Simulated sensitivity of African terrestrial ecosystem photosynthesis to rainfall frequency, intensity, and rainy season length

Our modeling framework quantifies the sensitivity of simulated ecosystem photosynthesis (as measured by gross primary productivity, GPP) and biome geographic distributions to small perturbations in one of the three rainfall characteristics, while the other two are fixed to their current climatological values. We assume that GPP is a function of rainfall intensity (α), rainfall frequency (λ) and rainy season length (T_w), i.e. GPP = G(α,λ,T_w). Following Rodriguez-Iturbe and Porporato (2004) and Guan et al (2014a), we assume there is only a single rainy season with negligible dry season rainfall, which is the case for 92% of all the vegetated area in the Africa continent. The detailed mathematical derivation of the stochastic rainfall characteristics and their implementation within the weather generator are provided in section 2.3.

Table 1. Experiment designs. 'Obs' refers that the rainfall is purely from the observation, 'Syn' refers that the rainfall forcing is synthetically generated based on the climatology of the corresponding rainfall characteristics. '±20%,±10%' means that we perturb a specific rainfall characteristic to increase/decrease 20% and 10% away from the current climatology value and then use the weather generator to create realistic rainfall and other climate forcings. The total rainfall amount changes by '±18%,±9%' as we assume only rainy season that covers 90% of the total annual rainfall (see details in section 2.3).

Experiment number	Mean climate	Rainfall characteristics			Purpose
	P	α	λ	Tw
Sclimatology	Obs	Obs	Obs	Obs	Climatology run
Scontrol	Syn	Syn	Syn	Syn	Control run
Exp 1(Sα)	±18%,±9%	±20%,±10%	Syn	Syn	To calculate $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial {\rm{\alpha }}/{\rm{\alpha }}}}$
Exp 2(Sλ)	±18%,±9%	Syn	±20%,±10%	Syn	To calculate $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial {\rm{\lambda }}/{\rm{\lambda }}}}$
Exp 3(STw)	±18%,±9%	Syn	Syn	±20%,±10%	To calculate $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial {{\rm{T}}_{\rm{w}}}/{{\rm{T}}_{\rm{w}}}}}$

We design the following sensitivity experiments (table 1). We vary one at a time of the three rainfall characteristics at four percentage change levels (−20%, −10%, +10% and +20%) from the current climatological state, while fixing the other two rainfall characteristics at their current climatological values. For each rainfall scenario, we generate six 500-year rainfall realizations (i.e. ensemble members) to account for the stochasticity of the synthetic rainfall model. These six rainfall realizations, with their corresponding climate variables, are used to drive the SEIB-DGVM from which the ensemble mean annual GPP is calculated and normalized by the annual mean GPP from the control run. Adjusting the change over the five percentage scenarios (−20%, −10%, 0%-control run, +10% and +20%) allows us to assess whether the derived GPP sensitivities are linear within the perturbation range. We find that these GPP sensitivities can be mostly treated as linear except for the tropical forests, which show a slight non-linearity (figure S5) but this non-linearity has minimal impact on our results and conclusion. The results shown in the main text are the GPP sensitivity derived from the −10% and +10% scenarios. Since we only apply small perturbations in the rainfall characteristics and assume GPP = G(α,λ,T_w), in the results we will analyze the relative changes in GPP normalized by the perturbation of each rainfall characteristics, i.e. $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial \alpha /\alpha }}$, $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial \lambda /\lambda }}$, and $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial {T_w}/{T_w}}}$, which will be unitless.

We use the well-validated dynamic vegetation model SEIB-DGVM (Sato and Ise 2012). This model explicitly simulates the dynamics of fine-scale ecosystem structure and function for a set of virtual vegetation patches to represent large-scale ecosystem features. Individual plants are simulated from establishment, through growth, and resource competition with other plants, and finally to senescence, in order to create gaps in which recruitment of plants can occur. The SEIB-DGVM includes mechanistically-based and empirically-based algorithms for land physical processes, plant physiological processes, and plant competition processes (Sato and Ise 2012).

