Annual number of 10mm rainfall days (days where there is greater than or equal to 10mm rainfall) averaged over 1991-2020, provided on a 2km BNG grid.This data contains a field for the average over the period, named 'Rainfall 10mm Days'.Data source:HadUK-Grid v1.1.0.0 (downloaded 11/03/2022)More about HadUK-Grid - https://www.metoffice.gov.uk/research/climate/maps-and-data/data/haduk-grid/haduk-grid This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

What does the data show?  The data shows the annual average of precipitation amount (mm) for the 1991-2020 period from HadUK gridded data. It is provided on a 12km British National Grid (BNG).    Limitations of the data  We recommend the use of multiple grid cells or an average of grid cells around a point of interest to help users get a sense of the variability in the area. This will provide a more robust set of values for informing decisions based on the data.    What are the naming conventions and how do I explore the data?    This data contains a field for the average over the 1991-2020 period. It is named 'pr' (precipitation).    To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578    Data source:   ·       Version: HadUK-Grid v1.1.0.0 (downloaded 21/06/2022)  ·       Source: https://catalogue.ceda.ac.uk/uuid/652cea3b8b4446f7bff73be0ce99ba0f  ·       Filename: rainfall_hadukgrid_uk_12km_ann-30y_199101-202012.nc      Useful links  ·       Further information on HadUK-Grid  ·       Further information on understanding climate data within the Met Office Climate Data Portal   

Annual averages of precipitation (mm) for 1991-2020 from HadUK 12km gridded data.This data contains a field for the average over the period. It is named 'pr' (precipitation).HadUK-Grid: https://www.metoffice.gov.uk/research/climate/maps-and-data/data/haduk-grid/haduk-grid Recommendations for use of this data:We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.Data source: https://catalogue.ceda.ac.uk/uuid/652cea3b8b4446f7bff73be0ce99ba0frainfall_hadukgrid_uk_12km_ann-30y_199101-202012.ncHadUK-Grid_v1.1.0.0 (downloaded 21/06/2022)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

This data has been updated and corrected for errors. The most up to date data can be found in the dataset Data from: Eleven years of mountain weather, snow, soil moisture and stream flow data from the rain-snow transition zone - the Johnston Draw catchment, Reynolds Creek Experimental Watershed and Critical Zone Observatory, USA. v1.1 This dataset is supplemental to the article "A hydrological modeling dataset for the Johnston Draw catchment, Reynolds Creek Experimental Watershed, Idaho, USA," which was submitted to Water Resources Research in December 2015. The data includes time-series measurements of precipitation at three different gauges (124, 125, and 124b) at the Johnston Draw watershed, a sub-watershed of the Reynolds Creek Critical Zone Observatory. The Johnston Draw watershed was established by the USDA's Agricultural Research Service in 2002 to study the rain-snow transition zone. Data was collected at gauges 124 and 125 from October 1, 2003 through September 30, 2014 and at gauge 124b from November 11, 2006 through September 30, 2014. Precipitation for 124 and 125 were wind-corrected using the dual-gauge method described by Hanson et al. (2004). Precipitation for 124b was wind-corrected using wind data and the standard World Meteorological Organization (WMO) method as applied by Yang et al. (1999). The percent snow was calculated using the methods developed by Marks et al. (2013), using the during-storm dew point temperature (Td) where: Td < ­‐0.5 °C 100 % Snow ­ ‐0.5 °C >= Td < 0 °C 75 % Snow 0 °C <= Td < +0.5 °C 25 % Snow 0.5 °C <= Td 0 % Snow 125 and 124b are dual gauge precipitation stations. The pair are modified Belfort Universal gauges, with 124b having a wind shield and 125 remaining unshielded. Each of the precipitation records is an ASCII comma-separated text file with one header row containing Date_time, WY (Water Year), Year, Month, Day, Hour, Minute, ppt_s (shielded precipitation; mm), ppt_u (unshielded precipitation; mm), ppt_a (wind corrected precipitation; mm), and pct_snow (percent of precipitation that is snow; %) separated by commas.

