The PRISM Climate Group gathers climate observations from a wide range of monitoring networks, applies sophisticated quality control measures, and develops spatial climate datasets to reveal short- and long-term climate patterns. The resulting datasets incorporate a variety of modeling techniques and are available at multiple spatial/temporal resolutions, covering the period from 1895 to the present.

Datasets and sources used for the current Africa Data Hub Climate Observer. https://www.africadatahub.org/data-resources/climate-observer

The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling.

The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.

Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. The NOAA Ocean Surface Bundle (OSB) Climate Data Record (CDR) consist of three parts: sea surface temperature, near-surface atmospheric properties, and heat fluxes. This portion of the OSB CDR is the NOAA Climate Data Record (CDR) of Sea Surface Temperature - WHOI. The SST data are found through modeling the diurnal variability in combination with AVHRR observations of sea surface temperature. The data cover a time period from January 1988 - December 2007 at a 3-hourly, quarter-degree resolution.

This list contains useful resources for environmental reporters covering the climate crisis in Africa. It contains links to data source, journalism organisations and training materials.

Climate Yaunde
This dataset falls under the category Environmental Data Climate Data.
It contains the following data: The climate here is tropical. Most months of the year are marked by significant rainfall. The short dry season has little impact. The Koppen-Geiger climate classification is Am. The temperature here is on average 23.0 C | In a year, the precipitation is 1727 mm.
This dataset was scouted on 2022-02-21 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://es.climate-data.org/africa/camerun/centre/yaunde-3987/

Spatial Resolution: 10km x 10km. Period: 2002 – Recent Temporal Resolution: Monthly and Daily Landcover ESA Worldcover - https://esa-worldcover.org/en Land cover classification. Spatial Resolution: 10m x 10m. Period: 2020

This dataset contains historical climate data and climate summaries from Long-Term Ecological Research sites and other climate stations in their vicinity. It has as its basis "A Climatic Analysis Of Long-Term Ecological Research Sites" created by David Greenland, Timothy KIttel, Bruce Hayden and David Schimel in 1996 (http://climhy.lternet.edu/documents/climdes/). The dataset includes monthly data and summaries aggregating years and months to produce climatic summaries.

Data deliverables from Arctic Network for 2021. Files may include: protocol, standard operating procedures, site maps, site visit worksheets, datalogger programs, photos, raw data, corrected data, operations report, sensor calibration certificates, and/or periodic reports.

Saudi Arabia hourly climate integrated surface data with the below data observations, WindSky conditionVisibilityAir temperatureDewSea level pressureNote: The dataset will contain the last 5 years hourly data, however, check the attachments section in this dataset if you need historical data.

This resource is part of the larger SnowClim Dataset (https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/). This resource contains present-day climate metrics. Climate metrics were created by downscaling outputs of the Weather Research and Forecasting Model (WRF; Rasmussen and Liu, 2017) for the present-day period (1 Oct 2000 to 30 Sep 2013) using a combination of local lapse rates and terrain corrections for solar radiation as described in Lute et al., (in prep). Climate metrics are available on a ~210 m grid for the western United States in both netCDF and GeoTiff formats.

Additional information is available in: Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.

Subak Climate Data Cooperative

We developed a new climate dataset for Europe referred to as ECLIPS (European CLimate Index ProjectionS), which contains gridded data for 80 annual, seasonal, and monthly climate variables for two past (1961-1990, 1991-2010) and five future periods (2011-2020, 2021-2140, 2041-2060, 2061-2080, 2081-2100). The future data are based on five Regional Climate Models (RCMs)driven by two greenhouse gas concentration scenarios, RCP 4.5 and 8.5.

The ECLIPS dataset has two versions; ECLIPS 1.1 contains data with spatial resolution of 0.11° × 0.11°, which is the resolution of underlying RCMs. ECLIPS1.1 is available at https://doi.org/10.5281/zenodo.1181780.

The ECLIPS 2.0 presented here contains a subset of climate indices of ECLIPS 1.1, downscaled to the resolution of 30 arcsec by means of the delta correction approach. Both ECLIPS versions were evaluated by testing their relationship with independent station data from the European Climate Assessment (ECA) dataset. Correlations of the empirical testing data to ECLIPS 1.1 ranged from 0.63 to 0.78,and to ECLIPS 2.0 from 0.78 to 0.93. suggesting substantial improvement due to downscaling. A large number of climate projections, time periods and indices as well as the availability of these data at two different spatial resolutions can support diverse studies across a range of disciplines and thus extend our understanding of climate-sensitive dynamics of many social-ecological systems

The zipfile ECLIPS2.0 contains 5 folders with subfolders

File naming system for the subfolders / folder are as follows

ECLIPS2.0_196191: past climate 1961-1990: < climate index>

ECLIPS2.0_199110: past climate 1991-2010 < climate index>

ECLIPS2.0_45 : future climate RCP4.5

ECLIPS2.0_85 : future climate RCP8.5

Incase zpfile reader 7zip is not available, please install from here: https://www.7-zip.org/

