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The share of solar power in Brazil''s electrical grid has rapidly increased, relieving GHG emissions and diversifying energy sources for greater energy security. Besides that, solar resource is susceptible to climate change, adding uncertainty to electrical grid resilience. This study uses satellite and reanalysis data to evaluate the performance of CMIP6 models in replicating and predicting surface solar irradiance (SSR) in Brazil. The results from the most reliable models indicate an increase in SSR by 2% to 8% in most regions, with a decrease of around 3% in the South. These findings highlight the potential for increased photovoltaic (PV) yield if backed by supportive public policies while underlining the importance of uncertainty assessment of climate models.
Since 2018, Brazil has been witnessing a significant surge in its installed PV capacity, which has now surpassed 30.7 GW in the second quarter of 202313,14,15. As PV power generation is set to play a more substantial role in Brazil''s future energy mix, it becomes imperative to delve into the impact of climate change on the spatial and temporal variability of solar energy.
The previous results revealed a high level of uncertainty in climate change impact assessments, partly due to the different methodologies and datasets adopted. A more rigorous selection of the climate models to be used in an ensemble analysis, focusing on selecting those with the best performance and ability to represent current climate patterns, is essential in improving the analysis of future climate scenarios. Bias-correction methods and statistical indicators to evaluate the model''s skill in reproducing spatial and seasonal patterns observed in historical reference datasets, like satellite-based or meteorological reanalysis, are fundamental to achieving more confidence in the climate change impact assessment.
Figure 1 provides a comprehensive view of the performance of CMIP6 models in reproducing (SSR) spatial patterns, providing visual information on the alternation between positive and negative bias for Brazilian territory. Uncertainty in model estimates is noticeable due to the large spread of deviations. The 40-models'' ensemble ((ENS)) reproduces the (SSR)''s spatial pattern over Brazilian Northeast and Central regions with reduced bias. Nevertheless, the (ENS) overestimates (around (50 W/m^2)) the climatological (SSR) in the Amazon region. These results agree with findings showing a negative bias for precipitation outputs of CMIP6 models for the north of the Amazon22,23,24.
The panel presents the mapping of the BIAS deviation (in W/m2)shown by the (SSR) estimates provided by the ensemble (upper left corner) and by each of the forty climate models from CMIP6 used in the study. The model names are positioned above the corresponding map. The authors prepared maps using the available Python libraries.
Model M25 (HadGEM3-C31) is the top-performing model in terms of (TSS), with the highest time correlation and lowest (uRMSD). The (ENS) has the second lowest ((SD)) but performs poorly in other statistical indexes compared to the ten best-performing models.
The (SSR) changes for future scenarios obtained from (SME) are presented in three timeslices: near-future (2015-2040), mid-term future (2041-2070), and end-of-century (2071-2100). Complete plots and maps for the three timeslices and both climate scenarios (SSP2.45 and SSP5.85) are available at https://doi /10.6084/m9 gshare.25396612 for public access.
Figure 4c shows an opposite seasonal pattern in the South of Brazil (area A2). The (CCF) shows negative values most of the year except for January and February, ranging from (0.5) to (1.5%) in both scenarios and timeslices. The decrease in (SSR) is more severe during the Wet-Dry transition months when the predicted (CCF) is around(-2.0%) ((-4.5%)) in SSP2-4.5 (SSP5-8.5) at the end-of-century.
The seasonal mean CCF predicted by the SME for the SSP2-4.5 in 2015-2040 (a), 2041-2070 (b), and 2071-2100 (c) timeslices; and for SSP5-8.5 in 2015-2040 (d), 2041-2070 (e) and 2071-2100 (f) timeslices. The columns are from left to right: summer, autumn, winter, spring, and annual. The gray dots over the maps represent the grid locations with statistical significance (p-value <0.05). The authors prepared maps using the available Python libraries.
Solar PV technologies have rapidly grown in Brazilian metropolitan regions (MR) due to a sharp cost reduction and recent regulations encouraging distributed generation13. The SSR''s spatial distribution and future trends highlight the challenges in optimizing solar power benefits for Brazil''s energy mix while reducing risks and GHG emissions to fulfill international commitments. Based on recent works using data from PV power systems operating in Brazil27, we used the performance ratio (PR) around 0.8 to evaluate the impact of climate change on solar PV yield.