Specifically, the soil hydrology follows Sato et al (2010), which assumes that soil water is retained as if it were in a bucket and is only lost when it exceeds a maximum volume. The model divides soil column into layers, each of 10 cm in depth, and liquid water in the soil fills from the deep soil layers until it reaches field capacity. The model assumes no bottom drainage, and hence runoff can only occurs as surface runoff when all soil layers are saturated with water. The 'effective' bucket depth is determined by the number of the layers from which evapotranspiration occurs: it is assumed to be the top five layers of the soil column. With this scheme, the model was calibrated to reconstruct annual GPP, biomass, and runoff averaged over the African continent. After this calibration, the model reasonably reconstructs geographical distributions of biomass, plant productivity, and biome (Sato and Ise 2012). It is worth noting that SEIB-DGVM only simulates saturation-excess runoff, but not infiltration-excess runoff. Considering SEIB-DGVM functions at the daily step, and infiltration-excess runoff happens at much shorter time scales, it is reasonable to only consider saturation-excess runoff process at the daily scale here.

SEIB-DGVM uses a representation of plant water stress based on soil moisture status (stat_water), as stat_water = (S − S_w)/(S_f − S_w), where S, S_w, and S_f refer to the fraction of volumetric soil water content within the rooting depth, at the wilting point, and at field capacity, respectively. A water stress factor (w) is then calculated as w = 2×stat_water − stat_water², where the quadratic term is included to capture the non-linear response of plant water stress to soil moisture (Sato and Ise 2012, Ronda et al 2001), and this water stress factor directly acts to scale the stomatal conductance for plant transpiration and carbon assimilation. For simplicity, all the soil-dependent parameters are derived from soil properties from Global Soil Wetness Project 2 (GSWP2, http://cola.gmu.edu/gswp/).

The model includes two tropical woody plant functional types (PFTs) and two grass PFTs. Specifically the two tropical woody PFTs were developed in the model based on the hydroclimatic conditions: (1) 'tropical evergreen trees' which only develop where water resources are sufficient all year around, so they can maintain leaves for all seasons; (2) 'tropical deciduous trees' which develop where dry and wet seasons both exist and only the wet season can provide enough to fulfill plant water needs, and the trees shed leaves during dry seasons to avoid water stress (figure S3) (Sato and Ise 2012). Furthermore, stat_water also controls leaf phenology in the tropical deciduous tree PFT through an empirically delivered time parameter, D_max. When the D_max day running average of stat_water exceeds 0.0, the phase changes from dormant to growth, whereas when the D_max day running average of stat_water falls below 0.0, the phase changes from growth into a dormant phase. D_max was calibrated to 10 d. SEIB-DGVM simulates two grass PFTs, i.e. C₃ or C₄ grass, depending on the environments. In tropical Africa C₄ grass usually dominates.

The SEIB-DGVM is run at one-degree spatial resolution and at a daily step. Although the spatial and temporal resolutions of the SEIB-DGVM are coarse, the model reasonably reconstructs geographical patterns of biomass, plant productivity, and biome type along the known aridity gradients for the African continent (Sato and Ise 2012), and thereby it is appropriate for this analysis. It is spun-up for 2000 years, during which it is driven by the observed climate (1970−2000) repeatedly to allow the soil carbon pool to reach steady state and then followed by a 500 year simulation driven by a specific rainfall scenario so that the biome distribution reaches to a new steady state.

To understand the direct impacts of intra-seasonal rainfall variability, we turn off the fire component of the SEIB-DGVM to exclude the effects of fire-mediated feedbacks. It is worth noting that though we did not simulate fire in the current work, the SEIB-DVGM was calibrated with allowance of wild fire. Actually, a fire suppression simulation of the SEIB-DGVM indicated that fire is required for reconstructing savanna ecosystems in some areas of Africa: without wildfire in these regions, it reconstructs forest ecosystems instead of savanna (Sato and Ise 2012). Though we are aware of the important role of fire in interacting with rainfall seasonality and in influencing Africa ecosystems (Archibald et al 2009, Bond et al 2005), we aim to focus our scenarios on the pure impact of rainfall characteristics and leave the study of the fire-rainfall interaction to other future work. For similar reasons, we fix the atmospheric CO₂ concentration at 380 ppmv to exclude possible impacts of CO₂ fertilization effects.

