The climate data were collected between 1988 and 1995 and include portions of 25 volumes of fan-fold line-printer computer printouts, with 10 columns of variables per page. The legacy weather data was entered into Excel spreadsheets and delivered to the Arizona State Climate Office, Navajo Nation, and the Desert and Southern Rockies LCCs. This project was completed by the USGS Arizona Water Science Center in cooperation with the Navajo Nation.

This climate and vegetation index dataset was collected from readily available open source data, such as Landsat. The data represents points across the northern Colorado plateau. The vegetation type was defined based on U.S. Geological Survey ReGAP data. Using compositing techniques by season we developed a dataset of lag and legacy for each point. We could then look to understand how both lag and legacy impacted vegetation production across the time series. In this dataset we focus on the soil adjusted vegetation index (SAVI), the standardized precipitation and evapotranspiration index (SPEI), and precipitation. Included in this dataset are climate lags of 3,6,9 and 12 months. Additionally, the legacy construct is included in the latter columns.

Note: This dataset version has been superseded by a newer version.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. This Climate Data Record (CDR) includes lower tropospheric, mid-tropospheric, and lower stratospheric temperatures over land and ocean derived from microwave radiometers on NOAA and NASA polar orbiting satellites. The temperatures are from measurements produced by Microwave Sounding Units (MSU) since 1978 and Advanced Microwave Sounding Unit-A (AMSU-A) since 1998 flying on NOAA polar orbiting satellites, on NASA Aqua satellite (operating since mid-1998) and on the European MetOp satellite (operating since late 2006). The instruments are cross-track through-nadir scanning externally-calibrated passive microwave radiometers. Brightness temperature measurements are derived at microwave frequencies within the 50-60 GHz oxygen absorption complex, and (in the case of AMSU-A) at a few microwave frequencies above and below that absorption complex. There are three atmospheric layers for which intermediate products are processed: (1) lower-tropospheric (TLT) deep-layer average temperature, computed as a weighted difference between view angles of AMSU-A channel 5, whose heritage comes from MSU channel 2, (2) mid-tropospheric (TMT) deep-layer temperature, computed as an average of the central portion of the scan of AMSU-A channel 5, whose heritage also comes from MSU channel 2, and (3) lower-stratospheric (TLS) deep layer temperatures, computed from the central portion of the scan of AMSU channel 9, whose heritage comes from MSU channel 4. This CDR includes several products. The global monthly anomaly data data are averaged onto a 2.5 x 2.5 degree latitude-longitude grid for each of the three atmospheric layers. Monthly anomalies are averaged for each of the three atmospheric layers over multiple regions, including Global, hemispheric, tropic, extratropic, polar and contiguous U.S. A mean annual cycle of monthly mean layer temperatures is also included. Anomalies are deviations from 1981-2010 mean. The datasets have been converted from the native ASCII format to CF-compliant netCDF-4 format.

This Global Summaries dataset, known as GSOY for Yearly, contains a yearly resolution of meteorological elements from 1763 to present with updates applied weekly. The major parameters are: – average annual temperature, average annual minimum and maximum temperatures; total annual precipitation and snowfall; departure from normal of the mean temperature and total precipitation; heating and cooling degree days; number of days that temperatures and precipitation are above or below certain thresholds; extreme annual minimum and maximum temperatures; number of days with fog; and number of days with thunderstorms. The primary input data source is the Global Historical Climatology Network - Daily (GHCN-Daily) dataset. The Global Summaries datasets also include a monthly resolution of meteorological elements in the GSOM (for Monthly) dataset. See associated resources for more information. These datasets are not to be confused with "GHCN-Monthly", "Annual Summaries" or "NCDC Summary of the Month". There are unique elements that are produced globally within the GSOM and GSOY data files. There are also bias corrected temperature data in GHCN-Monthly, which are not available in GSOM and GSOY. The GSOM and GSOY datasets replace the legacy U.S. COOP Summaries (DSI-3220), and have been expanded to include non-U.S. (global) stations. U.S. COOP Summaries (DSI-3220) only includes National Weather Service (NWS) COOP Published, or "Published in CD", sites.

