Temperature in Brazil increased to 26.02 celsius in 2024 from 25.92 celsius in 2023. This dataset includes a chart with historical data for Brazil Temperature.

Brazil's mean temperature was **** degrees Celsius warmer in 2022 than the average recorded from 1951 to 1980. Since 1961, the South American country recorded the largest mean temperature deviation in 2015 and 2019, both years at **** degrees Celsius above the long-term average. Temperature variations are becoming increasingly warmer in recent years.

This web map is a subset of Global Annual Mean Temperature Image Service. This layer represents CMIP6 future projections of mean annual temperature. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change.WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description:"Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)."Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100Units: deg CCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 Bioclimate

Average annual temperatures in Brazil are projected to rise under the different Representative Concentration Pathways (RCP), based on the historic baseline of **** degrees Celsius (°C). Under the RCP *** intermediate emission scenario, it is expected that temperatures will rise to ** °C in the next decades and to **** °C by mid-century. Temperatures will continue to rise to reach **** °C by 2099, following the same scenario.

ABSTRACT In recent decades, scientific and academic researchers around the world have been concerned with the assessment of regional and global climate trends. The assessment of changes in the climate system is a planning tool for society as it enables the consideration of possible consequences from the increasing air temperature and changes in precipitation that have been observed around the world. Under the hypothesis of climate change presence in Brazil, the aim of this study was to verify the presence of climate trends in 243 annual data from Brazilian cities of maximum, minimum and average air temperatures and rainfall. The Mann-Kendall (M-K.) and the Pettitt (Pett.) tests were applied in order to evaluate the presence of significant trends. Maps were developed for the spatial visualization of the observed trends. The statistical results show that, from all the studied cities, increasing trends in maximum temperature were observed in 35% of the series, decreasing in 1% and no trends were observed in 64%. For the minimum temperature, increasing trends were observed in 30% of the studied series, decreasing in 8% and no trends in 63%. For the average temperature, increasing trends were observed in 35%, decreasing in 3% and no trends in 62%. For rainfall, increasing trends were observed in 6%, decreasing in 4% and no trends in 91%. The observed trends may be related to the anthropic activities like urban expansion, industrial development and the increasing population density in each studied city.

Temperature Time-Series for some Brazilian cities

Do you ever wonder how are temperatures in Brazilian cities? Too hot? Cold weather sometimes? And what about climate changes? Is Brazil getting hotter?

This is your chance to check it out!

Context

This datasets are collected in order to provide some answers for the above question through Data Analysis. Maybe you want to try some Machine Learning model in order to practice and predict the evolution of temperature in some Brazilian cities.

Content

The content is provided by NOAA GHCN v4 and post-processed by NASA's GISTEMP v4.

In summary, each data file contains a temperature time series for a station named according to the city. The time series provides temperature records by month for each year. Some mean measurement is calculated, like metANN and D-J-F. I can't give details about these quantities, nor how they are calculated. Please refer for NASA GISTEMP website in this regard. The most important seems to be metANN, which is an annual temperature mean.

Acknowledgements

These datasets are provided through NASA's GISTEMP v4 and recorded by NOAA GHCN v4. Thanks for researchers and staffs for the really nice work!

Average monthly temperatures in Manaus, Brazil remain incredibly stable and warm throughout the year. This is characteristic of tropical climates, which see very little seasonal variation due to their proximity to the equator, as well as the self-regulatory nature of rainforest climates. In contrast, the examples of locations in the far north of Canada or in Finland are much further from the equator and are therefore much colder, and they also see the most seasonal variation.

