23 PFTs in BETHY (Knorr, 1997, based on Wilson and Henderson-Sellers, 1985). A grid cell can contain up to three different PFTs, with the amount specified by their fractional coverage.

Frankenberg et al. (2011, 2012), Joiner et al. (2011), and Guanter et al., (2012) have published maps of SIF from GOSAT (Kuze et al, 2009). The retrieval measures terrestrial emission at the frequencies of solar Fraunhofer lines (gaps in the solar spectrum). Chlorophyll fluorescence is the main contributor to emissions at these frequencies. GOSAT carries a Fourier Transform Spectrometer (FTS) measuring with high spectral resolution in the 755–775 nm range, which allows resolving individual Fraunhofer lines overlapping the fluorescence emission. The method described in Frankenberg et al. (2011) makes use of two spectral windows centred at 755 and 770 nm to derive SIF. Results from the line centred around 755 nm for the period June 2009 to December 2010 are used in this study. The fluorescence data we are using are monthly means mapped onto 2∘ ×  2∘ spatial resolution at global scale. The fluorescence product includes uncertainties.

The input data for the models we are using are of three main kinds: (i) the data for the canopy radiative transfer modules of SCOPE, (ii) the data characterizing the environmental conditions (i.e. meteorological and short and long wave radiation) relevant for both the canopy radiative transfer and biochemistry models, and (iii) the process parameters of the biochemistry models.

The model SCOPE requires incident radiation at the top-of-canopy as input. To take into account the atmospheric absorption bands properly, these data are needed at high resolution. The spectra of sun and sky fluxes at the top of the canopy are obtained from the atmospheric radiative transfer model MODTRAN (Berk et al., 2000). MODTRAN was run for 16 atmospheric situations representative of different regions (Verhoef et al., 2014). We use 4 types of these generated atmospheres. They are tropical atmosphere for the tropical zones, winter and summer atmospheres for high and middle latitudes. In addition, we have at our disposal data for an atmosphere which is representative of the whole globe (hereafter standard atmosphere). We have tested the sensitivity of SIF and GPP to these four types of atmospheres. Results show only negligible differences between the inferred SIF and GPP. We consider the standard atmosphere for the idealized tests (Sect. 4.1) and the seasonal atmosphere for the simulations at global scale by using the CCDAS (Sect. 4.2).

The system needs forcing data to drive SCOPE within the CCDAS framework. Monthly observed climate, incident radiation, and fractional soil moisture for the period 2009–2010 are used (Weedon et al., 2011). The LAIs are obtained from BETHY simulation.

The main parameters that affect both the photosynthesis and fluorescence schemes are given in Table 2. The parameters are of two kinds: parameters that are PFT-specific (e.g., Vcmax and Cab) and global parameters. Prior and optimized values of Vcmax obtained by Koffi et al. (2012) are shown. The chlorophyll content Cab is related to the nitrogen content of the leaf which itself is linked to the maximum rate of carboxylation through the proteins of the Calvin Cycle and the thylakoids. Some investigators have related the photosynthetic capacity of leaves of some specific plants to their nitrogen content (e.g., Evans, 1989; Kattge et al., 2009; Houborg et al., 2013). Other investigators have derived some empirical relationships between the nitrogen content and the chlorophyll content (e.g., Shaahan et al., 1999; van den Berg and Perkins, 2004; Ghasemi et al., 2011). Since the current version of the model SCOPE does not include the nitrogen scheme of a leaf, we first use the same value of chlorophyll content Cab for all 13 PFTs. As a second step, Cab values for each of the 13 PFTs are optimized so that the simulated SIF reproduces the main spatial characteristics of observed SIF.

Figure 2 shows the sensitivity of both SIF and GPP to LAI, Vcmax, and Cab under moderate light conditions (Rin=500 W m-2). As expected, both the fluorescence SIF and GPP increase with the increase of LAI (Fig. 2). However, a weak sensitivity is found for LAI values greater than 4. As an illustration for the increase, for Vcmax=50 µmol m-2 s-1, SIF values of 0.5 and 1.25 W m-2 µm-1 sr-1 are found for LAI of 0.5 and 2, respectively (Fig. 2a). The fluorescence slightly increases with an increase of Vcmax. The sensitivity is relatively large for Vcmax, less than 70 µmol m-2 s-1. Then, SIF remains almost constant for Vcmax higher than 125 µmolm-2s-1 (Fig. 2a). As an illustration, for LAI = 2, the largest increase is of only 50 % of SIF for Vcmax between 10 and 70 µmol m-2 s-1. Under the studied configurations SIF increases with Vcmax when the GPP is controlled by the carboxylation enzyme Rubisco, and remains almost constant when photosynthesis is limited by electron transport.