Our analysis for the manuscript, "Planning for climate migration in Great Lake Legacy Cities" uses county level spatial data from the FEMA National Risk Index (USFEMA, 2021) and the CDC SVI ranking system (ATSDR, 2018) in the form of shapefiles(.shp). To create the geovisualization, we used boundaries of the Great Lakes that are published here https://www.glc.org/greatlakesgis. All analysis was conducted using R (2020), with code that can be found here: https://derekvanberkel.github.io/Planning-for-climate-migration-in-Great-Lake-Legacy-Cities/

ATSDR. (2018). Cdc/atsdr social vulnerability index. https://www.atsdr.cdc.gov/placeandhealth/svi/fact sheet/fact sheet.html.

USGCRP. (2018). Impacts, risks, and adaptation in the united states: Fourth national climate assessment. US Global Change Research Program.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information. This tool is called the Climate and Economic Justice Screening Tool. The tool uses datasets that are indicators of burdens in eight categories: climate change, energy, health, housing, legacy pollution, transportation, water and wastewater, and workforce development. The tool uses this information to identify communities that are experiencing these burdens. These are the communities that are disadvantaged because they are marginalized by underinvestment and overburdened by pollution. CEQ will update the tool, after reviewing public feedback, research, and the availability of new data. Version 2.0 Release update - Dec 20, 2024 New & Improved Added the low income burden to American Samoa, Guam, the Mariana Islands, and the U.S. Virgin Islands Tracts in these territories that are completely surrounded by disadvantaged tracts and exceed an adjusted low income threshold are now considered disadvantaged Additionally, census tracts in these four Territories are now considered disadvantaged if they meet the low income threshold only, because these Territories are not included in the nationally-consistent datasets on environmental and climate burdens used in the tool Updated the data in the workforce development category to the Census Decennial 2020 data for the U.S. territories of Guam, Virgin Islands, Northern Mariana Islands, and American Samoa Made improvements to the low income burden to better identify college students before they are excluded from that burden’s percentile Census tracts that were disadvantaged under version 1.0 continue to be considered as disadvantaged in version 2.0 Technical Fixes For tracts that have water boundaries, e.g. coastal or island tracts, the water boundaries are now excluded from the calculation to determine if a tract is 100% surrounded by disadvantaged census tracts User Interface Improvements Added the ability to search by census tract ID The basemap has been updated to use a free, open source map

We provide the data and R scripts from an experiment testing the role of warming history on the diversity and functional diversity of intertidal biofilm communities. In this study, 77 samples were collected throughout the experiment to investigate microbial community diversity (both taxonomic and functional). In addition, biomass measurements (chlorophyll a) were recorded. We also provide the associated metadata and analytical scripts used for processing and analyzing the data.

In 1954 researchers at the USGS Great Lakes Science Center conducted 11 research cruises on Lake Michigan during which 779 bathythermographs were cast to collect temperature profile data (temperature at depth). Bathythermographs of that era recorded water pressure and temperature data by mechanically etching them as a curve on a glass slide. Data was collected from the glass slide by projecting the image of the curve, superimposing a grid, and taking a photo of it, thereby creating a bathythermogram. Data collection personnel could then read the data from the curve. This USGS data release is a digitized set of those original bathythermogram print photos and the temperature and depth data the project team collected from them using the open-source software, Web Plot Digitizer, as well as metadata describing each. In addition, because of their historical value as well as potential future use, this data release includes the cruise logs, which include nautical and research notes beyond the logical scope of this data release.

We introduce a method that identifies from earnings conference calls the attention paid by financial analysts to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. The measures are useful in predicting important real outcomes related to the net-zero transition, notably job creation in disruptive green technologies and green patenting, and they contain information that is priced in options and equity markets. Updates [2024-08-17]: We have updated our data to 2023Q4. Updates [2023-11-21]: We have updated our data to 2022Q4. Updates [2023-02-15]: We have updated our data to ensure that the topic measures have zero values when CCExposure=0. Updates [2022-03-11]: We have updated our data to 2021Q4. Updates [2022-02-25]: We have expanded the number of variables provided in the datasets (we re-run the bigram searching algorithm so the original scores change but remain highly correlated with the legacy version.). Updates [2021-05-14]: We have updated our data to 2020Q4. Updates [2021-04-03]: Last update missed 2019 Q3 and Q4. We added the data of these two quarters in the latest version. Updates [2021-01-19]: We have updated our data to 2020Q3.