Brazil Heat Index data was reported at 0.320 Day in 2020. This records a decrease from the previous number of 0.710 Day for 2019. Brazil Heat Index data is updated yearly, averaging 0.220 Day from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 1.410 Day in 2015 and a record low of 0.000 Day in 1977. Brazil Heat Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Environmental: Climate Risk. Total count of days per year where the daily mean Heat Index rose above 35°C. A Heat Index is a measure of how hot it feels once humidity is factored in with air temperature.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

Brazil Mean Drought Index data was reported at -1.345 NA in 2021. This records an increase from the previous number of -1.705 NA for 2020. Brazil Mean Drought Index data is updated yearly, averaging -0.076 NA from Dec 1960 (Median) to 2021, with 62 observations. The data reached an all-time high of 2.688 NA in 1989 and a record low of -2.052 NA in 2015. Brazil Mean Drought Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Environmental: Climate Risk. The SPEI fulfills the requirements of a drought index since its multi-scalar character enables it to be used by different scientific disciplines to detect, monitor, and analyze droughts. Like the sc-PDSI and the SPI, the SPEI can measure drought severity according to its intensity and duration, and can identify the onset and end of drought episodes. The SPEI allows comparison of drought severity through time and space, since it can be calculated over a wide range of climates, as can the SPI.;Global SPEI database (SPEIbase). https://spei.csic.es/database.html;;

We analysed changes in mean air annual temperature (MAAT), vegetation and biomass burning on a long and continuous lake-peat sediment record from the Colônia basin, southeastern Brazil, examining the responses of a wet tropical rainforest over the last 180 ka. Stronger southern atmospheric circulation up to the latitude of Colônia was found for the penultimate glacial with lower temperatures than during the last glacial, while strengthening of the South American summer monsoon (SASM) circulation started during the last interglacial and progressively enhanced a longer wet summer season from 95 ka until the present. Past MAAT variations and fire history were possibly modulated by eccentricity, although with signatures which differ in average and in amplitude between the last 180 ka. Vegetation responses were driven by the interplay between the SASM and southern circulation linked to Antarctic ice volume, inferred by the presence of a cool mixed evergreen forest from 180 to 45 ka progressively replaced by a rainforest. We report cooler temperatures during the marine isotope stage 3 (MIS 3: 57-29 ka) than during the Last Glacial Maximum (LGM: 23-19 ka). Our findings show that tropical forest dynamics display different patterns than mid-latitude during the last 180 ka.

This data publication includes data on catches reported in the harbours of Santa Catarina state, southern Brazil, between the years 2000 and 2019. Historically, these harbours have concentrated a significant part of the demersal fishing fleet that operates widely on the Brazilian Meridional Margin (BMM), from 21°S to the southern border of Brazilian EEZ (34°S) and from the coastal areas down to 500 m depths. Landed catches were monitored by a research team based on the University of 'Vale do Itajaí' (UNIVALI) along a sequence of scientific projects and contracts developed to meet governmental demands for oceanic and deep fisheries development and management, and in support of the licensing processes of the offshore oil and gas exploration activities. This catch composition time series shows an annual compilation of total catches discriminated by species caught by trawlers (single trawlers, pair or twin trawlers and double trawlers) and gill net industrial fisheries. These species represent altogether over 81% of total reported catch in the period. The remainder amount of landings was reported as groups of species (e.g. sharks) or categories indiscriminated by species and were excluded from the database.The 'Mean Temperature of the Catch (MTC)' was estimated for each year of the time series, as proposed by Cheung et al. (2013). This variable is expressed in degrees Celsius. 'Sea Bottom Temperature (SBT)' was derived from estimates provided by the high-resolution ocean general circulation INALT20 model (Schwarzkopf et al., 2019) for the study period (2000-2019) and was calculated by averaging the temperatures over 0.25° x 0.25° grid cells of the BMM and a water column up to 50 m above the seafloor. This variable is expressed is degrees Celsius. 'Annual volume transports of the Brazil-Malvinas confluence (BCt)' was extracted and compiled from Artana et al. (2019). This index was estimated for the period between 2000 and 2017. More detailed information about this index can be accessed in Artana et al. (2019). This variable expressed in Sverdrups (Sv). 'Simpson diversity index (Dm)' express the diversity of demersal fishing métiers (i.e. combination of target species, gear, and time of the year) operating each year of the time series, as determined by a process of classification of individual fishing trips. […]

