The Impacts of Changes in Land Use/Land Cover and Increases in Greenhouse Gases on the Surface Energy Balance During the Rainy Season in the Metropolitan Region of Manaus

Impactos das Mudanças da Cobertura do Solo e Aumento dos Gases do Efeito Estufa no Clima da Região Metropolitana de Manaus

Abstract

This study analyzed the impact of land use and land cover (LULC) changes and increased in greenhouse gases (GHGs) on surface variables in the climate of the metropolitan region of Manaus (MRM). The numerical experiments were carried out using the BRAMS regional model for the MRM rainy season period and divided into four categories, namely: actual land cover, sensitivity to deforestation and urbanization expansions, sensitivity to increased GHGs, and a combined experiment driven by an extreme scenario. Changes in LULC produced local alterations in the energy and radiation balances and in surface temperature. In addition, the diurnal cycle of the precipitation showed an increase after peak hours over the urban area. In the scenario of increasing GHGs, significant changes in the components of the radiation and energy balances resulted in a positive surface temperature anomaly (∼10 °C) and a negative precipitation anomaly (∼50%). These changes were slightly intensified in the combined experiment. It was found that MRM's climate is more sensitive to an increase in GHGs than to a local change in LULC. Our results reinforce the urgent need to take measures to contain the global increase in GHGs because, in the face of such a scenario, the maintenance of the forest, its ecological processes, and its environmental services would be impossible.

Keywords
climate sensitivity; deforestation; urbanization; LULC; BRAMS; The Amazon

Resumo

Este estudo analisou a influência das mudanças no uso e cobertura da terra (LULC) e do aumento dos gases de efeito estufa (GEEs) nas variáveis de superfície do clima da região metropolitana de Manaus (RMM). Os experimentos numéricos foram realizados utilizando o modelo regional BRAMS, para o período chuvoso da RMM, e divididos em quatro categorias, a saber: experimento de cobertura do solo atual, experimento de sensibilidade às expansões de desmatamento e urbanização, experimento de sensibilidade ao aumento dos GEEs e experimento com efeitos combinados impulsionados por um cenário extremo. Mudanças no LULC produziram alterações locais nos balanços de radiação e energia e na temperatura da superfície. Além disso, o ciclo diurno da precipitação mostrou um aumento após os horários de pico na área urbana. No cenário de aumento dos GEE, alterações significativas nos componentes dos balanços de radiação e energia resultaram em uma anomalia positiva de temperatura superficial (∼10 °C) e uma anomalia negativa de precipitação (∼50%). Essas mudanças foram levemente intensificadas no experimento combinado. O clima da RMM foi considerado mais sensível ao aumento dos GEE do que à uma mudança local do LULC. Nossos resultados reforçam a necessidade urgente de tomar medidas para conter o aumento global de GEEs porque, diante de tal cenário, a manutenção da floresta, de seus processos ecológicos e de seus serviços ambientais seria impossível.

Palavras-chave
sensibilidade climática; desmatamento; urbanização; LULC; BRAMS; Amazônia

1. Introduction

The Amazon covers about 59% of the Brazilian territory and is thus considered to be the largest biome in Brazil (IBGE, 2021). This biome is characterized by its immense biodiversity (Peres et al., 2010), which plays an important role in the economic (Uhl et al., 1997; Homma, 2005) and climactic (Nobre et al., 2016; Alves de Oliveira et al., 2021) spheres, both on a local and regional scale. In this sense, the forest plays an important role with regard to the formation and maintenance of precipitation, temperature, radiation, aerosols and clouds. Furthermore, the forest is directly related to the storage and emission of carbon, both by storing carbon by keeping it intact and by releasing carbon through deforestation (Artaxo, 2023). According to data from the National Institute of Space Research, about 20% of the Amazon biome has already been deforested (INPE, 2021).

Several studies have been conducted to assess the impacts of deforestation on the climate of the Amazon. Observational studies have shown that deforestation affects surface parameters such as albedo, roughness, root system, soil moisture, and runoff (Dickinson and Kennedy, 1992; Sud; Yang and Walker, 1996; Costa and Foley, 2000; Correia et al., 2008). Such impacts are more noticeable on local scale, since deforestation alters important surface parameters, such as albedo, which affects the radiation balance of the deforested area (Gash and Nobre, 1997). Although evapotranspiration reduces when forest is replaced by pasture (Gash and Nobre, 1997; Fearnside, 2006), some studies have shown that precipitation may increase in deforested regions (Chu et al., 1994; Fearnside, 2006). This occurs due to the increase in sensible heat flux in the deforested area, leading to an increase in surface temperature, which in turn generates to upward air currents. The difference in temperature, and, consequently, in pressure between the deforested and nearby forest areas, intensifies the transport of moisture from nearby forest areas to the deforested region, thus favoring the formation of clouds (Chu et al., 1994; Gash and Nobre, 1997).

Numerical modeling has allowed us to evaluate the climatic impacts of deforestation in the Amazon at several spatial scales. Studies addressing total deforestation of the Amazon rainforest have shown that rainfall would be strongly reduced in the basin (Nobre, 1991; Costa and Foley, 2000; Changnon and Bras, 2005; Sampaio et al., 2007; Silveira et al., 2017). These results highlight the importance of the forest in the hydrological cycle of the Amazon. When deforestation occurs on a smaller scale (20-40% of the total forest area), there is an increase in local precipitation, which is caused mainly by increased moisture convergence, convection, and cloud cover over deforested areas. Such effects decrease as the spatial scale of the deforested area increases (Souza et al., 2000; D’Almeida et al., 2007; Sampaio et al., 2007, Correia et al., 2008; Roy, 2009; Wang et al., 2009; Saad, et al., 2010; Medvigy et al., 2011; Rocha et al., 2012; Silveira et al., 2017).

Another change in land use and cover that modifies important surface parameters is urbanization. Urban surfaces have a high heat absorption capacity and high soil impermeability, which contribute to the increase in the temperature of the urbanized surface (Oke, 1988; Changnon, 1992; Arnfield, 2003; Kalnay and Cai, 2003). Research in this area is scarce for the Amazon region. Using 9 years of observational data, Souza and Alvalá (2014) analyzed the phenomenon of an urban heat island (UHI) in Manaus. Their results highlight an increase in temperature (∼3 °C) and a reduction in relative humidity (∼1.7%) in the city when compared to a forest area nearby. These results were corroborated in the study of Corrêa et al. (2016). To analyze the impacts of the urban expansion of Manaus on its microclimate, via climate modeling, Souza et al. (2016) evaluated the impacts of the expansion of the urban area of Manaus in the year 2008 and what they would be if this area was doubled. Their results showed the increase in precipitation over the city for the urban expansion scenario. The authors associated this result with the intensification of the circulation of the breeze resulting from the increase in the intensity of the heat island and the intensification of convective activity over the city due, mainly, to the increase in the sensible heat flux and the increase in surface temperature. The study of the impact of the urban area on precipitation also indicates a possible impact on the increase in extreme cases (Rozoff et al., 2003; Bender et al., 2019).