What does the data show? The Drought Severity Index is not threshold based. Instead, it is calculated with 12-month rainfall deficits provided as a percentage of the mean annual climatological total rainfall (1981–2000) for that location. It measures the severity of a drought, not the frequency. 12-month accumulations have been selected as this is likely to indicate hydrological drought. Hydrological drought occurs due to water scarcity over a much longer duration (longer than 12 months). It heavily depletes water resources on a large scale as opposed to meteorological or agricultural drought, which generally occur on shorter timescales of 3-12 months. However this categorisation is not fixed, because rainfall deficits accumulated over 12-months could lead to different types of drought and drought impacts, depending on the level of vulnerability to reduced rainfall in a region. The DSI 12 month accumulations are calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. What are the possible societal impacts? The DSI 12-month accumulations measure the drought severity. Higher values indicate more severe drought. The DSI is based on 12-month rainfall deficits. The impacts of the differing length of rainfall deficits vary regionally due to variation in vulnerability. Depending on the level of vulnerability to reduced rainfall, rainfall deficits accumulated over 12 months could lead to meteorological, agricultural and hydrological drought. What is a global warming level? The DSI 12-month accumulations are calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the DSI 12-month accumulations, an average is taken across the 21 year period. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level. What are the naming conventions and how do I explore the data? This data contains a field for each global warming level and two baselines. They are named ‘DSI12’ (Drought Severity Index for 12 month accumulations), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. E.g. 'DSI12 2.5 median' is the median value for the 2.5°C projection. Decimal points are included in field aliases but not field names e.g. 'DSI12 2.5 median' is 'DSI12_25_median'.  To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578 Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘DSI12 2.0°C median’ values. What do the ‘median’, ‘upper’, and ‘lower’ values mean? Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, DSI 12 month accumulations were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble. This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty. ‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful links This dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report. Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal

This datasets contains local weather data collected by Council's maintained weather stations.

This datasets contains local weather data collected by Council's maintained weather stations.

This data package contains locally verified daily meteorological observations from a NOAA National Weather Service station located at the USDA Jornada Experimental Range headquarters in southern New Mexico, USA. Daily data has been collected there by USDA staff since 1914 for minimum and maximum air temperature and daily accumulated precipitation using standard U.S. climatological service instrumentation and procedures. The included data were verified and transcribed directly from the original paper data sheets and have undergone quality control and assurance procedures different than those in place at NOAA. These data therefore differ from those directly downloadable from NOAA servers. Local verification and transcription of observations from the data sheets ceased in 2006 and data are now directly entered to the NOAA system. Therefore, this dataset is complete and will no longer be added to.All observations from this weather station have also undergone NOAA QA/QC procedures and those data are available by accessing the Jornada Experimental Range, NM US GHCN station through the National Climatic Data Center portal (https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00294... - daily and monthly data are available).

This data package contains locally verified monthly meteorological observations from a NOAA National Weather Service station located at the USDA Jornada Experimental Range headquarters in southern New Mexico, USA. Monthly summary data (based on daily observations) has been collected there by USDA staff since 1914 for minimum and maximum air temperature and daily accumulated precipitation using standard U.S. climatological service instrumentation and procedures. The included data were verified and transcribed directly from the original paper data sheets and have undergone quality control and assurance procedures different than those in place at NOAA. These data therefore differ from those directly downloadable from NOAA servers. Local verification and transcription of observations from the data sheets ceased in 1998 and data are now directly entered to the NOAA system. Therefore, this dataset is complete and will no longer be added to.All observations from this weather station have also undergone NOAA QA/QC procedures and those data are available by accessing the Jornada Experimental Range, NM US GHCN station through the National Climatic Data Center portal https://www.ncdc.noaa.gov/cdo-web/datasets/GSOM/stations/GHCND:USC002944... - daily and monthly data are available).

Monthly averages of global rainfall amount (mm) for 1981-2010 from CRU TS data, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'pr' (precipitation) and the month. E.g. 'pr March' is the mean of monthly-total rainfall in March throughout 1981-2010.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km. This is the same as the 60km grid used by UKCP18.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global rainfall amount (mm) for 2040-2069 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'pr' (precipitation), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'pr March Median' is the mean of monthly-total rainfall in March throughout 2040-2069, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.Data has been removed and replaced with 'Null' where the baseline 1981-2010 value was <1mm/month. This is because the percentage change may be unreliable with a very small baseline. 'Null' means that data is not provided, it doesn't mean 0mm precipitation. The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with a percentage change relative to 1981-2010 from UKCP18. Where the baseline value was <1mm/month, the projection value has been replaced with 'Null' because the percentage change may be unreliable with a very small baseline.The percentage change data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2040-2069 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