This data set has been replaced with a newer version. Note: If necessary

Overview: The model of watershed hydrology and water management used for the Lower Nooksack Water Budget is Topnet-WM, developed for Water Resources Inventory Area 1 (WRIA 1) in an effort led by researchers from Utah State University, as reported in peer-reviewed publications (Bandaragoda et al., 2004; Ibbitt and Woods, 2004; Tarboton, 2007). The model has also been applied, at finer spatial resolution, to the Fishtrap Creek and Bertrand Creek watersheds (Bandaragoda, 2008; Bandaragoda and Greenberg, 2009). The model processes of Topnet-WM are described in detail in Chapter 2 Model Processes. The daily meteorological variables required by Topnet-WM are precipitation, temperature (minimum and maximum), and wind speed.

Prior to the Lower Nooksack Water Budget project, WRIA 1 Topnet-WM used interpolated climate data (1946-2006) from 19 weather stations located within or near the WRIA 1 boundary. A significant component of the Lower Nooksack Water Budget Project was to update Topnet-WM to use the high resolution (1/8 lat/long degree; approximately one data point every 8 miles) gridded climate dataset that is updated and distributed, on an ongoing basis, by the University of Washington (UW) Land Surface Hydrology Research Group1 , following methods described in Maurer et. al. (2002) and Hamlet and Lettenmaier (2005). This dataset includes daily precipitation, wind speed, and daily maximum and minimum temperatures over the 1915 through 2011 water years (October 1 through September 30).

Figure 1 shows the distribution of the updated mean annual precipitation distribution derived from the Lower Nooksack Water Budget Topnet-WM gridded climate data for the 172 drainages (black dots) in WRIA 1. The lowest annual precipitation values are around Lummi Island and Bellingham (31-38 inches per year) and the highest precipitation values are near Mount Baker (121-207 inches per year). The increase in annual precipitation follows a gradient of increase from the west coast of the watershed to the eastern mountains, reflecting the role of orographic uplift of moist oceanic air masses in generating precipitation in this region.

Purpose: The purpose for updating climate data used for watershed model inputs is to use the most current and up to date datasets. For the Lower Nooksack Water Budget Topnet-WM model, this includes new Snotel stations, an additional 8 years of daily climate data, and a higher resolution data product, compared to the initially developed Topnet-WM (Tarboton, 2007), which was populated with climate data ending in 2004. Updated climate data helps build our knowledge of the watershed system, since we have more information about when and where water is input to the system as rain and/or snow.

This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource.

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Ottawa airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.

This dataset contains hourly historical and future weather files for use in building simulations for the city of Ottawa, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.

The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.

The historical and future global warming thresholds and their corresponding time periods are as follows:

The following variables are included in the files:

The impact of climatic variability on the environment is of great importance to the agricultural sector in Canada. Monitoring the impacts on water supplies, soil degradation and agricultural production is essential to the preparedness of the region in dealing with possible drought and other agroclimate risks. Derived normal climate data represent 30-year averages (1961-1990, 1971-2000, 1981-2010, 1991-2020) of climate conditions observed at a particular location. The derived normal climate data represents 30-year averages or “normals” for precipitation, temperature, growing degree days, crop heat units, frost, and dry spells. These normal trends are key to understanding agroclimate risks in Canada. These normal can be used as a baseline to compare against current conditions, and are particularly useful for monitoring drought risk.

The Unified Sea Ice Thickness Climate Data Record, 1947 Onward is the result of a concerted effort to collect as many observations as possible of Arctic and Antarctic sea ice draft, freeboard, and thickness and to format them consistently with clear documentation, allowing the scientific community to better utilize what is now a considerable body of observations.

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates? (2) Are there significant regional differences in accuracy among data sets? (3) How accurate are their mean values compared with extremes? (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

The Berkeley website provides data and analysis for a number of weather stations within the North Slope region. Data download and summary graphs with trend are provided. The datasets presented are divided into three categories: Output data, Source data, and Intermediate data. The Berkeley Earth averaging process generates a variety of Output data including a set of gridded temperature fields, regional averages, and bias-corrected station data. Source data consists of the raw temperature reports that form the foundation of our averaging system. Source observations are provided as originally reported and will contain many quality control and redundancy issues. Intermediate data is constructed from the source data by merging redundant records, identifying a variety of quality control problems, and creating monthly averages from daily reports when necessary. The definitive repository for Source and Intermediate data is located in the SVN, which is built nightly. Sites include: Alpine, Ambler, Anaktuvuk, Atqasuk, Barrow, Cape Lisburne, Deadhorse, Dietrich Camp, Franklin Bluff, Galbraith Lake, Happy Valley, Lonely, Noatak, Nuiqsut, Oliktok, Point Lay, Prudhoe Bay, Red Dog, Sag River and UGNU Kuparuk