This dataset provides a comprehensive overview of global population trends, historical data, and future projections. It includes detailed information for various countries and regions, encompassing key demographic indicators such as population size, growth rates, and density.

The dataset covers a broad time span, from 1980 to 2050, allowing for analysis of long-term population dynamics. It incorporates data from reputable sources like the United Nations Population Division and World Population Review, ensuring data accuracy and reliability.

This database represents the historic, current and future estimates and projections with number of inhabitants for the world's largest urban areas from 1950-2050. The data covers cities and other urban areas with more than 750,000 people.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 Revision. ( 2 ) Census reports and other statistical publications from national statistical offices, ( 3 ) Eurostat: Demographic Statistics, ( 4 ) United Nations Statistical Division. Population and Vital Statistics Reprot ( various years ), ( 5 ) U.S. Census Bureau: International Database, and ( 6 ) Secretariat of the Pacific Community: Statistics and Demography Programme. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."

This layer shows the types and combinations of modeled change to the World Terrestrial Ecosystems (WTEs) from 2015 to 2050 using the CMIP6 SSP3-7.0 scenario. The SSP3-7.0 scenario includes nearly double the current CO2 levels by 2100 and an average global temperature increase of 3.6 deg C by the end of the century. The Paris Agreement targets an increase of no more than 2.0 deg C.To learn more about this work, read our open access peer-reviewed journal article in Global Ecology and Conservation, Volume 57, January 2025, e03370: Potential 2050 distributions of World Terrestrial Ecosystems from projections of changes in World Climate Regions and Global Land Cover. The WTEs include projections for temperature, precipitation, and land cover, using criteria determined by the Food and Agriculture Organization of the United Nations. This layer shows the different combinations of how those components may change in the future. Each year, Earth's changing climate affects about 1% of land, converting it do a different climate classification, e.g., subtropical temperatures warm to tropical. Depending on how we choose to act with respect to emissions, future climate and land change models for 2050 indicate about 35% of the Earth's terrestrial ecosystems will shift from what they were in 2015 to a different type. Specifically, the SSP3-7.0 scenario indicates 36.3% of land area could change ecosystem type. Ecosystems include where people live and land people depend on for food, building materials, and many other services that make life as we know it possible. When the change to our climate is large enough to affect the ecology around us, it is a signal that we may also need to change or adapt. This layer represents 1-km resolution locations of no change and seven types of change that may be sufficient to cause local ecosystems to change:Temperature OnlyAridity OnlyLand Cover OnlyTemperature and AridityTemperature and Land CoverAridity and Land CoverAll threeLayers of where and why ecosystems could change can be used as the basis for planning adaptation strategies for climate change given the needs of local populations. Therefore, this layer is intended for visualization and to be used in overlay analyses that intersect the types of change with the footprints of human activities and policies to learn what may be impacted by the changing climate.When considering the types of change, it is extremely important to understand that the locations of ecologically meaningful shifts in climate appears to be reliable, however, in these models, only the amount of land use over a region is relevant. The specific locations for land cover or use change are simply based on proximity to need, and do not account for existing local plans or land protection status. The value of the land change in this layer is that it provides an amount of change by 2050 over an area such as a country or state. By knowing this amount needed for human uses in 2050, we can develop strategies and plans for where to best locate the projected area of land use changes.Note, there is no popup field for this layer. Instead, we designed the Global Projections of Change Types for WTEs for 2050 by SSP-RCP for Popups layer to provide a single popup for this layer and the SSP1-2.6 and SSP5-8.5 Change Type layers. For temperature or aridity, sufficient change is defined based on the climate region classes presented in Sayre, et. al. (2020) which are based on the FAO's definition for climate zones presented in Figure 3A.5.2 of 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Volume 4, Agriculture, Forestry and Other Land Use. Thus, if some aspect of temperature or aridity changed sufficiently from 2015 to 2050, a change of that type is presented in this layer.For land change, we use a dataset produced by Chen et al (2022) that models land cover change by RCP-SSP. These projections of future land cover are modeled at 1-km resolution and include models for 2015 and various future projections and RCP-SSPs to 2100. These models implement a combination of environmental, demographic, and socioeconomic pressures that change land use and land cover according to ach RCP-SSP, however, they are not appropriate to use at the level of a single pixel, but rather to summarize over much larger region of hundreds or thousands of square kilometers. References:Sayre, R., D. Karagulle, C. Frye, T.Boucher, N. Wolff, S. Breyer, D.Wright, M. Martin, K. Butler, K. Van Graafeiland, J. Touval, L. Sotomayor, J. McGowan, E. Game, and H. Possingham. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems. Global Ecology and Conservation, 21, e00860, ISSN 2351-9894 https://doi.org/10.1016/j.gecco.2019.e00860.Chen, G., Li, X. & Liu, X. Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. Sci Data 9, 125 (2022). https://doi.org/10.1038/s41597-022-01208-6.