This dataset (.xlsx) is a compendium of climate change hazard data and adaptation resources for cultural heritage. It was created by JBA Consulting for Historic England and is accompanied by a research report which provides the background, methodology, and results of the project. One aim of the project was to identify and compile climate hazard resources (data and tools) that could assist those managing the UK historic environment, with specific attention paid to data availability, spatial resolution, and format.

The project identified 73 datasets and 38 tools. The datasets were linked to relevant climate hazards from a standardised hazard vocabulary (Thomas, 2024). The attached pdf file provides further details on how to use the dataset. Further information can be found in the report, and questions can be addressed to Kate Guest at Kate.Guest@HistoricEngland.org.uk.

The intensification of drought throughout the US Great Plains has the potential to have large impacts on grassland functioning, as has been shown with dramatic losses of plant productivity annually. Yet, we have a poor understanding of how grassland functioning responds after drought ends. This study examined how belowground nutrient cycling responds after drought and whether legacy effects persist post-drought. We assessed the two-year recovery of nutrient cycling processes following a four-year experimental drought in a mesic grassland by comparing two different growing season drought treatments - chronic (each rainfall event reduced by 66%) and intense (all rain eliminated until 45% of annual rainfall was achieved) – to the control (ambient precipitation) treatment. At the beginning of the first growing season post-drought, we found that in situ soil CO₂ efflux and laboratory-based soil microbial respiration were reduced by 42% and 22% respectively in the intense drought treatment compared to the control, but both measures had recovered by mid-season (July) and remained similar to the control treatment in the second post-drought year. We also found that extractable soil ammonium and total inorganic N were elevated throughout the growing season in the first year after drought in the intense treatment. However, these differences in inorganic N pools did not persist during the growing season of the second year post-drought. The remaining measures of C and N cycling in both drought treatments showed no post-drought treatment effects. Thus, although we observed short-term legacy effects following the intense drought, C and N cycling returned to levels comparable to non-droughted grassland within a single growing season regardless of whether the drought was intense or chronic in nature. Overall, these results suggest that key aspects of C and N cycling in mesic tallgrass prairie do not exhibit persistent legacies from four years of experimentally-induced drought.

This dataset includes data from the research article 'Unveiling Climate-Adaptive World Heritage Management Strategies: the Netherlands as Case Study', submitted to and accepted by the MDPI Sustainability journal, under the topic of 'World Heritage Sites and Values in Danger: Climate-Change Related Challenges and Transformation'.

The dataset comprises three sets of textual data obtained from the UNESCO World Heritage Convention website for the Netherlands. These include the Statement of Outstanding Universal Value (SOUV), Management Plan (MP), and State of Conservation (SoC) Reports by the State Parties. The codes and reference texts from each document set were used for qualitative clustering analysis and categorised into sub-themes and themes. The occurrence frequency of finalised codes and sub-themes was counted to support the visualisation of their numerical patterns. The resulting visualisations, a Sankey diagram and two semantic networks, facilitated unveiling two climate-adaptive World Heritage management strategies.

This Climate Data Record (CDR) includes lower tropospheric, mid-tropospheric, and lower stratospheric temperatures over land and ocean derived from microwave radiometers on NOAA and NASA polar orbiting satellites. The temperatures are from measurements produced by Microwave Sounding Units (MSU) since 1978 and Advanced Microwave Sounding Unit-A (AMSU-A) since 1998 flying on NOAA polar orbiting satellites, on NASA Aqua satellite (operating since mid-1998) and on the European MetOp satellite (operating since late 2006). The instruments are cross-track through-nadir scanning externally-calibrated passive microwave radiometers. Brightness temperature measurements are derived at microwave frequencies within the 50-60 GHz oxygen absorption complex, and (in the case of AMSU-A) at a few microwave frequencies above and below that absorption complex. There are three atmospheric layers for which intermediate products are processed: (1) lower-tropospheric (TLT) deep-layer average temperature, computed as a weighted difference between view angles of AMSU-A channel 5, whose heritage comes from MSU channel 2, (2) mid-tropospheric (TMT) deep-layer temperature, computed as an average of the central portion of the scan of AMSU-A channel 5, whose heritage also comes from MSU channel 2, and (3) lower-stratospheric (TLS) deep layer temperatures, computed from the central portion of the scan of AMSU channel 9, whose heritage comes from MSU channel 4. This CDR includes several products. The global monthly anomaly data data are averaged onto a 2.5 x 2.5 degree latitude-longitude grid for each of the three atmospheric layers. Monthly anomalies are averaged for each of the three atmospheric layers over multiple regions, including Global, hemispheric, tropic, extratropic, polar and contiguous U.S. A mean annual cycle of monthly mean layer temperatures is also included. Anomalies are deviations from 1981-2010 mean. The datasets have been converted from the native ASCII format to CF-compliant netCDF-4 format.

