The climate model shows that large-scale wind and solar farms in the Sahara increase rainfall and vegetation

Limiting global warming to 2 ° C is essential to mitigate the excessive damage caused by climate change (1–3). Major global efforts and long-term policies are needed to achieve the corresponding level of decarbonisation (4–6). Renewable energy sources such as wind power and solar energy have become viable options (7) because of their abundant supply and availability on Earth (8, 9). Extracting a small portion of the solar and wind energy available on Earth would be more than enough to meet the total global demand for energy in all its forms. This opens the possibility of feeding the world with wind and solar energy, which is possible and has been discussed in the literature (9–13).

To replace the fossil fuels that currently dominate global electricity generation, as well as the needs for transportation, heating and industrial energy, more large wind and solar farms around the world would need to be installed. The installed wind turbines and photovoltaic panels would cover the ground and alter the properties of the soil surface (particularly surface roughness and albedo, respectively) and, if large enough, could have unforeseen consequences on the surface. local and regional climate (14–16). Previous modeling studies have shown that large-scale implementation of wind and solar farms can lead to significant climate change on a continental scale (ten, 17). However, in these studies, vegetation is prescribed rather than dynamic – that is, vegetation types and properties do not respond to climate change caused by large wind and solar farms or changes in vegetation. vegetation does not affect the climate. The absence of vegetation feedback could make modeled climate impacts very different from their actual behavior (18, 19), as vegetation dynamics [e.g., albedo, evapotranspiration, roughness, and leaf area index (LAI)] played a key role in the land-climate interaction (20). Feedbacks on vegetation can either improve or suppress the initial climate changes caused by the change of terrain (21, 22).

In our study, we used a dynamic vegetation climate model to study the climate impacts of large wind and solar farms in the world's largest deserts. We mainly focused on the effects of these large wind and solar farms in the Sahara region (including the aridest areas of the Arabian Desert) and the neighboring Sahel region for several reasons: (i) the Sahara is the the world's largest desert has a large amount of solar and wind energy. (ii) The Sahara is sparsely inhabited and, therefore, the development of wind and solar farms would have minimal competition for terrestrial areas from natural and other land uses, such as agriculture (15). (iii) The Sahel is a transitional region between the desert savannah and the wooded savannah and, as such, is very sensitive to changes in terrain (18, 19, 23). (iv) The two regions are close to Europe and the Middle East, in regions with a huge energy demand and in sub-Saharan Africa, where energy demand is increasing sharply (see additional text). (v) Massive investments in solar and wind power generation could promote the economic development of the Sahel, one of the poorest regions in the world, and provide clean energy for desalination and water supply. cities and food production (24). The wind and solar farms simulated in this study would generate respectively about 3 and 79 TW of electricity, on average over a typical year (see additional text).

Our results show that the effects of large scale wind and solar farms in the Sahara are greater locally – ie, near or near wind and solar farms – with limited impacts at a distance (Fig. 1). The wind farm causes significant regional warming on near-surface air temperature (+2.16 K), with larger changes in the minimum temperature than the maximum temperature (+2.36 vs. +1 , 85 K) (Fig. S1). This asymmetrical impact on temperature has been reported in both empirical (16) and modeling studies (14, 25). Nighttime warming is all the more important as wind turbines can improve vertical brewing and lower warm air from top to bottom, especially during stable nights (14, 26). Wind farms also increase precipitation by + 0.25 mm / day, on average for wind farm areas, resulting in a doubling of precipitation compared to the control experiment (0.24 mm / day), especially in the Sahel region. average increase of +1.12 mm / day (Table S1). Indeed, the increased friction of the surface reduces the wind speed and the associated Coriolis force, which leads to a more dominant pressure gradient force towards the Saharan heat reduced by the wind-induced warming. This produces surface convergence and upward movement as well as moisture convergence and higher humidity (Figs S3 to S5). The increase in precipitation in turn causes an increase in the fraction of the vegetation cover (+0.084), the LAI (+0.50 m²/ m²), and root carbon (+0.08 kgC / m²) which further reduce the surface albedo (Figs S2 and S5). These changes together trigger a positive return albedo-rainfall-vegetation (21, 22). In addition, the vegetation recovered increases evaporation, surface friction, cloud cover (Figure S3) and, consequently, precipitation. Increased evaporation, which partially offsets the increase in superficial solar radiation, also plays an important role in improving local rainfall (21). Slight cooling is observed in the wetter Sahel region as the recovered vegetation increases evaporation and decreases the sensible heat flux. As expected, the increased drag at the surface due to wind turbines reduces wind speed by about 36% (Figure S1).

The impacts of solar farms on temperature and precipitation are substantially similar to those of wind farms in terms of spatial configuration. This is because the solar panels directly reduce the surface albedo and thus trigger a return of albedo – rainfall – positive vegetation similar to that of the wind farms, which leads to an increase in temperature and precipitation. The resulting warming is stronger in maximum temperature (+1.28 K) than in minimum temperature (+0.97 K) because the reduction of the albedo mainly affects the net radiation during the day (Fig S1) . Compared to the control experiment, a 50% increase in precipitation (+ 0.13 mm / day) is observed in solar farms in the Sahara and an increase of +0.57 mm / day in the Sahel (table S1). Unlike the wind farm experience, the solar farm experience produces very few changes in wind speed (Fig S1).

When wind and solar farms are deployed together in the Sahara, climate change is reinforced. Rainfall in the Sahara from 0.24 mm / day in the control cycle to 0.59 mm / day for combined wind and solar farms, an increase of ~ 150%, while the increase in temperature (+ 2.65 K) with this for the only solar park. Although the absolute level of mean precipitation variation over the whole of the Sahara is low compared to these experiments (up to +0.35 mm / day for the combined wind and solar farms), it is necessary to emphasize that the impact of rainfall is not uniform in the space. The largest precipitation increase occurs in the Sahel, with a range of variation between +200 and +500 mm / year (Table S1), which is large enough to have major ecological, environmental and societal impacts.

