OnPoint Weather is a global weather dataset for business available for any lat/lon point and geographic area such as ZIP codes. OnPoint Weather provides a continuum of hourly and daily weather from the year 2000 to current time and a forward forecast of 45 days. OnPoint Climatology provides hourly and daily weather statistics which can be used to determine ‘departures from normal’ and to provide climatological guidance of expected weather for any location at any point in time. The OnPoint Climatology provides weather statistics such as means, standard deviations and frequency of occurrence. Weather has a significant impact on businesses and accounts for hundreds of billions in lost revenue annually. OnPoint Weather allows businesses to quantify weather impacts and develop strategies to optimize for weather to improve business performance. Examples of Usage Quantify the impact of weather on sales across diverse locations and times of the year Understand how supply chains are impacted by weather Understand how employee’s attendance and performance are impacted by weather Understand how weather influences foot traffic at malls, stores and restaurants OnPoint Weather is available through Google Cloud Platform’s Commercial Dataset Program and can be easily integrated with other Google Cloud Platform Services to quickly reveal and quantify weather impacts on business. Weather Source provides a full range of support services from answering quick questions to consulting and building custom solutions. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery 瞭解詳情

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

• An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

• The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

• The property’s Flood Factor as well as data on economic loss.

• The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

• Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

• Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name	Type	Description
fsid	int	First Street ID (FSID) is a unique identifier assigned to each location
long	float	Longitude
lat	float	Latitude
zcta	int	ZIP code tabulation area as provided by the US Census Bureau
blkgrp_fips	int	US Census Block Group FIPS Code
tract_fips	int	US Census Tract FIPS Code
county_fips	int	County FIPS Code
cd_fips	int	Congressional District FIPS Code for the 116th Congress
state_fips	int	State FIPS Code
floodfactor	int	The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)
CS_depth_RP_YY	int	Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00
CS_chance_flood_YY	float	Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00
aal_YY_CS	int	The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low
hist1_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist1_event	string	Short name of the modeled historic event
hist1_year	int	Year the modeled historic event occurred
hist1_depth	int	Depth (in cm) of flooding to the building from this historic event
hist2_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist2_event	string	Short name of the modeled historic event
hist2_year	int	Year the modeled historic event occurred
hist2_depth	int	Depth (in cm) of flooding to the building from this historic event
adapt_id	int	A unique First Street identifier assigned to each adaptation project
adapt_name	string	Name of adaptation project
adapt_rp	int	Return period of flood event structure provides protection for when applicable
adapt_type	string	Specific flood adaptation structure type (can be one of many structures associated with a project)
fema_zone	string	Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders
footprint_flag	int	Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

This dataset includes climate change hazard projections and combined climate hazard index values for 42,786 drinking water utilities accross the continental United States (US). The projections are compiled from multiple sources, including the Climate Mapping for Resilience and Adaptation tool (CMRA) and Climate Risk and Resilience Portal (ClimRR), and use mid-century (2050) Representative Concentration Pathway 4.5 CMIP5 Localized Constructed Analogs (LOCA) CMIP5 Projections for North America. The included climate hazards are extreme heat, energy demand, freeze-thaw cycles, extreme precipitation, wildfires, water supply stress, and sea level rise. Each row of the dataset corresponds to a different community water system within the contiguous US, each identified using their assigned Public Water System Identification number More details about the data sources and modeled combined climate hazard index can be found in the publication: Lyle et al 2025, Environ. Res.: Climate, https://doi.org/10.1088/2752-5295/adab10. Code can be found here: https://github.com/zialyle/DW-climate-change-hazard-index

The columns in the database are as follows:

pwsid: Public Water System Identification Number

primacy_agency_code: Two character postal code for the state or territory having regulatory oversight for the water system.

pws_name: Name of the water system

State: State in which water system is located

city_served: City in which water system is located

County: County in which water system is located

population_served_count: Number of customers served by water system

service_connections_count: Number of service connections maintained by water system

service_area_type_code: Service area type code

owner_type_code: Code that dentifies the ownership category of the water system consisting of: F (Federal Government), L (Local Government), M (Public/Private), N (Native American), P (Private), or S (State Government)

is_wholesaler_ind: Indicates whether the system is a wholesaler of water

primacy_type: Code that indicates if the water system is regulated by a state, tribal, or territorial primacy program. Note that EPA direct implementation programs, except for Wyoming, are tribal primacy programs

primary_source_code: The code showing the differentiation between the sources of water: ground water (GW),groundwater purchased (GWP), surface water (SW), surface water purchased (SWP), groundwater under influence of surface water (GU), or purchased ground water under influence of surface water source (GUP)

centroid_lat: Latitude ocation of water system

centroid_lon: Longitude ocation of water system

NOAA.Region: NOAA Climate Region in which water system is located

heat_index: Extreme heat index value

historic_mean_maxtemp_5d: Annual highest maximum temperature averaged over a 5-day period [degF], historical mean

