Climate Change Assessment in Brazil: Utilizing the Köppen-Geiger (1936) Climate Classification

Abstract

Analyses and climate forecasts indicate significant changes in climate elements, particularly the global mean temperature, and variations in rainfall patterns, which can have profound effects on ecosystems and agriculture. This study aims to assess the impacts of climate change on the Brazilian territory using the Köppen-Geiger (1936) climate classification. Climate data were analyzed at 4,942 locations, encompassing municipalities in Brazil from 1989 to 2019. These data were obtained from the NASA/POWER platform and complemented with monthly temperature and rainfall projections from the BCC-CSM1-1 model, part of the CMIP5 (Coupled Model Intercomparison Project Phase 5), under four emission scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) for the periods 2041-2060 and 2061-2080. The findings reveal a temperature increase across all scenarios, with RCP 8.5 indicating the most significant rise, reaching 4.30 and 5.42 °C for the periods 2041-2060 and 2061-2080, respectively. Additionally, the least rainy month of the year exhibits precipitation values exceeding 60 mm, leading to the dominance of the tropical climate typology “A” in 82.94% of the current climate assessment. In contrast, under climate change scenarios, reductions in areas with typical temperate climate “C” and expansions in arid climate “B” and tropical climate classes were observed compared to the present climate pattern. Notably, the BSh class has a prevalence of 6.09% and 8.16% for the periods 2041-2060 and 2061-2080, respectively. The observed climate changes signal potential challenges for the preservation of species in Brazil, as higher temperatures may hinder their adaptability to drier and warmer conditions. As a result, careful measures and strategies are needed to address the implications of these changes in the coming decades.

Keywords
CMIP5 projections; emission scenarios; arid climate; global mean temperature

Resumo

Análises e previsões climáticas indicam mudanças significativas nos elementos climáticos, principalmente na temperatura média global, e variações nos padrões de precipitação, que podem ter efeitos profundos nos ecossistemas e na agricultura. Este estudo tem como objetivo avaliar os impactos das mudanças climáticas no território brasileiro usando a classificação climática de Köppen-Geiger (1936). Foram analisados dados climáticos em 4.942 localidades, abrangendo municípios do Brasil, de 1989 a 2019. Esses dados foram obtidos da plataforma NASA/POWER e complementados com projeções mensais de temperatura e precipitação do modelo BCC-CSM1-1, parte do CMIP5 (Coupled Model Intercomparison Project Phase 5), em quatro cenários de emissões (RCP 2.6, RCP 4.5, RCP 6.0 e RCP 8.5) para os períodos de 2041-2060 e 2061-2080. Os resultados revelam um aumento de temperatura em todos os cenários, com o RCP 8.5 indicando o aumento mais significativo, atingindo 4,30 e 5,42 °C para os períodos de 2041-2060 e 2061-2080, respectivamente. Além disso, o mês menos chuvoso do ano apresenta valores de precipitação superiores a 60 mm, levando ao predomínio da tipologia de clima tropical “A” em 82,94% da avaliação climática atual. Em contrapartida, nos cenários de mudança climática, foram observadas reduções nas áreas com clima temperado típico “C” e expansões nas classes de clima árido “B” e tropical em comparação com o padrão climático atual. Notavelmente, a classe BSh tem uma prevalência de 6,09% e 8,16% para os períodos de 2041-2060 e 2061-2080, respectivamente. As mudanças climáticas observadas sinalizam possíveis desafios para a preservação de espécies no Brasil, pois as temperaturas mais altas podem dificultar sua adaptabilidade a condições mais secas e quentes. Como resultado, são necessárias medidas e estratégias cuidadosas para lidar com as implicações dessas mudanças nas próximas décadas.

Palavras-chave
projeções CMIP5; cenários de emissão; clima árido; temperatura média global

1. Introduction

In summary, climate consists of climate elements observed in a given location (Rolim and Aparecido, 2016; Rigal et al., 2019). The mean pattern of the atmosphere requires an analysis of time series with at least 30 years (WMO, 2017; Arguez et al., 2012), achieving higher accuracy in the seasonal variation of climate elements (Teegavarapu et al., 2012). Climate classification systems are important tools for assessing the mean scenarios in a region (Terassi and silveira, 2013), consisting of determining conditions for synthesizing and delimiting areas under similar conditions (Martins et al., 2018), facilitating the comparison between climate variability of a particular location (Ayoade, 2010).

Several climate classification systems have been developed over time (Silva and Sales, 2018; Saifudeen et al., 2023) considering climate factors or the effect of climate elements on physical and biological systems on Earth (Nascimento et al., 2016; Netzel and Stepinski, 2016; Belda et al., 2017). The most used systems are global wind zones and air masses of Flonh (1950) and Strahler (1951), vegetation cover of Candolle (1874), Köppen-Geiger (1936), Thornthwaite (1948), Holdridge (1967), and Camargo (1991). The Köppen system is the most used because of its accuracy (Rubel and Kottek, 2010; Alvarez et al., 2013).

The Köppen system was based on the concept that native vegetation is the best expression of climate (De castro et al., 2007; Tatli, 2017; Fernandez et al., 2017), through the abundance and distribution of rainfall indices in the annual and monthly temperature variability (Medeiros et al., 2020). The Köppen (1900) climate classification was developed through the relationship between vegetation using five vegetation groups (Ruman, 2020). Subsequently, the system underwent continuous changes performed by Köppen (1936), Setzer (1966), and Trewartha (1954), with higher importance in meteorology from Geiger (1961), making the system known as Köppen-Geiger (Rahimi et al., 2020).

Climate change is one of the main threats to the ecosystems (Luis, 2015; Tamaki et al., 2017; Pecl et al., 2017; He and Silliman, 2019; Litke et al., 2023), affecting aspects of human life and the environment (Rahimi et al., 2020), with agricultural production being the most climate-dependent among all human activities (Adefisan, 2018). Several studies have assessed climate change around the world, for example, in Europe (Gallardo et al., 2013), Serbia (MihailoviC et al., 2015), Algeria (Zeroual et al., 2019), and Australia (Leao, 2014), but none of them assessed Brazil using the Köppen-Geiger climate classification.

The use of climate classification systems as tools for validating climate change models (Belda et al., 2014; Skalák et al., 2018) represents an important subsidy tool to characterize new areas suitable or unsuitable for agricultural activity according to future climate change scenarios (Lori et al., 2017; King et al., 2018; Fathi and Ezziyyani, 2019).

The Intergovernmental Panel on Climate Change (IPCC) was created in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Program (UNEP) (IPCC, 2014) to provide assessments on climate change and its possible implications and future risks (Waisman et al., 2019). According to IPCC (2012), climate change is the result of changes in climate, which are identified by changes in the mean and/or variability of its properties, with the value being maintained for a long period.

