Since 1961, Chile's highest maximum average temperature was recorded in 2016, at 20.4 degrees Celsius. More recent years also registered some of the highest maximum temperatures in the Latin American country, with 2020 and 2021 tying in second place. In fact, nine out of the top ten maximum temperatures were achieved in the last two decades, manifesting the consequences of a warming planet.

Since 1961, Chile's highest minimum average temperature was recorded in 1997, at 7.2 degrees Celsius. Meanwhile, the Latin American country registered its second highest minimum temperature – 7.1 degrees Celsius – in 2016. That same year, Chile's maximum average temperature reached a record high.

The dew point or dew temperature is the highest temperature at which the water vapor contained in the air begins to condense, producing dew, mist, any type of cloud or, if the temperature is low enough, Frost. This is one of the variables recorded by the meteorological network of the Chilean Meteorological Directorate (DMC). This collection contains the information stored by 321 stations that have recorded, at some point, the dew point since 1950, spaced every hour. It is important to note that not all stations are currently operational.

The data is updated directly from the DMC's web services and can be viewed in the Data Series viewer of the Itrend Data Platform.

In addition, a historical database is provided in .npz* and .mat** format that is updated every 30 days for those stations that are still valid.

*To load the data correctly in Python it is recommended to use the following code:

import numpy as np

with np.load(filename, allow_pickle = True) as f:
  data = {}
  for key, value in f.items():
    data[key] = value.item()

**Date data is in datenum format, and to load it correctly in datetime format, it is recommended to use the following command in MATLAB:

datetime(TS.x , 'ConvertFrom' , 'datenum')

Chile Cooling Degree Days data was reported at 123.550 Degrees Celsius in 2020. This records a decrease from the previous number of 130.260 Degrees Celsius for 2019. Chile Cooling Degree Days data is updated yearly, averaging 85.160 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 143.000 Degrees Celsius in 2016 and a record low of 26.300 Degrees Celsius in 1971. Chile Cooling Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.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;;

Chile's mean temperature was 0.41 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 2016, at 1.02 degrees Celsius above the long-term average. Temperature variations are becoming increasingly warmer in recent years.

Surface Observations from Punta Arenas, in extreme southern Chile. WMO station ID 85934; Period of record 1896-1954. The original forms were scanned at the Museo Salesiano Maggiorino Borgatello, a private regional museum in Punta Arenas with funding provided by NOAA, and keyed by a NOAA contractor. These data can be used to fill in missing data in the GHCN record for station GHCN ID CIN00520006. These data consist of three observations per day plus daily data for every day of the year for most years. Daily data elements are daily maximum and minimum air temperature, daily precipitation, and snow depth. Three-times daily elements are Air Pressure, Temperature, Wet Bulb Temperature, Wind Direction and Wind Speed, Cloud amount, and Visibility.

Detailed temperature reconstructions over the past 2,000 years are important for contextualizing modern climate change. The midlatitude SE Pacific is a key region in this regard in terms of understanding the climatic linkages between the tropics and southern high latitudes. Multicentennial timescale temperature variability remains, however, poorly understood, due to a lack of long, high‐temporal‐resolution temperature records from this region and from the southern high latitudes in general. We present a unique alkenone sea surface temperature (SST) record from 44°S on the southern Chilean margin in the SE Pacific spanning the last 2,300 years at decadal resolution. The record displays relatively large changes including a cooling transition from 14 to 12.5 °C between 1,100 and 600 cal yr BP, in line with other Chile margin SST records and coeval with Antarctic cooling. This cooling is attributable to reduced Southern Ocean deep convection, driven by a late Holocene sea‐ice increase in the Weddell Sea associated with increased El‐Niño Southern Oscillation variability. Superimposed on the late Holocene cooling, we observe multicentennial timescale SST variability, including relatively cool SSTs (12.5 °C) from 950 to 500 cal yr BP, corresponding to the Medieval Climate Anomaly, and warmer SSTs (13 °C) from 500 to 200 cal yr BP, corresponding to the Little Ice Age. These oscillations may reflect either multicentennial internal variability of the Southern Ocean deep convection and/or multicentennial variability in the phasing of El‐Niño Southern Oscillation and Southern Annular Mode events.