Monthly averages of global rainfall amount (mm) for 2070-2099 from CRU TS and UKCP18 RCP2.6, provided on an approximately 60km grid.This data contains a field for each month’s average over the period. They are named 'pr' (precipitation), the month, and 'upper' 'median' or 'lower' as per the description below. E.g. 'pr March Median' is the mean of monthly-total rainfall in March throughout 2070-2099, in the median ensemble member.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.Data has been removed and replaced with 'Null' where the baseline 1981-2010 value was <1mm/month. This is because the percentage change may be unreliable with a very small baseline. 'Null' means that data is not provided, it doesn't mean 0mm precipitation. The grid is a lat-long grid, with cells close to the equator measuring approximately 60kmx60km.More about CRU TS - https://crudata.uea.ac.uk/cru/data/hrg/More about UKCP - https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data combines a baseline 1981-2010 value from CRU TS with a percentage change relative to 1981-2010 from UKCP18. Where the baseline value was <1mm/month, the projection value has been replaced with 'Null' because the percentage change may be unreliable with a very small baseline.The percentage change data is from the UKCP18 regional projections using the RCP2.6 scenario. RCP2.6 is a low emissions scenario, representing a mitigation scenario aiming to limit the increase of global mean temperature to around 2°C above preindustrial levels .What do the 'median', 'upper', and 'lower' values mean?This scenario is run as 12 separate ensemble members. To select which ensemble members to use, a single value for the mean global precipitation for the period 2070-2099 was taken from each ensemble member. They were then ranked in order from lowest precipitation to highest. The 'lower' fields are this data is the second lowest ranked ensemble member. The 'higher' fields are the second highest ranked ensemble member. The 'median' fields are the central (7th) ranked ensemble member.This gives a median value, and a spread of the ensemble members indicating the level of uncertainty in the projections.Recommendations for use of this data:1. We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.2. Consider whether the lower, median, or upper projections, or a combination, are most suitable for your use case.As described above, the spread of the ensemble members shown by the lower, median, and upper values indicates the level of uncertainty in the projections.Data source: CRU TS v. 4.06 - (downloaded 12/07/22)UKCP18 v.20200110 (downloaded 17/08/22)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

What does the data show?  The data shows monthly averages of precipitation amount (mm) for 1991-2020 from HadUK gridded data. It is provided on a 12km British National Grid (BNG).    Limitations of the dataWe recommend the use of multiple grid cells or an average of grid cells around a point of interest to help users get a sense of the variability in the area. This will provide a more robust set of values for informing decisions based on the data.What are the naming conventions and how do I explore the data?  This data contains a field for each month’s average over the period. They are named 'pr' (precipitation) and the month. E.g. 'pr March' is the rainfall amount for March in the period 1991-2020.    To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578  Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘pr January’ values.  Data source:   ·       Version: HadUK-Grid v1.1.0.0 (downloaded 26/08/2022)  ·       Source: https://catalogue.ceda.ac.uk/uuid/652cea3b8b4446f7bff73be0ce99ba0f  ·       Filename: rainfall_hadukgrid_uk_12km_mon-30y_199101-202012.nc      Useful links  ·       Further information on HadUK-Grid  ·       Further information on understanding climate data within the Met Office Climate Data Portal

Monthly averages of precipitation (mm) for 1991-2020 from HadUK 12km gridded data.This data contains a field for each month’s average over the period. They are named 'pr' (precipitation) and the month. E.g. 'pr July'.Data defaults to displaying January averages. Each monthly average is a field in the data. Use 'show table' to view all values, and 'change style' to change which month is displayed in the map.HadUK-Grid: https://www.metoffice.gov.uk/research/climate/maps-and-data/data/haduk-grid/haduk-grid Recommendations for use of this data:We don't recommend using this data at the resolution of a single cell.The higher resolution of this data improves representation of topography, coasts, etc. but at the same time increases some of the uncertainty for individual grid cells. And so it is recommended to work with multiple grid cells, or an average of grid cells around a point to improve certainty.Data source: https://catalogue.ceda.ac.uk/uuid/652cea3b8b4446f7bff73be0ce99ba0frainfall_hadukgrid_uk_12km_mon-30y_199101-202012.ncHadUK-Grid_v1.1.0.0 (downloaded 21/06/2022)This dataset forms part of the Met Office’s Climate Data Portal service. This service is currently in Beta. We would like your help to further develop our service, please send us feedback via the site - https://climate-themetoffice.hub.arcgis.com/

This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.