GPP monotonically increases as Vcmax increases with large sensitivity for small Vcmax (less than 75 µmol m-2 s-1), then it becomes weakly sensitive for large values of Vcmax (Fig. 2b). A moderate positive correlation is found between SIF and GPP for Vcmax less than 125 µmol m-2 s-1, as shown by the increase of both SIF and GPP with Vcmax (Fig. 2a and b). Then, for larger Vcmax (i.e., 125 µmol m-2 s-1), a very weak negative correlation between SIF and GPP is obtained. The reason for this weak negative correlation is that SIF slightly decreases for large Vcmax, while GPP even limited by the carboxylation enzyme Rubisco still slightly increases (Fig. 2a and b). In fact, the value of irradiance at which the fluorescence yield at leaf level ΦFt (Eq. 1) or SIF peaks increases with the increase of Vcmax. Thus, for the case presented in Fig. 2a with the short wave radiation Rin of 500 W m-2, the peak of SIF occurs at about Vcmax=200 µmol m-2 s-1.

In the current version of the fluorescence model in SCOPE, the concentration of chlorophyll Cab is set as a parameter and it is linked to SIF through the transmittance and reflectance of the leaves. Figure 2c portrays the variations of SIF as a function of Cab and for various LAIs. For a given LAI, SIF increases with Cab with large sensitivity for Cab less than 20 µg cm-2. For larger Cab values (i.e., > 50 µg cm-2), SIF remains almost constant with a tendency to slightly decrease as Cab increases. For a given Cab, the variance in SIF due to the LAI can be significant.

Fig. 2d displays GPP as a function of Cab (Fig. 2d). Except for small values of Cab (less than 5 µg cm-2), GPP is not sensitive to Cab. The very weak sensitivity of GPP to Cab comes from the impact of the chlorophyll content on the transmittance and reflectance at the top of the canopy when computing the aPAR. This lack of sensitivity of GPP to Cab contradicts the established positive relationship between the two variables as reported in Fleischer (1935) and more recently in Gitelson et al. (2006).

For a given LAI, both SIF and GPP increase with the top of canopy short wave radiation (Rin) (Fig. 2e and f). Thus, a strong positive linear correlation is obtained between SIF and Rin (Fig. 2e), while a non-linear (i.e., curvilinear) relationship is obtained between GPP and Rin (Fig. 2f). For large Rin, GPP increases with a slower rate indicating that the photosynthesis is limited by the carboxylation enzyme Rubisco. For the selected values of LAI, large variance is found between SIF and Rin (Fig. 2e). We also investigate the relationship between the simulated aPAR and both computed SIF and GPP (see Sect. S1 in the Supplement). As expected, a very strong linear relationship between SIF and aPAR is obtained. This relationship is less sensitive to the LAI as it is for the relation between SIF and Rin (as shown in Fig. 2e). GPP shows similar variations with aPAR as it does with the short wave radiation in Fig. 2f.

Finally, the sensitivities of SIF and GPP to both Rin and aPAR for various Vcmax are also investigated (Fig. 3). A strong linear relationship between SIF and both Rin and aPAR is obtained with slopes which are less sensitive to the values of Vcmax (Fig. 3a and c). Also, results clearly show that the sensitivity of SIF to Vcmax increases with the increase of aPAR (or Rin), with almost no sensitivity for low values of aPAR (< 250 µmol m-2 s-1). However, even with large values of aPAR (or Rin), the sensitivity of SIF to Vcmax remains small. In fact, the sensitivity of SIF to Vcmax slightly increases with increasing of incoming radiation only when Vcmax rapidly increases from low to high values (e.g. 5 to 250 µmol m-2 s-1; Fig. 3a and c). Such a rapid increase of Vcmax does occur only during the growing season of the plant. As expected, a curvilinear relationship is found between GPP and both Rin and aPAR with large variance in this relation for the selected Vcmax (Fig. 3b and d).