Chile Heat Index data was reported at 0.000 Day in 2020. This stayed constant from the previous number of 0.000 Day for 2019. Chile Heat Index data is updated yearly, averaging 0.000 Day from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 0.000 Day in 2020 and a record low of 0.000 Day in 2020. Chile Heat Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.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;;

Discover how Chile is set to achieve record-high fruit exports due to strong demand from Asia and favorable weather conditions, with cherries leading the way.

Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments.

Heating Degree Days data was reported at 5,939.790 Degrees Celsius in 2020. This records an increase from the previous number of 5,924.890 Degrees Celsius for 2019. Heating Degree Days data is updated yearly, averaging 6,304.620 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 6,828.850 Degrees Celsius in 1976 and a record low of 5,800.790 Degrees Celsius in 2016. Heating Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Environmental: Climate Risk. A heating degree day (HDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is below 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

Chile Transport Cost Index: SP: SO: Refrigeration: Refrigerant Temperature Sensor data was reported at 91.350 2023=100 in Dec 2024. This records a decrease from the previous number of 93.810 2023=100 for Nov 2024. Chile Transport Cost Index: SP: SO: Refrigeration: Refrigerant Temperature Sensor data is updated monthly, averaging 95.835 2023=100 from Jan 2024 (Median) to Dec 2024, with 12 observations. The data reached an all-time high of 99.770 2023=100 in Feb 2024 and a record low of 91.350 2023=100 in Dec 2024. Chile Transport Cost Index: SP: SO: Refrigeration: Refrigerant Temperature Sensor data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.I025: Transport Cost Index: 2023=100.

Comau Fjord, Northern Patagonia, Chile is a core monitoring area to reveal the impact of climate change on benthic cold-water communities. In order to record the water temperature at shallow cold-water coral banks, several long-term stations have been established in 2009. This data set provides the water temperature logged at an interval of 15 min at the station XHuinay in a depth of 17 m between 2017-01-30 and 2018-04-28.

Metal pollution is a worldwide problem and one of the greatest threats to ecosystem integrity due to its toxicity, persistence, and bioaccumulation in biological systems. Anthropogenic pollution impacts marine organisms and host-parasite dynamics, with the northern Chilean coast experiencing elevated copper levels in marine waters and sediments due to mining activities. In this study, we assessed the effects of exposure to copper concentrations at low and high-water temperatures on the survival and longevity of the marine parasite Himasthla sp. cercariae (Trematoda: Digenea) using the snail Echinolittorina peruviana as its first intermediate host. Snails were collected from intertidal rocky pools in northern Chile (23°S). To assess parasite survival and longevity, cercariae were collected from a pool of infected snails, and their mortality was recorded every 6 hours until all cercariae were dead. In a preliminary experiment conducted at 19°C, cercariae were exposed to different copper concentrations (0.2, 1.5, 3.0, and 6.0 mg/L) for 78 hours. Cercariae showed tolerance to copper. However, at the higher copper concentration (6 mg/L), survival was negatively impacted (50%) at 54 hours. In contrast, at the lower concentration (0.2 mg/L) and in the control group, cercariae sustained a 73–90% survival rate even after 54 hours. Based on these findings, we conducted subsequent experiments involving two copper treatments (0.2 and 3.0 mg/L) and two temperatures (14 and 22°C). Survival and longevity were significantly higher at lower temperature and copper concentration (14°C and 0.2 mg/L). Conversely, at higher temperature and copper concentration (22°C and 3 mg/L), survival and longevity decreased to only 66 hours. Our results show that Himasthla sp. cercariae tolerated most copper concentrations, with vulnerability observed primarily in high water temperatures, indicating an adverse effect on cercariae performance. This study contributes valuable insights into how parasites respond to environmental pollution, in marine ecosystems influenced by anthropogenic activities.