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Our stochastic rainfall model is based on the marked Poisson process (Rodriguez-Iturbe and Porporato 2004, Guan et al 2014a). A rainy day is defined when the daily rainfall amount is more than 0 mm. Since SEIB-DGVM runs at the daily step, we do not consider sub-daily rainfall events, and a rainy day is treated as a rainy event. Rainfall intensity is only defined for rainy days, thus it has the unit of mm. The arrival of rainfall events is a Poisson process, i.e. the distribution of time between rainfall events is exponential with mean 1/λ, and λ is estimated as the mean of rainfall frequency (unit: 1/day): f_t(t) = λe^−λt, fort ≥ 0 (with t as the time between events). The depth of rainfall events (i.e. rainfall intensity) is exponentially distributed with mean α, and α is estimated as the mean of rainfall depth only from the rainy days (mm): ${{\rm{f}}_{\rm{H}}}\left( {\rm{h}} \right) = \frac{1}{{\rm{\alpha }}}{{\rm{e}}^{ - \frac{1}{{\rm{\alpha }}}{\rm{h}}}},{\rm{for\ h}} \ge 0$ (with h as the dummy variable). The wet season length T_w is modeled as a beta distribution bounded from 0 to 1, scaled by 365 d. Thus the cumulative total rainfall for a certain period T_i has the mean of E[λ_i] E[α_i]E[T_i] (mm), with subscript i referring to a specific period, and E[x] referring to the statistical mean of the random variable x.

The mean annual precipitation (MAP) can be expressed as MAP=E[λ] E[α] E[T_w] / f_w, where f_w is the fraction of the MAP that occurs during the rainy season. In our model, we set f_w to be 0.9, resulting in the 'rainy (or wet) season' being the period that includes 90% of total annual rainfall, and 'dry season' contains the rest 10% of MAP. The starting/ending dates of the 'wet season' are determined in a more complex way than has been detailed in Guan et al (2015). Here, when generating a synthetic rainfall time series, we first randomly generate T_w using the beta distribution with derived parameters from the observation data (see below), and then simulate 'wet season' and 'dry season' rainfall respectively using two marked Poisson processes with specific derived parameters. We estimate all the rainfall parameters (including the mean and variance of rainfall frequency, intensity and length of wet and dry seasons) for the African continent from the satellite-gauge-merged rainfall measurement from Tropical Rainfall Measuring Mission (TRMM) 3b42−V7 daily rainfall product (Huffman et al 2007) for the period from 1998–2012. We derive these rainfall model parameters at the spatial resolution of the TRMM data (i.e. 0.25 degree, figure 1), and then aggregate these parameters to 1 degree and finally generate grid-based synthetic rainfall time series for the continental Africa. It is cautionary that rainfall characteristics are sensitive to the spatial scale, as local-scale rainfall frequency is usually smaller than those derived from regional aggregated rainfall data (e.g. from satellite or climate models) (del Jesus et al 2015). However, because our dynamic vegetation model was calibrated using the rainfall inputs at the spatial resolution of 0.5–1 degree (Sato et al 2010, Sato and Ise 2012), we keep the consistency at this spatial scale and match TRMM-derived rainfall characteristics to the same scale. This stochastic rainfall model provides a convenient way to adjust rainfall patterns by varying one of the rainfall characteristics.

Other climate variables besides rainfall are necessary for driving the SEIB-DGVM. We build a weather generator by conditionally resampling from the historical records the necessary climat variables based on the simulated rainfall conditions. This method preserves the co-variations between rainfall and other climatic variables, as well as those among different climate variables. The resampled climate variables include air temperature, wind, and humidity from the Global Meteorological Forcing Dataset (Sheffield et al 2006), and cloud fraction and soil temperature from the Climate Forecast System Reanalysis from National Centers for Environmental Prediction (Saha et al 2010) (more details can be found in the supplementary materials available at stacks.iop.org/ERL/13/025013/mmedia).

SEIB-DGVM has been widely validated for various ecosystems at global and regional scales (Sato and Ise 2012, Sato 2009, Sato et al 2010, 2007), and its simulations closely match ground and satellite observations of ecosystem composition, structure and function. Sato and Ise (2012) applied SEIB-DGVM to the whole African continent and found that this model can satisfactorily reproduce the spatial patterns of GPP and biomass measured from satellite remote sensing. Since the primary focus of the current work is GPP, we also compared GPP in the SEIB-DGVM simulation forced by the historical climate data (see details in 2.5) against the MODIS GPP for the Africa continent (figure S1) and found a high level of agreement that justifies the use of SEIB-DGVM for the current study.

