The data deposited here underlie an assessment of the exposure of UK habitats to climate change, and a linked assessment of how well current UK plant monitoring schemes cover these exposure gradients (see Wilson & Pescott, 2023 in press). The current dataset consists of spatially explicit (1 km gridded) classifications of predicted Köppen-Geiger climate types (Peel et al., 2007), based on both past (observed) and future (modelled) climate data. Full details about this dataset can be found at https://doi.org/10.5285/4aed4496-f9e2-494d-a0f9-adc297f033a4

Analysis of ‘UK weather by month’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tombutton/uk-weather-by-month on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Licence information and source

The MET Office copyright policy can be found at: https://www.metoffice.gov.uk/about-us/legal#licences Data source from: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data

--- Original source retains full ownership of the source dataset ---

The Flood Map for Planning includes several layers of information, which includes data created to support the use of Flood Zones in the planning process. This dataset shows the extent of land at risk of flooding to a defined annual exceedance probability (AEP) or chance of flooding each year, taking into account the possible effects of climate change as detailed below.

This dataset represents the following scenario:

● Defended: 3.3% AEP (1 in 30) Rivers/Sea

The defended products take into account the presence of flood defences and assume that they operate in the way they were intended (or designed) to function. This does not include any asset failure (or removal) scenarios.

Climate change scenarios have been produced to indicate the possible impacts of climate change on future risk. The climate change allowances are based on the latest UK Climate Projections (UKCP18) from the Met Office, using the Representative Concentration Pathway (RCP) 8.5. The specific climate change scenarios shown are as follows:

● the ‘Central’ allowance for the 2080s epoch (2070-2125) for risk of flooding from rivers
● the ‘Upper End’ allowance for risk of flooding from the sea, accounting for cumulative sea level rise to 2125

For climate change scenarios, it is assumed that existing flood defences continue to function in the same way as present day. No allowance is made for any future changes to flood defence design or operation.

These datasets are designed to only give an indication of flood risk to an area of land and are not suitable for showing whether an individual property is at risk of flooding. This is because we cannot know all the details about each property.

Information on flood depth, speed or volume of flow is not included.

What does the data show?

This data shows annual averages of precipitation (mm/day) for 2050-2079 from the UKCP18 regional climate projections. The data is for the high emissions scenario (RCP8.5).

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 period. They are named 'pr' (precipitation), the month, and 'upper' 'median' or 'lower'. E.g. 'pr Median' is the median value.

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 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 annual averages of precipitation for 2050-2079 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.

Data source

pr_rcp85_land-rcm_uk_12km_12_ann-30y_200912-207911.nc (median)

pr_rcp85_land-rcm_uk_12km_05_ann-30y_200912-207911.nc (lower)

pr_rcp85_land-rcm_uk_12km_04_ann-30y_200912-207911.nc (upper)

UKCP18 v20190731 (downloaded 04/11/2021)

Useful links

Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal

Context

Simple time series data for weather prediction time series projects.

Content

The data contains the following information from the UK Met Office location at London Heathrow Airport. The data runs from Jan 1948 to Oct 2020 and includes the following monthly data fields:

• yyyy = Year
• mm = Month
• tmax = Maximum temperature (Celsius)
• tmin = Minimum temperature (Celsius)
• af = Count of Air Frost days in the given month
• rain = Total rainfall (mm)
• sun = Sunshine duration (hrs)

Acknowledgements

Provided by the UK Met Office: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data Available under Open Government Licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Example code

The following Python code will load into a Pandas DataFrame:

colspecs = [(3, 7), (9,11),(14,18),(22,26),(32,34),(37,42),(45,50)] data = pd.read_fwf('../input/heathrow-weather-data/heathrowdata.txt',colspecs=colspecs)

The following will remove the first few lines of text

data = data[3:].reset_index(drop=True) data.columns = data.iloc[1] data = data[3:].reset_index(drop=True)

As part of the ESA Land Cover Climate Change Initiative (CCI) project a set of Global Land Cover Maps have been produced. These are available at 300m spatial resolution for three epochs centred on the year 2010 (2008-2012), 2005 (2003-2007) and 2000 (1998-2002), where each epoch covers a 5-year period.

Each pixel value corresponds to the label of a land cover class defined using UN-LCCS classifiers. For each epoch, the land cover map is delivered along with 4 quality flags which document the reliability of the classification. These are described further in the Product User Guides.

Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php

What does the data show?

Life expectancy at birth (years) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Health

Metric

Life expectancy at birth

Unit

Years

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

ONS 2018

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

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 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

What does the data show?

