The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

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.

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.

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.

"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."

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.

This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.

Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)

Method - demographic fractions

Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.

To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:

\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)

Where:

- \(\delta_{year,\ country,age}^{\text{wpp}}\) is the ratio of change in demographic for a given age and and country from the UN WPP dataset.

- \(f_{year,\ country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country, and year.

- \(f_{2010,country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.

The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.

For each subset of cells corresponding to a given country c, the fraction of population in a given age band is calculated as:

\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)

Where:

- \(f_{year,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for given year, for the grid cell c.

- \(f_{2010,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for 2010, for the grid cell c.

The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.

Method - demographic totals

Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.

The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.

Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.

The Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050, data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in ten-year increments, of population distribution and internal climate-related and other migration. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on crop productivity and water availability from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Three scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, and a more inclusive development scenario combining SSP2 and RCP8.5, and each scenario represents an ensemble of four model runs combining different climate impact models. The modeling work was funded and developed jointly with The World Bank, and covers most World Bank client countries, with reports released in 2018 and 2021 that address different regions and provide full methodological details.

This dataset provides population and other demographic estimates and projections from 1960 to 2050. They are disaggregated by age-group and gender and cover approximately 200 economies.

Enhance and apply the IMPACT system of models to examine multiple and likely conflicting trends and related goals at the global and regional scales, and for selected countries, in the context of changes in population, income, technology and climate to 2050. This dataset is directly applicable for use as the baseline feed data for the global economic model IMPACT. It is adaptable for use with models with similar representation of the global agricultural and food system.

This repository contains all data and R scripts to reproduce the figures in Van Dijk et al. (2021), A meta-analysis of global food demand and population at risk of hunger projections for the period 2010-2050, Nature Food. More specifically, it includes two databases: (1) A database with standardized information to describe the characteristics of 57 studies that were identified by the systematic literature review and (2) The Global Food Security Projections Database v1.0.1 with harmonized projections for three global food security indicators: food consumption in kcal per capita and total kcal, and population at risk of hunger. The database also includes projections for total global population that are required to derive the global food security indicators.

The two scripts (nf_figures.r and nf_meta_regression.r) can be used to reproduce the figures and tables in the main paper and the supplementary information. Please start with the first script, which sources the second script.

This is the first version of the Global Food Projections Database. We expect to update the data, including additional studies and variables in the future. For issues and suggestions, please contact michiel.vandijk@wur.nl.

FUME data on projected distributions of migrants at local level between 2030 and 2050.

The dataset contains a folder of data for each destination city as a gridded dataset at 100m resolution in GeoTIFF format. The examined destination cities are: Amsterdam, Copenhagen, Krakow and Rome. The dataset is provided as 100m grid cells based on the Eurostat GISCO grid of the 2021 NUTS version, using ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) as coordinate system. The file names consist of the projected year, the corresponding scenario, and the reference migrant group. The projections have been performed for the years 2030, 2040 and 2050. The investigated scenarios are the following: • benchmark (bs), • baseline (bs), • Rising East (re), • EU Recovery (eur), • Intensifying Global Competition (igc), and • War (war).

The migration background is derived from data about the Region of Origin (RoO) for migrants in Copenhagen and Amsterdam, and from Region of Citizenship (CoC) for migrants in Krakow and Rome.

The case study of Copenhagen covers the two central NUTS3 areas (DK011, DK012) and the groups presented are the following: • total population (totalpop), • native population (DNK), • Eastern EU European migrants (EU_East), • Western EU Europeans migrants (EU_West), • Non-EU European migrants (EurNonEU), • migrants from Turkey (Turkey), • the MENAP countries (MENAP; excluding Turkey), • other non-Western (OthNonWest), and • other Western countries (OthWestern).