It is worth noting that SIF values present in Fig. 3 in this study differ (here lower) from the fluorescence flux at leaf level shown in van der Tol et al. (2014). In fact, the authors argued that in the canopy, leaf illumination is variable among leaves, and the relationship after aggregating over all leaves (i.e., SIF) may differ from the fluorescence flux at leaf level.

The conclusions found from C3 plant relevant for the sensitivity of both SIF and GPP to the input variables (Vcmax, Cab, and Rin) are valid for C4 plant (see Sect. S1). However, the amplitude of these sensitivities is slightly larger for C4 plant.

The time series of both simulated SIF and GPP for 15–20 July 2004 are presented in Fig. 4. As expected, there is a strong correlation between aPAR and the short wave radiation Rin (Fig. 4b), hence we discuss the results as a function of the observed Rin. The temporal variations of SIF and GPP mainly follow that of Rin. Particularly, the variations of SIF mirror that of Rin, showing that the variance in SIF due to the temperature is low in this case study (Fig. 4a). At high irradiance GPP shows limitation by the carboxylation enzyme Rubisco, peaking early in the day whereas SIF follows Rin throughout the day. The small variations in GPP at certain episodes can be explained by the temporal variations of the temperature (Fig. 4a). Note that Vcmax, Cab, and LAI are set constant during this period. Consequently, for this case study, the short wave radiation (hence aPAR) is the main driver of the relationship between simulated SIF and GPP. A curvilinear relation is obtained between GPP and SIF. However, a relatively strong linear correlation coefficient of 0.95 is derived. This suggests that SIF is a good constraint of GPP even if it does not directly constrain Vcmax. The SCOPE model reproduces the observed diurnal GPP quite well with meaningful choices of both LAI and Vcmax values (Fig. 4d). Again, the simulated SIF is sensitive to Cab, while GPP is insensitive to Cab (Figs. 4c and d).

Furthermore, we have computed the seasonal variations of these quantities for some years at Hyytiala and Roccarespampani1 (acronym IT-Ro1, longitude/latitude of 11.93/42.408) (see Sect. S2 in the Supplement). Overall, the model reproduces quite well the observed GPP. However, the simulated SCOPE GPP peak over a year occurs earlier (within 1–2 months) than observed ones. This result is maybe caused by both LAI and Vcmax used for the simulation, which seem apparently large during the growing season of the vegetation at these sites. The results of these preliminary analyses can be then reinforced by using e.g., the satellite MODIS weekly LAI data relevant for these stations.

In summary, these idealized tests clearly show that the fluorescence SIF is more sensitive to Cab, while GPP is more sensitive to Vcmax and both quantities are strongly sensitive to the short wave radiation (or aPAR). However, GPP is limited by the carboxylation enzyme Rubisco for large values of short wave radiation (or aPAR). Consequently, in this case the relationship between SIF and GPP mainly driven by the short wave radiation (or aPAR) is curvilinear. The part of the variance in this relationship due to the GPP can be explained by Vcmax and environment conditions, while the variance in SIF is mainly due to Cab and possibly to the geometrical parameters (i.e., solar zenith angle and observation zenith angle) used in the retrieval of SIF.

Recent investigations by Zhang et al. (2014) show a strong sensitivity of SIF to Vcmax at in situ level at light saturation for cropland (corn and soybean) using SCOPE version 1.52. Zhang et al. (2014) found about 4 times our sensitivity of SIF (here computed at 755 nm; Fig. 3a) to Vcmax in the range of 10–200 µmol m-2 s-1. We have modified our experiments to bring them closer to those of Zhang et al. (2014). First, Zhang et al. (2014) calculate SIF at 740 nm vs. 755 nm in this study. Secondly, Zhang et al. (2014) average their calculations from 9:00–12:00 local time (LT), while we sample at 12:00 LT. Results show that:

The sensitivity of SIF to Vcmax is slightly larger at 740 than 755 nm and the difference increases with aPAR. However, as an example, for a relatively large aPAR (1400 µmol m-2 s-1), SIF at 740 nm is only 25 % higher than SIF at 755 nm.