Railway lines per area (m/km2) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Rail Infrastructure

Metric

Railway lines per area

Unit

m/km2

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

WFP 2014

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

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 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:

Further information on the UK SSPs can be found on the UK SSP project site and in this storymap. Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal

Licence information and source

The MET Office copyright policy can be found at: [https://www.metoffice.gov.uk/about-us/legal#licences] Data source from: [https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data]

Cover image: [https://pixabay.com/photos/scarborough-sunrise-seascape-2850597/]

As part of the ESA Land Cover Climate Change Initiative (CCI) project a static map of open water bodies at 150 m spatial resolution at the equator has been produced. The CCI WB v4.0 is composed of two layers:1. A static map of open water bodies at 150 m spatial resolution resulting from a compilation and editions of land/water classifications: the Envisat ASAR water bodies indicator, a sub-dataset from the Global Forest Change 2000 - 2012 and the Global Inland Water product.This product is delivered at 150 m as a stand-alone product but it is consistent with class "Water Bodies" of the annual MRLC (Medium Resolution Land Cover) Maps. The product was resampled to 300 m using an average algorithm. Legend : 1-Land, 2-Water2. A static map with the distinction between ocean and inland water is now available at 150 m spatial resolution. It is fully consistent with the CCI WB-Map v4.0. Legend: 0-Ocean, 1-Land.To cite the CCI WB-Map v4.0, please refer to : Lamarche, C.; Santoro, M.; Bontemps, S.; Dâ Andrimont, R.; Radoux, J.; Giustarini, L.; Brockmann, C.; Wevers, J.; Defourny, P.; Arino, O. Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. Remote Sens. 2017, 9, 36. https://doi.org/10.3390/rs9010036

The data describes future land use projections at 1 km^2 resolution developed by CRAFTY-GB. For each of six Shared Socioeconomic Pathways (SSP-RCP) scenarios, gridded land use maps for Great Britain are provided, each as a stacked raster file with seven bands representing land use at each decadal timestep, from 2020 to 2080. CRAFTY-GB is a new agent-based model of the British land system operating at a 1 km^2 resolution and based on a broad range of available land system data . The model is based on linked UK-RCP climate scenarios and UK-SSPs socio-economic pathway (SSP) scenarios, based on global SSPs developed by the Intergovernmental Panel on Climate Change (IPCC). It extrapolates the impact of these on the British Land system at decadal timesteps from 2020-2080. Full details about this dataset can be found at https://doi.org/10.5285/f9ab3051-4f85-415f-b691-371ff8e951f2

Context Simple time series data for weather prediction time series projects.

Content The data contains the following information from the UK Met Office location at Armagh, Northern Ireland. The data runs from Jan 1853 to Nov 2020 and includes the following monthly data fields:

yyyy = Year mm = Month tmax = Maximum temperature (Celsius) tmin = Minimum temperature (Celsius) af = Count of Air Frost days in the given month rain = Total rainfall (mm) sun = Sunshine duration (hrs) Acknowledgements Provided by the UK Met Office: https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data Available under Open Government Licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Example code The following Python code will load into a Pandas DataFrame:

colspecs = [(3, 7), (9,11),(14,18),(22,26),(32,34),(37,42),(45,50)] data = pd.read_fwf('../input/heathrow-weather-data/heathrowdata.txt',colspecs=colspecs)

The following will remove the first few lines of text

data = data[3:].reset_index(drop=True) data.columns = data.iloc[1] data = data[3:].reset_index(drop=True)

The Flood Map for Planning includes several layers of information, which includes data created to support the use of Flood Zones in the planning process. These datasets show the extent of land at risk of flooding to a defined annual exceedance probability (AEP) or chance of flooding each year, taking into account the possible effects of climate change (detailed below).

These datasets include the following scenarios:

● Undefended: 0.1% AEP (1 in 1000) Rivers/Sea ● Undefended: 1% AEP (1 in 100) Rivers/ 0.5% AEP (1 in 200) Sea ● Defended: 0.1% AEP (1 in 1000) Rivers/Sea ● Defended: 1% AEP (1 in 100) Rivers/ 0.5% AEP (1 in 200) Sea

The undefended products show flood extents that ignore the presence and condition of flood defences.

The defended products take into account the presence of flood defences and assume that they operate in the way they were intended (or designed) to function. This does not include any asset failure (or removal) scenarios.

Climate change scenarios have been produced to indicate the possible impacts of climate change on future risk. The climate change allowances are based on the latest UK Climate Projections (UKCP18) from the Met Office, using the Representative Concentration Pathway (RCP) 8.5. The specific climate change scenarios shown are as follows:

● the ‘Central’ allowance for the 2080s epoch (2070-2125) for risk of flooding from rivers
● the ‘Upper End’ allowance for risk of flooding from the sea, accounting for cumulative sea level rise to 2125

For climate change scenarios, it is assumed that existing flood defences continue to function in the same way as present day. No allowance is made for any future changes to flood defence design or operation.

These datasets are designed to only give an indication of flood risk to an area of land and are not suitable for showing whether an individual property is at risk of flooding. This is because we cannot know all the details about each property.

Information on flood depth, speed or volume of flow is not included.