Department of Energy and Climate performance reporting on the Queensland Government On-Time Payment Policy.

Additional information reported in lieu of inclusion in the annual report: consultancies, overseas travel, Queensland Language Services Policy. Read the complete annual reports: https://www.epw.qld.g…Show full descriptionAdditional information reported in lieu of inclusion in the annual report: consultancies, overseas travel, Queensland Language Services Policy. Read the complete annual reports: https://www.epw.qld.gov.au/news-publications/annual-report

The data has been collected during the Nansen Legacy Seasonal Study Q4 from 28 November - 17 December 2019 on research vessel RV Kronprins Haakon (cruise number 201971), along a transect in the northern Barents Sea from 76N to 82N. The dataset contains abundance of pelagic marine protists, including phytoplankton (autotrophic) and protozooplankton (heterotrophic). Protists were identified and counted with light microscopy using the Utermöhl method and the result are given as cells per liter (cells/L) called organismQuantity.

Quality

Sampling method:

The samples were collected with Niskin bottles attached to a CTD rosette at the following depths: 5, 10, 30, 60, 90 m and deep chlorophyll max (DCM). The samples were preserved using an aldehyde mixture of glutaraldehyde and hexamethylenetetramine-buffered formalin at final concentrations of 0.1% and 1% respectively.

Analyse method:

All samples have been analysed at Institute of Oceanology of the Polish Academy of Sciences (IOPAN). The organisms were identified and counted under an inverted microscope according to the Utermöhl method.

Header name index - events

• expedition: cruise number for R/V Kronprins Haakon
• eventID: UUID for the sample
• parentID: UUID for the gear deployment (each Niskin has a unique parentID)
• eventDate: the date-time when an event occurred, using ISO 8601-1:2019 format (2020-07-27T07:16:03.446Z).
• fieldNumber: human-readable sample ID (e.g. PHT-001)
• locationID: station name
• decimalLongitude: geographic latitude (in decimal degrees, using the spatial reference system given in geodetic datum)
• decimalLatitude: geographic longitude (in decimal degrees, using the spatial reference system given in geodeticDatum)
• bottomDepthInMeters: bottom depth in meters
• eventRemarks: comments or remarks about the event (free text field)
• gearType: the gear used to take the sample e.g. Niskin bottle
• samplingDepthInMeters: depth sampled
• sampleType: description of the sample type according to a standard list
• recordedBy: name of the person who took the samples
• principalInvestigatorName: name of the person in charge of the sample collection
• principalInvestigatorEmail: email address of the person in charge of the sample collection
• principalInvestigatorInstitution: affiliated institution of the person in charge of the sample collection

Header name index - occurrence

• scientificName: full scientific name of the identified organism at the lowest taxonomic level that can be ascertained. The scientificName should be selected from a drop-down menu linked to the list in taxonomy sheet. (e.g Thalassiosira hyalina).
• identificationQualifier: A standard term (sp., spp., and indet.) to express uncertainty in identification.
• lifeStage: the life stage (e.g. resting spore) of the organism.
• sizeGroupOperator: describes if the size group is less than or greater than a value (It = less than, gte = greater or equal to)
• sizeGroup: the size group in µm.
• organismRemark: indicates e.g. varieties, colony type
• identificationRemarks: a free text field for adding information relevant to the analysis
• identifiedBy: person who did the lab-analyse
• fieldsInCount: Number of fields counted in the microscope
• magnificationMicroscope: The magnification setting used during analysis. Selected from a drop-down menu linked to vocab-sheet
• maxFields: Number of fields in the entire sedimentation chamber (Related to magnification used)
• takenVolumeML: The volume taken for sedimentation in the Utermöhl chamber (the sub-sample taken for analysis)
• identifiedBy: Drop-down menu linked to list in people-sheet
• dateIdentified: Date for the analysis
• sampleSizeValue=(fieldsInCount/maxFields)*(takenVolumeML/convertionMLtoL)*dilutionFactorFormaldehyde), dilutionFactorFormaldehyde = 0.95
• sampleSizeUnit: liter (l)
• organismQuantity: the quantity of the organism per volume water in the environment (organismQuantity = individualCount/sampleSizeValue)
• organismQuantityType: cells/l