Earthworms are often related to fertile soils and frequently used as environmental quality indicators. However, to optimize their use as bioindicators, their populations must be evaluated together with environmental and anthropogenic variables regulating earthworm communities. In this review we identify the earthworm, soil chemical, physical, environmental and management-related variables evaluated in 124 published studies that quantified earthworm abundance (>7300 samples) in 765 sites with different types of climate, soils, land use and management systems in Brazil. Most soil chemical and physical attributes (except pH) were less reported (<50% of studies) than other environmental variables such as sampling date, altitude, temperature, precipitation, climate and soil type and land use (all >50% of studies). Earthworms were rarely identified (24%) and few studies (31%) measured their biomass, although most provided adequate information on sampling protocol. Based on the importance in regulating earthworm populations, we propose a set of variables that should be evaluated when studying earthworm communities and other macrofauna groups. This should help guide future studies on earthworms in Brazil and other countries, optimize data collection and replicability, allow comparisons between different studies and promote the use of earthworms as soil quality bioindicators.

Methods A dataset of earthworm abundance in Brazilian ecosystems was constructed using published literature on the topic. Published studies that evaluated earthworm populations in Brazilian ecosystems were searched in the literature from 1976 up to the year 2017. The literature review included searchable online databases such as Web of Science, Scielo, Lattes-CNPq Platform, Biblioteca Digital de Teses e Dissertações (BDTD- Brazilian digital library of theses and dissertations), Google Scholar and the Alice-Embrapa Repository. As we were aiming to review all studies available and determine which soil, environmental, earthworm and sampling-related factors were evaluated, we also included non-indexed journals, book chapters, and conference proceedings in soil science, zoology, ecology, agroecology and conservation agriculture. We also made personal contact with colleagues who work with earthworms and/or are members of the Brazilian CNPq research group “Biology, ecology and function of terrestrial Brazilian oligochaetes (earthworms and enchytraeids)”, in order to help us expand our data search.

The data on soil, environmental and earthworm sampling variables were extracted from each of the 124 publications and entered into an excel data file. Using the data for all sites, the number of publications with each environmental, earthworm and soil physical and chemical variables was quantified, as well as the corresponding number of points/sampling sites. The data contains information from 765 specific sites, representing over 7300 earthworm samples, from a wide range of soils, vegetation types and management systems in 135 counties in Brazil. Additional environmental and soils data were also obtained from these sites and complemented with information from various sources (e.g. online climate data, municipal GPS coordinates, Köppen climate database), or calculated (e.g. CEC or Sum of bases, pH, H+Al, C:N) and included, when possible. Climate information was corrected also when needed, following the data of Alvares et al. (2013). When not provided, geographic position data used was for the county seats, and when found to be incorrect, the county or state information was corrected. The data thus includes 13 climate and vegetation-related environmental variables (Sampling date, Sampling season, Locality, County, State, Geographical coordinates, Altitude, Mean annual precipitation, Mean annual temperature, Köppen climate, Biome, Soil cover/Vegetation type, Type of native vegetation), 8 management-related variables (Crop type, Soil management, Years in current land use, Previous land use, Pesticide use, Pesticides type, Fertilizers, Fertilizer type), 17 soil-related variables (Soil type, pH, H+Al, K, Ca , Mg, P , C, Sum of bases, CEC, Base saturation, N , C:N, Sand, Clay, Silt, Textural class), and 6 earthworm sampling-related variables (Size of holes dug, Number of holes, Depth, Density, Biomass, Species identification).

Native vegetation types were classified according to standard categoreies (IBGE, 2012), while forest plantations were divided into four main types: Eucalyptus or Pinus spp. trees, Araucaria angustifolia, and Others (contemplating all other tree species). Pasture grasses were separated into only two categories, Brachiaria spp. pastures (some of which are currently in the Urochloa genus), and Others, including all other types or species of pasture grasses. Soil types information was obtained from the publications or using the Brazilian national or state maps available online and from Embrapa databases (IBGE; Santos et al., 2018). Soil were classified according to the Brazilian (Santos et al., 2018) and the FAO (2015) soil classification systems. Soil tillage was classified into four categories, according to decreasing intensity: conventional tillage (CT), minimum tillage (MT), no tillage (NT) and permanent crop with no tillage (PC). Similarly, pesticide and fertilizer use information were searched for in each publication, including types (herbicides, fungicides or insecticides and fertilizer formulation) and active ingredients.