To test the validity of the synthetic rainfall/weather generator, we run the SEIB-DGVM using the historical climate record (S_climatology) and the synthetic forcing (S_control), with the latter generated using the weather generator based on the rainfall characteristics derived from the former. Figure 2 shows that the SEIB-DGVM simulations driven by these two different forcings generate similar biome distributions with a Cohen's Kappa coefficient of 0.78 (Cohen 1960), and similar GPP patterns for Africa, with the linear fit of annual GPP as GPP(S_control)= 1.03×GPP(S_climatology)+0.215 (R²= 0.89, P < 0.0001, figure S2). Biome and GPP patterns from both S_climatology and S_control are consistent with observations (Sato and Ise 2012). These results provide confidence in the appropriateness of using the synthetic weather generator to conduct our study.

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We find that within the realistic range of changes (−20% ~ +20%) in total rainfall amount, the simulated change in biome distribution is mainly determined by the total rainfall amount rather than specific rainfall characteristics (figure 3, figure S4). This indicates that MAP is still the first-order determinant for biome distribution. However, there is non-negligible difference among the three rainfall characteristics, principal among these is that a change in T_w can lead to an increase in tropical evergreen forests as well as more tropical woodland expansion into grassland, compared with the same percentage changes in intensity or frequency. This finding has two important implications. First, it indicates that the distributions of tropical evergreen and deciduous trees are also sensitive to the shifts in wet season length (equivalently, the dry season length). Second, this finding also indicates that rainy season length is also a major determinant of savanna structure (i.e. tree/grass fraction) and ecotone shifts.

We find that the GPP sensitivities to rainfall intensity and frequency have similar spatial patterns and magnitudes (figures 4(a) and, (c)). Three distinct response regimes apparently arise as a function of mean annual precipitation (figures 4(b) and (d)). For regions with MAP below 700 mm year⁻¹, GPP has high positive sensitivity to both intensity and frequency ($\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial \alpha /\alpha }}$ and $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial \lambda /\lambda }}$, i.e. for a 10% increase in intensity or frequency, there would be more than a 5% increase in GPP). There is also a large variation (standard deviation ≈ 0.4) in the simulated GPP sensitivity. The peak sensitivities to both intensity and frequency happen at approximately 500 mm year⁻¹. In the MAP range from 700 to 1400 mm year⁻¹, GPP has much lower, but still positive, sensitivity (~0.1±0.05) to intensity and to frequency. A general trend of decreasing sensitivity is seen from 500 to 1400 mm year⁻¹, with the most dramatic decrease happening from 500 to 800 mm year⁻¹. When MAP is larger than 1400 mm year⁻¹, GPP shows slightly negative sensitivity to intensity and frequency. These results demonstrate that changes in intensity and frequency affect drylands the most, especially in regions with MAP below 700 mm year⁻¹––consistent with the existing ecohydrology literature about the importance of rainfall frequency and intensity in determining dryland soil moisture dynamics and plant productivity (Daly et al 2004, Rodriguez-Iturbe and Porporato 2004).

GPP sensitivity to T_w is systematically different than the sensitivity to intensity and frequency (figures 4(e) and (f)). The GPP sensitivity to T_w is high when MAP is below 700 mm year⁻¹, which is similarly to intensity and frequency. However, in the intermediate MAP range (700−1400 mm year⁻¹), changes in T_w would lead to three to four times larger changes in GPP than would result from the same percentage changes in intensity or frequency. The sensitivity of GPP to T_w significantly decreases at 1400−1600 mm year⁻¹and becomes almost zero when MAP is larger than 1600 mm year⁻¹.

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Figure 5 further shows the scatterplots of GPP sensitivity to the three rainfall characteristics. Again, we find that GPP sensitivity to T_w is in general much larger than the GPP sensitivities to intensity (α) and to frequency (λ), with more than 93% of the grid cells showing higher $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial Tw/Tw}}$ than $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial \alpha /\alpha }}$ (i.e. above the 1:1 line in figure 5(a)), and more than 88% of the grids showing higher $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial Tw/Tw}}$ than $\frac{{\partial {\rm{GPP}}/{\rm{GPP}}}}{{\partial \lambda /\lambda }}$ (i.e. above the 1:1 line in figure 5(b)). Although the differences between the GPP sensitivities to frequency and intensity are subtle (figure 4), they are clearly identified in figure 5(c), which shows that the GPP sensitivity to intensity is only 78% of the GPP sensitivity to frequency.