For the discussion of the time series of modelled SIF and GPP at each CCDAS land pixel and the corresponding observed SIF we analyze only pixels for which we have at least 1 year satellite-based SIF data. Moreover, we consider only the time series of these quantities for which the satellite-based SIF data show consecutive values equal or greater than zero. Indeed, the SCOPE model does not allow simulating negative SIF values. Overall, the seasonality of the satellite-derived SIF is reasonably well reproduced by both the simulated SIF and GPP as illustrated in Fig. 5. In accordance with the idealized tests, the amplitudes of the satellite-derived SIF can be better fitted by appropriate values of Cab (Fig. 5a), while the simulated GPP is only weakly sensitive to small Cab values as discussed in Sect. 4.1. As expected, the amplitudes of the simulated GPP are strongly sensitive to Vcmax (Fig. 5b).

We have computed the Pearson correlation coefficient between the time series of satellite-based SIF and modelled SIF and GPP at each pixel. For each pixel, we consider only the pair of data for which the satellite-based SIF is greater than or equal to zero. At most, 18 pairs of data are available for each pixel. We treat only pixels with at least 14 data points for which the linear correlation is significant at least 10 % of level of significance for Pearson coefficient R greater than 0.43. For about half of the 3462 land pixels of CCDAS, the linear correlation coefficient R between the satellite-based SIF and either simulated SIF or GPP is less than 0.43. For these latter pixels, we have analyzed the time series of the satellite-based SIF (with their uncertainty) jointly with the simulated SIF and GPP together with the aPAR as representative of the short wave radiation. For brevity sake, we only enumerate the different cases with low correlation (i.e., R< 0.43) without quantification since this does not add anything valuable to our demonstration in the current study. We have cases for which:

The peaks in simulated quantities (i.e., SIF and GPP) lag the satellite-based SIF peak by at least 1 month. Other cases show opposite behaviour.

The model SCOPE simulates quite well the observed SIF (Fig. 6d). However, large observed SIF (> 2 Wm-2 µm-1 sr-1) are not simulated. Such large observed SIF mainly occur in the tropics. This result points out that short wave radiation used in the CCDAS simulations may be smaller than actual values. Also, the parameter Kn (Eq. 3) in the SCOPE model may explain part of these low SIF. In fact, the computation of the fluorescence yield ΦFm (Eq. 2) depends on the parameter Kn, which is unknown and there is no theoretical basis to constrain it. Thus, an empirical relationship of Kn is used to calculate ΦFm. In the current version of the model SCOPE, there are two parametrizations of Kn. In this paper, we use the parameterization of Kn from Flexas et al. (2002)'s data set that includes drought stress (see Eq. 3). Nevertheless, we have tested the other parameterization and large differences are found from their SIF output. The contribution of chlorophyll content Cab is low since the assigned value in tropics is already large (40 µg cm-2) and as shown by the idealized tests, the simulated fluorescence SIF remains almost constant for Cab value larger or equal to 40 µg cm-2 (Fig. 2c). The correlation coefficient between modelled GPP and satellite-based SIF is 0.70. This rises to 0.8 when we aggregate both quantities to 4 × 4 degrees in agreement with Frankenberg et al. (2011). Finally, as expected, a relatively good correlation is found between aPAR and satellite-based SIF (Fig. 6f).

Correlations are found to be larger between simulated quantities and satellite-derived SIF in the northern hemisphere and moderate in the tropics and lower in the southern hemisphere (not shown).

We compute the mean annual patterns of the satellite-based SIF and simulated SIF and GPP for 2010. We discuss the simulated quantities by using the experiments S3 (i.e., optimized Vcmax and constant Cab for all the 13 PFTs) and S4 (optimized Vcmax and Cab PTF-specific) (See Table 3).

Figure 7 displays the annual mean observed and simulated SIF as well as simulated GPP. Figure 7a shows the satellite-based SIF. Figure 7b displays the modelled SIF by using constant Cab for the 13 PFTs (experiment S3; Table 3), while Fig. 7c presents model results of SIF for Cab PTF-specific (experiment S4). Figure 7d exhibits the simulated GPP by using both Cab PFT-specific and optimized Vcmax (experiment S4). The model can reasonably reproduce the mean spatial patterns of the satellite-based SIF with an appropriate choice of Cab values for each of the 13 PFTs (Fig. 7a and c). The model with constant Cab cannot reproduce the locations of maximum observed SIF (Fig. 7a and b). Despite the good correlation, the computed SIF with PFT-specific Cab (Table 3) underestimates the satellite-based data (Fig. 7a and c). Some of this mismatch corresponds to unlikely simulated SIF, for example, in central Australia.