In the context of global warming, there is a gradual decrease in cold events while hot events are steadily increasing (Zhang and Gao, 2023). Simulation models project even greater changes for high-emission scenarios (Hamed et al., 2023). The combined consequences of these phenomena are severe for rural regions, which could experience production losses with implications for food security (Straffelini and Tarolli, 2023), primarily due to water resource scarcity (Lehner and Formayer, 2023).

Climate reports generated by the IPCC are eventually issued to estimate these climate variations, showing emission scenarios based on changes in the greenhouse gas concentrations, rainfall levels, and variable thermal indices (IPCC, 2014). The fifth report of the IPCC (AR5) stated that the mean surface air temperature over land areas has increased by about 0.85 °C since 1880 (IPCC, 2013; O'neill et al., 2016).

Representative concentration pathways (RCPs) are a set of four future climate change scenarios that form the basis for the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Taylor et al., 2012) and the assessment in the fifth report (AR5) issued by the IPCC (IPCC, 2013). CMIP5 represents the world's largest climate data project (Sanderson et al., 2015). RCPs are mitigation scenarios that assume that political actions will be taken to achieve certain emission targets (Taylor et al., 2012). RCPs present a scenario of lower greenhouse gas emissions (RCP2.6), two intermediate scenarios (RCP4.5 and RCP6.0), and a scenario with very high emissions (RCP8.5) (IPCC, 2014).

The RCP 2.6 scenario shows a peak in radiative forcing of 3 W m^-2 (∼490 ppm CO₂eq) before 2100, followed by a decrease to 2.6 W m^-2 in 2100. RCPs 4.5 and 6.0 show radiative forcing values of 4.5 W m^-2 (∼650 ppm CO₂eq) and 6.0 W m^-2 (∼850 ppm CO₂eq), respectively, both with stabilization after 2100, whereas RCP 8.5 demonstrates an increasing radiative forcing of 8.5 W m^-2 (∼1370 ppm CO₂eq) in 2100 (Van vuuren et al., 2011).

This study aimed to complement existing climate research in Brazil by evaluating the potential effects of climate change projections from CMIP5. The reference used for this assessment was the Köppen-Geiger (1936) climate classification, and the analysis focused on the Representative Concentration Pathways (RCP) scenarios, specifically RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5. The primary objectives were to investigate the impacts on rainfall and temperature, as well as the spatial distribution of Köppen-Geiger climate zones across the Brazilian territory under varying scenarios.

2. Material and Methods

2.1. Study area

The work was carried out in the Brazilian territory (Fig. 1) located in South America, corresponding to an area of 8,547,403.5 km2 (Rebouças, 2003; IBGE, 2011). Brazil has great economic prominence for the agribusiness sector, representing 37% of GDP (Gross Domestic Product) (Bruno, 2019) especially the production of grains (Soybean and Corn) and livestock in the Midwest region (Sauer and Leite, 2012). In Brazil, five biomes predominate: Amazon, Pantanal, Cerrado, Atlantic Forest and Pampas, providing different characteristics for the vegetation (Coutinho, 2016).

Figure 1
Study area location map.

2.2. Database

The current scenario was calculated with climate data for temperature (°C) and precipitation (mm) obtained from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources - NASA/POWER platform (Sparks, 2018) in the period 1989 - 2019 for 4942 municipalities distributed over the Brazilian territory (Fig. 1).

Using the geographic information system (GIS), the spatial interpolation of all climatic elements for the current scenario was performed using the Kriging method (Krige, 1951), with a spherical model, a neighbor and a spatial resolution of 0.25 °.

We used to evaluate climate change scenarios, data from monthly projections of temperature and precipitation extracted from the BCC-CSM1-1 model (Xiao-ge et al., 2013), available on the CHELSA V1.2 platform (Climatologies at high resolution) were used. for the earth's land surface areas - https://chelsa-climate.org) (Karger, 2017). The model belonging to phase 5 of the CMIP corresponding to the years 2041-2060 and 2061-2080, associated with four RCP scenarios (2.6, 4.5, 6.0, 8.5).

The BCC-CSM1-1 model was developed by the Beijing Climate Center (BCC) (Xin et al., 2018), it is a model of a coupled climate system including atmosphere, ocean, land surface and sea ice (Wu et al., 2013).). The model is run on the National Center for Atmospheric Research (NCAR) coupler version 5 (Xiao-ge et al., 2013).

2.3. Calculation of potential evapotranspiration

The calculation of potential evapotranspiration (PET) was performed using the method of Camargo (1971) using Eq. (1).

(1)$$PET=F Qo T ND$$

where Qo (mm day^-1) is the daily extraterrestrial solar radiation expressed in evaporation equivalent, in the considered period, T (°C) is the average air temperature during the period; F is the adjustment factor that varies with the average annual temperature (Ta) of the site (for Ta up to 23 °C, F = 0.01; Ta = 24 °C, F = 0.0105; Ta = 25 °C, F = 0.011; Ta = 26 °C, F = 0.0115; Ta > 26 °C, F = 0.012); and ND is the number of days in the period.

2.4. Climatological water balance

For the purpose of climatological characterization of current and future climate conditions, the climatic water balance (CWB) was employed. This method was based on the approach proposed by Thornthwaite and Mather (1955) (Fig. 2). A soil available water capacity of 100 mm was utilized, as it was found to be more suitable for regional climate characterization (De carvalho et al., 2010; Rodrigues et al., 2018).

Figure 2
Diagram of the modified water balance model by Thornthwaite and Mather (1955). where PET is the potential evapotranspiration (mm), AWC is the available water capacity in the soil (mm), SWS is the soil water storage (mm), NAC is the accumulated negative, i.e., accumulated precipitation minus potential evapotranspiration, P is the precipitation (mm), DEF is the water deficit in the soil-plant-atmosphere system (mm), ETR is the real evapotranspiration (mm), EXC is the water surplus of the soil-plant-atmosphere system (mm), ALT is the soil water storage for the current month minus soil water storage for the previous month (mm), and i is the monthly period. Adaptado de Rolim et al., 2020.

2.5. Köppen-Geiger climate classification

The classification proposed by Köppen-Geiger uses a system composed of 3 letters to define climatic zones. Seeking to indicate the vegetation group based on temperature and precipitation values (first letter), annual precipitation distribution (second letter) and seasonal temperature variations (third letter) (Rahimi et al., 2020), depending on the seasonality of temperature or precipitation (Köppen 1936; Geiger 1961).

The climatic classification was carried out using the Köppen-Geiger method (1936), following the descriptions made by Peel et al. (2007), Kriticos et al. (2012), and Beck et al. (2018) (Fig. 3).