Rain Forecast for Maitland from BOM

Abstract:Rain on Snow is a statewide coverage of rain-on-snow zones.  Rain-on-snow zones are based on average amounts of snow on the ground in early January, relative to the amount of snow that could reasonably be melted during a model storm event.  Five Rain on Snow zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation.  Rain on Snow was digitized from 1:250,000 USGS quads.Purpose:The Rain-on-snow coverage was created as a screening tool to identify forest practice applications that may be in a significant rain-on-snow zone (WAC 222-22-100).Description:Five ROS zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on snow is a process that exhibits spatial and temporal variation under natural conditions, with the effects of vegetation on snow accumulation and melt adding additional complications in prediction. There is no map that shows the magnitude and frequency of water inputs to be expected from rain on snow events, so we have attempted to create an index map based on what we know about the process controls and their effects in the various climatic zones. If we assume that, averaged over many years, the seasonal storm tracks that bring warm, wet cyclonic storms to the Northwest have access to all parts of Washington , then the main factors controlling and/or reflecting the occurrence and magnitude of a R/S event in any particular place are: 1) Climatic region: especially the differences between windward and leeward sides of major mountain ranges, which control seasonal climatic patterns;2) Elevation: controls temperature, thus the likelihood and amount of snow on the ground, and affects orographic enhancement of storm precipitation; 3) Latitude: affects temperature, thus snow;4) Aspect: affects insolation and temperature (especially in winter), thus melting of snow; 5) Vegetation: the species composing forest communities can reflect the climate of an area (tolerance of warmth or cold, wet or dry conditions, deep and/or long lived snowpacks); the height and density of vegetation also partly controls the amount of snow on the ground. As natural vegetation integrates the effects of all of these controls, we tried to find or adapt floral indicators of the various zones of water input. We designed the precipitation zones to reflect the amount of snow likely to be on the ground at the beginning of a storm. We assumed that some middle elevation area would experience the greatest water input due to Rain on Snow, because the amount of snow available would be likely to be approximately the amount that could be melted. Higher and lower elevation zones would bear diminished effects, but for opposite reasons (no snow to melt, vs too cold to melt much). These considerations suggested a three or five zone system. We chose to designate five zones because a larger number of classes reduces the importance of the dividing lines, and thus of the inherent uncertainties of those lines. The average snow water equivalents (SWE) for the early January measurements at about 100 snow courses and snow pillows were compiled; snow depths for the first week in January at about 85 weather stations were converted into SWE. For each region (western North Cascades, Blue Mountains, etc.), the snow amounts were sorted by station elevation to derive a rough indicator of the relationship between snow accumulation and elevation. (Sub regional differences in snow accumulation patterns were also recognized.) After trying various combinations of ratios for areas where the snow hydrology is relatively well known, we adopted the following designations: 5. Highlands: >4 5 times ideal SWE; high elevation, with little likelihood of significant water input to the ground during storms (precipitation likely to be snow, and liquid water probably refreezes in a deep snow pack); effects of harvest on snow accumulation are minor; 4. Snow dominated zone: from "1.25 1.5 ideal SWE, up to "4; melt occurs during R/S (especially during early season storms), but effects can be mitigated by the lag time of percolation through the snowpack; 3. Peak rain on snow zone: "0.5 0.75 up to "1.25 ideal SWE; middle elevations: shallow snow packs are common in winter, so likelihood and effects of R/S in heavy rainstorms are greatest; typically more snow accumulation in clearings than in forest; 2. Rain dominated zone: "0.1 0.5 ideal SWE; areas at lower elevations, where rain occasionally falls on small amounts of snow; 1. Lowlands: <0.1 ideal SWE; coastal, low elevation, and rain shadow areas; lower rainfall intensities, and significant snow depths are rare. Precipitation zones were mapped on mylar overlays on 1:250,000 scale topographic maps. Because snow depth is affected by many factors, the correlation between snow and elevation is crude, and it was not possible to simply pick out contour markers for the boundaries. Ranges of elevations were chosen for each region, but allowance was made for the effects of sub regional climates, aspect, vegetative indicators of snow depth, etc. Thus, a particular boundary would be mapped somewhat lower on the north side of a ridge or in a cool valley (e.g. below a glacier), reflecting greater snow accumulations in such places. The same boundary would be mapped higher on the south side of the ridge, where inter-storm sunshine could reduce snow accumulation. Conditions at the weather stations and snow courses were used to check the mapping; but in areas where measurements are scarce, interpolation had to be performed. The boundaries of the precipitation zones were entered in the DNR's GIS. Because of the small scale of the original mapping and the imprecision of the digitizing process, some errors were introduced. It should not be expected that GIS images can be projected to large scales to define knife edge zone boundaries (which don't exist, anyway), but they are good enough to locate areas tens of acres in size. Some apparent anomalies in the map require explanation. Much of western Washington is mapped in the lowland or highland zones. This does not mean that R/S does not occur in those areas; it does, but on average with less frequency and hydrologic significance than in the middle three zones. Most of central and eastern Washington is mapped in the rain dominated zone, despite meager precipitation there; this means only that the amount of snow likely to be on the ground is small, and storm water inputs are composed dominantly of the rain itself, without much contribution from snow melt. Much of northeastern Washington is mapped in the peak Rain Snow zone, despite the fact that such events are less common there than in western Washington. This is due to the fact that there is less increase in snow depth with elevation (i.e. the snow wedge is less steep), so a wider elevation band has appropriate snow amounts; plus, much of that region lies within that elevation band where the 'ideal' amount of snow is liable to be on the ground when a model Rain Snow event occurs. This does not reflect the lower frequency of such storms in that area.