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Based on the above results, we propose three types of vegetation responses to explain the different GPP sensitivities (figure 6). (1) In the low MAP regime (below 700 mm year⁻¹), ecosystem GPP would increase with MAP, regardless of whether the increase in MAP is achieved by increasing rainfall intensity, or rainfall frequency, or the length of the rainy season. Vegetation in this MAP regime experiences water scarcity in both the wet seasons (i.e. $\frac{{\partial \rm GPP/GPP}}{{\partial \alpha /\alpha }}$, $\frac{{\partial \rm GPP/GPP}}{{\partial \lambda /{\rm{\lambda }}}}$) and dry seasons (i.e. $\frac{{\partial \rm GPP/GPP}}{{\partial {T_w}/{T_w}}}$), we describe this sensitivity as the 'chronic water stress' response. (2) For plants in the intermediate MAP range (700−1600 mm year⁻¹), GPP is largely insensitive to additional rainfall within the rainy season (from either intensity or frequency increases), because sufficient water is available during the rainy season (i.e. $\frac{{\partial \rm GPP/GPP}}{{\partial \alpha /\alpha }}$ and $\frac{{\partial \rm GPP/GPP}}{{\partial \lambda /\lambda }}$ range from [0, 0.3]); yet GPP is still highly sensitive to the changes in T_w (i.e. $\frac{{\partial \rm GPP/GPP}}{{\partial {T_w}/{T_w}}}$). Vegetation in this regime is mostly constrained by the length of the dry season, and we describe this sensitivity as 'acute water stress' response. (3) Finally, in the case of high MAP (above 1600 mm year⁻¹), vegetation has no sensitivity or sometimes a slightly negative sensitivity to increases in intensity or frequency within the rainy season, indicating little water stress. The slight negative sensitivity (i.e. $\frac{{\partial \rm GPP/GPP}}{{\partial \alpha /\alpha }} <$, $\frac{{\partial \rm GPP/GPP}}{{\partial \lambda /\lambda }}$) likely arises from the correlation of rainfall and cloudiness. When rainfall amount is not a constraint, ecosystems are prone to radiation limitation instead; any increased cloudiness associated with more rain decreases incoming shortwave radiation and lowers potential ET demand. These regions are almost insensitive to the change in T_w (i.e. $\frac{{\partial \rm GPP/GPP}}{{\partial {T_w}/{T_w}}} \cong$), mostly because their rainy season is close to the whole year and could not be further extended. We describe the sensitivity for this regime as 'minimum water stress' response.

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We further construct a 'GPP sensitivity space' (figure 7) to synthesize our findings. The 'GPP sensitivity space' has its primary axis as the averaged GPP sensitivity to intensity and to frequency (these two are combined due to their similar responses in both magnitudes and spatial patterns) and its secondary axis as the GPP sensitivity to T_w. The three water stress regimes can be distinguished using the K-mean classification algorithm (Seber 1984), with the centroids of each type of water stresses shown in figure 7(a) and their boundaries delineated in figure 7(b). The 'minimum water stress' type has little sensitivity to T_w, and very small (and mostly negative) sensitivity to intensity and frequency. The 'acute water stress' type has large positive sensitivity to T_w, and very small (but positive) sensitivity to intensity and frequency. The 'chronic water stress' type has large positive sensitivity to increases in all the rainfall characteristics; this type has the largest variation. When spatially mapping back this GPP sensitivity space (figure 7(b)), we find that the spatial distribution of the clusters largely resembles the current distribution of biomes in Africa (figure 2), as the 'chronic water stress' type mostly overlaps with tropical grasslands, the 'acute water stress' type overlaps with tropical woodlands, and the 'minimum water stress' type is occupied by tropical forests.

We summarize that these biome-specific sensitivities to rainfall would be generated in the model primarily by PFT specific plant-physiological-properties. First, in savanna and grassland biomes, where the C4-type grass PFT dominates plant productivity, drought tolerance should be higher than in forest ecosystems. Second, while the grass PFT and the deciduous woody PFT can adjust their leafing days to changes in dry season length, the evergreen woody PFT has to maintain leafs even during the dry season, resulting in lower carbon uptake. The latter factor would primarily determine different responses of evergreen and deciduous forests to rain-days, because evergreen and deciduous woody PFTs shares identical stomatal conductance parameters in the model. It should be noted that changes in soil types would also affect these sensitivities, because soil parameters determine capacity of the "bucket" in the hydrology sub-model.