The system is identical to that adopted by Köppen (1936) with some differences, where the climates and cold “D” and temperate “C” are delimited using a limit of 0 °C for the coldest month. The arid subclimates “B” W (desert) and S (steppe) were identified, corresponding to 70% of the precipitation in summer or winter, and the subclimates s (dry summer), w (dry winter) and f (no dry season) within the C and D climates were made mutually exclusive, tropical “A”, temperate “C”, cold “D” and polar “E” climates can intersect with arid class “B”, to avoid this, climate type B had preference over other classes. Seeking to normalize the temperature and precipitation indices during the seasons, summer and winter were defined as the period of six hottest and coldest months between October to March and April to September.

Figure 3
Köppen-Geiger climate classification flowchart.

2.6. Statistical indicators

The climate data collected from the virtual stations (NASA/POWER) and the data estimated by the BCC-CSM1-1 model for the climate change scenarios were compared using statistical indicators: precision and accuracy (Table 1). Precision, indicating the degree of dispersion between estimated and observed values, was estimated by the coefficient of determination (R²) as described by Cornell and Berger (1987). The accuracy, which determines the distance between estimated and observed values, was estimated using the Willmott index (d), mean square error (RMSE) and mean absolute error (MAPE).

Table 1
Precision and accuracy of the statistical indices used. Where Yesti is the estimated value of y; Yobsi is the observed value of; $\overline{Y}$ is the observed mean value of y; N is data number.

Statistical index	Equation
Precision
R 2	$R^2 =1-\mathrm{}\frac{\sum _{i=1}^n{\left(Yesti-\overline{Y}obs\right)}^2}{\sum _{i=1}^n{\left(Yesti-\overline{Y}obs\right)}^2-\sum _{i=1}^n{\left(Yesti-Yobsi\right)}^2}$
Accuracy
d	$d =1-\mathrm{}\frac{\sum _{i=1}^n{\left(Yobsi-Yesti\right)}^2}{\sum _{i=1}^n{\left(\left|Yesti-\overline{Y}\right|+\left|Yobsi-\overline{Y}\right|\right)}^2}$
RMSE	$RMSE = \sqrt{\frac{\sum _{i=1}^n{\left(Yobsi-Yesti\right)}^2}{N}}$
MAPE	$MAPE = \frac{\sum _{i=1}^n\left(\frac{\left|Yobsi-Yest\right|}{Yobsi}\right)}{N}*100$

3. Results and Discussion

The mean rainfall for Brazil was 1987 (± 725) mm (Fig. 4A), corroborating the values found by Casaroli et al. (2018). However, variations from 409 to 3625 mm were found between regions. The states of Rio Grande do Norte and Amapá presented the lowest and highest mean annual rainfall, with values of 800.86 (± 213.18) and 2999.79 (± 305.32) mm, respectively. Similar results were observed by Almeida et al. (2017) and Gonçalves and Back (2018) on rainfall variability in Brazil. Air temperature for Brazil presented a mean of 22.20 (± 3.20) °C (Fig. 4B). Amapá and Santa Catarina showed the highest and lowest means of air temperature, with values of 27.10 (± 0.46) and 18.02 (± 1.51) °C, similar to what was reported by Medeiros et al. (2005) and Alvares et al. (2013a).

Figure 4
Rainfall and temperature for current climate conditions from 1989 to 2019.

The statistical precision test reveals a higher dispersion between the mean annual temperature data for the periods 2041-2060 (Table 2) and 2061-2080 (Table 3). The period 2041-2060 shows an R² dispersion analysis of 0.64 and 0.58 for RCP 4.5 and 8.5, respectively, while the Willmott agreement index (d) remained below 0.50 for all RCPs. This dispersion coincides with the mean temperature rise projected by the scenarios. RMSE and MAPE for the RCP 8.5 scenario reached values of 4.50 and 19.97%, respectively. The following period (2061-2080) (Table 3) registered little difference relative to the previous period, showing a higher dispersion for RCP 8.5, with RSME of 25.04% and R² of 0.44.

The mean annual rainfall presents a lower dispersion between the observed data and the stipulated for the periods 2041-2060 (Table 2) and 2061-2080 (Table 3). The R² dispersion analysis remained above 0.90 for all assessed scenarios and the d index presented values close to 1, indicating little variation between the mean annual rainfall estimated by the model and that observed. RCP 2.6 for the period 2041-2060 presented the highest RSME, with a value of 41.31, and the RCP 8.5 scenario showed the highest MAPE, with a value of 25.24%, indicating higher data segregation. In the period from 2061 to 2080, the RCP 6.0 scenario presented lower RSME and MAPE indices, reaching values of 33.22 and 21.97, respectively.

Table 2
Statistical analysis for the period 2041-2060.

Statistical index	2041-2060
	Mean annual temperature				Mean annual precipitation
	RCP_2.6	RCP_4.5	RCP_6.0	RCP_8.5	RCP_2.6	RCP_4.5	RCP_6.0	RCP_8.5
R2	0,68	0,64	0,74	0,58	0,96	0,97	0,98	0,93
d	0,49	0,45	0,47	0,42	0,88	0,91	0,93	0,88
RMSE	3,55	4,06	3,88	4,50	41,31	35,03	29,70	39,93
MAPE	15,54	17,93	17,16	19,97	22,51	19,84	17,81	25,24

Table 3
Statistical analysis for the period 2061-2080.

Statistical index	2061-2080
	Mean annual temperature				Mean annual precipitation
	RCP_2.6	RCP_4.5	RCP_6.0	RCP_8.5	RCP_2.6	RCP_4.5	RCP_6.0	RCP_8.5
R2	0,79	0,56	0,73	0,44	0,91	0,94	0,98	0,92
d	0,52	0,44	0,43	0,37	0,87	0,89	0,91	0,90
RMSE	3,32	4,20	4,34	5,59	42,24	38,73	33,22	37,33
MAPE	14,65	18,51	19,27	25,04	23,04	24,25	21,67	22,69

Rainfalls under climate change scenarios in the period 2041-2060 showed a decrease in the mean annual volume for Brazil, ranging from -159 to -255 mm (Fig. 5). In general, the spatial rainfall distribution between scenarios showed no variation. However, it evidenced an increase mainly in the west of the state of Amazonas, in the north of Pará, and the entire Amapá, while the northeast region of Brazil had the lowest values of annual rainfall. The highest difference between scenarios was observed for the annual rainfall volume.

Figure 5
Mean annual rainfall for climate change scenarios in the period 2041-2061.

RCP 2.6 showed an annual mean of 1827 (± 677) mm, being the scenario with the highest rainfall volume. RCP 8.5 showed the second-highest volume, with an annual mean of 1808 (± 657) mm. RCP 6.0, on the other hand, had the lowest rainfall among all scenarios, with a value of 1731 (± 603) mm per year. Moreover, RCP 4.5 presented a mean of 1772 (± 637) mm. The observed data were similar to those assessed by Dabanli (2018) when analyzing the relationship of the difference between temperature and rainfall under climate change scenarios in Turkey.