The soil chemical (pH, H+Al, K, Ca, Mg, P, C, sum of Bases, CEC, Base saturation, N and C:N ratio) and physical (sand, clay and silt proportions) attributes included were those generally provided in routine soil fertility analyses in Brazilian soil analysis laboratories (van Raij, 1987), except for C and N by combustion (which are not routine in most laboratories). Soil pH values were transformed to equivalent of pH in CaCl2 or KCl for all samples, using a conversion factor of 0.6 when measured in water (i.e., pH in water was considered to be 0.6 points higher than that estimated in CaCl2 or KCl). Values obtained in CaCl2 or KCl were maintained and considered of similar magnitude for these two methods. All values of H+Al, K, Ca, Mg, sum of Bases and CEC not in standard units (cmolc dm-3) were transformed. Phosphorus values were standardized to mg dm-3 and only studies using the Mehlich-extraction method were included. Carbon and Nitrogen values were in g dm-3 and based on analyses performed using combustion or Walkley-Black digestion. When OM values were given, C was estimated using the “Van Bemmelen” factor, dividing OM by 1.72. Total sand, clay and silt contents were all in g kg-1. Textural soil classes were based on the soil texture triangle of IBGE (2007), similar to that of FAO (2015). When several sources of data were available for the same collection site, or when it was sampled more than once (for example, at different times of the year, or different years), the mean values of chemical and physical attributes of soils were calculated.

Only studies performed using the standard method for collecting earthworms in tropical soil conditions, based on ISO (2018) and the Tropical Soil Biology and Fertility (TSBF) method (Anderson & Ingram, 1993) were included in the database. Information on the size of excavated area to collect the earthworms, the number of samples taken per site, and the depth of the sample were extracted from each publication. Mean earthworm density and biomass data (when available) were entered into the database as well as information on species identification (if performed or not). Earthworm data was entered as number of individuals per m2 (no. indiv. m-2) and biomass expressed as fresh mass in g m-2 (fresh weight in preservative liquid, including intestinal contents). When the data were not expressed in these units, they were obtained by using sample number and the size (area, in m2) at each date and site.

When more than one source of data was available by the same authors for the same site, i.e., if it was sampled more than once (e.g., at different times of the year or different years), mean density and biomass values were calculated for each sample site or treatment evaluated, so that each individual treatment or site was represented by only one line in the database. A preliminary analysis of climate effects showed that earthworm abundance and biomass at the same site could be significantly affected by the sampling date, particularly at sites in climates with pronounced dry and wet seasons. For sites in these climates, dry season sample dates were excluded from the database, as they could not be compared with samples of earthworm populations taken in the wet season. For cases where multiple samples were taken during the wet season, data were combined and means calculated per sample site. For climate types where there is no dry season, data from all sampling dates, irrespective of season were combined and means calculated per site.

All data is provided in excel format, and includes four tabs in the data file: Legend, Data base, count and References. The Legend tab provides a detailed explanation for each variable included in the Data base tab. The counts tab provides a table with the total number and percentage of times each variable was provided in the 124 publications used. The References tab gives the full bibliographic information on each of the 124 studies (148 publications) used, of which most (80%) are in Portuguese.

Brazil Cooling Degree Days data was reported at 4,610.220 Degrees Celsius in 2020. This records a decrease from the previous number of 4,796.490 Degrees Celsius for 2019. Brazil Cooling Degree Days data is updated yearly, averaging 4,281.610 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 4,891.640 Degrees Celsius in 2015 and a record low of 3,742.630 Degrees Celsius in 1974. Brazil Cooling Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Environmental: Climate Risk. A cooling degree day (CDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is above 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

Dataset Name: Dengue Cases in Brazil, 2012-2021

File format: Comma Seperated Values (CSV)

Dataset Files and Decriptions: - Brazil_Dengue_Model_Data_w_pop.csv - Dengue Data for Brazil as a whole country, 2012 - 2021 - State_Dengue_Model_Data_w_pop.csv - Dengue Data for Individual States / Federative Units in Brazil, 2012 - 2021