A good agreement between the spatial patterns of GPP and satellite-based SIF is found (Fig. 7a and d). Overall, we have a co-occurrence of hot spots of observed SIF and simulated SIF and GPP. Moreover, maximum simulated SIF coincides with maximum aPAR (not shown).

The small sensitivity of simulated SIF to Vcmax suggests that it may be difficult to use observations of SIF to constrain it. We can test this in a more realistic context by comparing the differences between simulated SIF for prior and optimized values of Vcmax. If differences are large compared to uncertainties in the observation then SIF observations would allow constraining Vcmax. We compute the differences between simulated SIF by using prior Vcmax (experiment S2 in Table 3) and optimized Vcmax (experiment S4). Then, we normalize these differences by the uncertainties in satellite-based SIF. The derived root mean square over the year 2010 at pixel level can reach up to 67 % of the observed uncertainties, but the global average is only 6 %. This suggests that SIF measurements can only weakly constrain Vcmax within the current CCDAS.

We compute the global and regional (i.e., Northern hemisphere [30∘ N–90∘ N] Tropics [30∘ S–30∘ N] and Southern hemisphere [90∘ S–30∘ S]) means at each month of the year and over June 2009 to December 2010 over land pixels. Results of both simulated SIF and GPP from our best experimental set up (i.e., optimized Vcmax with Cab PTF-specific; experiment S4 in Table 3) are discussed. The results show a reasonably good agreement between satellite-based SIF and both simulated SIF and GPP in terms of seasonality (Fig. 8). However, on average, the simulated quantities peak 1 month earlier than the peak of the satellite-based SIF (Fig. 8a). In the Northern hemisphere, satellite-based SIF peaks in July, while simulated SIF reaches its maximum in June (Fig. 8b). The seasonality at global scale is dominated by the Northern hemisphere (Fig. 8a and b). In the Tropics, there is no significant seasonality in the satellite-based SIF, which is also reproduced by the model (Fig. 8c). In the Southern hemisphere, the satellite-based SIF peaks in January, while modelled peaks in December (Fig. 8d). This weak seasonality shift in the CCDAS simulations is driven by the visible radiation at the top of the canopy (or aPAR) and LAI.

Quantitatively, the mean values of the simulated SIF are slightly smaller than that of satellite-based (about 93 %) in the Northern hemisphere and the Tropics. Since the above-mentioned regions dominated the amplitude of SIF, a good agreement between simulated and satellite-based SIF is consequently found at global scale. The simulated SIF in the Southern hemisphere is about 1.47 times the value of satellite-based SIF. The main differences occur in Australia where the relatively large values of modelled SIF are not shown in the satellite-based SIF data (see Fig. 7a and c).

The zonal averages over the CCDAS land pixels of the satellite-based SIF and the simulated quantities (SIF and GPP) are shown in Fig. 9. A good agreement is found between the latitudinal variations of the satellite-based SIF and the simulated SIF by using the Cab PFT-specific (Fig. 9). Also, a good agreement is obtained between the satellite-based SIF and the GPP (Fig. 9) and between SIF and aPAR (see Sect. S3 in the Supplement). All of these quantities show maxima in the tropics and around 45∘ N. Simulated SIF values are smaller than the satellite-based SIF in the tropics. Between -15∘ and -45∘, the differences are mainly due to C4 grass for which both the model's Vcmax and Cab are apparently small. Around -35∘ latitude, the differences are mainly due to the fact that the model simulates a large SIF signal over Australia, while the satellite-based SIF shows only a small SIF signal. This discrepancy might be explained by the uncertainty in the LAIs set to the evergreen shrub in the CCDAS in this area. Apparently, the LAIs in the CCDAS seem larger than expected values that give satellite based SIF measurements.

In summary, the agreement between simulated and observed SIF is better as we move to larger and larger scales.