A percentage rainfall variation of -60% to +60% is observed relative to the different scenarios (Fig. 6). However, predominant variations of 0-30% are observed in a large part of the Brazilian territory. The largest reductions in rainwater supply were between the states of Roraima, extreme north of Amazonas, Mato Grosso and Bahia, west of Rondônia, Acre and Mato Grosso do Sul, the center of the state of Pará, and small areas dispersed throughout the state of Minas Gerais, with values ranging from 30 to 60%. On the other hand, an increase of up to 60% is observed in the west of Amazonas, the coastline of Rio de Janeiro, and small locations of the north and coastline of Paraná.

Figure 6
Dynamics of mean annual rainfall for climate change scenarios during the period 2041-2060.

A high variation of the mean air temperature was observed in the 2041-2060 scenarios relative to the current scenario, varying 3.6 (± 0.49) °C on average (Fig. 7). RCP 2.6 showed the lowest variation compared to the current scenario, with an annual mean of 25.54 (± 2.28) °C, concentrated in the North and Northeast regions (Fig. 7A). Conversely, RCP 8.5 reached 26.54 (± 2.40) °C, with the highest variation compared to the current scenario (Fig. 7D).

Figure 7
Mean annual temperature for climate change scenarios in the period 2041-2060.

The RCP 4.5 scenario showed the second-highest annual mean of air temperature, with a value of 26.06 (± 2.30) °C (Fig. 5B), exceeding the mean of the RCP 6.0 scenario, which presented a value of 25.90 (± 2.31) °C (Fig. 7C). Miao et al. (2014) also observed a similar difference when assessing CMIP5 climate models and projecting temperature changes in Northern Eurasia.

Reductions in the mean temperature of -10% can be found in the states of Pará, Amapá, northern Mato Grosso and Maranhão, eastern Rondônia, and southern Amazonas for RCPs 2.6, 4.5, and 6.0 (Figs. 8A, B, and C). An increase in temperature of more than 20% was observed in small points in western São Paulo and Minas Gerais, Rio de Janeiro, and Espírito Santo for RCP 2.6 (Fig. 8A). In the other scenarios, this increase can also be observed in the north of Roraima for RCP 4.6 and 6.0 (Figs. 8B and C) and a few places in the states of Bahia, Piauí, Paraíba, and Ceará for RCP 8.5 (Fig. 8D).

Figure 8
Mean annual temperature variation for climate change scenarios in the period 2041-2060.

The scenarios of the period 2061-2080 showed rainfall with a small reduction relative to the previous period, with values of 3.64 mm. The mean observed between scenarios reached 1781.24 (± 41.18) mm per year (Fig. 9). The spatial distribution of rainfall showed no change relative to the period 2041-2060, with the North region having the highest rainfall volumes. RCP 2.6 presented a mean of 1835 (± 638) mm (Fig. 9A), being the scenario with the highest volume. The RCP 8.5 scenario had the lowest rainfall volume, with a mean of 1741 (± 644) mm (Fig. 9D). RCP 4.5 and 6.0 had annual means of 1790 (± 659) and 1750 (± 635) mm (Figs. 9B and C).

Figure 9
Mean annual rainfall for climate change scenarios in the period 2061-2080.

Rainfall variation for RCP 2.6 and 4.5 (Figs. 10A and B) remained equal to the previous period. RCP 6.0 and 8.5 showed reductions in rainfall of up to -60%, with higher intensity in the states of Pará, Roraima, and Amapá and also in the entire Northeast region (Fig. 10).

Figure 10
Mean annual rainfall variation for climate change scenarios in the period 2061-2080.

Air temperature for the scenarios in the period 2061-2080 showed an increase compared to the period 2041-2060 (Fig. 11). RCP 8.5 (Fig. 11D) showed the highest increase compared to the previous period (1.12 °C), with an annual mean of 27.62 (± 2.42) °C, being the scenario with the highest mean temperature. This increase can negatively influence agricultural production in the region due to the large temperature variation (Srivastava et al., 2018).

The RCP 2.6 scenario (Fig. 11A) showed a small reduction in temperature compared to the previous period, with an annual mean of 25.36 (± 2.29) °C, characterizing the scenario with the lowest mean air temperature during this period. RCP 4.5 and 6.0 (Figs. 11B and C) showed similar means of air temperature, with values of 26.19 (± 2.35) and 26.35 (± 2.31) °C, respectively.

Figure 11
Mean annual temperature for climate change scenarios in the period 2061-2080.

A 10% reduction was observed in the mean annual temperature, with a predominance in the same locations highlighted for the previous period in RCP 2.6, 4.5, and 6.0 (Figs. 12A, B, and C), except for RCP 8.5, showing an increase in temperature. An increase of more than 20% in the mean temperature occurred at small points in the west of São Paulo and Minas Gerais, Rio de Janeiro, Espírito Santo, and Amapá for RCP 2.6 and 4.5 (Figs. 12A and B) and few locations in the states of Bahia, Piauí, Paraíba, and Ceará for RCP 6.0 (Fig. 12C). The states of the South, Southeast, Northeast, and Midwest regions of Brazil and the west of Acre and Amazonas showed a 20% increase in the mean temperature for RCP 8.5 (Fig. 12D).

Figure 12
Mean annual temperature variation for climate change scenarios in the period 2061-2080

The analysis of the vulnerability of Brazilian states to changes in rainfall patterns and mean air temperature showed that the state of Paraná (Fig. 13PR) had the highest increase in rainfall, with increments of +200 (1788 ± 41) mm, +69 (1657 ± 40) mm, +92 (1680 ± 39) mm, and +217 (1805 ± 55) mm for RCPs 2.6, 4.5, 6.0, and 8.5, respectively, in the period 2041-2060. The Federal District (Fig. 13DF) represents the second most favorable location for increased rainfall in Brazil, with an increase of +148 mm for RCPs 2.6 (1680 ± 116) mm, 4.5 (1680 ± 120) mm, 6.0 (1746 ± 118) mm, and 8.5 (1745 ± 118) mm.

Roraima (Fig. 13RR), Amapá (Fig. 13AP), and Rondônia (Fig. 13RO) represent the states with the highest reduction in rainfall for the period 2041-2060. The state of Roraima presented reductions of -725 (1589 ± 91) mm, -753 (1562 ± 88) mm, -853 (1462 ± 84) mm, and -723 (1592 ± 101) mm for RCPs 2.6, 4.5, 6.0, and 8.5 respectively, being the Brazilian state with the highest reduction for the period 2041-2060. The state of Amapá had RCPs 2.6 and 6.0 with the highest levels of reduction in rainfall, with values of -579 (2420 ± 132) mm and -541 (2458 ± 139) mm, respectively. The state of Rondônia showed reductions of -304 (2072 ± 128) mm, -531 (1845 ± 104) mm, -475 (1901 ± 114) mm, and -463 (1913 ± 111) for RCPs 2.6, 4.5, 6.0, and 8.5 respectively.