50 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2050.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents. Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley. How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life? Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

What does the data show?

This data shows the monthly averages of surface temperature (°C) for 2040-2069 using a combination of the CRU TS (v. 4.06) and UKCP18 global RCP2.6 datasets. The RCP2.6 scenario is an aggressive mitigation scenario where greenhouse gas emissions are strongly reduced.

The data combines a baseline (1981-2010) value from CRU TS (v. 4.06) with an anomaly from UKCP18 global. Where the anomaly is the change in temperature at 2040-2069 relative to 1981-2010.

The data is provided on the WGS84 grid which measures approximately 60km x 60km (latitude x longitude) at the equator.

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 each month’s average over the period. They are named 'tas' (temperature at surface), the month and ‘upper’ ‘median’ or ‘lower’. E.g. ‘tas Mar Lower’ is the average of the daily average temperatures in March throughout 2040-2069, in the second lowest ensemble member.

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 Jan 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.

To select which ensemble members to use, the monthly averages of surface temperature for the period 2040-2069 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

CRU TS v. 4.06 - (downloaded 12/07/22)

UKCP18 v.20200110 (downloaded 17/08/22)

Useful links

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

This dataset provides statistical indicators of tides, storm surges and sea level that can be used to characterize global sea level in present-day conditions and also to assess changes under climate change. The indicators calculated include extreme-value indicators (e.g. return periods including confidence bounds for total water levels and surge levels), probability indicators (e.g. percentile for total water levels and surge levels). They provide a basis for studies aiming to evaluate sea level variability, coastal flooding, coastal erosion, and accessibility of ports at a global scale. The extreme value statistics for different return periods can be used to assess the frequency of an event and form the basis of risk assessments. The global coverage allows for world-wide assessments that are particularly useful for the data scarce regions where detailed modelling studies are currently lacking. The indicators are computed from time series data available in a related dataset in the Climate Data Store named Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections (see Related data), where further details of the modelling are provided. The indicators are produced for three different 30-year periods corresponding to historical, present, and future climate conditions (1951-1980, 1985-2014, and 2021-2050). The future period is based on global climate projections using the high-emission scenario SSP5-8.5. The dataset is based on climate forcing from ERA5 global reanalysis and 4 Global Climate Models (GCMs) of the high resolution Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate projection dataset from the High Resolution Model Intercomparison Project (HighResMIP) multi-model ensemble. An estimate of the uncertainties associated with the climate forcing has been obtained through the use of a multi-model ensemble. Each of the indicators provides ensemble statistics computed across the 4 members of the HighResMIP ensemble (e.g. median, mean, standard deviation, range). Absolute and relative changes for the future period (2015-2050) relative to the present-day (1985-2014) are provided to assess climate change impacts on water levels. This dataset was produced on behalf of the Copernicus Climate Change Service.

Monthly averages of precipitation (mm/day) for 2050-2079 from UKCP18 regional projections (12km grid), using the RCP8.5 pathway.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 July Median'.UKCP: https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/indexWhat is the data?The data is from the UKCP18 regional projections using the RCP8.5 scenario. RCP8.5 is the highest of the plausible future emissions scenarios used by the IPCC, sometimes referred to as 'business as usual'.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 UK precipitation for the period 2050-2079 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:pr_rcp85_land-rcm_uk_12km_12_mon-30y_200912-207911.nc (median)pr_rcp85_land-rcm_uk_12km_05_mon-30y_200912-207911.nc (lower)pr_rcp85_land-rcm_uk_12km_04_mon-30y_200912-207911.nc (upper)UKCP18 v20190731 (downloaded 04/11/2021)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/