The wettest months in the United Kingdom tend to be at the start and end of the year. In the period of consideration, the greatest measurement of rainfall was nearly 217 millimeters, recorded in December 2015. The lowest level of rainfall was recorded in April 2021, at 20.6 millimeters. Rainy days The British Isles are known for their wet weather, and in 2024 there were approximately 164 rain days in the United Kingdom. A rainday is when more than one millimeter of rain falls within a day. Over the past 30 years, the greatest number of rain days was recorded in the year 2000. In that year, the average annual rainfall in the UK amounted to 1,242.1 millimeters. Climate change According to the Met Office, climate change in the United Kingdom has resulted in the weather getting warmer and wetter. In 2022, the annual average temperature in the country reached a new record high, surpassing 10 degrees Celsius for the first time. This represented an increase of nearly two degrees Celsius when compared to the annual average temperature recorded in 1910. In a recent survey conducted amongst UK residents, almost 80 percent of respondents had concerns about climate change.

As part of the ESA Land Cover Climate Change Initiative (CCI) project a new set of Global Land Cover Maps have been produced. These maps are available at 300m spatial resolution for each year between 1992 and 2015.

Each pixel value corresponds to the classification of a land cover class defined based on the UN Land Cover Classification System (LCCS). The reliability of the classifications made are documented by the four quality flags (decribed further in the Product User Guide) that accompany these maps. Data are provided in both NetCDF and GeoTiff format.

Further Land Cover CCI products, user tools and a product viewer are available at: http://maps.elie.ucl.ac.be/CCI/viewer/index.php . Maps for the 2016-2020 time period have been produced in the context of the Copernicus Climate Change service, and can be downloaded from the Copernicus Climate Data Store (CDS).

What does the data show?

This data shows monthly averages of surface temperature (°C) for 2050-2079 from the UKCP18 regional climate projections. The data is for the high emissions scenario (RCP8.5).

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 period. They are named 'tas' (temperature at surface), the month, and 'upper' 'median' or 'lower'. E.g. 'tas July Median' is the median value for July.

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 ‘tas January 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 monthly averages of temperature for 2050-2079 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.

Data source

tas_rcp85_land-rcm_uk_12km_12_mon-30y_200912-207911.nc (median)

tas_rcp85_land-rcm_uk_12km_05_mon-30y_200912-207911.nc (lower)

tas_rcp85_land-rcm_uk_12km_04_mon-30y_200912-207911.nc (upper)

UKCP18 v20190731 (downloaded 04/11/2021)

Useful links

Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal

This data is to illustrate the theory, data analysis, and exemplary results of the array processing described in the corresponding publication entitled 'First map of coherent low frequency continuum radiation in the sky'.

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale.

The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage.

Climate Just Map Tool includes maps on:

• Flooding (river/coastal and surface water)
• Heat
• Fuel poverty.

The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data.

Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls

Indicators include:

Climate Just-Flood disadvantage_2011_Dec2014.xlsx

Fluvial flood disadvantage index
Pluvial flood disadvantage index (1 in 30 years)
Pluvial flood disadvantage index (1 in 100 years)
Pluvial flood disadvantage index (1 in 1000 years)

Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx

Percentage of area at moderate and significant risk of fluvial flooding
Percentage of area at risk of surface water flooding (1 in 30 years)
Percentage of area at risk of surface water flooding (1 in 100 years)
Percentage of area at risk of surface water flooding (1 in 1000 years)

Climate Just-SSVI_indices_2011_Dec2014.xlsx

Sensitivity - flood and heat
Ability to prepare - flood
Ability to respond - flood
Ability to recover - flood
Enhanced exposure - flood
Ability to prepare - heat
Ability to respond - heat
Ability to recover - heat
Enhanced exposure - heat
Socio-spatial vulnerability index - flood
Socio-spatial vulnerability index - heat

Climate Just-SSVI_indicators_2011_Dec2014.xlsx

% children < 5 years old
% people > 75 years old
% people with long term ill-health/disability (activities limited a little or a lot)
% households with at least one person with long term ill-health/disability (activities limited a little or a lot)
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% households rented from social landlords
% households rented from private landlords
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% people with % unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Crime score (IMD)
% area not road
Density of retail units (count /km2)
% change in number of local VAT-based units
% people with % not home workers
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (Pounds)
% all pensioner households
% born outside UK and Ireland
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Travel time to nearest GP by walk/public transport (mins - representative time)
% of at risk pop

Natural England has developed a GIS model that provides an assessment of the vulnerability of priority habitats to climate change based on principles of adaptation for biodiversity. It identifies why areas are vulnerable and which possible interventions can have the biggest impact in increasing resilience to the changing climate. This will inform prioritisation of adaptation action and assist in the development of adaptation strategies for biodiversity both within Natural England and with our partners. The NBCCVA uses a 200m x200m GIS grid model to assess priority habitats for their Sensitivity to climate change, Adaptive Capacity; including habitat fragmentation, topographic variety and current management and condition and Conservation Value. The metrics can then be added together to produce an overall vulnerability assessment. Key outputs are maps showing the metric results and the range of relative vulnerability across the country, giving a visual representation of the areas vulnerable to climate change.

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