Figure 13
Boxplot for rainfall for climate change scenarios in the period 2041-2060.

All 26 states and the Federal District showed an increase in the mean annual temperature in the assessed scenarios for the period 2041-2060 (Fig. 14), especially the Federal District (Fig. 14DF), location with the highest increase in temperature, with values of +3.92 (24.90 ± 1.20) °C, 4.62 (25.60 ± 1.29) °C, 4.17 (25.15 ± 1.14) °C, and 4.92 (25.90 ± 1.28) °C for RCPs 2.6, 4.5, 6.0, and 8.5, respectively. Minas Gerais represents the second Brazilian state with the highest climate vulnerability related to the increase in the mean temperature (Fig. 14MG), with increases of +3.67 (23.74 ± 1.87) °C, +4.19 (24.26 ± 1.80) °C, 3.87 (23.94 ± 1.82) °C, and +4.38 (24.45 ± 1.73) °C for RCPs 2.6, 4.5, 6.0, and 8.5, respectively.

Figure 14
Boxplot for mean air temperature for climate change scenarios in the period 2041-2060.

Little rainfall variation was observed for the period 2061-2080 in the Brazilian states relative to the previous period. The state of São Paulo (Fig. 15SP) presented an increase in rainfall of +83 (1471 ± 68) mm, 77 (1465 ± 72) mm, 60 (1448 ± 77) mm, and 82 (1470 ± 76) mm for RCPs 2.6, 4.5, 6.0, and 8.5 respectively, which are above those recorded for the previous period (Fig. 15SP). Paraná and the Federal District presented the highest increase in rainfall, with a slight increase for the period 2041-2060. Roraima, Rondônia, and Amapá presented the highest reductions in rainfall, with levels below those registered in the previous period. The state of Acre (Fig. 15AC) showed reductions of -443 (1842 ± 93) mm, -416 (1869 ± 88) mm, -377 (1908 ± 93) mm, and -396 (1889 ± 89) mm for RCPs 2.6, 4.5, 6.0, and 8.5, respectively, which are lower than the values recorded in the previous period.

Figure 15
Boxplot for rainfall for climate change scenarios in the period 2041-2060.

The mean temperature of the Brazilian states remained high. The state of Minas Gerais presented high means, as registered in the previous period. Piauí (Fig. 16PI) also stood out with the highest increases in the mean temperature, reaching values of +2.81 (28.72 ± 1.30) °C, +3.53 (29.44 ± 1.31) °C, 3.57 (29.49 ± 1.29) °C, and 4.91 (30.83 ± 1.38) °C for RCPs 2.6, 4.5, 6.0, and 8.5, respectively, for the period 2061-2080.

Figure 16
Boxplot for Air Temperature in Climate Change Scenarios during the Period 2061-2080.

The analysis of the likely impacts of climate change on the pattern of air temperature, rainfall, evapotranspiration, soil water storage, water surplus, and monthly water deficit for the Brazilian territory in the period 2041-2060 shows an increase in the mean air temperature in both scenarios for all months (Fig. 13), with RCP 8.5 hottest among all scenarios, reaching 27.33 °C in November (Fig. 17A).

Monthly rainfall (Fig. 17B) remained below that for the current scenario for all RCPs in the assessed months. RCP 2.6 reached the highest level of monthly rainfall in January, with a value of 259 mm. Monthly evapotranspiration of RCPs was higher than the current scenario, with a variation from 88 to 134 mm for RCP 6.0 and 8.5 in June and December, respectively. The biggest surplus was registered in the 2.6 scenario, with 140 mm in January. The current scenario showed a high water surplus compared to RCPs. The monthly water deficit (Fig. 17F) was higher from March to September for all RCPs, with a value of 55 mm recorded in RCP 8.5 in August.

Figure 17
Monthly variation of (A) temperature (°C) (T), (B) rainfall (mm) (P), (C) potential evapotranspiration (mm) (PET), (D) soil water storage (mm) (STO), (E) water surplus (mm) (EXC), and (F) water deficit (mm) (DEF) for the Brazilian territory in the period 2041-2060.

The period 2061-2080 (Fig. 18) showed few differences from the previous period for the monthly patterns of temperature, rainfall, evapotranspiration, soil water storage, water surplus, and water deficit in the Brazilian territory. However, RCP 8.5 stood out with a monthly increase in the mean air temperature (Fig. 18A), an increase in evapotranspiration levels (Fig. 18C), and a higher water deficit (Fig. 18D). RCP 2.6 showed an increase in the levels of water surplus (Fig. 18E) and soil water storage (Fig. 18D) compared to the other RCPs, as found in the previous period. RCP 2.6 was the most favorable scenario for the occurrence of rainfall (Fig. 18B).

Figure 18
Monthly variation of (A) temperature (°C) (T), (B) rainfall (mm) (P), (C) potential evapotranspiration (mm) (PET), (D) soil water storage (mm) (STO), (E) water surplus (mm) (EXC), and (F) water deficit (mm) (DEF) for the Brazilian territory in the period 2061-2080.

Three climate zones and eight climate classes were identified under the current climate pattern condition for the Köppen-Geiger (1936) system applied in the Brazilian territory (Fig. 19). It represents one less than that recorded by Peel, Finlayson & Mcmahonet (2007), but similar to global studies carried out by Beck et al. (2018). Zone “A” was the most frequent, representing 82.94% of the Brazilian territory (Fig. 19), but it was not present in the states of the South region, that is, Paraná, Santa Catarina, and Rio Grande do Sul. Alvares et al. (2013b) found a similar result. The equatorial climate classes Af, Am, and Aw represent the most predominant classes in Brazil within zone “A” with 21.72, 28.41, and 32.80%, respectively. It represents 82.94% (Table 1) of the territory, with higher occurrence in the North, Midwest, and Coastal regions (Fig. 15). Aparecido et al. (2020) showed the predominance of the climate class Aw in 58.50% of the Midwest region of Brazil.

The arid class BSh represents 3.89% (Fig. 19) of the territory, with a higher occurrence in the Northeast region. The temperate classes Cfa, Cfb, Cwa, and Cwb present 6.42, 2.38, 2.34, and 2.00% of predominance, respectively, representing a total of 13.16% (Fig. 19) of the Brazilian territory, with higher occurrence in the South and Southeast regions (Fig. 19). Dubreil et al. (2018) showed a variation of the temperate class for the same regions, with values of 18.8 and 16.3% for the period 1964-1989 and 1990-2015, respectively. Some studies using the Köppen-Geiger classification with the CMIP6 model can be found in the literature, but only abroad, such as the work by Hamed et al. (2023) for Southeast Asia.

Figure 19
Köppen-Geiger (1936) climate classification for Brazil under the current scenario.