The ECHAM climate model has been developed from the ECMWF atmospheric model (therefore the first part of its name: EC) and a comprehensive parameterisation package developed at Hamburg therefore the abbreviation HAM) which allows the model to be used for climate simulations. The model is a spectral transform model with 19 atmospheric layers and the results used here derive from experiments performed with spatial resolution T42 (which approximates to about 2.8 degrees longitude/latitude resolution). The model has also been used at resolutions in the range T21 to T106. ECHAM4 is the current generation in the line of ECHAM models (Roeckner, et al., 1992). A summary of developments regarding model physics in ECHAM4 and a description of the simulated climate obtained with the uncoupled ECHAM4 model is given in Roeckner et al. (1996). The initial sea surface temperature and sea-ice data is the COLA/CAC AMIP SST and sea-ice data set. The mean terrain heights are computed from high resolution US Navy data set. The fraction of grid area covered by vegetation based on the Wilson and Henderson-Sellers (1985) data set. The ocean albedo is a function of solar zenith angle and the land albedo from the satellite data of Geleyn and Preuss (1983). A diurnal cycle and gravity wave-drag is included. The time-step of the model is 24 minutes, except for radiation which uses two hours. The ocean model is an updated version of the isopycnal model (OPYC3) developed by Josef Oberhuber (Oberhuber, 1993) at the Max-Planck-Institute for Meteorology, Hamburg, Germany. The name OPYC is derived from Ocean and isoPYCnal co-ordinates. The concept to use isopycnals as the vertical co-ordinate system for an OGCM is based on the observation that the interior ocean behaves as a rather conservative fluid. Even over long distances the origin of water masses can be traced back by considering the distribution of active or passive tracers. Treating the ocean as a conservative fluid fails in areas of significant turbulence activity such as the surface boundary layer. A surface mixed-layer is therefore coupled to the interior ocean in order to represent near-surface vertical mixing and to improve the response time-scales to atmospheric forcing which is controlled by the mixed-layer thickness. Since the model is designed for studies on large scales, a sea ice model with rheology is included and serves the purpose of de-coupling the ocean from extreme high-latitude winter conditions and promotes a realistic treatment of the salinity forcing due to melting or freezing sea ice. The experiments from which results are used here are the 1000-year unforced control simulation using the coupled ECHAM4/OPYC3 model and then two climate change simulations. The greenhouse gas only forced experiment (referred to as GGa1) used historical greenhouse gas forcing from 1860 to 1990 followed by a 1 per cent annum increase in radiative forcing from 1990 to 2099. The greenhouse gas and sulphate aerosol forced experiment (referred to as GSa1) used the GGa1 forcing, plus the negative forcing due to sulphate aerosols. This was represented by means of an increase in clear-sky surface albedo proportional to the local sulphate loading. The indirect effects of aerosols were not simulated. For 1860 to 1990 the historic sulphate aerosol forcing estimate was used and for 1990 to 2049 the aerosol forcing estimated for the IS92a emissions scenario. The GSa1 experiment did not extend beyond 2049. Fuller details of the ECHAM4/OPYC3 coupled model can be found at the DDC Yellow Pages. Several papers describe results using this version of the model - see Bacher et al. (1998), Oberhuber et al. (1998), Zhang et al. (1998). The climate sensitivity of ECHAM4 is about 2.6 degrees C.The A2 world consolidates into a series of roughly continental economic regions, emphasizing local cultural roots. In some regions, increased religious participation leads many to reject a materialist path and to focus attentionon contributing to the local community. Elsewhere, the trend is towards ncreased investment in education and science and growth in economic productivity. Social and political structures diversify with some regions moving towards stronger welfare systems and reduced income inequality, while others move towards "lean" government. Environmental concerns are relatively weak, although some attention is paid to bringing local pollution under control and maintaining local environmental amenities. Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially.

Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median pr... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.160.3 for complete metadata about this dataset.

ggACene (global gridded Air Conditioning energy) projections

Output AC and AC electricity gridded data

This repository hosts output data for SSPs126, 245, 370 and 585 on the estimated and future projected ownership of residential air conditioning (% of households), the related energy consumption (TWh/yr.), and the underlying population counts (useful to quantify the per-capita average consumption or the headcount of people affected by the cooling gap). These data are contained in the multi-layer .nc (NCDF) files, which can be opened and processed in any GIS software/library, or visualised in softwares such as Panoply.