Dataset Sources: - Records of Dengue Cases in Brazil: Brazilian Government’s Sistema de Informação de Agravos de Notificação (SINAN) -URL Link: https://data.mendeley.com/datasets/2d3kr8zynf/4 - Brazil State Codes / Federative Unit Codes: Brazilian Government’s Instituto Brasileiro de Geografia e Estatística (IBGE) -URL Link: https://github.com/datasets-br/state-codes - Evironmental Data in Brazil (Temperature and Percipitation): World Bank Climate Knowledge -URL Link: https://climateknowledgeportal.worldbank.org/country/brazil/climate-data-historical - Brazil Population Data: Brazilian Government’s Instituto Brasileiro de Geografia e Estatística (IBGE) -URL Link: https://www.ibge.gov.br/en/statistics/social/population/18448-estimates-of-resident-population-for-municipalities-and-federation-units.html?edicao=28688&t=conceitos-e-metodos

Dataset Managers: - Jimmy Zhang | jz876@drexel.edu - Jonathan Watkins | jfw68@drexel.edu - Jascha Brettschneider | jmb598@drexel.edu

Column Headers: Year - a Year Between 2012 and 2021 State - Brazil or a Brazillian State / Federative Unit Mean_Tmp - Mean Temperature in Degrees Celsius Min_Tmp - Min Temperature in Degrees Celsius Max_Tmp - Max Temperature in Degrees Celsius Percipitation - Annual Percipitation Given in Millimeters Change_Tmp - Max_Tmp minus Min_Temp in Degrees Celsius State_ID - Abbreviation for State / Federative Unit Cases - Number of Recorded Dengue Cases Region - Directional Location Relative to Brazil's Center. Possible Values: North (N), Northeast (NE), Center-West (CO), Southeast (SE), South (S) State_Area(km2) - Area of State / Federative Unit Given in Squared Kilometers Population - Estimation of Total Population

Abstract Aim Our study aimed to evaluate changes in the phytoplankton functional groups brought about by increases in temperature and nutrients predicted by the Intergovernmental Panel on Climate Change (IPCC) scenario for semi-arid regions. Methods Two experiments were performed, one in the rainy season and another in the dry season. The nutrient enrichment was based on the annual mean values (August 2012-August 2013) of soluble reactive phosphorus and nitrate verified in the reservoir. The microcosms were exposed to two different temperatures, the five-year average of air temperature in the reservoir (control) and 4°C above the control temperature (warming). The experiment was conducted over 12 days; every three days water samples of approximately 60 mL in volume were taken from the reservoir for chemical and phytoplankton analysis. All species were classified by Reynolds Functional Groups (RFG). Results The functional groups H1, X1, LO and S1 were the most representative in both seasons (rainy and dry). Our results showed that bloom-forming cyanobacteria, in particular the species of functional groups H1 and M, commonly reported in reservoirs in semi-arid regions of Brazil, were not significantly benefited by the warming and nutrient enrichment. The recruitment of other blue-green species, as well as diatoms and green algae, could be observed. Conclusions The effects of warming and/or nutritional enrichment can change the structure of the phytoplankton community. However, as not expected as the pessimist scenario, in our study the bloom-forming phytoplankton functional groups did not show changes in relative biomass. Instead, the recruitment of diatoms and green algae currently found in enriched environments was verified, specifically in the rainy period, when nutrient dilution typically occurs.

Millennial-scale oscillations are known to be important in the climatic evolution of the Atlantic basin, but which internal processes originates these oscillations are still uncertain. In this study, we investigated how the Greenland and Antarctic climates affect the SW Atlantic through basin-wide oceanographic features (such as the NADW formation and the Agulhas leakage). We reconstructed sea surface and subsurface temperatures (SST and subT) using three lipid-based biomarker proxies (UK'37, TEX86 and LDI indexes) from a sediment core (NAP 63-1) retrieved from the SW Atlantic slope (24.8°S, 44.3°W). This location permitted to evaluate the temperature oscillations of the Brazil Current without any terrigenous or upwelling-derived biases. Both TEX86-based and LDI-based estimates represent the mean annual SST, while the UK'37-based estimates represent the subT (around 30 m water depth). The periods with the most well-mixed water column were observed during intervals of cooling orbital trends due to the time required to transfer the surface cooling to the subsurface. The temperature reconstructions showed a general colder MIS 3 when compared to the MIS 4. They also showed evidence of a late response to the deglaciation, with its onset in the SW Atlantic occurring in the middle of the Last Glacial Maximum. Based on these reconstructions, the NAP 63-1 SST orbital-scale trend seems to be linked to the Antarctic climate, influenced by local insolation changes. These temperature records also presented a clear millennial periodicity around 8 kyr. On this timescale, the millennial oscillations in the SW Atlantic's SST are likely linked to the NADW formation.