Significant changes were found in areas of different climate classes in the four assessed scenarios, with the highest changes projected for RCP 8.5 and the lowest changes for RCP 2.6 in the period 2041-2060. The North region stands out with a higher trend to climate change for the Am class (Fig. 20). Tropical climate type “A” showed an increase in the covered area in the projected scenarios, with 87.42% for RCP 2.6 (Fig. 21A) and 88.00% for RCP 4.5 (Fig. 21B), a scenario with the highest increase in this type of climate (Fig. 22). Moreover, a predominance of 86.57% and 87.16% (Fig. 22) was observed for RCPs 6.0 (Fig. 21C) and 8.5 (Fig. 21D), respectively, with higher coverage in the North, Midwest, and Southeast regions and the coastline. This increase is caused by a reduction of areas with a predominance of temperate climate type “C”.

Figure 20
Regions of climate vulnerability in the period 2041-2060.

Figure 21
Köppen-Geiger (1936) climate classification for Brazil in the period 2041-2060.

Figure 22
Prevalence of Köppen climate classes within each scenario.

The temperate climate type “C” presented 7.31% of predominance (Fig. 22) in RCP 2.6, the largest area among the assessed scenarios. RCPs 4.5 and 6.0 showed 6.60% and 7.18% (Fig. 22), respectively. The lowest record occurred in RCP 8.5, with 6.50% (Fig. 22), covering mainly the South and Southeast regions. The increase in the local mean temperature represents the main factor for a reduction of this climate type in the assessed RCPs.

On the other hand, the arid climate type “B” presented a significant increase in coverage in the Brazilian territory, with 5.26% in RCP 2.6 (Fig. 22). The RCP 8.5 scenario showed the highest increase in the arid climate type, with 6.33% (Fig. 22) and higher occurrence in the Northeast region, standing out for its low annual precipitation rate.

Class Af shows a reduction of geographic limits from 21.25% in RCP 2.6 to 12.54% in RCP 8.5 (Fig. 22). The Aw class presents absolute predominance in all RCPs, showing an increase in covered area from 48.92% in RCP 2.6 to 52.00% in RCP 8.5 (Fig. 22). The BSh class also had an increase in occurrence from 5.25% in RCP 2.6 to 6.09% in RCP 8.5.

The BWh class only occurred in RCPs scenarios, with 0.02% in RCP 2.6 and 0.24% in RCP 8.5 and higher concentration in the extreme north of the state of Bahia. RCP 4.5 showed extinction of the climate class Cwb and a decrease in the other warm temperate classes, that is, Cfa (6.11%) and Cfb (0.10%). RCP 8.5 also showed extinction of climate classes type “C” (Cwb and Cfb).

The Aw class stood out for the projected period from 2061 to 2080, with higher commutation in all assessed scenarios, mainly in the states of Mato Grosso, Rondônia, Mato Grosso do Sul, Minas Gerais, and São Paulo (Fig. 23). Eight climate classes were observed, with the Cwb climate class ceasing to exist in the assessed scenarios. Climate type “A” presents an expansion of the covered area for RCP 6.0 (Fig. 24C), with an increase of 1.56% (88.13%).

Figure 23
Regions of climate vulnerability in the period 2061-2080.

Figure 24
Köppen-Geiger (1936) climate classification for Brazil in the period 2061-2080.

Climate type “C” presents an increase in the covered area in RCPs 2.6 and 4.5, with 0.81% (8.12%) and 0.29% (6.89%), respectively, and area loss for RCPs 6.0 and 8.5, with values of 0.87 (6.31%) and 0.95 (5.50%), respectively (Fig. 22), compared to the previous period. Arid climate classes “B” showed an increase in the RCP 4.5 and 8.5 scenarios, with 1.34% (6.77%) and 2.50% (8.83%), respectively, and a decrease of 0.20% (5.06%) for RCP 2.6 and 0.68% (5.54%) for RCP 6.0 (Fig. 22). Rubel and Kottek (2010) and Beck et al. (2018) also recorded an increase in arid zones in global studies of climate change at the end of the century.

The BWh climate class shows a higher expansion, with values ranging from 0.08% in RCP 2.6 to 0.68% in RCP 8.5. On the other hand, classes Af and Am present a reduction in surface, with values ranging from 14.69 and 26.40% in RCP 2.6 to 12.57 and 20.43% in RCP 8.5, respectively (Fig. 22).

The Northeast region of Brazil had the occurrence of five climate classes (Af, Am, Aw, BSh, and BWh) in the assessed RCPs for the periods 2041-2060 and 2061-2080 (Figs. 21 and 24). The reduction in local rainfall and increase in temperature provided an increase in the territory coverage by the BSh and BWh climate classes in the municipalities of Petrolina and Juazeiro. The Northeast forest zone presented the Af and Am classes restricted to the coastline of the state of Bahia, mainly in the municipalities of Canavieiras, Marau, and Jaguaripe. Jenkins and Warren (2015) sought to assess the occurrence of drought events and observed an increase in their intensity and duration in the North and Northeast regions of Brazil.

The states of Paraíba and Rio Grande do Norte present a predominance of 77.40 and 89.37% of the BSh class (Table 4), respectively, for RCP 8.5 in the period 2061-2080 (Fig. 24). Bahia showed the highest climate diversity in the Northeast region, with the occurrence of five climate classes, with Aw and BSh predominating in more than 80% of the territory (Table 4) in all assessed scenarios. The BWh class, observed on the border between Bahia and Pernambuco in the future scenarios, characterizes the region of the São Francisco Valley, a fruit-producing center of foremost importance in Brazil, with a hot desert climate with a mean annual temperature above 18 °C, a factor that can affect fruit production due to severe water restriction.

Table 4
Percentage of Köppen-Geiger (1936) climate classes in each Brazilian state under climate change scenarios.