Input data and analysis replication

The repository also hosts input data to replicate the entire data generating process. A twin Github repository hosts code (https://github.com/giacfalk/ggACene) to run the model generating the ggACene (global gridded Air Conditioning energy) projections dataset.

InstructionsTo reproduce the model and generate the dataset from scratch, please refer to the following steps:- Download input data "replication_package_input_data.7z" by cloning the repository- Decompress the folder using 7-Zip (https://www.7-zip.org/download.html)- Open RStudio and adjust the path folder in the sourcer.R script- Run the sourcer.R script to train the ML model, make projections, and represent result files

Figures replication package

Finally, the source_code_data_replication_figures.zip archive contains an R script and processed input data to replicate all the figures contained in the manuscript.

ReferenceFalchetta, G., De Cian, E., Pavanello, F., & Wing, I. S. Inequalities in global residential cooling energy use to 2050. Nature Communications. https://www.nature.com/articles/s41467-024-52028-8

The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8 degrees horizontal grid and 17 vertical levels for the oceanic part. Flux adjustment for atmosphere-ocean heat and water exchange is applied to prevent a drift of the modelled climate. The atmospheric model adopts a radiation scheme based on the k-distribution, two-stream discrete ordinate method (DOM) (Nakajima and Tanaka, 1986). This scheme can deal with absorption, emission and scattering by gases, clouds and aerosol particles in a consistent manner. In the calculation of sulphate aerosol optical properties, the volumetric mode radius of the sulphate particle in dry environment is assumed to be 0.2 micron. The hygroscopic growth of the sulphate is considered by an empirical fit of d'Almeida et al. (1991). The vertical distribution of the sulphate aerosol is assumed to be constant in the lowest 2 km of the atmosphere. The concentrations of greenhouse gases are represented by equivalent-CO2. Three integrations are made for 200 model years (1890-2090). In the control experiment (CTL), the globally uniform concentration of greenhouse gases is kept constant at 345 ppmv CO2-equivalent and the concentration of sulphate is set to zero. In the experiment GG, the concentration of greenhouse gases is gradually increased, while that of sulphate is set to zero. In the experiments GS, the increase in anthropogenic sulphate as well as that in greenhouse gases is given and the aerosol scattering (the direct effect of aerosol) is explicitly represented in the way described above. The indirect effect of aerosol is not included in any experiment. The scenario of atmospheric concentrations of greenhouse gases and sulphate aerosols is given in accordance with Mitchell and Johns (1997). The increase in greenhouse gases is based on the historical record from 1890 to 1990 and is increased by 1 percent / yr (compound) after 1990. For sulphate aerosols, geographical distributions of sulphate loading for 1986 and 2050, which are estimated by a sulphur cycle model (Langer and Rodhe, 1991), are used as basic patterns. Based on global and annual mean sulphur emission rates, the 1986 pattern is scaled for years before 1990; the 2050 pattern is scaled for years after 2050; and the pattern is interpolated from the two basic ones for intermediate years to give the time series of the distribution. The sulphur emission rate in the future is based on the IPCC IS92a scenario. The sulphate concentration is offset in our run so that it starts from zero at 1890. The seasonal variation of sulphate concentration is ignored. Discussion on the results of the experiments will be found in Emori et al. (1999). Climate sensitivity of the CCSR/NIES model derived by equilibrium runs is estimated to be 3.5 degrees Celsius. Global-Mean Temperature, Precipitation and CO2 Changes (w.r.t. 1961-90) for the CCSR/NIES model. From the IPCC website: The A1 Family storyline is a case of rapid and successful economic development, in which regional averages of income per capita converge - current distinctions between poor and rich countries eventually dissolve. In this scenario family, demographic and economic trends are closely linked, as affluence is correlated with long life and small families (low mortality and low fertility). Global population grows to some nine billion by 2050 and declines to about seven billion by 2100. Average age increases, with the needs of retired people met mainly through their accumulated savings in private pension systems. The global economy expands at an average annual rate of about three percent to 2100. This is approximately the same as average global growth since 1850, although the conditions that lead to a global economic in productivity and per capita incomes are unparalleled in history. Income per capita reaches about US$21,000 by 2050. While the high average level of income per capita contributes to a great improvement in the overall health and social conditions of the majority of people, this world is not w... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.313.2 for complete metadata about this dataset.