No description found

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The Global Energy and Water Cycle Experiment (GEWEX) Continental-scale International Project (GCIP) Enhanced Observing Period (EOP) takes place in the Mississippi River basin, which provides a number of watershed areas that are potentially useful for hydrologic focused studies. The temporal coverage for this dataset is as follows: Begin datetime: 1996-05-01 00:00:00, End datetime: 2001-12-31 23:59:59. This dataset contains the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) 4 KM GRIB radar estimate (no bias removal) "RAD" data. A prototype, real-time, hourly, multi-sensor National Preciptation Analysis (NPA) has been developed at NCEP in cooperation with the Office of Hydrology (OH). This analysis merges two data sources that are currently being collected in real-time by OH and NCEP. Hourly digital precipitation (HDP) radar estimates are created by the WSR-88D Radar Product Generator on a 131 X 131 4-km grid centered over each radar site. Data analysis routines, including a bias correction of the radar estimates using rain gage data, have been adapted by NCEP on a national 4-km grid from algorithms developed by OH and executed regionally at NWS River Forecast Centers (RFC). This dataset only contains the NCEP 4 KM GRIB Data hourly, 6-hourly, and daily radar estimate (no bias removal). 6-hourly data are generally available at 00Z, 06Z, 12Z, and 18Z. Daily data are generally available at 12Z. Depending on the time period selected, all three datasets may or may not be available. Other NCEP 4 KM GRIB Data including gage-only analysis, multi-sensor analysis (gage and unbiased radar), radar estimate after bias removal, and gage-only analysis using 24h accumulated ("RFC") data are available as independent datasets. Depending on the time period selected, all five types may or may not be available. Please see GCIP/EOP: Surface NCEP Ancillary Catalogue of Available GCIP Precipitation Data (NCEP/EMC). The format of the files is GRIB. The files are compressed using the UNIX "compress" command and "uncompress" must be used before decoding.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The Global Energy and Water Cycle Experiment (GEWEX) Continental-scale International Project (GCIP) Enhanced Observing Period (EOP) takes place in the Mississippi River basin, which provides a number of watershed areas that are potentially useful for hydrologic focused studies. The temporal coverage for this dataset is as follows: Begin datetime: 1997-04-23 00:00:00, End datetime: 2001-12-31 23:59:59. This dataset contains the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) 4 KM GRIB radar estimate after bias removal ("UBR") data. A prototype, real-time, hourly, multi-sensor National Preciptation Analysis (NPA) has been developed at NCEP in cooperation with the Office of Hydrology (OH). This analysis merges two data sources that are currently being collected in real-time by OH and NCEP. Hourly digital precipitation (HDP) radar estimates are created by the WSR-88D Radar Product Generator on a 131 X 131 4-km grid centered over each radar site. Data analysis routines, including a bias correction of the radar estimates using rain gage data, have been adapted by NCEP on a national 4-km grid from algorithms developed by OH and executed regionally at NWS River Forecast Centers (RFC). This dataset only contains the NCEP 4 KM GRIB Data hourly, 6-hourly, and daily radar estimate after bias removal. 6-hourly data are generally available at 00Z, 06Z, 12Z, and 18Z. Daily data are generally available at 12Z. Depending on the time period selected, all three datasets may or may not be available. Other NCEP 4 KM GRIB Data including gage-only analysis, multi-sensor analysis (gage and unbiased radar), radar estimate (no bias removal), and gage-only analysis using 24h accumulated ("RFC") data are available as independent datasets. Depending on the time period selected, all five types may or may not be available. Please see GCIP/EOP: Surface NCEP Ancillary Catalogue of Available GCIP Precipitation Data (NCEP/EMC). The format of the files is GRIB. The files are compressed using the UNIX "compress" command and "uncompress" must be used before decoding.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The NESOB 1997 Daily Precipitation Composite is one of several precipitation datasets provided in the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Near-Surface Observation Data Set (NESOB) 1997. This precipitation composite is composed of data from several sources (i.e., National Weather Service (NWS) Cooperative Observers, National Centers for Environmental Prediction (NCEP), and the daily precipitation data extracted from the NESOB 1997 Hourly Precipitation Composite). Data from these sources were quality controlled and merged to form this precipitation composite. After the datasets were merged to form the NESOB 1997 Daily Precipitation Composite, a statistics program was executed to ensure that the quality of the individual datasets had been retained. This composite contains data for the NESOB 1997 domain (approximately 94.5 W to 100.5 W longitude and 34 N to 39 N latitude) and time period (01 April 1997 through 31 March 1998). The NCEP Daily Precipitation dataset was formed by extracting incremental precipitation values. The value reported for any daily observation represents data collected during the previous 24 hours. The Daily Precipitation Composite contains six metadata parameters and four data parameters. The metadata parameters describe the station location and time at which the data were collected. The four data parameters repeat once for each day in the monthly record. Every record has 31 days reported, regardless of the actual number of days in the month. For months with less than 31 days, the extra days are reported as missing (i.e., '-999.99 7 M'). Each 24 hour precipitation value has an associated observation hour. The observation hour is the ending UTC hour for the 24 hour period for which the precipitation value is valid.