Areas with the 'acute water stress' response largely overlap with tropical woodlands (figure 2), and exhibits little water stress during the rainy season. Both satellite-based (Huete et al 2002) and model-simulated productivity corroborate this finding (figure S6), and indicate that a majority of woodlands have similarly high maximum productivity during the growing season, regardless of widely different annual averaged values in productivity of the broad woodlands. In other words, most woodlands can achieve similarly high peak GPP, but their annual total GPP is largely constrained by the growing season length, which is tightly linked with T_w in tropical Africa (Guan et al 2014b). Though fire impacts have not been modeled in the current framework, we expect that feedbacks between fire occurrences and dry season length would further amplify the controls of dry season length on woodland productivity (Archibald et al 2009).

The GPP sensitivities to intensity and frequency (figures 4(a) and (d)) are similar, mostly because both intensity and frequency describe rainfall characteristics during the rainy season instead of dry season. Thus, only considering the ecological impacts from intensity or frequency cannot capture the effects arising from shifts in rainy season length. Nevertheless, there are still subtle differences between the effects of intensity and frequency, as shown by the fact that the GPP sensitivity to intensity is only 78% of the GPP sensitivity to frequency (figure 5(c)). Plants have little use for higher intensity (here, daily accumulation) if most of it ends up as runoff. Because SEIB-DGVM only considers 'saturation-excess runoff', here we envision the case in which daily rainfall and antecedent soil moisture are high, so that any newly added rainfall overfills the soil 'bucket' and produces saturation-excess runoff. This rainfall intensity impact should be differentiated from the mechanisms of 'infiltration-excess runoff'. The finding that tropical ecosystems in general have larger positive GPP sensitivity to frequency than to intensity is consistent with previous field and modeling studies suggesting that increases in rainfall frequency may be more beneficial to tropical ecosystems than increases in rainfall intensity, given the same change in total rainfall amount (Porporato et al 2004, Knapp et al 2002, Good and Caylor 2011).

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In the current study, we exclude the effects of fire and focus instead on the pure effect of rainfall, which is the first-order determinant for arid and semi-arid ecosystems (Strickland et al 2016, Lehmann et al 2014). We fully recognize the importance of fire, which has been found to strongly affect vegetation dynamics and composition for the savanna ecosystems (Dantas et al 2016, Bond et al 2005). Fire regime depends on both the soil moisture associated with rainfall scenarios and the fuel load associated with vegetation structure. This double dependency leads to a complex vegetation-fire-climate feedback (Lasslop et al 2016, Lehmann et al 2014), which has been challenging to model so far (Hantson et al 2016). Yet we have reasons to believe that our conclusions would largely hold even when considering fire effects. First, fire effects would likely only influence the results for moist woodland or forest regions where rainfall is higher than 1000 mm (Staver et al 2011); fire is less of a concern for GPP in regions with chronic and acute water stress. Second, previous literature largely emphasized the fire impacts on the biome distribution, and the current study shows that intra-seasonal rainfall characteristics have little impact on the biome shifts. Therefore, this study can be regarded as an appropriate first estimate of the potential ecosystem response to changes in rainfall variability and we suggest that analysis of the interactions between fire and intra-season rainfall characteristics can be delayed to future studies.

This study relies on a single ecosystem model, with one way of modeling plant water stress (primarily through soil moisture) and vegetation dynamics. The simulated responses to the three intra-seasonal rainfall characteristics can be largely attributed to the parameterization of soil moisture stress on stomatal functioning. The spatial variations of the responses also arise from the heterogeneous rainfall patterns across space (figure 1) and the specific parameterizations of biophysical, phenological and biome determinations in SEIB-DGVM. The current study using SEIB-DGVM is a continental-scale modeling sensitivity analysis that demonstrates the importance of explicitly considering intra-seasonal rainfall characteristics. Thus, other models should confirm our results before the latter can inform climate change adaptation. Still, the relevant parametrizations in SEIB-DGVM are in line with the state-of-the-art in ecosystem models, and the model contains the necessary processes to be realistic.

Finally, we caution that our synthetic rainfall model is most suitable for the case of a single rainy season or a year-round rainy season (such as in the tropical forests). This covers 92% of the vegetated landscapes in Africa. The only exception is East Africa (i.e. Kenya, Ethiopia), which experiences two rainy seasons per year. Thus while we are confident in our results for the vast majority of the African continent, we are less confident for the East Africa region.