Processed monthly variables for members 1-10 of World Avoided rcp8.5 members

Processed monthly variables for members 1-10 of World Avoided ozone depleting substances only (unchanged ozone) rcp4.5 members

Please note this dataset supersedes previous versions on the Climate Data Portal. It has been uploaded following an update to the dataset in March 2023. This means sea level rise is approximately 1cm higher (larger) compared to the original data release (i.e. the previous version available on this portal) for all UKCP18 site-specific sea level projections at all timescales. For more details please refer to the technical note.What does the data show?The time-mean sea-level projections to 2100 show the amount of sea-level change (in cm) for each coastal location (grid-box) around the British Isles for several emission scenarios. Sea-level rise is the primary mechanism by which we expect coastal flood hazard to change in the UK in the future. The amount of sea-level rise depends on the location around the British Isles and increases with higher emission scenarios. Here, we provide the relative time-mean sea-level projections to 2100, i.e. the local sea-level change experienced at a particular location compared to the 1981-2000 average, produced as part of UKCP18.For each grid box the time-mean sea-level change projections are provided for the end of each decade (e.g. 2010, 2020, 2030 etc) for three emission scenarios known as Representative Concentration Pathways (RCP) and for three percentiles.The emission scenarios are:RCP2.6RCP4.5RCP8.5The percentiles are:5th percentile50th percentile95th percentileImportant limitations of the dataWe cannot rule out substantial additional sea-level rise associated with ice sheet instability processes that are not represented in the UKCP18 projections, as discussed in the recent IPCC Sixth Assessment Report (AR6). Although the time-mean sea-level projections presented here are to 2100, past greenhouse gas emissions have already committed us to substantial additional sea level rise beyond 2100. This is because the ocean and cryosphere (i.e. the frozen parts of our planet) are very slow to respond to global warming. So, even if global average air temperature stops rising, as global emissions are reduced, sea level will continue to rise well beyond the time changes in global average air temperature level off or decline. This is illustrated by the extended exploratory time-mean sea level projections and discussed further in AR6 (Fox-Kemper et al, 2021).What are the naming conventions and how do I explore the data?The data is supplied so that each row corresponds to the combination of a RCP emissions scenario and percentile value e.g. 'RCP45_50' is the RCP4.5 scenario and the 50th percentile. This can be viewed and filtered by the field 'RCP and Percentile'. The columns (fields) correspond to the end of each decade and the fields are named by sea level anomaly at year x, e.g. '2050 seaLevelAnom' is the sea level anomaly at 2050 compared to the 1981-2000 average.Please note that the styling and filtering options are independent of each other and the attribute you wish to style the data by can be set differently to the one you filter by. Please ensure that you have selected the RCP/percentile and decade you want to both filter and style the data by. Select the cell you are interested in to view all values. To understand how to explore the data please refer to the New Users ESRI Storymap.What are the emission scenarios?The 21st Century time-mean sea level projections were produced using some of the future emission scenarios used in the IPCC Fifth Assessment Report (AR5). These are RCP2.6, RCP4.5 and RCP8.5, which are based on the concentration of greenhouse gases and aerosols in the atmosphere. RCP2.6 is an aggressive mitigation pathway, where greenhouse gas emissions are strongly reduced. RCP4.5 is an intermediate ‘stabilisation’ pathway, where greenhouse gas emissions are reduced by varying levels. RCP8.5 is a high emission pathway, where greenhouse gas emissions continue to grow unmitigated. Further information is available in the Understanding Climate Data ESRI Storymap and the RCP Guidance on the UKCP18 website.What are the percentiles?The UKCP18 sea-level projections are based on a large Monte Carlo simulation that represents 450,000 possible outcomes in terms of global mean sea-level change. The Monte Carlo simulation is designed to sample the uncertainties across the different components of sea-level rise, and the amount of warming we see for a given emissions scenario across CMIP5 climate models. The percentiles are used to characterise the uncertainty in the Monte Carlo projections based on the statistical distribution of the 450,000 individual simulation members. For example, the 50th percentile represents the central estimate (median) amongst the model projections. Whilst the 95th percentile value means 95% of the model distribution is below that value and similarly the 5th percentile value means 5% of the model distribution is below that value. The range between the 5th to 95th percentiles represent the projection range amongst models and corresponds to the IPCC AR5 “likely range”. It should be noted that, there may be a greater than 10% chance that the real-world sea level rise lies outside this range. Data sourceThis data is an extract of a larger dataset (every year and more percentiles) which is available on CEDA at https://catalogue.ceda.ac.uk/uuid/0f8d27b1192f41088cd6983e98faa46eData has been extracted from the v20221219 version (downloaded 17/04/2023) of three files:seaLevelAnom_marine-sim_rcp26_ann_2007-2100.ncseaLevelAnom_marine-sim_rcp45_ann_2007-2100.ncseaLevelAnom_marine-sim_rcp85_ann_2007-2100.ncUseful links to find out moreFor a comprehensive description of the underpinning science, evaluation and results see the UKCP18 Marine Projections Report (Palmer et al, 2018).For a discussion on ice sheet instability processes in the latest IPCC assessment report, see Fox-Kemper et al (2021). Technical note for the update to the underpinning data: https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/ukcp/ukcp_tech_note_sea_level_mar23.pdfFurther information in the Met Office Climate Data Portal Understanding Climate Data ESRI Storymap.