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The NESOB 1997 Daily Precipitation Composite is one of several precipitation datasets provided in the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Near-Surface Observation Data Set (NESOB) 1997. This precipitation composite is composed of data from several sources (i.e., National Weather Service (NWS) Cooperative Observers, National Centers for Environmental Prediction (NCEP), and the daily precipitation data extracted from the NESOB 1997 Hourly Precipitation Composite). Data from these sources were quality controlled and merged to form this precipitation composite. After the datasets were merged to form the NESOB 1997 Daily Precipitation Composite, a statistics program was executed to ensure that the quality of the individual datasets had been retained. This composite contains data for the NESOB 1997 domain (approximately 94.5 W to 100.5 W longitude and 34 N to 39 N latitude) and time period (01 April 1997 through 31 March 1998). The NCEP Daily Precipitation dataset was formed by extracting incremental precipitation values. The value reported for any daily observation represents data collected during the previous 24 hours. The Daily Precipitation Composite contains six metadata parameters and four data parameters. The metadata parameters describe the station location and time at which the data were collected. The four data parameters repeat once for each day in the monthly record. Every record has 31 days reported, regardless of the actual number of days in the month. For months with less than 31 days, the extra days are reported as missing (i.e., '-999.99 7 M'). Each 24 hour precipitation value has an associated observation hour. The observation hour is the ending UTC hour for the 24 hour period for which the precipitation value is valid.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The National Climatic Data Center (NCDC) Summary of the Day Co-operative Dataset is one of several surface datasets provided for the Southern Great Plains (SGP) 1997 project. This NCDC Co-operative Observer (COOP) dataset contains data from sixty-two stations for the SGP 1997 time period (18 June 1997 through 18 July 1997) and in the SGP 1997 domain (approximately 97W to 99W longitude and 34.5N to 37N latitude). The primary thrust of the cooperative observing program is the recording of 24-hour precipitation amounts, but approximately 55% of the stations also record maximum and minimum temperatures. The observations are for the 24-hour period ending at the time of observation. Observer convenience or special program needs mean that observing times vary from station to station. However, the vast majority of observations are taken near either 7:00 AM or 7:00 PM local time. The NCDC Summary of the Day Co-operative Dataset (TD-3200) contains eight metadata parameters and fifteen data parameters and flags. The metadata parameters describe the date/time, network, station and location at which the data were collected. All times are UTC. Data values are valid for the 24 hours preceding the time of observation.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The region selected for investigation is the best instrumented site for surface soil moisture, hydrology and meteorology in the world. This includes the USDA/ARS Little Washita Watershed, the USDA/ARS facility at El Reno, Oklahoma, the ARM/CART central facility, as well as the Oklahoma Mesonet. The National Climatic Data Center (NCDC) Summary of the Day Co-operative Precipitation Dataset is one of several surface precipitation datasets provided in the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) by UCAR/JOSS. The primary thrust of the cooperative observing program is the recording of 24-hour precipitation amounts. The observations are for the 24-hour period ending at the time of observation. Observer convenience or special program needs mean that observing times vary from station to station. However, the vast majority of observations are taken near either 7:00 AM or 7:00 PM local time. The National Weather Service (NWS) Cooperative Observer Daily Precipitation dataset was formed by extracting the daily incremental precipitation values provided in the National Climatic Data Center (NCDC) TD 3200 dataset. The Daily Precipitation data set contains six metadata parameters and four data parameters. The metadata parameters describe the station location and time at which the data were collected. The four data parameters repeat once for each day in the monthly record. Every record has 31 days reported, regardless of the actual number of days in the month. For months with less than 31 days, the extra days are reported as missing (i.e., '-999.99 7 M'). Each 24 hour precipitation value has an associated observation hour. The observation hour is the ending UTC hour for the 24 hour period for which the precipitation value is valid.