The case study of Amsterdam covers one NUTS3 area (NL329) and the presented groups are the following: • total population (totalpop), • native population (NLD), • Eastern EU European migrants (EU East), • Western EU European migrants (EU West), • migrants from Turkey and Morocco (Turkey + Morocco), • migrants from the Middle East and Africa (Middle East + Africa), • migrants from the former colonies (Former Colonies), and • migrants from the rest of the world (Other Europe etc).

The case study of Krakow covers the Municipality of Krakow, and the presented groups are the following: • total population (totalpop), • native population (POL), • EU/EFTA European migrants (EU), • non-EU European migrants (Europe_nonEU), and • migrants from the rest of the world (Other).

The case of Rome covers the Municipality of Rome, and the presented groups are the following: • total population (totalpop), • native population (ITA), • migrants from Romania (ROU), • Philippines (PHL), • Bangladesh (BGD), • the EU (EU; excluding Romania), • Africa (Africa), • Asia (Asia; excluding Philippines and Bangladesh) and • America (America).

The Groundswell Africa Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050 data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in five-year increments, of population distribution and internal climate-related and other migration for West Africa and the Lake Victoria Basin. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on water availability, crop productivity, and pasturelands (where cropping does not occur), as well as flood impacts, from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Four scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, a more inclusive development scenario combining SSP2 and RCP8.5, and an optimistic scenario combining SSP2 and RCP2.6. Each scenario provides an ensemble average of four model runs combining different climate impact models as well as confidence intervals to better capture uncertainties. The modeling work was funded and developed jointly with The World Bank.

We assess the potential impact of international migration on population ageing in Asian countries by estimating replacement migration for the period 2022-2050.

This open data deposit contains the code (R-scripts) and the datasets (csv-files) for the replacement migration scenarios and a zero-migration scenario:

• Constant chronological old-age dependency ratio (Constant OADR scenario)
• Constant prospective old-age dependency ratio (Constant POADR scenario)
• Constant chronological working-age population (Constant WA scenario)
• Constant prospective working-age population (Constant PWA scenario)
• Zero-migration (ZM scenario)

Countries included in the analysis: Armenia, China, Georgia, Hong Kong, Japan, Macao, North Korea, Singapore, South Korea, Taiwan, Thailand.

Please note that for Armenia and Hong Kong (2023) and Georgia (2024) later baseline years are applied due to the UN country-specific assumptions on post-Covid-19 mortality.

For detailed information about the scenarios and parameters:

Dörflinger, M., Potancokova, M., Marois, G. (2024): The potential impact of international migration on prospective population ageing in Asian countries. Asian Population Studies. https://doi.org/10.1080/17441730.2024.2436201

All underlying data (UN World Population Prospects 2022) are openly available at:

https://population.un.org/wpp/Download/Archive

Code

1_Data.R:

• Load and merge data from UN World Population Prospects 2022
• Define sample
• Prepare data (prospective old-age thresholds, model sex and age pattern of migrants)

2_Scenarios.R:

• Replacement migration scenarios:
  • Constant chronological old-age dependency
  • Constant prospective old-age dependency
  • Constant chronological working-age population
  • Constant prospective working-age population
• Zero-migration scenario

3_Robustness_checks.R:

• Run replacement migrations scenarios with different model sex and age patterns for net migration

Program version used: RStudio "Chocolate Cosmos" (e4392fc9, 2024-06-05). Files may not be compatible with other versions.

Datasets

The datasets contain the key information on population size, the relevant indicators (OADR, POADR, WA, PWA) and replacement migration volumes and rates by country and year. Please see readme_datasets.txt for detailed information.

Acknowledgements

Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the German National Member Organization.

India Population Projection: Single Year data was reported at 1,667,873,933.000 Person in 2050. This records an increase from the previous number of 1,658,330,351.000 Person for 2049. India Population Projection: Single Year data is updated yearly, averaging 1,394,461,787.000 Person from Mar 2001 (Median) to 2050, with 50 observations. The data reached an all-time high of 1,667,873,933.000 Person in 2050 and a record low of 1,019,001,911.000 Person in 2001. India Population Projection: Single Year data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Demographic – Table IN.GAI001: Population Projection: Single Year.