RCP4.5_mid_mean_maxtemp_5d: Annual highest maximum temperature averaged over a 5-day period [degF], RCP 4.5 mid-century

RC_maxtemp_5d: Relative change in annual highest maximum temperature averaged over a 5-day period [degF] from historical to RCP 4.5 mid-century

Diff_maxtemp_5d: Absolute change in annual highest maximum temperature averaged over a 5-day period [degF] from historical to RCP 4.5 mid-century

extremeprecip_index: Extreme precipitation index value

historic_mean_highest_precip_5d: Annual highest precipitation total over a 5-day period [inches] , historical mean

RCP4.5_mid_mean_highest_precip_5d: Annual highest precipitation total over a 5-day period [inches] , RCP 4.5 mid-century

RC_highest_precip_5d: Relative change in annual highest precipitation total over a 5-day period [inches] from historical to RCP 4.5 mid-century

Diff_highest_precip_5d: Absolute change in annual highest precipitation total over a 5-day period [inches] from historical to RCP 4.5 mid-century

SLR_index: Sea level rise index value

SLR_indicator: Sea level rise indicator, where 0 indicates utility is not in a county expecting some amount of sea level rise by 2100 and 1 indicates utility is in a county expecting some amount of sea level rise by 2100.

wildfirerisk_index: Wildfire index value

RC_avg_wildfire: Relative change in Fire Weather Index from historical to RCP 4.5 mid-century

D_avg_wildfire: Absolute change in Fire Weather Index from historical to RCP 4.5 mid-century

FT_index: Freeze-Thaw cycle index value

RCP_mid_mean_FT: Number of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC), RCP 4.5

historical_mean_FT: Number of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC), historical mean

RC_FT: Relative change in the umber of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC) from historical to RCP 4.5 mid-century

Diff_FT: Absolute change in the umber of freeze-thaw days (days as those with a maximum daily temperature above 0 degC and a minimum temperature below 0 degC) from historical to RCP 4.5 mid-century

waterrisk_index: Water stress index value, using (Dickson & Dzombak, 2019)

water_stress: Change in water supply stress from historical to RCP 4.5 mid-century, using Water Supply Stress Index from (Dickson & Dzombak, 2019)

energydemand_index: Energy demand index value, using regression model developed by (Sowby & Burian, 2022)

energy_demand: Change in energy demand by mid-century under RCP 4.5 scenarios, using utility energy use model from (Sowby & Hales, 2022).

historic_mean_avg_temp: Daily average temperature [degF] , historical mean

RCP4.5_mid_mean_avg_temp: Daily average temperature [degF] , RCP 4.5 mid-century

RC_avg_temp: Relative change in daily average temperature [degF] from historical to RCP 4.5 mid-century

Diff_avg_temp: Absolute change in daily average temperature [degF] from historical to RCP 4.5 mid-century

historic_mean_avg_precip: Daily average precipitation [inches] , historical mean

RCP4.5_mid_mean_avg_precip: Daily average precipitation [inches] , RCP 4.5 mid-century

RC_avg_precip: Relative change in daily average precipitation [inches] from historical to RCP 4.5 mid-century

Diff_avg_precip: Absolute change in daily average precipitation [inches] from historical to RCP 4.5 mid-century

hazard_index: Combined climate change hazard index value, normalized from 0 to 1

hazard_index_group: Classification group for combined climate change hazard index value (minimal, low, moderate, high)

heat_threshold: Binary value indicating whether PWS exceeded risk threshold level for extreme heat (0 indicating no, 1 indicating yes)

precip_threshold: Binary value indicating whether PWS exceeded risk threshold level for extreme precipitation (0 indicating no, 1 indicating yes)

SLR_threshold: Binary value indicating whether PWS exceeded risk threshold level for sea level rise (0 indicating no, 1 indicating yes)

wildfire_threshold: Binary value indicating whether PWS exceeded risk threshold level for wildfires (0 indicating no, 1 indicating yes)