The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8 degrees horizontal grid and 17 vertical levels for the oceanic part. Flux adjustment for atmosphere-ocean heat and water exchange is applied to prevent a drift of the modelled climate. The atmospheric model adopts a radiation scheme based on the k-distribution, two-stream discrete ordinate method (DOM) (Nakajima and Tanaka, 1986). This scheme can deal with absorption, emission and scattering by gases, clouds and aerosol particles in a consistent manner. In the calculation of sulphate aerosol optical properties, the volumetric mode radius of the sulphate particle in dry environment is assumed to be 0.2 micron. The hygroscopic growth of the sulphate is considered by an empirical fit of d'Almeida et al. (1991). The vertical distribution of the sulphate aerosol is assumed to be constant in the lowest 2 km of the atmosphere. The concentrations of greenhouse gases are represented by equivalent-CO2. Three integrations are made for 200 model years (1890-2090). In the control experiment (CTL), the globally uniform concentration of greenhouse gases is kept constant at 345 ppmv CO2-equivalent and the concentration of sulphate is set to zero. In the experiment GG, the concentration of greenhouse gases is gradually increased, while that of sulphate is set to zero. In the experiments GS, the increase in anthropogenic sulphate as well as that in greenhouse gases is given and the aerosol scattering (the direct effect of aerosol) is explicitly represented in the way described above. The indirect effect of aerosol is not included in any experiment. The scenario of atmospheric concentrations of greenhouse gases and sulphate aerosols is given in accordance with Mitchell and Johns (1997). The increase in greenhouse gases is based on the historical record from 1890 to 1990 and is increased by 1 percent / yr (compound) after 1990. For sulphate aerosols, geographical distributions of sulphate loading for 1986 and 2050, which are estimated by a sulphur cycle model (Langer and Rodhe, 1991), are used as basic patterns. Based on global and annual mean sulphur emission rates, the 1986 pattern is scaled for years before 1990; the 2050 pattern is scaled for years after 2050; and the pattern is interpolated from the two basic ones for intermediate years to give the time series of the distribution. The sulphur emission rate in the future is based on the IPCC IS92a scenario. The sulphate concentration is offset in our run so that it starts from zero at 1890. The seasonal variation of sulphate concentration is ignored. Discussion on the results of the experiments will be found in Emori et al. (1999). Climate sensitivity of the CCSR/NIES model derived by equilibrium runs is estimated to be 3.5 degrees Celsius. Global-Mean Temperature, Precipitation and CO2 Changes (w.r.t. 1961-90) for the CCSR/NIES model. From the IPCC website: The A1 Family storyline is a case of rapid and successful economic development, in which regional averages of income per capita converge - current distinctions between poor and rich countries eventually dissolve. In this scenario family, demographic and economic trends are closely linked, as affluence is correlated with long life and small families (low mortality and low fertility). Global population grows to some nine billion by 2050 and declines to about seven billion by 2100. Average age increases, with the needs of retired people met mainly through their accumulated savings in private pension systems. The global economy expands at an average annual rate of about three percent to 2100. This is approximately the same as average global growth since 1850, although the conditions that lead to a global economic in productivity and per capita incomes are unparalleled in history. Income per capita reaches about US$21,000 by 2050. While the high average level of income per capita contributes to a great improvement in the overall health and social conditions of the majority of people, this world is not w... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.292.2 for complete metadata about this dataset.