In addition to changing land use and land cover, other anthropogenic actions can cause changes in the climate by increasing the emission of greenhouse gases (GHGs) into the atmosphere. In this sense, the United Nations brings together various observational and climate modeling works in their reports (Intergovernmental Panel on Climate Change -IPCC) with the aim of analyzing the origins, causes and effects of the increase in GHGs and proposing actions to mitigate such effects. The latest IPCC report (AR6), released in 2021, showed that the increase in GHG emissions that has occurred since 1750 is unequivocally caused by human activities. This increase has been more pronounced in recent decades. In 2019, GHG emissions reached 59 gigatons of CO₂ equivalent (GtCO₂e) - about 12% more than in 2010 and 54% more than in 1990. As a result of this increase, the global climate system is undergoing several changes. The main climate change is global warming, which melts glaciers and sea ice, and thus raises the sea levels. In addition, studies indicate that global warming can increase and/or intensify the occurrence of extreme weather events such as heat waves, hurricanes, droughts, and floods (IPCC, 2021). Climate change affects people's health, lives, livelihoods in addition to energy and transportation systems. In the Amazon, the observed warming from 1949 to 2017 ranged from 0.6 to 0.7 °C (Marengo et al., 2018); however, numerical modeling studies have revealed a worrying scenario. Under the RCP 8.5 emission scenario (IPCC AR5), the air temperature will increase and cause changes in the hydrological cycle. Precipitation in the Amazon will be greatly affected, and there may be a reduction in precipitation and an increase in the number of consecutive days without rain. These effects affect several environmental services of the Amazon rainforest, such as the water storage, the preservation of biodiversity and the reduction in the ability to absorb carbon, in addition to increasing the frequency and intensity of fires (Brito et al., 2019; Rocha et al., 2019; Gomes et al., 2020). Therefore, the present study aims to analyze how the processes of changes in land use and cover, the most pessimistic scenario of increase in GHGs, and their combined effects can affect the microclimate of the metropolitan region of Manaus (MRM).

2. Materials and Methods

2.1. Study area

The creation of the metropolitan region of Manaus (MRM) represented an important advance in the process of metropolization of the Brazilian Amazon. Created on May 30^th, 2007, the MRM consists of 13 municipalities in the state of Amazonas: Manaus, Iranduba, Manacapuru, Novo Airão, Careiro da Várzea, Rio Preto da Eva, Itacoatiara, Presidente Figueiredo, Careiro, Autazes, Silves, Itapiranga and Manaquiri (Fig. 1). In total, these municipalities comprise an area of approximately 127,000 km² (IBGE, 2021), with Manaus being the most populous city in the MRM and the 7ª most populous city in the country, according to the IBGE (2020).

Figure 1
Study area - Metropolitan Region of Manaus (MRM) highlighted in the larger figure on the right.

The climate in the Amazon region is influenced by several factors, such as high amounts of water vapor via moisture transport from the Atlantic Ocean and forest evapotranspiration, and high rates of radiation incident on the surface throughout the year (Reboita et al., 2010). The Amazon has low seasonal thermal variation, of the order of 1-2 °C, with average values between 24 and 26 °C. The city of Manaus is located in the central part of the Amazon and has the highest solar radiation totals in the period from September to October and the lowest from December to February (Horel et al., 1989). In addition, Manaus has temperature extremes in the months of September (27.9 °C) and April (25.8 °C), according to Fisch et al. (1998).

2.2. Numerical models

For this study, we used version 4.2 of the BRAMS regional model (Brazilian developments on the Regional Atmospheric Modeling System) developed and maintained by the Center for Weather Forecasting and Climate Studies of the National Institute of Space Research - CPTEC/INPE, University of São Paulo (USP) and other institutions in Brazil and abroad (Freitas et al., 2009). The BRAMS model was adapted for the tropics from the regional RAMS (Regional Atmospheric Modeling System) model (Pielke et al., 1992). BRAMS is an open-source software available for free at http://www.cptec.inpe.br/brams. For the present study, three nested grids with horizontal resolutions of 60, 15 and 3.7 km were used for grids 1, 2 and 3, respectively. Vertically, 38 levels in sigma-z coordinates were used. Version 4.2 of BRAMS is based on version 4.0 of BRAMS, and presents the following characteristics: i) new vegetation data with 1 km resolution derived from the International Geosphere-Biosphere Program - IGBP, the Brazilian Institute of Geography and Statistics - IBGE and the Proveg project (Sestini et al., 2002) - INPE, ii) the surface scheme (soil-vegetation-atmosphere interaction) of the model based on the Land Ecosystem Atmosphere Feedbacks - LEAF (Walko et al., 2000; Walko and Tremback, 2005), iii) assimilation of heterogeneous soil moisture data based on Gevaerd et al. (2006), iv) Binary reproducibility (same result using different numbers of processors), v) improvement in software portability and quality, vi) performance improvement in serial and parallel simulations, vii) parameterization of shallow cumulus (Souza and da Silva, 2003), viii) new deep-convection parameterization based on a mass flux scheme with different closures (Freitas et al., 2007), ix) inclusion of the numerical model of transport of aerosols and atmospheric tracers - Coupled Aerosol and Tracer Transport (CATT), and x) inclusion of the energy balance model in urban areas - TEB (Town Energy Budget).

The TEB model uses a generalized canyon geometry to represent urban (and suburban) areas. In addition, TEB covers industrial and vehicular contributions to sensible and latent heat fluxes (Masson, 2000). The vegetation cover of the Amazon basin was obtained via the deforestation monitoring project in the Legal Amazon (INPE, 2021). The TEB model uses capacity data and thermal conductivity of streets, roofs and walls in addition to sensible heat and latent heat emission from vehicular, industrial and domestic sources. For this, we used the values used in the study by Souza et al., 2016 for the city of Manaus. The methodology for estimating these parameters was presented in Souza, 2012.

To represent the use and land cover of the MRM, the maps of 2017 and 2100 from Santos et al. (2022) were used. As initial and boundary conditions for the BRAMS model, the outputs of the Earth System Model HadGEM2-ES (Hadley Centre's Global Environmental Model, version 2) from the UK Met Office Hadley Centre (MOHC) were used. The HadGEM2-ES model features a horizontal resolution of 1.25° x 1.875° latitude and longitude (Collins et al., 2011; Martin et al., 2011).

The HadGEM2-ES model is one of the global models used in the preparation of the IPCC (Intergovernmental Panel on Climate Change) Assessment Reports (Jones et al., 2011). The IPCC has separated the so-called RCPs (Representative Concentration Pathways) into four possible future climate scenarios. RCP 2.6 assumes that global annual greenhouse gas emissions (measured in CO₂ equivalents) peak between 2010-2020, with emissions decreasing substantially thereafter. Emissions in RCP 4.5 peak around 2040 and then decline. In RCP 6, emissions peak around 2080 and then decline. In the most pessimistic scenario, RCP 8.5, emissions continue to increase throughout the 21^st century and stabilize around the year 2250 at just under 2000 ppm (Van Vuuren et al., 2011; Caesar et al., 2013).

Thus, the outputs of the HadGEM2-ES model of the present time and the outputs of the most pessimistic scenario of the IPCC (RCP 8.5) were used as initial and boundary conditions in the regional BRAMS model. The configurations of the BRAMS model followed the configurations of Souza (2012) and Souza et al., (2016), see too supplementary materials ^{Fig. S-1} and ^{Table S-1}.

2.3. Numerical experiments

Four numerical experiments were performed, one control and tree sensitivity experiments (EXP-CTRL, EXP-LULC, EXP-RCP and EXP-LULC+RCP). For each experiment, 3 runs of 5 months were performed (from December to April, representing the rainy season of the region) for each year, and the first month (December) was discarded due to the spin up effect of the model. These 3 runs are called ensemble members. The analyses were carried out based on the average of the ensemble members performed point by point. For the CTRL experiment, the MRM map for the year 2017 and the initial and boundary conditions from HadGEM2-ES for the present climate (2002-2005) were used. For the EXP-LULC, the map of the MRM for the year 2100 and the same initial and boundary conditions as the EXP-CTRL were used. The map of 2100 was obtained from the model of environmental dynamics Dinamica EGO (Santos et al., 2022). This model uses environmental variables, rates of deforestation and urbanization, and considers the presence of a permanent protection area to simulate the progress in changing land use and cover. In the future climate experiments, EXP-RCP and EXP-LULC+RCP, the greenhouse gas emission scenario RCP 8.5, from the HadGEM2-ES model was used, as well as the map of coverage and land use of the MRM for the year 2017 and 2100, respectively. Then, the ensemble of the outputs of each experiment was performed.

The RPC 8.5 emission scenario projects a temperature anomaly above 2 °C around 2037, which reaches almost 6 °C in 2100, with an increase in precipitation of the 6% in relation to pre-industrial levels (Caesar et al., 2013). Martins et al. (2015) evaluated the performance of IPCC models in representing the rains of the Amazon, and their results showed that HadGEM2-ES satisfactorily represents the seasonality of rainfall in the Amazon and the convergence of humidity for the rainy summer period (DJF), although it presents a drier and warmer atmosphere than the observed data. Figure 2 briefly shows the characteristics of the numerical integrations that were performed in this study. We work with ensemble members with different initial conditions to try to minimize the impact of uncertainty associated with internal climate variability (IPCC, 2022). For example, for the months of December through April 2002-2003, the region was completing a moderate El Nino event. On the other hand, 2003-2004 was considered a neutral year while for December and January 2004-2005, the region was under the influence of a weak El Nino event. It was not possible to carry out long simulations due to lack of machine.

Figure 2
Characteristics of numerical integrations for each experiment using the BRAMS/HadGEM2-ES model.

To analyze the entire MRM, the outputs of domain 2 (15 km) were used for the analysis of the results. The averages of the three simulations (ensemble) of each experiment were calculated. The analyzes were performed from the ensemble of each experiment.

2.4. Model validation

To evaluate the performance of the BRAMS/HadGEM2-ES model, we used the method of bias and statistical significance through Student's t. For this, data from the fifth generation of ECMWF (European Centre for Medium-Range Weather Forecasts) - ERA5 reanalysis were used (Hersbach et al., 2018a; Hersbach et al., 2018b). ERA5 is the fifth generation ECMWF reanalysis for the global climate, it has hourly data from 1959 to the present. Reanalysis combines numerical simulation data with observations from around the world into a globally complete and consistent dataset.

3. Results and Discussions

3.1. Model validation

The Amazon region has been shown to be a region particularly challenging in representing phenomena convective as it experiences a wide range of convective regimes and complex interactions between the surface and the atmosphere (Adams et al., 2009). The results of temperature, relative humidity and precipitation bias and the Student's t-test for the MRM are shown in Fig. 3. The averages of the variables temperature and relative humidity given per hour were calculated. The average precipitation is relative to daily accumulated values for the study period. It is observed that the temperature differences (Fig. 3a) present positive values throughout the MRM - about 8% in relation to the data observed (Fig. A-2a Supplementary Material), indicating that the temperature simulated by BRAMS/HadGEM2-ES was higher than that observed in the reanalysis ERA5 (warm bias). On the other hand, the differences in relative humidity (Fig. 3b) showed negative values throughout the region (on average -18%) in relation to the ERA5 values (Fig. A-2b), indicating that relative humidity simulated by BRAMS/HadGEM2-ES was lower than that observed (dry bias). The differences in daily accumulated precipitation showed significant negative values in most of the MRM (about -25% of the observed value - Fig. A-2c), indicating that the simulated precipitation was lower than that observed in the reanalysis ERA5. It is noteworthy that precipitation in ERA 5 is generally closer to what is observed compared to most model-based precipitation products, however, in the northern region of Brazil, ERA 5 precipitation is higher than observed, as shown in the study by Farias et al., 2021.

In addition, the study by Silveira et al. (2013) showed that the models of CMIP5, to which HadGEM2-ES belongs, diverge in terms of the amount of precipitation in the Amazon region both for the historical period (1901 to 1999) and in their projections (RCP8.5 - 2010 to 2099). Their study aimed to evaluate the ability of the CMIP5 models to predict the seasonal, interannual and interdecadal precipitation regimes in specific regions of South America, in addition to evaluating the projections of the RCP8.5 scenario for the 21^st century. Their results showed that, for the Amazon region, the HadGEM2-ES model underestimates the precipitation observed in the first quarter of the year and overestimates the other months, showing phase error in the representation of seasonality. As for the seasonal evaluation, the HADGEM2-ES model presented the best evaluation of the analyzed models.

Even though the models presented some differences when compared to the observations, the BRAMS regional model and the HadGEM2-ES global model are indicated for studies of the Amazon. The BRAMS model has been applied in several numerical studies in Brazil (Piva and Anabor, 2008; Araujo, 2010; Souza, Alvalá and Nascimento, 2016; Henkes, 2017; Freitas et al., 2017) and particularly in the Amazon (Herrmann and Freitas, 2011; Silva and Freitas, 2015; Viana et al., 2016; Moreira et al., 2017) due to its ability to represent the climate of the region. When forced by observational and/or reanalysis data, BRAMS can satisfactorily represent the characteristics of the Amazon climate (Cabral et al., 2007; Herrmann and Freitas, 2011). The HadGEM2-ES global model, implemented at the National Institute of Space Research (INPE), has also shown itself to be able to simulate the climate of South America satisfactorily (Silveira et al., 2013).

Figure 3
Temperature bias (a), relative humidity (b) and daily accumulated precipitation (c) relative to BRAMS data and ERA5 reanalysis over the MRM in the period from JAN to ABR between 2003 and 2005. The dotted areas represent statistical significance at the level of 95% using Student's t-test.

3.2. Impacts of changes in land use and land cover on the climate of the MRM

The impacts of the changes in land use and land cover in the fields of anomalies air temperature, net radiation balance, latent and sensible heat fluxes in the MRM, from the average of the ensembles of the EXP-LULC and EXP-CTRL, are shown in Fig. 4. As observed, the surface temperature anomaly field presented slight variations over the MRM (Fig. 4a), with emphasis on the central urban area of Manaus, which showed a slight positive anomaly, and the southern region, which showed a negative anomaly. The surface radiation balance over the MRM is strongly altered, especially in the central sector of the city of Manaus (Fig. 4b), where surface temperature anomalies are positive. The changes in land use and cover also produced intense negative net radiation anomalies over the MRM. This spatial pattern of anomalies can be better understood from the anomalous fields of short-wave (Fig. 4c) and long-wave (Fig. 4d) radiation fluxes. The short-wave radiation balance presents positive anomalies in the central sector of the city of Manaus and negative anomalies in the surrounding areas. In most of the MRM, the anomalies of the short-wave radiation balance are negative, i.e., with the changes in land use and cover there is less absorption of short-wave radiation, which contributed to the reduction in the radiation balance (Fig. 4b). In the central sector of the city of Manaus, the balance of long-wave radiation presents positive anomalies, indicating that the amount of energy emitted by the surface is greater with changes in land use and cover. The highest available energy at the surface (Fig. 4b), in this region, was used to heat the surface (Nobre et al., 1991; Oke, 1988). This can be seen through air temperature and sensible heat flux (Figs. 4a and 4e). The change in land use and cover resulted in the reduction of latent heat flux in Manaus. With the change in land use and cover, the smaller area of vegetation coverage reduces the rate of evapotranspiration and, consequently, decreases the latent heat flux (Potter et al., 1975; Henderson-Sellers and Gornitz, 1984; Dickinson and Kennedy, 1992; Culf et al., 1995; Gash e Nobre, 1997) (Fig. 4f).

Figure 4
Anomalies of the fields of air temperature (a), radiation balance (Rn) (b), short-wave radiation balance (SW) (c), long-wave radiation balance (LW) (d), sensible heat flux (SH) (e) and latent heat flux (LH) (f) over the MRM for the LULC experiment.

The impacts of the changes in land use and land cover on the water balance components of the MRM are presented in Fig. 5. To evaluate the water balance in the atmosphere, the methodology proposed by Marengo (2005) was used, by which moisture convergence (P-E) can be estimated using precipitation (P) minus evapotranspiration (E). This methodology was also used in the study of Llopart et al. (2021).

The daily accumulated precipitation (Fig. 5a) showed a slight reduction for the JAN-ABR period in almost the entire area of the MRM. This result agrees with the studies of Nobre et al. (1991), Dickinson and Kennedy (1992) and Gomes et al. (2020); however, the reduction in precipitation in these studies was greater. The difference in the intensity of the reduction in precipitation can be attributed to the spatial scale of land use and land cover change analyzed, since these studies used a larger area of deforestation, whereas, in the present study, the area of deforestation/urbanization is concentrated only in the MRM. As can be seen, evapotranspiration (Fig. 5b) decreases in almost the entire MRM. Replacing the forest with degraded soil modifies the leaf area index in the region, which reduces evapotranspiration. Moisture convergence (Fig. 5c) also decreased in almost the entire MRM, with emphasis on the central area of Manaus where there was greater moisture convergence. This may be due to the greater amount of energy on the urban surface, which in turn produces increased temperature, increased convection, intense upward vertical motion, and moisture convergence (Oke, 1988; Li et al., 2020). This increase in moisture convergence offset the reduction in evapotranspiration in the urban area of Manaus, which led to a slight increase in precipitation in the region. Another factor that may contribute to the increase in precipitation over urban areas is the concentration of aerosols (van den Heever and Cotton, 2007).

Figure 5
Anomalies of the fields of daily accumulated precipitation (a), accumulated evapotranspiration (b) and moisture convergence (P-E) (c) over the MRM for the LULC experiment.

To evaluate how the growth of deforested areas and urban areas affects meteorological variables, two regions of the MRM were selected in which these processes occurred more intensively. Region A, centered in the urban area of Manaus, showed extensive urban expansion, while region B showed deforestation expansion up until the end of the 21^st century (see ^{Fig. S-3}).Table 1 shows the mean values of the meteorological variables for regions A and B for the period from JAN-ABR. The variables: P-E, E and P are presented in terms of accumulated values for the study period. The surface albedo exerts influence on the amount of energy that is absorbed by the urban surface and acts directly on the balance of radiation and energy (Oke, 1988; Ouyang, 2022). By reflecting less incident solar radiation, a greater amount of energy reaches the surface and will be available to be partitioned into turbulent fluxes in region A. In addition, the replacement of forest and/or deforested area by urban surfaces alters different thermal properties (e.g., high thermal conductivity, high heat absorbability and thermal inertia), which contribute to the daytime energy gain being stored (Oke, 1982). In this way, the net radiation balance increases over the surface of region A and, with the change in land use and cover, this means that there is more energy available at the surface for conversion into fluxes.

Table 1
Mean values of the meteorological variables of the control experiment and difference in the LULC experiment for regions A and B.

Variable		Region A		Region B
	Control Exp.	Difference [%] LULC Exp.	Control Exp.	Difference [%] LULC Exp.
SW_dn (W·m-2)	280.6	1.60 [0.57]	282.44	3.47 [1.23]
SW_up (W·m-2)	51.44	-2.66 [-5.18]	58.72	9.41 [16.03]
SW_net (W·m-2)	229.16	4.27 [1.86]	223.72	-5.94 [-2.65]
LW_dn (W·m-2)	392.49	-7.66 [-1.95]	396.09	0.02 [0.00]
LW_up (W·m-2)	438.09	-24.79 [-5.66]	444.76	0.23 [0.05]
LW_net (W·m-2)	-45.61	17.13 [-37.56]	-48.67	-0.21 [0.44]
Rn (W·m-2)	183.55	21.40 [11.66]	175.05	-6.15 [-3.51]
Albedo (%)	18.35	-1.03 [-5.62]	20.8	3.06 [14.73]
LH (W·m-2)	137.3	-23.91 [-17.42]	142.7	-6.59 [-4.62]
SH (W·m-2)	40.03	18.16 [45.38]	32.63	-2.48 [-7.6]
G (W·m-2)	6.23	27.15 [436.05]	-0.28	2.92 [1043.86]
Temp (°C)	27.71	0.33 [1.19]	27.56	0.00 [0.00]
Tmax (°C)	31.79	0.37 [1.16]	31.47	-0.06 [-0.18]
Tmin (°C)	24.55	0.33 [1.33]	24.48	0.06 [0.26]
wind at 10 m (m·s-1)	1.22	0.42 [34.72]	1.22	0.27 [22.12]
q (g·kg-1)	16.82	-0.27 [-1.62]	17.06	-0.03 [-0.16]
pw (mm)	51,54	-0.04 [-0.07]	5.25	0.00 [-0.02]
P - E (mm)	351.83	63.13 [17.94]	315.64	-40.69 [-12.89]
E (mm)	409.97	-71.41 [-17.42]	426.09	-19.67 [-4.62]
P (mm)	761.8	-8.27 [-1.09]	741.73	-60.36 [-8.14]

As expected, the greatest amount of available energy was used to increase the sensible heat fluxes and of the soil heat fluxes; while there was a reduction in latent heat flux associated to reduction in evapotranspiration. There was no significant difference in the values of air temperature and specific humidity. The small change in the temperature of the region can be explained by the type of change in use and soil cover. In 2017, region A consisted mainly of an urban area and a deforested area (see ^{Fig. A-3}). These two soil cover types are characterized by a high surface temperature in relation to the forest surface. In 2100, region A was converted into an urban area while maintaining a high surface temperature. Increased moisture convergence was observed and calculated through the difference between precipitation and evapotranspiration (Su and Lettenmaier, 2009). This increase may be due to a typical local circulation mechanism (Silveira, 2017). In deforested areas and urban areas, the air near the surface becomes warmer than that in the adjacent forest, taking cooler and wetter air from the forest to the deforested/urban area. The humid air rises over this area forming clouds and, if it has sufficient moisture, there may be increased precipitation, as observed in the central area of Manaus (Fig. 5a). Evapotranspiration decreased due to the reduction in leaf area and, consequently, there was a reduction in rain interception. This result agrees with the studies of Lean (1989); Shukla (1990); Nobre (1991) and Costa and Pires (2010). Increased moisture convergence minimized the effect of reduced evapotranspiration of the region, which contributed to a small difference in precipitation, and which agrees with other studies (Rozoff et al., 2003; Shepherd, 2005; Han and Baik, 2008; Zhong and Yang, 2015). In urban areas, the increase in surface temperature and energy fluxes to the surface, coupled with greater convergence and greater moisture transport, intensifies the upward movement of air. Such phenomena directly contribute to the onset of precipitation over urban areas and in downwind regions of urban areas.

Region B is characterized by a strong expansion of deforestation at the end of the century. This change in land use and cover is characterized by increased albedo (Dickinson and Henderson-Sellers, 1988; Nobre et al., 1991; Berbet and Costa, 2003) and reduction in net radiation (Rn) to the surface, as can be observed in the last column of Table 1. Eltahir (1996) showed that the reduction in liquid radiation cools the upper atmosphere over the deforested area and induces a thermally oriented circulation that results in subsidence. Wang et al. (2009) observed that shallow clouds tend to appear over deforested surfaces, which results from a stronger lifting mechanism caused by mesoscale circulations driven by heterogeneities that are induced by deforestation. No changes were observed in the components of the long wave balance. The net radiation decreased in region B, which was caused by the increase in the shortwave radiation reflected by the surface, a result that is in accordance with Eltahir and Bras (1994). The smaller amount of energy at the surface led to the smaller amount of energy fluxes. This reduction was most pronounced in sensible heat flux (∼ 15%). On the other hand, there were no changes in the variables air temperature, and minimum and maximum temperatures in region B. A lower convergence of humidity was observed for the region followed by a reduction in evapotranspiration, which caused a reduction in daily accumulated precipitation, and this result agrees with the studies of Eltahir and Bras (1994) and Gandu, Cohen and Souza (2004).

The impacts of changes in land use and land cover on the diurnal cycle of meteorological variables in region A are presented in Fig. 6. The radiation fluxes in region A did not show great differences (Fig. 6a and 6b). Changes in the energy balance at the surface were observed, such as an increase in the balance of radiation and sensible heat flux and a reduction in latent heat flux (Fig. 6c). There was no difference in air temperature between the LULC and CTRL experiments. Precipitation (Fig. 6d) showed similar behavior to the CTRL experiment, but the mean value of this variable was higher after the peak time (14:00 - local time). This result shows that, although the accumulated daily precipitation reduced over the urban area (see Table 1), a greater volume of precipitation can occur in a short time interval. Our results are in line with the studies of Huff and Changnon (1973) and Liu and Niyogi (2019). Liu and Niyogi (2019) conducted a meta-analysis study based on 85 papers that deal with the modification of rainfall due to urbanization. Their results show that average rainfall is increased by 18% downwind of the city, 16% over the city, 2% left and 4% right relative to the direction of the storm. In addition, increased precipitation occurred approximately 20-50 km from the city center.

Figure 6
Diurnal cycle of short-wave radiation balance and components (a), long-wave radiation balance and components (b), radiation balance and energy flows (c), and precipitation and surface temperature (d) in region A for the LULC experiment.

The diurnal cycle of the components of the radiation and energy balance at the surface, in addition to precipitation and surface temperature for region B, is shown in Fig. 7. As in region A, the components of the radiation balance did not show major differences in the LULC experiment. The reduction in latent heat flux was lower in region A. This was because region A is characterized by an urban area, while region B is characterized by deforestation. In addition, no change in surface temperature was observed. Precipitation in the deforested region presented the same behavior as in the CTRL experiment, but with lower values.

Figure 7
Diurnal cycle of short-wave radiation balance and components (a), long-wave radiation balance and components (b), radiation balance and energy flows (c), precipitation and surface temperature (d) in region B for the LULC experiment.

3.3. Impacts of increased GHGs on the climate of the MRM

The impacts of increased GHGs on the spatial fields of temperature, radiation balance and energy balance in the MRM are shown in Fig. 8. The temperature at the surface was elevated (Fig. 8a), which may be associated with the increase in the radiation balance at the surface (Fig. 8b). On the other hand, this increase in the radiation balance may be associated with the increase in the short-wave radiation balance (Fig. 8c), which was greater than the reduction in the long-wave radiation balance (Fig. 8d). The long-wave radiation balance decreased in most of the MRM, with the exception of areas with water coverage (Fig. 1). The greater amount of available energy was responsible for the increased sensible heat flux at the surface (Fig. 8e). In contrast, the latent heat flux (Fig. 8f) suffered a greater reduction than in the change in land use and cover experiment (LULC, Fig. 4f).

Figure 8
Anomalies of the temperature fields (a), radiation balance (Rn) (b), short wave radiation balance (SW) (c), long wave radiation balance (LW) (d), sensible heat flux (SH) (e) and latent heat flux (LH) (f) in the MRM for the RCP experiment.

Figure 9 shows the impact of increased GHGs on the components of the water balance in the MRM. Precipitation was greatly reduced (Fig. 9a) in this experiment and was accompanied by reduced evapotranspiration (Fig. 9b). Reduced moisture convergence (Fig. 9c) over the MRM affected moisture transport and precipitation. This may be associated with an increase in air temperature and a reduction in evapotranspiration. These mechanisms would lead to a warmer, drier and deeper boundary layer in the region affecting the surface energy budget, planetary boundary layer and the convective potential energy of the atmospheric column (Betts et al., 2004). Langenbrunner et al. (2019) showed that increasing temperature implies that more moisture would be needed to reach condensation and lifting condensation level (LCL) and level of free convection (LFC) increase. These changes limit the amount of convective available potential energy (CAPE) available for deep convection, which decreases precipitation over the Amazon.

Figure 9
Anomalies of the fields of daily accumulated precipitation (a), accumulated evapotranspiration (b) and moisture convergence (P-E) (c) in the MRM for the RCP experiment.

Table 2 presents the mean values of the meteorological variables of the control experiment and their anomalies from the RCP experiment for regions A and B. For region A, the influence of albedo on the amount of short-wave radiation reflected over the region was observed. The anomaly of the short-wave radiation balance indicates that a greater amount of energy was available for the turbulent exchanges in the planetary boundary layer. The long-wave radiation that is incident at the surface increased mainly due to the increase in GHGs in the atmosphere. Increasing GHG concentrations in the atmosphere cause more infrared radiation to be absorbed by the atmosphere and re-emitted back to the Earth's surface, creating a more efficient greenhouse effect. The increase in long wave radiation emitted by the surface can be explained by the increase in surface temperature in this region. However, the balance of long-wave radiation reduced because of the emitted long-wave radiation being greater than incident long-wave radiation. In other words, the surface is emitting more long-wave radiation than it receives. However, the net radiation balance increased in region A. This results in a greater amount of energy being available to be converted into energy fluxes. The sensible heat flux doubled when compared to the CTRL experiment, while the latent heat flux decreased, which was mainly due to the high reduction in evapotranspiration. As a result, in the experiment, the minimum and maximum air temperatures and mean temperature showed a strong increase, with an increase in the concentration of greenhouse gases (RCP). This increase is above the projected values for the region in the fifth IPCC report (AR5) of 2013. According to this report, warming in the Amazon could reach up to 6 °C by the end of the 21^st century, considering the RCP8.5 scenario. This difference is related to the fact that the BRAMS/HadGEM2-ES model overestimates the temperature for the region (see Fig. 3). The water balance in the region was greatly affected in both regions. Precipitable water showed a slight increase, and this result is in accordance with Trenberth (2011). According to this author, the atmosphere has a greater capacity to retain water vapor - about 7% for every 1 °C of warming. The reduction in daily accumulated precipitation can be explained by the combination of certain factors, such as reduced evapotranspiration, reduced moisture convergence and increased water vapor retention capacity. This reduction in precipitation represented approximately half of the precipitation of the control experiment. This result is greater than the projections (up to 15-20% in the central and eastern Amazon) of AR5. It is also noteworthy that the RCP scenarios do not include deforestation or urbanization rates in their elaboration (Marengo, 2018).

Region B underwent changes in almost all variables analyzed for the RCP experiment. The increased short-wave radiation balance resulted in increased net radiation at the surface, which increased the sensible heat flux by around 200%. On the other hand, the latent heat flux reduced due to reduced evapotranspiration. The air temperature, maximum temperature (Tmax) and minimum temperature (Tmin) increased. A strong reduction in moisture convergence was observed, followed by a reduction in evapotranspiration that resulted in reduced precipitation (∼54%).

Table 2
Mean values of the meteorological variables of the control experiment and difference in the RCP experiment for regions A and B.

Variable	Region A		Region B
	Control Exp.	Difference [%] RCP Exp.	Control Exp.	Difference [%] RCP Exp.
SW_dn (W·m-2)	280.6	30.60 [10.91]	282.44	28.56 [10.11]
SW_up (W·m-2)	51.44	7.62 [14.82]	58.72	7.69 [13.09]
SW_net (W·m-2)	229.16	22.98 [10.03]	223.72	20.87 [9.33]
LW_dn (W·m-2)	392.49	46.20 [11.77]	396.09	47.31 [11.95]
LW_up (W·m-2)	438.09	53.15 [12.13]	444.76	55.17 [12.4]
LW_net (W·m-2)	-45.61	-6.95 [15.24]	-48.67	-7.85 [16.13]
Rn (W·m-2)	183.55	16.03 [8.73]	175.05	13.02 [7.44]
Albedo (%)	18.35	0.62 [3.39]	20.8	0.55 [2.62]
LH (W·m-2)	137.3	-41.59 [-30.29]	142.7	-50.36 [-35.29]
SH (W·m-2)	40.03	45.18 [112.86]	32.63	50.92 [156.02]
G (W·m-2)	6.23	12.45 [199.92]	-0.28	12.46 [4456.67]
Temp (°C)	27.71	10.03 [36.20]	27.56	9.78 [35.47]
Tmax (°C)	31.79	12.81 [40.29]	31.47	12.51 [39.76]
Tmin (°C)	24.55	5.76 [23.45]	24.48	5.59 [22.84]
wind at 10 m (m·s-1)	1.22	0.48 [39.70]	1.22	0.58 [47.75]
q (g·kg-1)	16.82	-0.23 [-1.39]	17.06	-0.05 [-0.32]
pw (mm)	51,54	8.97 [17.41]	5.25	0.95 [18.15]
P - E (mm)	351.83	-272.84 [-77.55]	315.64	-246.51 [-78.10]
E (mm)	409.97	-124.20 [-30.29]	426.09	-150.36 [-35.29]
P (mm)	761.8	-397.04 [-52.12]	741.73	-396.87 [-53.51]

Figure 10 shows the diurnal cycle of radiation fluxes, energy fluxes, precipitation, and temperature in region A for the RCP experiment. The region showed changes in the components of the radiation and energy balance. The increase in the variable incident short-wave radiation flux, short-wave radiation balance, incident long-wave radiation flux and emitted long-wave radiation flux led to an increase in the radiation balance in the region (Figs. 10a and 10b). The latent heat flux was greatly reduced, and its peak was 3 h earlier than the control experiment (10:00 - local time). On the other hand, the sensible heat flux (Fig. 10c) increased, and its peak was delayed by 2 h (13:00 - local time). In other words, the surface evaporated less moisture and earlier, as well as became hotter and reached its peak later. This may be related to the change in the precipitation pattern. The average precipitation value showed a relatively intense reduction and its peak occurred during the first hours of the day (05:00 - local time). The temperature showed the same behavior as in the CTRL experiment, but with higher values (about 10 °C) (Table 2).

Figure 10
Diurnal cycle of short-wave radiation balance (SW) and components (a), long-wave radiation balance (LW) and components (b), radiation balance and energy flows (c), and precipitation and surface temperature (d) in region A for the RCP experiment.

The diurnal cycle of the components of the radiation and energy balance, precipitation and temperature for region B from the RCP experiment is shown in Fig. 11. The daytime cycle followed the behavior of region A. The increase in the short-wave radiation balance led to the increase in the radiation balance. Increased energy availability increased sensible heat flux, while the reduction in evapotranspiration (Table 3) reduced the latent heat flux. The temperature showed similar behavior to the CTRL experiment, but with higher intensity (about 10 °C). Precipitation decreased throughout the day, presenting two peaks, one at 05:00 (local time) and another lower at 14:00 (local time). This result shows that these variables are more sensitive to the increase in GHGs than to the change in land use and cover.

Figure 11
Diurnal cycle of short-wave radiation balance (SW) and components (a), long-wave radiation balance (LW) and components (b), radiation balance and energy flows (c), precipitation and surface temperature (d) in region B for the RCP experiment.

3.4. Impacts of the joint effects of increased GHGs and land use and cover changes

The impacts of the joint effects of increased GHGs and changes in land use and cover on temperature, and the radiation and energy balances at the surface, are shown in Fig. 12. The results show a pattern that is similar to the greenhouse gas increase experiment (RCP experiment). However, the radiation balance anomaly presented lower and negative values, and this was due to the lower balance of long wave radiation at the surface.

Figure 12
Anomalies of the fields of temperature (a), radiation balance (Rn) (b), short-wave radiation balance (SW) (c), long-wave radiation balance (LW) (d), sensible heat flux (SH) (e) and latent heat flux (LH) (f) in the MRM for the LULC+RCP experiment.

The impacts of the combined effects of the increase in GHGs and changes in land use and land cover on the components of the water balance are presented in Fig. 13. The components of the water balance showed similar behavior to the RCP experiment. Increased warming leads to greater evaporation and thus to reduced surface moisture (Trenberth, 2011). The reduction in moisture convergence followed by a high reduction in evapotranspiration resulted in a strong reduction in precipitation over the MRM. This result is in accordance with Silva and Hass (2016).

Figure 13
Anomalies of the fields of daily accumulated precipitation (a), accumulated evapotranspiration (b) and moisture convergence (P-E) (c) over the MRM for the LULC+RCP experiment.

Table 3 presents the mean values of the meteorological variables over regions A and B for the LULC+RCP experiment. All the components of the short-wave radiation balance increased for both regions. However, the components of the long-wave radiation balance showed different values. For region A, the balance of short-wave radiation and its components increased, while the long-wave radiation balance for region B was negative due to the emitted long-wave radiation being greater than the incident long-wave radiation. The increase in the long wave radiation balance in region A may be associated with the intensification of the greenhouse effect caused by the increase in GHGs. As a result, net radiation increased by about 20% in relation to the CTRL experiment. As expected, the sensible heat flux (SH) more than doubled in the experiment with increased GHGs and land use change (LULC+RCP), while the latent heat flux (LH) decreases. The air temperature, the maximum temperature and the minimum temperature showed a strong increase. The water balance in the region was greatly affected. The reduction in daily accumulated precipitation can be explained by reduced evapotranspiration and reduced moisture convergence, and this reduction in the components of the water balance represented approximately half of the values of the CTRL experiment.

For region B, surface temperature, maximum temperature and minimum temperature showed similar values to the RCP experiment. Also noteworthy is the complete reduction in moisture convergence for the region, followed by a greater reduction in evapotranspiration, which resulted in the reduction in precipitation.

Table 3
Mean values of the meteorological variables of the control experiment and difference in the LULC+RCP experiment for regions A and B.

Variable		Region A		Region B
	Control Exp.	Difference [%] LULC+RCP Exp.	Control Exp.	Difference [%] LULC+RCP Exp.
SW_dn (W·m-2)	280.6	29.93 [10.67]	282.44	29.45 [10.43]
SW_up (W·m-2)	51.44	2.99 [5.81]	58.72	17.19 [29.27]
SW_net (W·m-2)	229.16	26.95 [11.76]	223.72	12.26 [5.48]
LW_dn (W·m-2)	392.49	39.07 [9.95]	396.09	47.36 [11.96]
LW_up (W·m-2)	438.09	29.08 [6.64]	444.76	55.88 [12.56]
LW_net (W·m-2)	-45.61	9.99 [-21.90]	-48.67	-8.52 [17.50]
Rn (W·m-2)	183.55	36.94 [20.12]	175.05	3.75 [2.14]
Albedo (%)	18.35	-0.82 [-4.49]	20.8	3.53 [16.97]
LH (W·m-2)	137.3	-68.36 [-49.79]	142.7	-57.63 [-40.38]
SH (W·m-2)	40.03	53.03 [132.48]	32.63	45.36 [139.00]
G (W·m-2)	6.23	52.26 [839.51]	-0.28	16.01 [5728.73]
Temp (°C)	27.71	10.29 [37.15]	27.56	9.75 [35.35]
Tmax (°C)	31.79	12.70 [39.95]	31.47	12.50 [39.73]
Tmin (°C)	24.55	6.18 [25.18]	24.48	5.73 [23.40]
wind at 10 m (m·s-1)	1.22	0.91 [74.44]	1.22	0.99 [80.80]
q (g·kg-1)	16.82	-0.48 [-2.86]	17.06	-0.08 [-0.49]
pw (mm)	51,54	8.90 [17.26]	5.25	0.95 [18.06]
P - E (mm)	351.83	-180.48 [-51.30]	315.64	-235.43 [-74.59]
E (mm)	409.97	-204.12 [-49.79]	426.09	-172.07 [-40.38]
P (mm)	761.8	-384.60 [-50.49]	741.73	-407.51 [-54.94]

The diurnal cycle of the radiation balance and its components, the energy balance, precipitation and temperature for regions A and B are presented in Fig. 14. With the increase in net radiation in region A, a large amount of this energy was used to raise the surface temperature by increasing the sensible heat flux. On the other hand, region A showed the greatest reduction in latent heat flux. Despite this, the temperature and precipitation did not show great differences when compared to the RCP experiment (Fig. 10d).

Figure 14
Diurnal cycle of short-wave radiation balance (SW) and components (a), long-wave radiation balance (LW) and components (b), radiation balance and energy flows (c), precipitation and surface temperature (d) in region A for the LULC+RCP experiment.

The diurnal cycle of the radiation balance and its components, energy balance, precipitation, and temperature over region B for the LULC+RCP experiment are shown in Fig. 15. Although the radiation balance and its components present slight variations in relation to the control experiment, the sensible heat flux increased while the latent heat flux reduced. Precipitation and temperature showed similar behavior to the RCP experiment. Precipitation was sharply reduced with two peaks, the first at 05:00 (local time) and the second at 14:00 (local time).

Figure 15
Diurnal cycle of the balance of short-wave radiation (SW) and components (a), balance of long-wave radiation (LW) and components (b), balance of radiation and energy fluxes (c), precipitation and surface temperature (d) in region B for the LULC+RCP experiment.

4. Conclusions

In this study, we discuss the effects of changes in land use and cover, the increase in GHGs and their combined effects on the climate of the MRM towards the end of the 21^st century. Projection maps of deforestation and urbanization expansion for the year 2100 were used for the MRM along with projections of increases in GHGs according to the IPCC RCP8.5 scenario. For this purpose, numerical experiments were performed idealized from the BRAMS mesoscale model.

The results show that the change in land use and land cover generated slight local changes in the spatial pattern of surface temperature, accompanied by changes in the radiation balance and energy fluxes at the surface. In addition, precipitation also showed changes in its spatial pattern. Despite the reduction in evapotranspiration, it was observed that the slight increase in humidity convergence for the central region of Manaus was able to generate increased precipitation over the urban area.

The regional model showed itself to be more sensitive to the increase in GHGs. The temperature showed a strong increase throughout the MRM. Thus, the sensible heat flux and the components of the water balance showed greater differences in relation to the control experiment. Latent heat flux has been shown to be more sensitive to increased GHGs than to changes in earth cover (Supplementary ^{Tables S-2} and ^S-3).

About the diurnal cycle, it was observed that the expansion of the urban area modifies the diurnal cycle of precipitation over this area. Accumulated precipitation showed higher values after peak hours, when only the effects of land use and land cover change are observed. While in the deforested area, the accumulated precipitation showed the same behavior as the control experiment but with lower values. The change in land use and land cover did not affect the diurnal surface temperature cycle in both urban and deforested regions. However, it caused an increase in precipitation in the urban area and a slight reduction in the diurnal cycle of precipitation in the deforested area.

All experiments showed increased surface radiation balance in urban areas. The greater amount of energy at the surface produced an increase in temperature in all experiments. When only the process of change of land use and cover is analyzed, small variations were observed in the variables air temperature and precipitation. This may be a result of the greater convergence of moisture to the central region of Manaus. On the other hand, the effects of the increase in GHGs and the change in land use and cover modified practically all the variables analyzed in the study area. Reductions in evapotranspiration and moisture convergence decreased the amount of rainfall, thus reinforcing the positive feedback mechanism. Temperature and precipitation showed no differences between the RCP and LULC+RCP experiments, which shows that the increase in GHGs is a predominant factor in the changes in these variables. These results can contribute to decision-making regarding water supply, agriculture and survival conditions of Amazonian peoples.

For future work, we recommended extending the expansion of the simulated deforestation and urbanization to the entire Amazon basin considering the current rates of change in land use and land cover, as done in this study for the MRM.

Acknowledgments

The authors would like to thank the National Institute for Research in the Amazon (INPA) and the University of the State of Amazonas (UEA) for their logistical support. This study was supported by the Amazonas State Research Support Foundation (FAPEAM), notices: RESOLUTION N. 002/2018 and N. 005/2019 - PAPAC/FAPEAM, the National Council for Scientific and Technological Development (CNPq) GM/GD #131459/2020-1 and the São Paulo Research Foundation (FAPESP), and CAPES.

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Supplementary Material

Figure S-1 Domains of the nested grids of the BRAMS model: G1, G2 and G3, with resolution of 60 km, 15 km and 3.7 km, respectively. Figure S-2 Bias in percentage of temperature (a), relative humidity (b) and daily accumulated precipitation (c) between BRAMS and ERA5 data over the MRM in the period from JAN to ABR. Dotted areas are significant at the level of 95% significance using Student's t-test. Figure S-3 Simulated MRM land use and land cover map for 2100 obtained from Santos et al. (2020). In region A (figure in the upper right corner), the expansion of urbanization is highlighted and, in region B (figure in the lower right corner), the expansion of deforestation is highlighted. Table S-1 Configuration of the BRAMS model used in the numerical integrations. Table S-2 Mean values of the meteorological variables of the control experiment and differences in the LULC, RCP and LULC+RCP experiments for the region A. Table S-3 Mean values of the meteorological variables of the control experiment and differences in the LULC, RCP and LULC+RCP experiments for region B.

Publication Dates

• Publication in this collection
  04 Nov 2024
• Date of issue
  2024

History

• Received
  08 Dec 2023
• Accepted
  31 July 2024