The Co-operative Agency Reservoir dataset is one of various hydrological datasets provided for the Southern Great Plains 1997 (SGP97) Project. This dataset contains reservoir data from 26 Co-operative Agency stations within the Enhanced Observing Period (EOP) domain and time period. No additional quality control was performed by the University Corporation for Atmospheric Research/Joint Office for Science Support (UCAR/JOSS). The Co-operative Agency reservoir dataset is provided "as is" in the original format. The Co-operative Agency data is in a non-consistent, ASCII format.

The United States Geological Survey (USGS) Reservoir dataset is one of various hydrological datasets provided for the Southern Great Plains 1997 (SGP97) project. This dataset contains reservoir data from stations in SGP97 domain. The data collected at USGS gaging stations consist of records of stage and measurements of discharge of streams or canals, and stage, surface area, and contents of lakes or reservoirs. This dataset contains only the USGS reservoir data. For a lake or reservoir, capacity tables giving the contents for any stage are prepared from stage-area relation curves defined by surveys. The application of the stage to the capacity table gives the contents, from which the daily, monthly, or yearly change in contents is computed. If the stage-capacity curve is subject to changes because of deposition of sediment in the reservoir, periodic resurveys of the reservoir are necessary to define new stage-capacity curves. During the period between reservoir surveys, the computed contents may be increasingly in error due to the gradual accumulation of sediments. For some gaging stations there are periods when no gage-height record is obtained or the recorded gage height is so faulty that it cannot be used to compute daily discharge or contents. This happens when the recorder stops or otherwise fails to operate properly, intakes are plugged, the float is frozen in the well, or for various other reasons. For such periods, the daily contents may be estimated on the basis of operator's log, prior and subsequent records, inflow-outflow studies, and other information. The USGS reservoir data are provided in a single file and are provided "as is" in their original card image format. There are six different types of "cards images" which appear in the USGS reservoir dataset. Each card has a unique format, but the first character of a card image always indicates the card type. Depending upon the card type, the card image may contain metadata and/or data. No additional quality control was performed by the University Corporation for Atmospheric Research/Joint Office for Science Support (UCAR/JOSS).

The United States Geological Survey (USGS) stream flow dataset is one of various datasets provided for the Southern Great Plains 1997 (SGP97) project. This dataset contains stream flow data from 997 USGS stations in the SGP97 domain. The data collected at USGS gaging stations consist of records of stage and measurements of discharge of streams or canals, and stage, surface area, and contents of lakes or reservoirs. This dataset contains only the USGS stream flow data. For USGS stream-gaging stations, the daily mean discharge is computed from gage heights and rating tables. These rating tables are prepared from stage-discharge-relation curves and give the discharge for any stage. If the stage-discharge relation for a station is temporarily changed by the presence of aquatic growth or debris on the control, the daily mean discharge is computed by what is basically the shifting-control method. At some USGS gaging stations, acoustic velocity meter (AVM) systems are used to compute discharge. The AVM system measures the stream's velocity at one or more paths in the cross section. Coefficients are developed to relate this path velocity to the mean velocity in the cross section. Cross-sectional area curves are developed to relate stage to cross section area. Discharge is computed by multiplying path velocity by the appropriate stage related coefficient and area. Changing stage, backwater from reservoirs, tributary streams, or other sources, and ice in the winter affect the stage-discharge relation. Special methods, such as using comparable records of discharge for other stations, are then used to compute discharge. If no gage-height record can be obtained from a gaging station due to failed equipment, etc., daily discharge values are estimated using various means. The USGS stream flow dataset contains three metadata parameters and three data parameters. The metadata parameters identify the network, station, and time at which the data was collected. Each record contains one month's data. The three data parameters (stream flow, stage, and hour of observation) are repeated once for each UTC day (0000 to 2300). All records contain data for 31 days regardless of the actual number of days in a month. Months with less than 31 days are padded with missing values (e.g., -999.99). The stream flow values are reported in cubic meters per second and are 24 hour averages. There are no stage values in this dataset, so the stage values are shown as missing. The hour of observation is the beginning UTC hour for the 24 hour period for which the stream flow value is valid. No additional quality control was performed by the University Corporation for Atmospheric Research/Joint Office for Science Support (JOSS).

