## 4 Discussion
This pattern has also been observed for Mediterranean forests, with GPP peaking during solar radiation and temperature optima [28; 101], although for pine forest in semi-arid conditions GPP seems to maximise earlier in the year, representing a shift from radiation-controlled to moisture-controlled conditions [102]. Overall, Mediterranean forests seem to achieve a lower maximum and annual GPP, compared to tropical, subtropical and temperate forests [100]. However, the relative control of climate, canopy structure and leaf physiology on forest GPP has been studied less within similar ecosystem types (see, however, [103; 104]). Among our study plots, found within relatively short distances from each other (max distance 28.2 km), similar climatic conditions prevail (Figure A1). Furthermore, no differences in the photosynthetic capacity parameters have been identified between the study sites (Table 4) and thus variability in stand and canopy structure can be considered as the main source of simulated GPP variation among the sites. Across our study sites, canopy structure variability is related to the time since the last stand-replacing fire and thus the four study plots can be used to represent the expected GPP pattern with post-fire time. In accordance with ecosystem theory, GPP seems to peak at mature stands, and thus maintenance of this forest state can be important for carbon management purposes [7]. In our simulations, seasonal variation of GPP (Figure 3) seems to be mainly driven by seasonal patterns of PAR [100]. Simulation of GPP based on satellite-driven models is frequently made through light use efficiency models [105], where primary productivity is estimated as the product of incoming PAR, the fraction of absorbed PAR and various environmental scalars (functions that do or do not limit GPP based on prevailing weather conditions and nutrient availability). These models frequently assume a constant (throughout the year) maximum efficiency of the foliage to convert the absorbed energy to carbon (like our average photosynthetic capacity models), which is being reduced by the environmental scalars [66]. Field observations suggest, however, that the photosynthetic capacity of leaves varies substantially throughout the year [84; 106], as our results also show (Figure 2E-P and Table 4), and thus the use of constant photosynthetic parameter values may be inappropriate when simulating GPP [84]. In our varying photosynthetic capacity model parameterisation, we used monthly estimates of the parameters of the photosynthetic light response curve to take into account non-stomatal limitation shifts in photosynthetic capacity, such as reduced mesophyll conductance and photochemical and enzymatic limitations [107]. We further assumed that short-term effects of factors such as temperature and water availability would be less important in determining daily carbon assimilation rates, and thus avoided the use of scalars or functions that regulate canopy conductance, for example through the implementation of stomatal conductance regulation algorithms [108]. Incorporating such short-term responses of photosynthesis to weather variability would probably increase the accuracy of our simulations and reduce simulated GPP fluxes. However, in this study our purpose was to describe a simplified scheme for upscaling measurements of leaf gas-exchange to the stand level, by avoiding the use of detailed physiological algorithms, and assuming that once variation in some key foliage properties has been taken into account, patterns of incoming solar radiation might be the key determinants of forest primary productivity [52]. We however acknowledge that our simplified framework depends strongly on the availability of monthly photosynthetic data that require a significant time spent in the field. During our study period, a strong reduction in the photosynthetic rate in July 2020 has been observed, with \(A_{sat}=5.365\) (\(\mu\)mole m\({}^{-2}\)s\({}^{-1}\)) being 22.3% lower than the respective July 2019 \(A_{sat}\).