Figure 6 shows the annual PV yield from 1980 to 2100 assessed using the SSR outcomes of the SME for SSP2-4.5 and SSP5-8.5 pathways in seven MRs and two remote areas, covering different climate regimes. We assumed that technological advancements in PV technology will offset the losses in solar energy conversion due to the rise in ambient temperature. Table 1 lists the trend slope and p-value of the linear regression fitted for the nine locations and climate pathways. The statistically significant trends are highlighted in bold blue numbers.
Annual trends of (SSR) generated from (SME) outputs for seven metropolitan areas of Brazil - (a) Petrolina, (b) Fortaleza, (c) Brasília, (d) Belo Horizonte, (e) São Paulo, (f) Porto Alegre and (g) Manaus - and two remote areas (h) Boa Vista (located further north in the Brazilian Amazon), and (i) Colniza (located in the deforestation belt in the Southern Amazon). The geographical location of the nine spots is shown in Fig. 2.
Fortaleza and Petrolina are MRs located in the Northeastern region of Brazil, where (SSR) is at its highest. Fortaleza is on the coast near the Equator and holds more than three million inhabitants. It also has abundant wind energy resources throughout the year28,29, which allows hybrid wind-solar projects to take place, reducing power intermittence. On the other hand, Petrolina is located in the semiarid region close to the largest regional hydropower reservoir, Sobradinho (1050 GW), where floating PV power plants could improve water storage and management during extreme drought periods and meet water demands for other uses besides power generation30,31.
São Paulo is the largest Brazilian MR, with nearly 22 million inhabitants living in around (8000 km^2) in the Brazil Southeast. São Paulo is the country''s primary energy consumption center and has seen an exponential increase in PVDG since 202013,14. Figure 6 and Table 1 indicate that climate change will not particularly affect the annual PV yield as the trend slope is slight and has no statistical significance.
The three remaining locations, Manaus, Boa Vista, and Colniza, are facing a pressing issue of energy access. Despite being far from the leading consumer centers, these regions urgently need to address their energy challenges. Manaus, the largest urban center in the Brazilian Amazon region, is a hub of economic activity. Boa Vista, the northernmost state capital in Brazil, is not served by the National Interconnected Electricity System (SIN). Still, diesel-powered thermal generation mainly meets its electricity demand, with a small fraction imported from neighboring countries. Colniza, a small town in the southern Amazon region, has an economy heavily based on agriculture, with a large portion of the population living in rural areas without access to electricity utilities.
Most of the Amazon region is not linked with the Brazilian Interconnected Electricity Distribution System (SIN). Instead, isolated power systems that rely on fossil fuels are spread throughout the region, and their costs are financed by compulsory taxes included in the energy tariff paid by all Brazilian electricity consumers. Solar PV systems are the primary alternative for isolated power systems to reduce greenhouse gas emissions and lower high taxes on electricity bills. According to the SME outcomes, the PV yield scenarios show the highest increasing trend in the Brazilian Amazon region for both SSP pathways. In the SSP5-8.5 scenario, the PV yield may increase up to (4%), strengthening the solar power option to meet the power demand in the region.
The study area comprises continental Brazilian territory, the fifth-largest country in the world. From North to South, Brazil extends for almost 4400 km, with the Equator and the Tropic of Capricorn running through it. Most of the population lives near the Atlantic coast, and the largest cities are in the Southeastern region. Brazil encompasses diverse important biomes, including the Amazon Forest in the North region, Pantanal wetland in the Mid-west area, Caatinga (semiarid) in the Northeast, and Pampa in the South.
Figure 7 illustrates the analytical steps for assessing the impacts of climate change on the solar energy resource in Brazilian territory. The investigation used (SSR) data from three repositories: the Coupled Model Intercomparison Project Phase 6 (CMIP6), the ERA5 reanalysis provided by ECMWF (European Centre for Medium-Range Weather Forecasts), and satellite-based data provided by INPE (Brazilian Institute for Space Research).
The flowchart shows the step sequence of the methodology used to investigate future solar energy resource scenarios based on CMIP6 climate models.
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