Tha data set comprises three files:

1. Long-term soil loss and runoff observations (nov/2011 to apr/2022) at experimental plot;
2. Daily meterological monitoring from a close station (2011-2022).

Field monitoring took place in the central region of the State of São Paulo, Brazil, at the Arruda Botelho Institute (IAB), situated at a latitude of 22º10'S and a longitude of 47º52'W, with an elevation of 790 meters above sea level. This region experiences an average annual precipitation of 1486 mm and has a humid subtropical climate (classified as Cwa in the Köppen system). This climate features hot and humid summers from October to March, with a mean temperature of 23.6 °C and 77% of the annual rainfall, as well as dry winters from April to September, with a mean temperature of 19.5 °C and only 23% of the annual rainfall. The predominant soil type in the study area is Orthic Quartzarenic Neosol (RQo), characterized by its sandy texture, deep profile, good drainage, acidity, and low nutrient content.

Since 2011, continuous monitoring has been in place to track soil loss and runoff within experimental plots. These plots measure 5 meters in width and 20 meters in length, featuring a uniform slope of 9% and enclosed by metal sheets that are approximately 30 centimeters in height. To ensure the reliability of the data, the experiments are conducted in triplicate for each type of land cover, and the mean values are utilized in subsequent analyses to minimize the impact of random variations. The plots encompass five different land covers:

A. Sugarcane, which is cultivated using contour techniques with 1.5 meters of spacing between the rows. In its mature stage, sugarcane reaches a height of 2 meters, is harvested annually in November, and is replanted every four years;

B. Pasture, specifically Brachiaria decumbens, with a canopy height ranging from 5 to 30 centimeters. This pasture is utilized for cattle grazing, employing a 30-day rotation system where approximately 10 cattle, each weighing around 420 kilograms, graze on one hectare of land for a period of 5 days. The pasture plots were substituted with soybean in November 2019 to align with the agricultural land cover changes implemented on surrounding farms;

C. Cerrado, also known as wooded Cerrado, an undisturbed woodland typical of the central area of Brazil;

D. Bare soil, established in 2011 and later in 2020, which is maintained devoid of vegetation through glyphosate application and manual weeding;

E. Soybean, which is cultivated annually during the rainy season (November to March) and left fallow during the remaining months of the year.

Global climate change poses a major challenge for contemporary forestry. Macadamia is an economically valuable tree genus that is widely cultivated across multiple countries and regions. However, few studies have focused on its adaptive distribution and spatiotemporal dynamics under projected global warming scenarios. In this study, we collected the global occurrence records of two commercial Macadamia species (Macadamia integrifolia Maiden & Betche and Macadamia tetraphylla L.A.S. Johnson) and employed a parameter-optimized MaxEnt model to project their suitable habitats under current and future climate scenarios. The optimized model exhibited excellent predictive performance (AUC = 0.979), with a regularization multiplier of 0.5 and linear–quadratic–hinge feature combination. Key bioclimatic variables include: annual Mean temperature (bio1), isothermality (bio3), min temperature of coldest month (bio6), annual precipitation (bio12), and precipitation of driest month (bio14), which collectively comprise 88.2% of the model’s explanatory power. Under the current scenario, the most suitable cultivation areas were determined to be located in Australia, China, South Africa, Brazil, Madagascar, Argentina, and the United States. Compared with the current scenario, total habitat areas under future scenarios (specifically SSP126/585 in the 2030s and 2050s; SSP126/245/370 in the 2070s) are projected to increase by 1.13–7.51%, while reductions of 0.03–2.98% are projected under the other scenarios (SSP245/370 in the 2030s and 2050s; SSP585 in the 2070s). Notably, Brazil exhibits habitat reductions of 2.59–20.06% across all scenarios, while China shows increases of 0.70–45.11%. Furthermore, M. integrifolia was determined to exhibit greater cultivation potential and global expansion feasibility in range than M. tetraphylla. This study elucidates the dominant environmental drivers, current habitat suitability, and climate-driven shifts in Macadamia distribution, providing an empirical basis for sustainable cultivation under climate change.