This metadata record describes a mix of intertidal seawater and air temperature data collected at Chilean Memorial, Washington, USA, by PISCO. Measurements were collected using StowAway TidbiT Temperature Loggers (Onset Computer Corp. TBI32-05+37) beginning 2001-08-17. Temperature loggers are bolted down in a wire cage at one location within each site at Mean Sea Level (MSL). Temperature is recorded at 1 hour intervals.

PatagoniaMet v1.0 (PMET from here on) is a new dataset for Western Patagonia that consists of two datasets: i) PMET-obs, a compilation of quality-controlled ground-based hydrometeorological data, and ii) PMET-sim, a daily gridded product of precipitation, and maximum and minimum temperature. PMET-obs was developed using a 4-step quality control process applied to 523 hydro-meteorological time series (precipitation, air temperature, potential evaporation, streamflow and lake level stations) obtained from eight institutions in Chile and Argentina. Based on this dataset and currently available uncorrected gridded products (in this case ERA5), PMET-sim was developed using statistical bias correction procedures (i.e. quantile mapping), spatial regression models (random forest) and hydrological methods (Budyko framework). Details are given below.

• PMET-obs is a compilation of five hydrometeorological variables obtained from eight institutions in Chile and Argentina. The daily quality controlled data of each variable are stored in separate .csv files with the following naming convention: variable_PMETobs_timeperiod_version/timestep.csv. Each column represents a different gauge with its "gauge_id". Each variable has an additional .csv file containing the metadata for each station (variable_PMETobs_version_metadata.csv). In order to make transparent the possible erroneous data that were discarded from the quality-controlled version, a .zip file with the raw data of all variables is attached. The metadata file (final and raw versions) contains the station name (gauge_name), the institution, the station location (gauge_lat and gauge_lon), the NASADEM elevation (gauge_alt) and the total number of daily records (length). In addition, the precipitation and temperature metadata include the number of monthly outliers (step Nº3 in the methods) and the number of changepoints (step Nº4 in the methods).

The streamflow metadata file (Q_PMETobs_version_metadata.csv) contains more than just the location data. Following current guidelines for hydrological datasets, the upstream area corresponding to each stream gauge was delimited (.shp file in Basins_PMETobs_version.zip), and several climatic and geographic attributes were derived. The details of the attributes can be found in the README file. For the basins that were part of the hydrological modelling (and that achieved a Kling-Gupta efficiency greater than 0.5), the file Q_PMETobs_version_water_balance.csv is attached, which contains the water balance for each basin estimated for the period 1985-2019.

• PMET-sim is a daily gridded product with a spatial resolution of 0.05° covering the period 1980-2020. The data for each variable (precipitation and maximum and minimum temperature) are stored in separate netcdf files with the following naming convention: variable_PMETsim_1980_2020_v10d.nc.

Citation: Aguayo, R., León-Muñoz, J., Aguayo, M., Baez-Villanueva, O., Fernandez, A. Zambrano-Bigiarini, M., and Jacques-Coper, M. (2023) PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia. Sci Data 11, 6 (2024). https://doi.org/10.1038/s41597-023-02828-2