FT_threshold: Binary value indicating whether PWS exceeded risk threshold level for freeze-thaw cycles (0 indicating no, 1 indicating yes)

waterstress_threshold: Binary value indicating whether PWS exceeded risk threshold level for water stress (0 indicating no, 1 indicating yes)

energydemand_threshold: Binary value indicating whether PWS exceeded risk threshold level for enegery demand (0 indicating no, 1 indicating yes)

sum: Total number of climate hazard risk threshold values exceeded

exposure: Product of combined climate change hazard index value and population served

This data is utilized in the Lesson 1.1 What is Climate activity on the MI EnviroLearning Hub Climate Change page.Station data accessed was accessed from NOAA. Data was imported into ArcGIS Pro where Coordinate Table to Point was used to spatially enable the originating CSV. This feature service, which incorporates Census Designated Places from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics, was used to spatially join weather stations to the nearest incorporated area throughout Michigan.Email Egle-Maps@Michigan.gov for questions.Former name: MichiganStationswAvgs19912020_WithinIncoproatedArea_UpdatedName Display Name Field Name Description

STATION_ID MichiganStationswAvgs19912020_W Station ID where weather data is collected

STATION MichiganStationswAvgs19912020_1 Station name where weather data is collected

ELEVATION MichiganStationswAvgs19912020_6 Elevation above mean sea level-meters

MLY-PRCP-NORMAL MichiganStationswAvgs19912020_8 Long-term averages of monthly precipitation total-inches

MLY-TAVG-NORMAL MichiganStationswAvgs19912020_9 Long-term averages of monthly average temperature -F

OID MichiganStationswAvgs1991202_10 Object ID for weather dataset

Join_Count MichiganStationswAvgs1991202_11 Spatial join count of weather station data to specific weather station

TARGET_FID MichiganStationswAvgs1991202_12 Spatial Join ID

Current place ANSI code MichiganStationswAvgs1991202_13 Census codes for identification of geographic entities (used for join)

Geographic Identifier MichiganStationswAvgs1991202_14 Geographic identifier (used for join)

Current class code MichiganStationswAvgs1991202_15 Class (CLASSFP) code defines the current class of a geographic entity

Current functional status MichiganStationswAvgs1991202_16 Status of weather station

Area of Land (Square Meters) MichiganStationswAvgs1991202_17 Area of land in square meters

Area of Water (Square Meters) MichiganStationswAvgs1991202_18 Area of water in square meters

Current latitude of the internal point MichiganStationswAvgs1991202_19 Latitude

Current longitude of the internal point MichiganStationswAvgs1991202_20 Longitude

Name MichiganStationswAvgs1991202_21 Location name of weather station

Current consolidated city GNIS code MichiganStationswAvgs1991202_22 Geographic Names Information System for an incorporated area

OBJECTID MichiganStationswAvgs1991202_23 Object ID for point dataset

A complete description of the dataset is given by Jones et al. (2023). Key information is provided below.

Background

A dataset describing the global warming response to national emissions CO₂, CH₄ and N₂O from fossil and land use sources during 1851-2021.

National CO₂ emissions data are collated from the Global Carbon Project (Andrew and Peters, 2024; Friedlingstein et al., 2024).

National CH₄ and N₂O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).

We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH₄ and N₂O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021).

Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST).

The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO₂, CH₄, N₂O or 3-GHG total) and source (fossil emissions, land use emissions or the total).

Data records: overview

The data records include three comma separated values (.csv) files as described below.

All files are in ‘long’ format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns.

Component specifies fossil emissions, LULUCF emissions or total emissions of the gas.

Gas specifies CO₂, CH₄, N₂O or the three-gas total (labelled 3-GHG).

Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country.

Data records: specifics

Data are provided relative to 2 reference years (denoted ref_year below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992.

EMISSIONS_ANNUAL_{ref_year-20}-2023.csv: Data includes annual emissions of CO₂ (Pg CO₂ year^-1), CH₄ (Tg CH₄ year^-1) and N₂O (Tg N₂O year^-1) during the period ref_year-20 to 2023. The Data column provides values for every combination of the categorical variables. Data are provided from ref_year-20 because these data are required to calculate GWP* for CH₄.

EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2023.csv: Data includes the cumulative CO₂ equivalent emissions in units Pg CO₂-e₁₀₀ during the period ref_year+1 to 2023 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables.

GMST_response_{ref_year+1}-2023.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period ref_year+1 to 2023 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables.

Accompanying Code

Code is available at: https://github.com/jonesmattw/National_Warming_Contributions .

The code requires Input.zip to run (see README at the GitHub link).

Further info: Country Groupings

We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:

• Annex I countries (number of countries, n = 42)
• Annex II countries (n = 23)
• economies in transition (EITs; n = 15)
• the least developed countries (LDCs; n = 47)
• the like-minded developing countries (LMDC; n = 24).

And other country groupings:

• the organisation for economic co-operation and development (OECD; n = 38)
• the European Union (EU27 post-Brexit)
• the Brazil, South Africa, India and China (BASIC) group.

See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the state, congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

Context

Data description

The FAOSTAT Temperature Change domain disseminates statistics of mean surface temperature change by country, with annual updates. The current dissemination covers the period 1961–2023. Statistics are available for monthly, seasonal and annual mean temperature anomalies, i.e., temperature change with respect to a baseline climatology, corresponding to the period 1951–1980. The standard deviation of the temperature change of the baseline methodology is also available. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).

Content

Statistical concepts and definitions

Statistical standards: Data in the Temperature Change domain are not an explicit SEEA variable. Nonetheless, country and regional calculations employ a definition of “Land area” consistent with SEEA Land Use definitions, specifically SEEA CF Table 5.11 “Land Use Classification” and SEEA AFF Table 4.8, “Physical asset account for land use.” The Temperature Change domain of the FAOSTAT Agri-Environmental Indicators section is compliant with the Framework for the Development of Environmental Statistics (FDES 2013), contributing to FDES Component 1: Environmental Conditions and Quality, Sub-component 1.1: Physical Conditions, Topic 1.1.1: Atmosphere, climate and weather, Core set/ Tier 1 statistics a.1.

Statistical unit: Countries and Territories.

Statistical population: Countries and Territories.

Reference area: Area of all the Countries and Territories of the world. In 2019: 190 countries and 37 other territorial entities.

Code - reference area: FAOSTAT, M49, ISO2 and ISO3 (http://www.fao.org/faostat/en/#definitions). FAO Global Administrative Unit Layer (GAUL National level – reference year 2014. FAO Geospatial data repository GeoNetwork. Permanent address: http://www.fao.org:80/geonetwork?uuid=f7e7adb0-88fd-11da-a88f-000d939bc5d8.

Code - Number of countries/areas covered: In 2019: 190 countries and 37 other territorial entities.

Time coverage: 1961-2023

Periodicity: Monthly, Seasonal, Yearly

Base period: 1951-1980

Unit of Measure: Celsius degrees °C

Reference period: Months, Seasons, Meteorological year

Acknowledgements

Documentation on methodology: Details on the methodology can be accessed at the Related Documents section of the Temperature Change (ET) domain in the Agri-Environmental Indicators section of FAOSTAT.

Quality documentation: For more information on the methods, coverage, accuracy and limitations of the Temperature Change dataset please refer to the NASA GISTEMP website: https://data.giss.nasa.gov/gistemp/

                                                                          Source: http://www.fao.org/faostat/en/#data/ET/metadata

Inspiration

Climate change is one of the important issues that face the world in this technological era. The best proof of this situation is the historical temperature change. You can investigate if any hope there is for stopping global warming :)

• Can you find any correlation between temperature change and any other variable? (Using ISO3 codes for merging any other countries' data sets possible.)

• Prediction of temperature change: there is also an overall world temperature change in the country list as 'World'.

These data were cited in the following:

        Marr, J.W. 1967. Data on mountain environments. I. Front
        Range, Colorado, sixteen sites, 1952-1953. University of Colorado Studies,
        Series in Biology 27, 110 pp.

  Marr, J.W., A.W. Johnson, W.S. Osburn, and O.A. Knorr. 1968. Data on 
        mountain environments. II. Front Range, Colorado, four climax regions,
        1953-1958. University of Colorado Studies, Series in Biology 28, 169 pp.
  Marr, J.W., J.M. Clark, W.S. Osburn, and M.W. Paddock. 1968. Data on 
        mountain environments. III. Front Range, Colorado, four climax regions,
        1959-1964. University of Colorado Studies, Series in Biology 29, 181 pp.

  Precipitation data were collected from a ridgetop climate station east of Niwot
      Ridge (B-1 @ 2591 m) throughout the year using a chart recorder. Initially,
      instrumentation consisted of an 8-inch metal rain gauge with the receiving rim about
      3 feet above the ground and measurements were made on an approximately weekly basis.
      More recently, instrumentation consisted of a Fergusson-type weighing rain gauge.
      Precipitation was caught in a bucket containing ethylene glycol (to melt snow) and
      light oil (to prevent evaporation). As the weight of the bucket increased, the pen
      moved up via a spring mechanism and recorded on a rotating chart. Precipitation was
      recorded on a continuous basis. 
 NOTE: The LTER data portal display does not display important maintenance/log
      information or other EML metadata features. Please be sure to view the EML file (a
      text file that contains XML tags) which is included in the zip archive (click on
      "Download zip archive") pertaining to each dataset. The EML file name will have the
      following format: knb-lter-nwt.[3 digit dataset number].[version number].xml. Most
      web browsers can parse the EML so it's easier to read.

This repository contains the resulting networks (.nc) and derived metrics (.csv) files from (1) a join capacity and dispatch optimization with 62 different weather years (design years) from 1960 to 2021 as input, and (2) a dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year. All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".

We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located here: https://github.com/ebbekyhl/multi-weather-year-assessment

Tropical forest dynamics and composition have changed over recent decades, but the proximate drivers of these changes remain unclear. Investigations into these trends have focused on increasing drought stress, CO2, temperature, and fires, whereas convective storms are generally overlooked. We argue that existing literature provides clear support for the importance of storms as drivers of forest change. We reanalyze the largest plot-based study of tropical forest carbon dynamics to show that lightning frequency – an indicator of storm activity – strongly predicts forest carbon storage and residence time, and its inclusion improves model fit and weakens evidence for effects of high temperatures. Convective storm activity has increased 5-25% per decade over the past half century. Extrapolating from historic trends, we estimate that storms likely contribute ca. 50% of the reported increases in biomass mortality across Amazonia, with all realistic combinations of assumptions indicating a possible range of 12-118%. Spatial variation in storm activity shows weak relationships with drought, demonstrating that forests can experience high drought stress, high storm activity, or both. Accordingly, we hypothesize that convective storms are amongst the most important drivers tropical forest change, and as such, they require significant research investment to avoid misguiding science, policy, and management.File and folder list, includes:amazon.zip: This folder contains all of the data for producing maps of storm activity and drought stress across the Amazon region and adjacent forests. This includes 4 folders and 3 files (1 csv and 2 tif). The code to run these analyses is in folder "Code_for_Amazon_analyses" along with the metadata for these files.climVars: .Rdata files for each month-year combination from 1971-2019. This is an output from scripts/parseClimData.R, which parses the netcdf files and isolates the target variables of CAPE (cape thresholds) and VPDfigures: figures for this publication, Gora et al., 2025mcwd: Evapotranspiration (et_stacks_annual) and precipitation (precip_stacks_annual) rasters stacked annually for calculating Maximum Cumulative Water Deficit. Evapotranspiration (pet_penman) and precipitation (pr) data were downloaded from Chelsa (see below). MCWD was reset annually for each raster cell via reset_mask.tif after reaching the month the focal cell experiences the greatest water deficit. Annual calculated values for MCWD are aggregated across our study period in MCWD_precip_reset_2009_2019.tifnetcdf: Climate variables downloaded from Chelsa and ERA5 via the NCAR repository (for more details on the data sources and specifics about each variable, see the ReadMe file for this project)processedClimVars1990.csv: aggregated csv with one value of each climate variable for each pixel in the study area, averaged across the timeframe of 1990-2019. This is an output from scripts/parseClimData.RanalysisRastTemplate.tif: raster template that we use as the base gridded structure to visualize the data. This is created using the extent of the Feng et al., 2023 Amazon shapefile and the pixel resolution of the ERA5 dataprocessedClimVars1990_2019.tif: aggregated raster with each pixel containing one value for each climate variable, averaged across the timeframe of 1990-2019. This is an output from scripts/parseClimData.RCode_for_Amazon_analyses.zip: Repository with code for parsing climate data and creating figures of storm activity and drought stress across the Amazon. Contains the complete Github code repository as of 14 May 2025. (see Github reference below).scriptsPublication: 5 R filesdataSources.md - contains references and notes on all data sourcesmetadata.md - list of amazon folder contents, descriptions for all climate variables, and methods of calculationREADME.mdstorm_contributions: This folder contains the R script for estimating the contributions of increased storm activity to increases in biomass mortality across the Amazon over the past several decades. This folder only includes 1 R code file.increased_storm_contributions_submitted.RSullivan_etal2020_reanalysis: These files are for the reanalysis of global forest biomass carbon originally published in Sullivan et al. 2020. "Long-term thermal sensitivity of Earth's tropical forests." Science. This folder contains 3 files (1 csv, 1 R file, 1 txt file),ENTLN_for_Sullivan-etal.csvGora_Sullivan2020_reanalysis_submitted.RReadMe_reanalysis_Sullivan-etal2020.txt

Dataset Description This dataset contains aggregated meteorological variables for U.S. counties and ZIP Code Tabulation Areas (ZCTAs) derived from the gridMET dataset. The gridMET product combines high-resolution spatial climate data (e.g., temperature, precipitation, humidity) from the PRISM Climate Group with daily temporal attributes and additional meteorological variables from the NLDAS-2 regional reanalysis dataset. The final product includes daily meteorological data at approximately 4km x 4km spatial resolution across the contiguous United States. This dataset has been processed to provide spatial (ZCTA, County) and temporal (daily, yearly) aggregations for broader climate analysis. This dataset was created to support climate and public health research by providing ready-to-use, high-resolution meteorological data aggregated at county and ZCTA levels. This allows for efficient linking with health and socio-demographic data to explore the impacts of climate on public health. Contributors: Harvard T.H. Chan School of Public Health, NSAPH (National Studies on Air Pollution and Health) The data is organized by geographic unit (County and ZCTA) and temporal scale (daily, yearly). The dataset is structured to facilitate the computation of climate exposure variables for health impact studies. A data processing pipeline was used to generate this dataset.

Background: Climate change effect on flora and fauna has been scientifically documented, but the effect on North American venomous snakebites is unknown. The objectives were to examine Californian snakebite incidence and correlate with weather patterns and climate changes. Methods: A retrospective analysis of snakebites reported to the Californian Poison Control System from 1 September 1997 to 30 September 2017. Venomous snakebite reports were aggregated by caller zip code, and correlated per county with weather data, air temperature, precipitation, population data, eco-regions, and land characteristics. Time series decomposition by seasonality and trend, regression, and autocorrelation were used to assess association between climate variables and incidence. Results: There were 5365 reported venomous snakebites during the study period, with a median age of 37 years (22–51) with 76% male (p 

Local climate zones have been developed in the climatology field to characterize the landscape surrounding climate monitoring stations, toward adjusting for local landscape influences on measured temperature trends. For example, a station surrounded by tall buildings may be influenced by the urban heat island effect compared to a station in an agricultural area. The local climate zone classification system was developed by Iain Stewart and Tim Oke at the University of British Columbia. The classification scheme has been adopted by the World Urban Database Access and Tools Portal (WUDAPT) project, which aims to produce local climate zone maps for the entire world at a scale of ~ 100m. Local climate zones take building and vegetation type and height into account, and therefore serve as indicators of urban form, from dense urban (high building with little vegetation) to industrial/commercial (large lowrise buildings with paved areas) and natural (dense trees, low plants, water). How local climate zones are related to human health is a new area of research.CANUE staff and students worked in collaboratation with WUDAPT researchers to map local climate zones for Canada, using scripts developed in Google Earth Engine and applied to LandSat imagery for key time periods. Each postal code has been assigned to one of 14 local climate zone classes. In adition, seven groups have been created by aggregating similar local climate zones, and the percentage of group in the neighbourhood (1km2) around each postal code has been calculated.

Predicting effects of climate change on plant disease is critical for protecting ecosystems and food production. Here, we show how disease pressure responds to short-term weather, historical climate, and weather anomalies by compiling a global database (4339 plant–disease populations) of disease prevalence in both agricultural and wild plant systems. We hypothesized that weather and climate would play a larger role in disease in wild versus agricultural plant populations, which the results supported. In wild systems, disease prevalence peaked when temperature was 2.7°C warmer than the historical average for the same time of year. We also found evidence of a negative interactive effect between weather anomalies and climate in wild systems, consistent with the idea that climate maladaptation can be an important driver of disease outbreaks. Temperature and precipitation had relatively little explanatory power in agricultural systems, though we observed a significant positive effect of curr..., , , # Impacts of weather anomalies and climate on plant disease

https://doi.org/10.5061/dryad.p8cz8wb0h

Description of the data and file structure

Data was collected through a systematic literature review and by extracting relevant climatic data from available databases.

Files and variables

File: ELE_-_climate_and_plant_disease_code_and_data.zip

Description:Â Files:

• Kirk_et_al_ELE_README.docx
• data folder

   Contains two CSV files: Plant disease survey data from literature review, Climate and weather data associated with plant disease surveys

• output folder

  Empty folder to store output from R code

• Kirk_ELE_climate_and_plant_disease_code.R

    R script to run analyses to run models, create tables, create figures, and create supplemental figures.

 Climate and weather data CSV details

-Â Â Â Â Â Â obs is an observation column to link the data to the disease survey data

-Â Â Â Â Â Â bio01 represents the annu...

A complete description of the dataset is given by Jones et al. (2023). Key information is provided below. Background A dataset describing the global warming response to national emissions CO2, CH4 and N2O from fossil and land use sources during 1851-2021. National CO2 emissions data are collated from the Global Carbon Project (Andrew and Peters, 2023; Friedlingstein et al., 2023). National CH4 and N2O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2023). We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH4 and N2O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021). Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST). The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO2, CH4, N2O or 3-GHG total) and source (fossil emissions, land use emissions or the total). Data records: overview The data records include three comma separated values (.csv) files as described below. All files are in ‘long’ format with one value provided in the Data column for each combination of the categorical variables Year, Country Name, Country ISO3 code, Gas, and Component columns. Component specifies fossil emissions, LULUCF emissions or total emissions of the gas. Gas specifies CO2, CH4, N2O or the three-gas total (labelled 3-GHG). Country ISO3 codes are specifically the unique ISO 3166-1 alpha-3 codes of each country. Data records: specifics Data are provided relative to 2 reference years (denoted ref_year below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992. EMISSIONS_ANNUAL_{ref_year-20}-2022.csv: Data includes annual emissions of CO2 (Pg CO2 year-1), CH4 (Tg CH4 year-1) and N2O (Tg N2O year-1) during the period ref_year-20 to 2022. The Data column provides values for every combination of the categorical variables. Data are provided from ref_year-20 because these data are required to calculate GWP* for CH4. EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2022.csv: Data includes the cumulative CO2 equivalent emissions in units Pg CO2-e100 during the period ref_year+1 to 2022 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables. GMST_response_{ref_year+1}-2022.csv: Data includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period ref_year+1 to 2022 (i.e. since the reference year). The Data column provides values for every combination of the categorical variables. Accompanying Code Code is available at: https://github.com/jonesmattw/National_Warming_Contributions . The code requires Input.zip to run (see README at the GitHub link). Further info: Country Groupings We also provide estimates of the contributions of various country groupings as defined by the UNFCCC: Annex I countries (number of countries, n = 42) Annex II countries (n = 23) economies in transition (EITs; n = 15) the least developed countries (LDCs; n = 47) the like-minded developing countries (LMDC; n = 24). And other country groupings: the organisation for economic co-operation and development (OECD; n = 38) the European Union (EU27 post-Brexit) the Brazil, South Africa, India and China (BASIC) group. See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.

Nowcast is a representation of present conditions for any location at any point in time. OnPoint Weather is described exactly as it sounds, weather data for any location at any point in time. Unlike other providers who rely on singular inputs that in many instances may be many miles away from your location of interest to be meaningful or actionable, Weather Source ingests all of the best weather sensing inputs available including:

Airport observation stations NOAA & NWS data Satellites Radar IoT Devices and other sensor information Telematics Weather analyses and model outputs

Weather Source unifies and homogenizes the inputs on our high resolution global grid. The globally consistent OnPoint Grid covers every land mass in the world and up to 200 miles offshore. Each grid point - millions in total - represents a “virtual” weather station with a unique OnPoint ID from which weather data can be mapped to any lat/lon coordinates or specified geographically bounded areas such as:

Census tract/block County/parish or state Designated Market Area (DMA) ZIP/Postal Code

All Weather Source data is available in hourly or daily format. Daily format includes minimum and maximum values as well as daily averages for each supported weather parameter. * Purchasing a subscription through Mobito will provide instant access to all Weather Source tiles and resources listed in the Mobito Marketplace including historical, forecast and climatology subject to the subscription tier purchased.

Precipitation data were collected on a daily time-scale from the C-1 climate station (3018 m) since 1952. Over time various circumstances have led to days with missing values. These values were estimated from nearby climate stations.