We analysed the alkenone unsaturation ratio (UK'37) in 87 surface sediment samples from the western South Atlantic (5°N–50°S) in order to evaluate its applicability as a paleotemperature tool for this part of the ocean. The measured UK'37 ratios were converted into temperature using the global core-top calibration of Müller et al. (1998, doi:10.1016/S0016-7037(98)00097-0) and compared with annual mean atlas sea-surface temperatures (SSTs) of overlying surface waters. The results reveal a close correspondence (<1.5°C) between atlas and alkenone temperatures for the Western Tropical Atlantic and the Brazil Current region north of 32°S, but deviating low alkenone temperatures by -2° to -6°C are found in the regions of the Brazil–Malvinas Confluence (35–39°S) and the Malvinas Current (41–48°S). From the oceanographic evidence these low UK'37 values cannot be explained by preferential alkenone production below the mixed layer or during the cold season. Higher nutrient availability and algal growth rates are also unlikely causes. Instead, our results imply that lateral displacement of suspended particles and sediments, caused by strong surface and bottom currents, benthic storms, and downslope processes is responsible for the deviating UK'37 temperatures. In this way, particles and sediments carrying a cold water UK'37 signal of coastal or southern origin are transported northward and offshore into areas with warmer surface waters. In the northern Argentine Basin the depth between displaced and unaffected sediments appears to coincide with the boundary between the northward flowing Lower Circumpolar Deep Water (LCDW) and the southward flowing North Atlantic Deep Water (NADW) at about 4000 m.

A country with continental dimensions like Brazil, characterized by heterogeneity of climates, biomes, natural resources, population density, socioeconomic conditions, and regional challenges, also exhibits significant spatial variation in dengue outbreaks. This study aimed to characterize Brazilian territory based on epidemiological and climate data to determine the optimal time to guide preventive and control strategies. To achieve this, the Moving Epidemics Method (MEM) was employed to analyze dengue historical patterns using 14-year disease data (2010–2023) aggregated by the 120 Brazilian Health Macro-Regions (HMR). Statistical outputs from MEM included the mean outbreak onset, duration, and variation of these measurements, pre- and post-epidemic thresholds, and the high-intensity level of cases. Environmental data used includes mean annual precipitation, temperature, and altitude, as well as the Köppen Climate Classification of each area. A multivariate cluster analysis using the k-means algorithm was applied to MEM outputs and climate data. Four clusters/regions were identified, with the mean temperature, mean precipitation, mean outbreak onset, high-intensity level of cases, and mean altitude explaining 80% of the centroid variation among the clusters. Region 1 (North-Northwest) encompasses areas with the highest temperatures, precipitation, and early outbreak onset, in February. Region 2a (Northeast) has the lowest precipitation and a later onset, in March. Region 3 (Southeast) presents higher altitude, and early outbreak onset in February; while Region 4 (South) has a lower temperature, with onset in March. To better adjust the results, the unique Roraima state HMR state was manually classified as Region 2b (Roraima) because of its outbreak onset in July and the highest precipitation volume. The results suggested preventive and control measures should be implemented first in Regions North-Northwest and Southeast, followed by Regions Northeast, South, and Roraima, highlighting the importance of regional vector control measures based on historical and climatic patterns. Integrating these findings with monitoring systems and fostering cross-sector collaboration can enhance surveillance and mitigate future outbreaks. The proposed methodology also holds potential for application in controlling other mosquito-transmitted viral diseases, expanding its public health impact.