The CSIRO Atmospheric Research Mark 2b climate model (Hirst et al., 1996, 1999) has recently been used for a number of more sophisticated climate change simulations. These start from 1880 to avoid the "cold start problem". This version of the CSIRO model includes the Gent-McWilliams mixing scheme in the ocean and shows greatly reduced climate drift relative to earlier versions (e.g. Dix and Hunt, 1998). The drift in global mean surface temperature in the new control run is about -0.02 degrees C/century. Note that the model uses flux correction. The model atmosphere has 9 levels in the vertical and horizontal resolution of spectral R21 (approximately 5.6 by 3.2 degrees). The ocean model has the same horizontal resolution with 21 levels. The equilibrium sensitivity to doubled CO2 of a mixed layer ocean version of the model is 4.3 degrees. This is at the high end of the range of model sensitivities (e.g. IPCC 1995, Table 6.3). In the basic greenhouse gas experiment the model combines the effect of all radiatively active trace gases into an "equivalent" CO2 concentration. Observed concentrations are used from 1880 to 1990 and the IS92a projections into the future. This gives close to a 1%/year compounding increase of equivalent CO2. Another model experiment includes the negative radiative forcing from atmospheric sulphate aerosol. The direct aerosol forcing is included via a perturbation of the surface albedo, similarly to the Hadley Centre experiments described by Mitchell et al (1995) and Mitchell and Johns (1997) . The sulphate concentrations are the same as used in the Hadley Centre experiments. However the chosen aerosol optical properties are different, giving a present day forcing due to anthropogenic sulphate of about -0.4 W/m^2. This can be compared to the 1880-1990 greenhouse gas forcing of about 2 W/m^2. The magnitude of the 20th century warming in the model including aerosol matches the observed reasonably well. However there are a number of forcings missing from the model, including solar variability, sulphate indirect effect and the effect of soot. The climate sensitivity of CSIRO-Mk2 is about 4.3 degrees C (Watterson et al.,1997). From the IPCC website: The A1 Family storyline is a case of rapid and successful economic development, in which regional averages of income per capita converge - current distinctions between poor and rich countries eventually dissolve. In this scenario family, demographic and economic trends are closely linked, as affluence is correlated with long life and small families (low mortality and low fertility). Global population grows to some nine billion by 2050 and declines to about seven billion by 2100. Average age increases, with the needs of retired people met mainly through their accumulated savings in private pension systems. The global economy expands at an average annual rate of about three percent to 2100. This is approximately the same as average global growth since 1850, although the conditions that lead to a global economic in productivity and per capita incomes are unparalleled in history. Income per capita reaches about US$21,000 by 2050. While the high average level of income per capita contributes to a great improvement in the overall health and social conditions of the majority of people, this world is not without its problems. In particular, many communities could face some of the problems of social exclusion encountered by the wealthiest countries in the 20th century and in many places income growth could come with increased pressure on the global commons. Energy and mineral resources are abundant in this scenario family because of rapid technical progress, which both reduce the resources need to produce a given level of output and increases the economically recoverable reserves. Final energy intensity (energy use per unit of GDP) decreases at an average annual rate of 1.3 percent. With the rapid increase in income, dietary patterns shift initially significantly towards increased consumption of meat and dairy products, but may decrease subsequently with increasing emphasis on health of an aging society. High incomes also translate into high car ownership, sprawling suburbanization and dense transport networks, nationally and internationally. Land prices increase faster than income per ... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.79.5 for complete metadata about this dataset.