 NOTE: The LTER data portal display does not display important maintenance/log information
  or other EML metadata features. 
  Please be sure to view the EML file (a text file that contains XML tags) which is included in 
  the zip archive (click on "Download zip archive")
  pertaining to each dataset. The EML file name will have the following format:
  knb-lter-nwt.[3 digit dataset number].[version number].xml. Most web browsers can 
  parse the EML so it's easier to read.

zip displays ZIP Code areas. Known Errors/Qualifications: Metropolitan boundaries are delineated well. Rural boundaries are extrapolated in some areas. Refer to US Postal Service for latest ZIP Code routes and for exact individual addresses. Zip codes defined by the US Postal Service as "unique", "military", and "post office box" are not included in this layer as they have no defined areas.

10/2013: While this layer is maintained as a feature class in gdbmaint, the coverage format is still required for certain nightly processing. See Steve Whitney. Lineage: Modified 85704 and 85718 zip code polygons as per USPS records requested by Matt from Tucson Water. Modified 85641 based on boundary of Coyote Creek Lots 1-395. Added 85756 zip code based on KOLD on-line news story, 7/1/08. Modified 85718 boundary per anonymous mapguide printout from City, 5/14/09. Modified 85756/85706 boundary using USPS lookup from parcel layer, 5/19/09. Added 85622 based on Green Valley news article and zip+4 lookup, 8/17/09. Removed 85744 based on zip+4 lookup and it was a single business "unique" zip code for IBM. Modified 85629 based on zip+4 notified by Michelene, 4/22/14. Modified boundary of 85742 and 85737 per Irene Swanson 9/2/14. Modified 85653 as per Robin Freiman 9/11/14. Modified 85704 as per Robin Freiman, 10/31/14. Modified 85658 as per Jennifer Patterson (recorders office) 9/10/15. Modified 85641 as per Michelene 4/8/16. Modified 85741 per Robin 1/13/17. Modified 85755 1/27/17 per Robin. 10/25/17 Modified 85341 and 85321 per Robin. Modify 85653 per Marana. 4/19/21 Modified 85629,85756 and 85641 per Nicholas Jordan. Spatial Domain: Pima County Rectified: parcel Maintenance Organization: PC ITD GIS Maintenance Format: GDB Std Export Primary Source Organization: US Postal Service Primary Source Document: http://new.usps.com Primary Source Date: 2000 Primary Source Format: Paper GIS Contact: Ray Brice

MapGuide Layer Name: Zip Code Areas MapGuide Scale Range: 0 - 100000000

Date: 2002-04-01Date type: publicationDateOriginator: Ministry of Environment and TourismResponsible party: Ministry of Environment and TourismResponsible party role: custodianAbstract:Rainfall variation calculated as the standard deviation of annual totals as a percentage of average annual rainfall.Purpose:To provide rainfall variation figures.Status: completedMaintenance and update frequency: asNeededEntity/attribute description:PERCENT, PER_VALUECompleteness:The data set covers the whole country.Logical consistency:UnknownPositional accuracy:Created from a grid interpolated from points - accuracy will be reasonable in areas with clusters of stations and low in areas with few stations.Temporal quality:UnknownThematic accuracy:UnknownLineage:Data was derived from the following report:Namibia Resource Consultants. 1999. Rainfall distribution in Namibia: Data analysis and mapping of spatial, temporal,and Southern Oscillation Index aspects. Windhoek: Ministry of Agriculture, Water and Rural Development.Process description:This shapefile was created from the Arcview grid file. This grid file was created from data for almost 300 stations.Rainfall variation calculated as the standard deviation of annual totals as a percentage of average annual rainfall. To illustrate the co-efficient of variation, consider the following example: Take a location (station) with an average rainfall of 400 mm per year and a co-efficient of variation of 40%. A value of 40% means that the standard deviation is 160mm (40% = (160/400)*100), and that area would see annual rainfall totals of between 240 and 540 mm in two-thirds of all years. In the remaining third of all years, annual rainfall totals would be below 240 or above 540 mm.Logical consistency report:UnknownSpatial reference information: EPSG:900999Metadata date: 2008-07-09Metadata date type: creationMetadata contact name: Namibia Statistics AgencyMetadata contact role code: pointOfContactMetadata contact person: Nevel Ngahahe-HangeroMetadata contact address postal code:P. O. Box 2133, WindhoekMetadata contact telephone: +264 61 431 3200Metadata standard name: ISO:19115 (NSA custom schema)Metadata standard version: 2014/NSA-1:2016 version