Our simulations show that wind farms and solar farms in the Sahara contribute to increased rainfall, especially in the Sahel region, thanks to the positive return of albedo – rainfall – vegetation. This positive feedback is established by different mechanisms for wind and solar farms. For wind farms, the higher surface roughness reinforces convergence at low altitude, leading to increased precipitation in the Sahara (27). For solar farms, the reduction of albedo associated with solar panels (ie the effective lower albedo of solar panels compared to Sahara sand) leads to an increased absorption of solar radiation and therefore a superficial warming . area, as well as convergence, upward movement and, therefore, more precipitation (23, 28). The increase in precipitation induced by wind or solar farms in turn increases the vegetation cover and LAI, resulting in further reduction of albedo and increased roughness, both factors favoring the convergence of # 39; moisture. These friction feedbacks – rainfall – vegetation (wind farms) and albedo – rainfall – vegetation (wind and solar farms) are known as the South (27) and the mechanisms of Charney (23, 28), respectively. To quantify the contribution of these two mechanisms, we performed additional experiments in wind farms in which both mechanisms are present and can separate climate changes induced by initial roughness and subsequent albedo changes due to return of vegetation (Fig. 2). We found that, for the change of temperature, the roughness and the return of the vegetation contribute almost equally (+1.00 against +1.16 K). The roughness-induced warming occurs because wind reduction weakens the transport of vertical turbulence to the surface (29). In contrast, for variation in rainfall, 80% of the increase (+0.20 mm / day) comes from the return of vegetation, while roughness only plays a secondary role except as as initial trigger. These results suggest that the absence of vegetation feedback in the model (18, 19) would significantly underestimate the temperature and precipitation impacts of the large Saharan wind farms.

Although wind and solar farms both improve precipitation during our experiments, solar farms will not necessarily always increase precipitation through albedo changes. The direction of precipitation change is largely determined by the sign of albedo change before and after the installation of a solar farm. Specifically, it depends on the efficiency of solar panel conversion and albedo of the background environment. The increase in precipitation in our experiments on solar installations is due to the relatively low conversion efficiency of the panels (15%, typical conversion efficiency of photovoltaic panels), which leads to a decrease in albedo (30). However, if the efficiency of the solar panels and the associated efficient albedo are high enough to cause an increase in the albedo relative to the environment (such as an efficiency of 45%), the climate would have a surface effect with precipitation suppression (Fig. S6), similar to the impact of overgrazing in the desert (23). Assuming an intermediate conversion efficiency greater than 15% for solar panels (for example, an efficiency of 30%), this results in a negligible change in albedo and, consequently, insignificant climatic impacts (fig. S6).

In this study, we used a model operating at a relatively coarse spatial resolution to simulate the impact of wind and solar farms. However, it has been shown that the model is able to capture large scale impacts of albedo evolution, roughness and vegetation responses (20–22, 31) has comparable skills to other higher resolution models in modern climate simulation and multi-year variability in this region as part of several model comparison projects (32, 33). However, uncertainties remain as to the magnitude of the climate response and the strength of the vegetation feedbacks. The complexity of an overall model also limits its ability to capture impacts on synoptic and mesoscale weather processes. It is unclear whether all our results are directly applicable to wind and solar farms that are much smaller in size than the model. Nevertheless, the impacts of the temperature of wind and solar farms in our study (ie the warming effect of wind farms and the impact of solar panels dependent on albedo) are consistent with those of the studies. conducted at the local level (34, 35). For precipitation change, the impact is more uncertain due to the region-specific nature and scale. We addressed these uncertainties by designing additional experiments (30). We have found that the expansion of the Sahara's wind and solar farms to other deserts in the world does not significantly increase the impact on the climate (Figure S7). The most significant impact is still concentrated in the Sahara and the surrounding areas, while the impact is not significant in many other deserts, due to their scattered geographical distributions, larger sizes smaller and smaller changes in albedo (Fig S8). Even in the Sahara, the impacts of wind and solar farms also depend on their specific location and spatial distribution, with uneven impacts when deployed with different spatial configurations ("damier" and "quarter" wind farm experiences). Shown in Fig. S9). Therefore, in order to assess the impacts of smaller scale wind farms and solar farms installed at specific sites, additional studies are needed, particularly those using more advanced global and regional climate models with higher spatial resolutions (25).

Our results obtained from experiments conducted with a climate model suggest that for installations of wind and solar farms whose current conversion efficiency in the desert on a scale large enough to feed the whole world, the impacts on the climate would be beneficial rather than detrimental. and the impacts on global average temperature are still low compared to those induced by CO₂ fossil fuel emissions (3, ten). If they were carefully planned, these farms could also trigger more rainfall, largely because of a return of previously neglected vegetation. This points out that in addition to the anthropogenic greenhouse gas emissions from fossil fuels and the resulting warming, wind and solar energy could have other unexpected beneficial impacts when it occurs. is deployed on a large scale in the Sahara, where conditions are particularly favorable. Efforts to build such large-scale wind and solar farms for power generation can still face many technological (eg, transmission, efficiency), socio-economic (costs, policies) and environmental challenges, but this goal has become more and more feasible. effective (36) (additional text). These results indicate that renewable energy can have multiple benefits for climate and sustainable development and that it could therefore be widely adopted as a key solution to the challenges of global energy, change and climate change. climate change and environmental and social sustainability (4).