The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8 degrees horizontal grid and 17 vertical levels for the oceanic part. Flux adjustment for atmosphere-ocean heat and water exchange is applied to prevent a drift of the modelled climate. The atmospheric model adopts a radiation scheme based on the k-distribution, two-stream discrete ordinate method (DOM) (Nakajima and Tanaka, 1986). This scheme can deal with absorption, emission and scattering by gases, clouds and aerosol particles in a consistent manner. In the calculation of sulphate aerosol optical properties, the volumetric mode radius of the sulphate particle in dry environment is assumed to be 0.2 micron. The hygroscopic growth of the sulphate is considered by an empirical fit of d'Almeida et al. (1991). The vertical distribution of the sulphate aerosol is assumed to be constant in the lowest 2 km of the atmosphere. The concentrations of greenhouse gases are represented by equivalent-CO2. Three integrations are made for 200 model years (1890-2090). In the control experiment (CTL), the globally uniform concentration of greenhouse gases is kept constant at 345 ppmv CO2-equivalent and the concentration of sulphate is set to zero. In the experiment GG, the concentration of greenhouse gases is gradually increased, while that of sulphate is set to zero. In the experiments GS, the increase in anthropogenic sulphate as well as that in greenhouse gases is given and the aerosol scattering (the direct effect of aerosol) is explicitly represented in the way described above. The indirect effect of aerosol is not included in any experiment. The scenario of atmospheric concentrations of greenhouse gases and sulphate aerosols is given in accordance with Mitchell and Johns (1997). The increase in greenhouse gases is based on the historical record from 1890 to 1990 and is increased by 1 percent / yr (compound) after 1990. For sulphate aerosols, geographical distributions of sulphate loading for 1986 and 2050, which are estimated by a sulphur cycle model (Langer and Rodhe, 1991), are used as basic patterns. Based on global and annual mean sulphur emission rates, the 1986 pattern is scaled for years before 1990; the 2050 pattern is scaled for years after 2050; and the pattern is interpolated from the two basic ones for intermediate years to give the time series of the distribution. The sulphur emission rate in the future is based on the IPCC IS92a scenario. The sulphate concentration is offset in our run so that it starts from zero at 1890. The seasonal variation of sulphate concentration is ignored. Discussion on the results of the experiments will be found in Emori et al. (1999). Climate sensitivity of the CCSR/NIES model derived by equilibrium runs is estimated to be 3.5 degrees Celsius. Global-Mean Temperature, Precipitation and CO2 Changes (w.r.t. 1961-90) for the CCSR/NIES model. From the IPCC website: The A1 Family storyline is a case of rapid and successful economic development, in which regional averages of income per capita converge - current distinctions between poor and rich countries eventually dissolve. In this scenario family, demographic and economic trends are closely linked, as affluence is correlated with long life and small families (low mortality and low fertility). Global population grows to some nine billion by 2050 and declines to about seven billion by 2100. Average age increases, with the needs of retired people met mainly through their accumulated savings in private pension systems. The global economy expands at an average annual rate of about three percent to 2100. This is approximately the same as average global growth since 1850, although the conditions that lead to a global economic in productivity and per capita incomes are unparalleled in history. Income per capita reaches about US$21,000 by 2050. While the high average level of income per capita contributes to a great improvement in the overall health and social conditions of the majority of people, this world is not w... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.291.2 for complete metadata about this dataset.

The experiments with the GFDL model used here were performed using the coupled ocean-atmosphere model described in Manabe et al. (1991) and Stouffer et al., (1994) and references therein. The model has interactive clouds and seasonally varying solar insolation. The atmospheric component has nine finite difference (sigma) levels in the vertical. This version of the model was run at a rhomboidal resolution of 15 waves (R15) yielding an equivalent resolution of about 4.5 degrees latitude by 7.5 degrees longitude. The model has global geography consistent with its computational resolution and seasonal (but not diurnal) variation of insolation. The ocean model is based on that of Byan and Lewis (1979) with a spacing between gridpoints of 4.5 degrees latitude and 3.7 degrees longitude. It has 12 unevenly spaced levels in the vertical dimension. To reduce model drift, the fluxes of heat and water are adjusted by amounts which vary seasonally and geographically, but do not change from one year to another. The model also includes a dynamic sea-ice model (Bryan, 1969) which allows the system additional degrees of freedom. The 1000-year unforced simulation used here is described in Manabe and Stouffer (1996). The drift in global-mean temperature during this unforced simulation is very small at about -0.023 degrees C per century. The two GFDL-R15 climate change experiments used here use the IS92a scenario of estimated past and future greenhouse gas (GGa1) and combined greenhouse gas and sulphate aerosol (GSa1) forcing for the period 1765-2065 (Haywood et al., 1997). For the GGa1 experiment only the 100-year segment from 1958-2057 are available through the IPCC DDC. The radiative effects of all greenhouse gases is represented in terms of an equivalent CO2 concentration, and the direct radiative sulphate aerosol forcing is parameterised in terms of specified spatially dependent surface albedo changes (following Mitchell et al., 1995). Results from these climate change experiments are discussed in Haywood et al. (1997). The model's climate sensitivity is about 3.7 degrees C. Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R and D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence a... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.181.2 for complete metadata about this dataset.