[update 28/03/24 - This description previously stated that the the field “2001-2020 (recent past) change” was a percentage change. This field is actually the difference, in units of mm/day. The table below has been updated to reflect this.]What does the data show? This dataset shows the change in summer precipitation rate for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, summer is defined as June-July-August. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions. The dataset uses projections of daily precipitation from UKCP18 which are averaged over the summer period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a percentage change (%) relative to the 1981-2000 value. This enables users to compare summer precipitation trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below. Period Description 1981-2000 baseline Average value for the period (mm/day) 2001-2020 (recent past) Average value for the period (mm/day) 2001-2020 (recent past) change Change (mm/day) relative to 1981-2000 1.5°C global warming level change Percentage change (%) relative to 1981-2000 2°C global warming level change Percentage change (%) relative to 1981-2000 2.5°C global warming level change Percentage change (%) relative to 1981-2000 3°C global warming level change Percentage change (%) relative to 1981-2000 4°C global warming level change Percentage change (%) relative to 1981-2000 What is a global warming level? The Summer Precipitation Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Summer Precipitation Change, an average is taken across the 21 year period. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level. What are the naming conventions and how do I explore the data? These data contain a field for each warming level and the 1981-2000 baseline. They are named 'pr summer change', the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'pr summer change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'pr summer change 2.0 median' is named 'pr_summer_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘pr summer change 2.0°C median’ values. What do the 'median', 'upper', and 'lower' values mean? Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Summer Precipitation Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble. This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty. ‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful links For further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal.

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The Walnut Gulch Experimental Watershed (WGEW) sediment collection program, established in 1953, provides event-based data for semiarid rangeland erosion, sediment transport, and yield research. Sediment loads carried through the channel network on the WGEW are high, but are typical of semiarid rangelands, and are influenced by soils, geologic parent material, and geomorphology. Typical monsoon thunderstorm generated flows in dryland regions are characterized by high velocities, short durations, and heavy and coarse sediment loads. Sediment is measured in conjunction with discharge measurements [Stone et al., 2008] that are integral to converting sample values to runoff event-based values. Sampling initiated in the 1960s was done with point intake pump samplers. The single point sampler intake tubes were later replaced with tubes that rise in response to flow and are perforated to collect depth integrated samples. Sampling with each of these systems is limited to suspended sediment smaller than the 0.635 cm diameter of the intake slots. Pump samplers are in use at the outlet of small watersheds where overland flow is the dominant hydrologic driver of sediment transport, and particles are small. As watershed size increases on the WGEW, in general, the channel network can dominate sediment delivery processes as it evolves to carry an increasingly coarse, and vertically sorted, sediment load. A traversing slot sediment sampler was designed in response to limitations of alternative sampling methods such as the pump sampler. The data collection network was expanded in 2002 and pit traps were added below the overfall at flumes 63.103 and 63.104. Analysis of these data, and efforts to process and make available the historic data, are ongoing.

Daily point rainfall values. This data is collected with rainfall telemetry devices installed at the catchment storage dams, and verified with field observations daily.  This data provides daily rainfall observed for the 24hr period from 8am to 8am. This data provides our customers and community with the daily observed rainfall information. This data is best used in long term rainfall analysis at the four major melbourne storage dams (Maroondah; O'Shannassy; Thomson; and Upper Yarra). This dataset is not suitable for event modelling which requires higher frequency observations.NOTE: Whilst every effort has been taken in collecting, validating and providing the attached data, Melbourne Water Corporation makes no representations or guarantees as to the accuracy or completeness of this data. Any person or group that uses this data does so at its own risk and should make their own assessment and investigations as to the suitability and/or application of the data. Melbourne Water Corporation shall not be liable in any way to any person or group for loss of any kind including damages, costs, interest, loss of profits or special loss or damage, arising from any use, error, inaccuracy, incompleteness or other defect in this data.

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[update 28/03/24 - This description previously stated that the the field “2001-2020 (recent past) change” was a percentage change. This field is actually the difference, in units of mm/day. The table below has been updated to reflect this.]What does the data show? This dataset shows the change in winter precipitation rate for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, winter is defined as December-January-February. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions. The dataset uses projections of daily precipitation from UKCP18 which are averaged over the winter period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a percentage change (%) relative to the 1981-2000 value. This enables users to compare winter precipitation trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below. Period Description 1981-2000 baseline Average value for the period (mm/day) 2001-2020 (recent past) Average value for the period (mm/day) 2001-2020 (recent past) change Change (mm/day) relative to 1981-2000 1.5°C global warming level change Percentage change (%) relative to 1981-2000 2°C global warming level change Percentage change (%) relative to 1981-2000 2.5°C global warming level change Percentage change (%) relative to 1981-2000 3°C global warming level change Percentage change (%) relative to 1981-2000 4°C global warming level change Percentage change (%) relative to 1981-2000 What is a global warming level? The Winter Precipitation Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Winter Precipitation Change, an average is taken across the 21 year period. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level. What are the naming conventions and how do I explore the data? These data contain a field for each warming level and the 1981-2000 baseline. They are named 'pr winter change', the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'pr winter change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'pr winter change 2.0 median' is named 'pr_winter_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘pr winter change 2.0°C median’ values. What do the 'median', 'upper', and 'lower' values mean? Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Summer Precipitation Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble. This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty. ‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful links For further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal.