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We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series. [ABSTRACT FROM AUTHOR]

This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007–2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov–Smirnov test, the corrected Akaike criterion AICc, the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28–32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. [ABSTRACT FROM AUTHOR]

Many regions around the world are facing climate changes, with substantial increase in air temperature over the past decades, which is mainly related to continental and global warming forced by the higher greenhouse gas (GHG) emissions. The objectives of this study were to use the Köppen climate classification to detect local climate change based on a historical series of 100 years and to assess if such change is related to those that are occurring in other spatial scales as a likely consequence of increasing GHG. This paper brings a content full of innovative results. The study area presented an average annual air temperature increase by 0.9 °C between 1917 and 2016, rising from 21.4 °C for the first climatological normal (1917–1946) to 22.3 °C for the last one (1987–2016). Furthermore, in the summer months, the temperature rose from 24.5 to 25.3 °C, and in the winter months, such increase was from 17.1 (1917–1946) to 18.3 °C (1987–2016). Our findings showed the subtropical conditions (Cfa in Köppen's classification) in the study area persisted from the beginning of the analysis (1917–1946) until the climatological normal of 1979–2008, with a clear tendency of tropicalization after that with a change in the climate type of Piracicaba from subtropical to tropical, which can now be classified as tropical with dry winter (Aw climate type). The local average air temperature showed concordances with the long-term air temperature anomalies from regional, continental, and global scales, indicating that all of them may be linked with increasing GHG emissions, since well-defined long-term linear relationships (r2 = 0.99) were observed between continental and global average air temperature anomalies and atmospheric CO2 concentration observed at the NOAA Lab in Mauna Loa in the last 59 years. While the local and regional forcing effects remain to be fully unraveled, our study provided a valid and strong scientific sound evidence that climate change occurred in Piracicaba, southeastern Brazil, in the last 100 years. [ABSTRACT FROM AUTHOR]

Remotely sensed sea-surface temperature (SST) retrievals with a significant positive bias during the occurrence of upwelling phenomena along the southeastern coast of Brazil were reported in our companion paper. As a result, this paper proposes an automated bias correction algorithm to improve the MODIS long-wave multichannel SST (MCSST) retrievals during the abovementioned conditions in this region. In this paper, MODIS daytime SST data (SSTMODIS) and differences between brightness temperatures in MODIS channels 31 and 32 (BT31 − BT32) are analyzed simultaneously with hourly wind surface conditions, in situ SST at 0.3 and 10 m in depth (SSTbuoy03 and SSTbuoy10), and sensible and latent heat fluxes from the Cabo Frio buoy data (at 23° S, 42° W) during 2014. The obtained results show that some upwelling events present air temperature ($T_{\mathrm {air}}$) greater than SSTbuoy03 and low-atmospheric water vapor content. A simultaneous occurrence of these factors during upwelling conditions may lead to a warm-skin layer effect and may cause BT31 to be greater than SSTbuoy03 and BT31 − BT32 to be small (−0.18 °C ± 0.22 °C), affecting the MCSST performance. The proposed bias correction algorithm uses a least-squares curve between SSTbuoy03 and SSTMODIS retrievals when BT31 − BT32 ≤ 0.5 °C (i.e., dry atmospheric conditions). The bias correction algorithm has significantly improved the SSTMODIS bias (RMSE) from 1.43 °C to −0.2 °C (1.60 °C to 0.58 °C) when applied to 22 cloud-free pixels of MODIS during January–March of 2015. [ABSTRACT FROM AUTHOR]

The study of climatic variables in large scales with surface meteorological stations is limited due to the low density of these stations in many regions, possible sources of errors related to missing data, and uncertainties about the calibration sensors. Global gridded data (GD) systems can minimize these problems. Thus, studies that validate GDs with "ground truth" are important for several applications such as climate change. The objective of this study was to compare long series of surface data with 10-day estimates of average air temperature (T) and precipitation (P) using data from the European Center for Medium-Range Weather Forecast (ECMWF) and the National Aeronautics and Space Administration (NASA) for important agricultural locations in the states of Minas Gerais and São Paulo in Brazil. Despite the different spatial resolutions between ECMWF and NASA, the purpose of this paper was to evaluate the two data sources as they are readily available. The GD performance was evaluated by linear regression analysis. Analyses were performed for each meteorological variable for entire years and separated by seasons. The estimates of T from both ECMWF and NASA systems were accurate with the minimum Willmott concordance index (d) and RMSEp of 0.86, 0.37 °C, respectively, and precision with R2 0.61. The estimates of P had a minimum R2, d, and RMSEp of 0.48, 0.79, 2.15 °C respectively. The decreasing orders of (R2) were autumn > winter > spring > summer for T and winter > autumn > spring > summer for P, varying from 0.93 to 0.61 for T and from 0.77 to 0.48 for P. [ABSTRACT FROM AUTHOR]

The aim of this paper was to investigate the relationship between land use and topography on the variations of temperature and relative humidity in urban and rural municipalities in the Contagem and Betim, south-central state of Minas Gerais- Brazil. The measurement of climatic data occurred during the winter (20/06 - 21/09/2011) continuously and simultaneously, at intervals of sixty minutes, using the automatic sensors (dataloggers - Icel HT model 4000). The weather station INMET, headquartered on the campus of UFMG were used for calibration of instruments and control of the local variations of climatic elements. The data obtained were analyzed in a comparative way in a time sequence taking into account the environmental characteristics (topography and land use) and atmospheric. The temperature of the air taken in urban showed count on "delay" in its development in hourly and lower amplitude when compared to data measured in Betim (rural area). Was attributed these facts to the topographical situation of each of the positions and characteristics of conservation and heat production in the urban environment associated with low specific heat of the materials used in construction, higher density of buildings, relative lack of green areas and participation additional sources of anthropogenic heat. [ABSTRACT FROM AUTHOR]

Abstract: This invited review paper tends to summarise the results based on the variability of occurrence of temperature extremes in South America. The first thing to note is that there is a geographical imbalance with respect to the number of published studies on temperature extremes. Most of the results come from the southern part of South America, east of the Andes, and a few from the northern part of the continent and for the Altiplano. The workshop organised by the ETCCDMI in Brazil was the first time to have the opportunity to collect information in a regional way and present trends in extreme daily temperatures. A better geographical picture enhanced with more data show significant geographical trends in warm (positive) and cold (negative) nights over Southern South America and over the northern South America coast. All other studies based on smaller regions also agree in finding the most significant trends in the evolution of the minimum temperature, with positive trends in almost all studies on the occurrence of warm nights (or hot extremes of minimum temperature) and negative trend in the cold extremes of the minimum. On the other hand, there is little agreement on the variability of maximum temperature. Generally the maximum temperature in southern South America has decreased, in opposition to the case of northern South America where it has increased. Strong decadal and interannual variability have been found in the occurrence of cold extremes. Reanalysis and climate models underestimate the intensity of extremes, mainly near the Andes. The studies trying to understand the dynamics of the circulation that leads to the occurrence of these extremes are analysed from its occurrence in almost all scales from the synoptic, intraseasonal, seasonal, annual, and multi-year linear trend with different methodologies, also, indentifying the local and remote forcing. A gap was found in studies that relate some specific local forcing (like changes in land use) and compare it with the remote ones. Different aspects of the occurrence of the temperature extremes are still missing in some regions of the continent. [Copyright &y& Elsevier]

• Evaporation trends of tropical reservoirs in Brazil were investigated. • Both positive and negative trends in evaporation were observed. • In the industrial region, the negative trend was potentially caused by the impact of pollutant released to the atmosphere. • In the coastal region, the positive trend was related to increasing air temperature. This paper aims to analyze the trends (Mann-Kendall test) of evaporation and climatological variables, including air temperature, relative humidity and wind speed in four reservoirs located in the Brazilian Northeast. The evaporation was estimated through remote sensing using the Surface Energy Balance System for Water (AquaSEBS) model from 1985 to 2018. In addition, it was verified which meteorological parameter has the highest correlation (R2) with the evaporation in order to assess why this process is changing throughout the years in each reservoir. The model performed well, with root mean square error (RMSE) ≤ 1.25 mm/day, percent bias (PBIAS) ≤ 13.70% and coefficient of determination (R2) ≥ 0.51. One reservoir located closer to a coastal zone presented a positive trend (+0.24 mm/34 years), corroborating the measurements of a reference class A pan, and Penman-Monteith equation, while the others, located closer to industrial areas, displayed negative trends (−0.26 to −0.080 mm/34 years). This reduced evaporation was attributed to the impact of industrial pollutant release to the atmosphere, while the increasing effect in the other reservoir was due to its proximity to the coast, which implies an air renewal as the sea breeze contributes for an improvement in the air quality. The meteorological data also presented positive and negative correlations: Temperature exhibited a positive trend (+0.57 °C/34 years), whereas humidity (−4.83%) and wind speed (−0.29 m/s), a negative trend. Evaporation displayed moderate coefficient of determination (R2 > 0.31) with wind speed and humidity. Yet, the reduction of evaporation may be associated with a possible counterbalancing effect of wind speed over humidity. Overall, the results contribute to a better understanding of evaporation trends in a water-scarce region and can be used in a water resources management context as the increasing/reduction of evaporation might significantly impact water availability in this area. [ABSTRACT FROM AUTHOR]