In this research, we quantify combined point-source inputs of nutrients, microplastics, a chemical (triclosan) and a pathogen (Cryptosporidium) to 10,226 rivers in 2010, 2050 and 2100, and show how pollutants are related. In the future, 80% of the global population could be living in urbanized areas where waters are polluted with multiple pollutants. We could formulate scenarios where future water pollution from growing cities is avoided by advanced waste water treatment in many world regions, but not in Africa. Date Submitted: 2020-11-27

Enhance and apply the IMPACT system of models to examine multiple and likely conflicting trends and related goals at the global and regional scales, and for selected countries, in the context of changes in population, income, technology and climate to 2050

Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This set of files includes downscaled projections of decadal means of monthly total precipitation (in millimeters, no unit conversion necessary) for each month of decades 2020-2029, 2050-2059, and 2060-2069 at 2x2 kilometer spatial resolution. Each file represents a mean monthly total in a given decade.

The spatial extent is clipped to a Seward REA boundary bounding box.

Overview:

Most of SNAP#8217;s climate projections come in multiple versions. There are 5 climate models, one 5 model average, 3 climate scenarios, 12 months, and 100 years. This amounts to 21,600 files per variable. Some datasets are derived products such as monthly decadal averages or specific seasonal averages, among others. This specific dataset is one subset of those.

Each set of files originates from one of five top ranked global circulation models or is calculated as a 5 Model Average. These models are referred to by the acronyms: cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, miroc3_2_medres, or 5modelavg.

For a description of the model selection process, please see Walsh et al. 2008. Global Climate Model Performance over Alaska and Greenland. Journal of Climate. v. 21 pp. 6156-6174

Each set of files also represents one projected emission scenario referred to as: sresb1, sresa2, or sresa1b.

Emmission scenarios in brief:

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) created a range of scenarios to explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting greenhouse gas emissions. The B1 scenario describes a convergent world, a global population that peaks in mid-century, with rapid changes in economic structures toward a service and information economy. The Scenario A1B assumes a world of very rapid economic growth, a global population that peaks in mid-century, rapid introduction of new and more efficient technologies, and a balance between fossil fuels and other energy sources. The A2 scenario describes a very heterogeneous world with high population growth, slow economic development and slow technological change.

These files are bias corrected and downscaled via the delta method using PRISM (http:prism.oregonstate.edu) 1961-1990 2km data as baseline climate. Absolute anomalies are utilized for temperature variables. Proportional anomalies are utilized for precipitation variables. Please see http:www.snap.uaf.eduabout for a description of the downscaling process.

File naming scheme:

[variable]_[metric]_[units]_[format]_[assessmentReport] [groupModel][scenario]_[timeFrame].[fileFormat]

[variable] pr, tas, logs, dot, dof, veg, age, dem etc

[metric] mean, total, decadal mean monthly mean, etc

[units] mm, C, in, km

[format] optional, if layer is formatted for special use

[assessmentReport] ar4, ar5

[groupModel] cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, miroc3_2_medres, 5modelavg, cru_ts30

[scenario] sresb1, sresa2, sresa1b

[timeFrame] yyyy or mm_yyyy or yyyy_yyyy or mm_yyyy_mm_yyyy

[fileFormat] txt, png, pdf, bmp, tif

examples:

tas_mean_C_ar4_cccma_cgcm3_1_sresb1_05_2034.tif

this file represents mean May, 2034 temperature from the 4th Assessment Report on Climate Change from the CCCMA modeling group, using their CGCM3.1 model, under the B1 climate scenario.

pr_total_mm_ar4_5modelAvg_sresa1b_09_2077.tif

this file represents total September, 2077 precipitation from the 4th Assessment Report on Climate Change from the 5 Model Average, under the A1B climate scenario.

tas = near-surface air temperature

pr = precipitation including both liquid and solid phases