Funding:

The Nansen Legacy is funded by the Research Council of Norway and the Norwegian Ministry of Education and Research. They provide 50% of the budget while the participating institutions contribute 50% in-kind. The total budget for the Nansen Legacy project is 740 mill. NOK.

This file contains the data used in the paper: Dryland sensitivity to climate change and variability using nonlinear dynamics. The first dataset (Data for dryland sensitivity_annual productivity and climates.csv) contains the time-series data of annual productivity and climate variables (annual precipitation, annual mean temperature, summer mean temperature, and annual SPEI). The second dataset (Data for dryland sensitivity_productivity and interannual climate variability.csv) contains the data of mean annual productivity and interannual climate variability (interannual precipitation variability, interannual temperature variability, interannual summer temperature variability, and interannual SPEI variability) in 6y moving windows.

This dataset (.xlsx) is a vocabulary of climate change hazards for heritage. Hazards are the potential occurrences of natural or physical events that may cause damage or loss. Previously there had been no definitive list of climate hazards for heritage that were directly connected to changing climatic processes. This project addresses this gap by linking the created hazards to the Climatic Impact-Drivers (CIDs) produced by the Intergovernmental Panel on Climate Change (IPCC). The vocabulary consists of 52 primary and key related hazards for heritage. It is international in its remit.

The list will be published as a vocabulary on the Forum on Information Standards in Heritage (FISH) where it can be accessed in multiple formats including linked data. This .xlsx format places the hazards in relationship to each other and in their CID context. Candidate terms can be submitted to the research group Heritage Environmental Risk and Data Analytics herada@ucl.ac.uk (terms submitted to Terminologies@HistoricEngland.org.uk will be directed to the research group for approval). An accompanying Historic England Research Report provides more information, including the methodology of the project (available in both English and Welsh).

The authors are interested in hearing from users of the vocabulary, specifically those that link the hazards to observed impacts of climate change on parts of the historic environment. This dataset was produced as part of a funded 6-month project between Historic England and the UCL Institute for Sustainable Heritage on developing a standardised vocabulary of climate change hazards for the historic environment. The Welsh version of the dataset was translated in 2025 by Lingo Soar, in collaboration with Fforest Fawr UNESCO Geopark, and as part of the UK National Commission for UNESCO's Climate Change and UNESCO Heritage project.

The NMPF’s climate change policies seek to support management of potential impacts of proposals in two ways. Firstly, the way in which the proposal may affect natural and / or physical features that play a role in mitigation (e.g. carbon sequestration) or adaptation (e.g. flood defence. Secondly the way in which the proposal has considered its own direct and indirect contributions to mitigation (e.g. measures included in the proposal to reduce emissions) and adaptation (e.g. ensuring the proposal is future-proofed in relation to changing operating conditions due to climate change).

X means that the variant was used to study the respective topic of each row. LSTM = LSTM model using the full depth of the Landsat time series and climate data; LSTMperm = LSTM model but the temporal patterns of both the predictive and the target variables were randomly permuted while instantaneous relationships between predictive and target variables were kept; LSTMmsc = LSTM model but the Landsat time series for each band were replaced by their mean seasonal cycle, while using the actual values of air temperature (Tair), precipitation (P), global radiation (Rg), and vapor pressure deficit (VPD); LSTMannual = LSTM model but the Landsat time series for each band were replaced by their annual mean, while using the actual values of Tair, P, Rg, and VPD, RF = Random Forest model using the actual values of the Landsat time series and climate data.