Estado	Symbol	Actual	Period 2041 0 2060				Period 2061 0 2080
			RCP_2.6	RCP_4.5	RCP_6.0	RCP_8.5	RCP_2.6	RCP_4.5	RCP_6.0	RCP_8.5
AC	Af	80,80	14,75	17,95	12,86	9,07	0,41	7,21	6,14	12,44
AC	Am	19,20	55,00	59,43	60,43	63,32	63,51	67,46	58,21	59,44
AC	Aw	0,00	30,25	22,62	26,71	27,61	36,08	25,32	35,66	28,11
AL	Am	10,39	14,55	5,46	4,95	1,92	11,71	0,31	5,10	0,00
AL	Aw	79,54	59,42	66,82	68,70	68,04	65,30	68,30	68,20	67,19
AL	BSh	10,08	26,03	27,72	26,35	30,04	23,00	31,39	26,71	32,67
AL	BWh	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,14
AM	Af	79,33	73,03	70,39	62,99	49,90	56,30	59,37	55,77	52,99
AM	Am	20,67	26,97	29,45	36,68	49,52	43,69	40,59	43,69	44,73
AM	Aw	0,00	0,00	0,16	0,34	0,59	0,01	0,04	0,54	2,29
AP	Af	0,00	50,29	22,49	17,33	22,33	15,49	28,19	40,51	2,37
AP	Am	100,00	48,97	77,51	82,67	77,67	84,51	71,81	56,79	97,63
AP	Aw	0,00	0,73	0,00	0,00	0,00	0,00	0,00	2,70	0,00
BA	Af	8,72	10,85	6,51	5,66	3,90	8,76	6,51	6,20	2,93
BA	Am	2,15	0,53	2,62	2,86	3,15	3,19	1,62	2,22	2,17
BA	Aw	55,12	48,73	48,21	45,85	45,37	48,30	42,88	46,40	39,38
BA	BSh	32,15	39,64	42,33	43,64	44,29	38,50	45,61	44,47	47,77
BA	BWh	0,00	0,26	0,33	1,99	3,30	1,24	3,39	0,71	7,74
BA	Cfa	0,04	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
BA	Cfb	0,16	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
BA	Cwa	0,49	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
BA	Cwb	1,18	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
CE	Aw	94,28	80,73	85,19	70,62	80,12	88,51	66,39	87,88	42,11
CE	BSh	5,72	19,27	14,81	29,38	19,88	11,49	33,61	12,12	57,89
DF	Aw	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00
ES	Af	1,10	10,89	0,00	0,00	0,00	4,23	0,00	0,00	0,00
ES	Am	12,40	3,69	12,58	9,21	6,46	27,71	7,66	8,14	5,12
ES	Aw	55,10	84,49	86,87	89,56	93,29	66,24	92,10	91,62	94,88
ES	Cfa	16,35	0,80	0,00	0,72	0,00	1,58	0,00	0,00	0,00
ES	Cfb	9,50	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
ES	Cwa	0,37	0,13	0,55	0,51	0,25	0,25	0,24	0,24	0,00
ES	Cwb	5,18	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
GO	Am	3,97	0,00	0,00	0,00	0,00	0,44	0,00	0,00	0,00
GO	Aw	96,03	100,00	100,00	100,00	100,00	99,56	100,00	100,00	100,00
MA	Am	13,86	7,90	10,25	11,88	9,47	11,58	8,12	5,58	6,13
MA	Aw	86,14	92,10	89,75	88,08	89,71	88,42	91,87	94,42	90,68
MA	BSh	0,00	0,00	0,00	0,04	0,82	0,00	0,01	0,00	3,18
MG	Am	0,02	0,00	0,00	0,00	0,00	0,03	0,00	0,00	0,05
MG	Aw	48,68	88,43	93,04	89,78	93,71	83,26	88,81	92,08	94,72
MG	BSh	0,00	2,43	1,64	2,50	1,66	1,04	4,55	2,89	3,25
MG	Cfa	0,08	1,52	0,37	0,71	0,00	1,72	0,49	0,06	0,20
MG	Cfb	0,26	0,22	0,00	0,00	0,00	0,22	0,00	0,00	0,00
MG	Cwa	25,35	7,35	4,95	6,88	4,63	13,73	6,15	4,96	1,78
MG	Cwb	25,61	0,04	0,00	0,13	0,00	0,00	0,00	0,00	0,00
MS	Af	5,44	4,21	0,62	1,12	0,00	3,97	0,00	0,42	0,13
MS	Am	44,87	15,14	3,65	8,93	5,17	18,72	12,69	4,35	7,02
MS	Aw	39,47	80,43	95,73	89,69	94,83	76,08	87,31	95,23	92,85
MS	Cfa	10,22	0,22	0,00	0,26	0,00	1,23	0,00	0,00	0,00
MT	Am	47,55	11,06	2,46	2,73	2,61	11,88	5,71	2,52	1,06
MT	Aw	52,45	88,94	97,54	97,27	97,39	88,12	94,29	97,48	98,94
PA	Af	22,15	20,07	9,05	8,40	9,22	11,51	13,27	13,26	5,80
PA	Am	73,18	41,65	48,71	48,48	51,15	66,90	51,66	37,74	41,67
PA	Aw	4,67	38,28	42,24	43,12	39,63	21,60	35,07	48,99	52,52
PB	Am	1,11	0,35	0,00	0,05	0,00	0,12	0,00	0,02	0,00
PB	Aw	68,36	64,43	62,32	54,71	55,82	62,92	50,06	60,26	19,25
PB	BSh	30,53	35,22	37,68	45,23	44,18	36,95	49,94	39,72	77,40
PB	BWh	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00	3,35
PE	Am	4,24	4,19	2,33	2,52	1,03	3,82	0,23	2,47	0,00
PE	Aw	44,26	35,89	35,11	31,49	31,27	38,98	28,79	35,96	19,93
PE	BSh	51,50	59,92	62,51	65,31	66,14	57,13	67,88	61,56	69,10
PE	BWh	0,00	0,00	0,04	0,69	1,55	0,07	3,10	0,00	10,98
PI	Aw	80,02	72,22	73,04	68,30	62,45	70,83	69,74	75,32	46,58
PI	BSh	19,98	27,78	26,96	31,70	37,54	29,17	30,26	24,68	52,98
PI	BWh	0,00	0,00	0,00	0,00	0,01	0,00	0,00	0,00	0,44
PR	Af	0,00	25,80	20,83	17,98	5,08	20,09	4,95	15,86	21,81
PR	Am	0,11	4,14	14,86	11,15	24,24	5,83	17,80	20,18	27,28
PR	Aw	0,65	0,49	3,30	1,63	9,66	0,07	10,91	8,92	6,17
PR	Cfa	63,01	63,35	61,01	69,24	61,01	74,01	66,34	55,04	44,73
PR	Cfb	36,23	6,22	0,00	0,00	0,00	0,00	0,00	0,00	0,00
RJ	Af	0,00	18,47	0,00	0,11	0,00	7,74	0,00	0,00	0,00
RJ	Am	3,79	7,13	27,00	24,93	26,13	23,94	26,02	23,81	26,67
RJ	Aw	41,42	63,05	63,86	63,27	65,48	54,79	65,48	68,01	69,11
RJ	Cfa	25,86	10,19	5,64	8,84	3,10	12,44	6,62	5,75	2,49
RJ	Cfb	6,78	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
RJ	Cwa	15,08	1,17	3,50	2,86	5,29	1,08	1,88	2,44	1,73
RJ	Cwb	7,08	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
RN	Aw	66,21	60,71	56,33	38,01	46,12	55,47	34,97	51,59	10,63
RN	BSh	33,79	39,29	43,67	61,99	53,88	44,53	65,03	48,41	89,37
RO	Am	100,00	48,64	21,69	26,30	17,06	43,70	27,17	22,67	17,82
RO	Aw	0,00	51,36	78,31	73,70	82,94	56,30	72,83	77,33	82,18
RR	Af	58,62	71,37	51,98	46,80	37,63	35,69	59,77	48,11	28,14
RR	Am	39,49	12,49	27,43	28,75	38,79	41,95	22,57	28,04	35,01
RR	Aw	1,88	16,14	20,59	24,44	23,59	22,36	17,66	23,85	36,85
RS	Cfa	87,14	97,17	99,07	97,81	100,00	98,04	98,50	98,69	100,00
RS	Cfb	12,86	2,83	0,93	2,19	0,00	1,96	1,50	1,31	0,00
SC	Af	0,00	3,31	3,38	3,45	4,38	2,53	3,13	4,49	10,52
SC	Am	0,00	0,00	1,21	0,00	0,23	0,00	1,79	1,02	0,00
SC	Cfa	38,95	76,73	88,77	85,68	95,38	86,77	86,98	86,94	88,11
SC	Cfb	61,05	19,95	6,64	10,87	0,00	10,70	8,09	7,54	1,37
SE	Am	12,94	0,73	2,81	0,04	0,00	0,08	1,22	0,00	0,00
SE	Aw	75,78	83,75	76,42	82,20	77,31	88,47	72,72	82,28	65,72
SE	BSh	11,29	15,52	20,76	17,76	22,69	11,45	26,06	17,72	34,28
SP	Af	0,09	6,69	4,41	3,91	1,87	5,03	1,59	3,18	4,80
SP	Am	0,00	7,42	10,77	7,59	6,29	12,83	6,34	5,86	10,32
SP	Aw	31,11	60,95	66,69	64,22	75,48	50,70	71,12	76,97	78,79
SP	Cfa	37,28	24,42	16,88	23,64	13,11	30,30	19,85	11,62	4,83
SP	Cfb	11,45	0,35	0,00	0,00	0,00	0,18	0,00	0,00	0,00
SP	Cwa	16,62	0,17	1,25	0,59	3,26	0,96	1,11	2,37	1,26
SP	Cwb	3,45	0,00	0,00	0,04	0,00	0,00	0,00	0,00	0,00
TO	Am	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00	0,00
TO	Aw	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00