Code repository: https://github.com/rodaguayo/PatagoniaMet

The termination of the last ice age (Termination 1; T1) is crucial for our understanding of global climate change and for the validation of climate models. There are still a number of open questions regarding for example the exact timing and the mechanisms involved in the initiation of deglaciation and the subsequent interhemispheric pattern of the warming. Our study is based on a well-dated and high-resolution alkenone-based sea surface temperature (SST) record from the SE-Pacific off southern Chile (Ocean Drilling Project Site 1233) showing that deglacial warming at the northern margin of the Antarctic Circumpolar Current system (ACC) began shortly after 19,000 years BP (19 kyr BP). The timing is largely consistent with Antarctic ice-core records but the initial warming in the SE-Pacific is more abrupt suggesting a direct and immediate response to the slowdown of the Atlantic thermohaline circulation through the bipolar seesaw mechanism. This response requires a rapid transfer of the Atlantic signal to the SE-Pacific without involving the thermal inertia of the Southern Ocean that may contribute to the substantially more gradual deglacial temperature rise seen in Antarctic ice-cores. A very plausible mechanism for this rapid transfer is a seesaw-induced change of the coupled ocean–atmosphere system of the ACC and the southern westerly wind belt. In addition, modelling results suggest that insolation changes and the deglacial CO2 rise induced a substantial SST increase at our site location but with a gradual warming structure. The similarity of the two-step rise in our proxy SSTs and CO2 over T1 strongly demands for a forcing mechanism influencing both, temperature and CO2. As SSTs at our coring site are particularly sensitive to latitudinal shifts of the ACC/southern westerly wind belt system, we conclude that such latitudinal shifts may substantially affect the upwelling of deepwater masses in the Southern Ocean and thus the release of CO2 to the atmosphere as suggested by the conceptual model of [Toggweiler, J.R., Rusell, J.L., Carson, S.R., 2006. Midlatitude westerlies, atmospheric CO2, and climate change during ice ages. Paleoceanography 21. doi:10.1029/2005PA001154].

The Chilean heat pump market was finally on the rise to reach $91M in 2024, after two years of decline. The market value increased at an average annual rate of +1.4% over the period from 2012 to 2024; however, the trend pattern indicated some noticeable fluctuations being recorded throughout the analyzed period. Over the period under review, the market hit record highs at $117M in 2019; however, from 2020 to 2024, consumption stood at a somewhat lower figure.

Chile Transport Cost Index: SP: Refrigeration: Refrigerant Temperature Sensor data was reported at 87.950 2018=100 in May 2019. This records an increase from the previous number of 87.230 2018=100 for Apr 2019. Chile Transport Cost Index: SP: Refrigeration: Refrigerant Temperature Sensor data is updated monthly, averaging 98.860 2018=100 from Jan 2018 (Median) to May 2019, with 17 observations. The data reached an all-time high of 104.330 2018=100 in Feb 2018 and a record low of 87.230 2018=100 in Apr 2019. Chile Transport Cost Index: SP: Refrigeration: Refrigerant Temperature Sensor data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.I017: Transport Cost Index: 2018=100.

Evolutionary change of thermal traits (i.e. heat tolerance and behavioral thermoregulation) is one of the most important mechanisms exhibited by organisms to respond to global warming. However, the evolutionary potential of heat tolerance, estimated as narrow-sense heritability, depends on the methodology employed. An alternative adaptive mechanism to buffer extreme temperatures is behavioral thermoregulation, although the association between heat tolerance and thermal preference is not clearly understood. We suspect that methodological effects associated with the duration of heat stress during thermal tolerance assays are responsible for missing this genetic association. To test this hypothesis, we estimated the heritabilities and genetic correlations for thermal traits in Drosophila subobscura, using high-temperature static and slow ramping assays. We found that heritability for heat tolerance was higher in static assays (h2 = 0.134) than in slow ramping assays (h2 = 0.084), suggesting that fast assays may provide a more precise estimation of the genetic variation of heat tolerance. In addition, thermal preference exhibited a low heritability (h2 = 0.066), suggesting a reduced evolutionary response for this trait. We also found that the different estimates of heat tolerance and thermal preference were not genetically correlated, regardless of how heat tolerance was estimated. In conclusion, our data suggest that these thermal traits can evolve independently in this species. In agreement with previous evidence, these results indicate that methodology may have an important impact on genetic estimates of heat tolerance and that fast assays are more likely to detect the genetic component of heat tolerance.