The equatorial climate type “A” predominated throughout the North region, with three climate classes (Af, Am, and Aw) in all assessed RCPs. There is a transition from the Af and Am climate zones to Aw in the south-center of the state of Acre and Pará, east of Roraima, and Rondônia, representing 28.11, 52.52, 36.85, and 82.18% of occurrence, respectively, in RCP 8.5 in the period 2061-2080 (Table 4). There is also a predominance of Am climate zones in southern Amazonas, with 55.6% of occurrence (Table 4) for RCP 8.5 in the period 2061-2080 compared to the current scenario.

The Brazilian Midwest showed a predominance of climate classes Af, Am, Aw, and Cfa in most RCPs, with the Cfa class present in RCPs 2.6 and 6.0 for the periods 2041-2060 (Figs. 21A and C) and 2061-2080 (Fig. 24A), located in the extreme south of the state of Mato Grosso do Sul, with a predominance of 0.22 to 1.23% (Table 4) mainly in the municipalities of Paranhos and Ponta-Porã. The Aw climate class has a higher predominance in RCP 8.5 for the period 2061-2080 (Fig. 24D), with 100% in Goiás, 92.85% in Mato Grosso do Sul, and 98.94% in Mato Grosso (Table 4). This expansion is directly related to a decrease in local rainfall rates.

The Cfb class in the South of Brazil remains restricted to the southeast of Santa Catarina on the border with Rio Grande do Sul, with 1.37% (Table 4) of occurrence in RCP 8.5 for the period 2061-2080 relative to the current scenario (Fig. 24D). The rest of the state showed a predominance of the Cfa class, as well as the entire state of Rio Grande do Sul and the center-south of Paraná. The northern Paraná concentrates the climate classes Am, Aw, and Af.

The Brazilian Southeast region presented the occurrence of eight climate classes (Am, Aw, BSh, BWh, Cfa, Cfb, Cwa, and Cwb) in the assessed RCPs. There is a reduction in areas with warm temperate climate classes (Cfa, Cfb, Cwa, and Cwb) and the Am class, with higher occurrence in the south of the State of São Paulo in RCP 8.5 for the period 2061-2080 (Fig. 24D) compared to the current period. The state of Minas Gerais showed an increase in areas with classes Aw and BSh, representing 94.72 and 3.25% (Table 4), respectively. Tavares et al. (2018) assessed the negative impacts on coffee production in the Southeast region using future scenarios for the end of the century, showing a potential loss of yield of 25%, a factor conditioned by an increase in areas with high climate risk due to an increase in the mean temperature.

The decreased rainfall in the assessed scenarios, associated with an increase in the mean air temperature, represents factors closely related to crop development and yield. Assad et al. (2019) observed an increase in Brazil's vulnerability as the world's largest food supplier given the 4 °C increase in the mean temperature. Changes in climate patterns can directly affect the economic development of Brazil due to the negative effects of crops. According to Santos et al. (2021), higher economic losses are projected for locations with an economy dependent on agriculture, especially soybean, such as the central regions of the Midwest and part of the Northeast. Souza and Haddad (2021) predicted future losses of Brazilian gross domestic product, ranging from 0.4 to 1.8% at the end of the century due to climate change.

4. Conclusions

The conclusion highlights the significant impacts of climate change on the Brazilian territory, as projected by the BCC-CSM1-1 model under different Representative Concentration Pathway (RCP) scenarios. The study investigated eight Köppen-Geiger climate classes in the region for the current scenario (1989-2019), with Af, Am, and Aw classes being the most predominant, particularly in the North, Midwest, and Southeast regions.

The climate change scenarios analyzed indicate potential shifts in the distribution of climate classes, with an increase in arid climate zones BSh and BWh in the Northeast region of Brazil. This change may lead to an increased demand for irrigation in affected areas. Additionally, there is a reduction in the Af class and temperate classes “C” in the future scenarios. The Aw class remains predominant in all assessed scenarios.

The findings suggest that in the coming decades, climate change is likely to bring significant alterations in temperature and/or rainfall patterns, potentially impacting the overall climate conditions in the country. These changes could have substantial implications for agriculture, water resources, and ecosystems, necessitating adaptive measures and informed decision-making to address the challenges posed by these shifts.

Overall, the study underscores the importance of understanding and preparing for the potential impacts of climate change on Brazil's climate and ecosystems, providing valuable insights for policymakers, researchers, and stakeholders to develop effective strategies for climate resilience and sustainable development.

Acknowledgment

This work was done with financial support from Instituto Federal de Mato Grosso do Sul “IFMS”.

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Publication Dates

• Publication in this collection
  05 Jan 2024
• Date of issue
  2023

History

• Received
  14 July 2023
• Accepted
  04 Sept 2023