This dataset presents essential statistics related to global population dynamics. It includes information such as the year, economy, economy label, absolute population values in thousands, and urban population percentages. The dataset covers the period from 1950 to 2050, providing insights into population trends and urbanization patterns across various economies. The columns in data set is

Year Economy
Economy Label
Absolute value in thousands
Absolute value in thousands Missing value
Urban population as percentage of total population
Urban population as percentage of total population Missing value

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

This dataset was kindly made available by the World Bank.

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 from 2015. Our own 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, the country of 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 next 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 each year.

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 the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.

"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."

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11723377%2F59bc70fb3d13d9f954e317aacbfd2bd6%2FPopulation.png?generation=1681981140865261&alt=media" alt="">

The population data from the United Nations is a dataset that contains information on the estimated population of each country in the world for various years between 1980 and 2050. The dataset includes the following columns:

• place: Name of the country or region
• pop1980: Estimated population for the year 1980
• pop2000: Estimated population for the year 2000
• pop2010: Estimated population for the year 2010
• pop2022: Estimated population for the year 2022
• pop2023: Estimated population for the year 2023
• pop2030: Estimated population for the year 2030
• pop2050: Estimated population for the year 2050
• country: ISO 3166-1 alpha-3 code of the country
• area: Total land and water area of the country (in square kilometers)
• landAreaKm: Land area of the country (in square kilometers)
• cca2: ISO 3166-1 alpha-2 code of the country
• cca3: ISO 3166-1 alpha-3 code of the country
• netChange: Annual net change in population (in thousands)
• growthRate: Annual population growth rate (as a percentage)
• worldPercentage: Percentage of world population
• density: Population density (in persons per square kilometer)
• densityMi: Population density (in persons per square mile)
• rank: Rank of the country by population

The dataset provides a comprehensive overview of the population of each country over time and can be used to analyze population trends, make population projections, and compare the population of different countries. The dataset can also be used in combination with other data sources to explore correlations between population and various social and economic indicators.

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.

Content

The current population of the United States of America is 334,464,117 as of Saturday, April 16, 2022, based on Worldometer elaboration of the latest United Nations data. This three datasets contain population data of USA (2020 and histIndiaorical), population forecast and population in major cities.

Attribute Information

• Year - Years from 2020-1955
• Population - Population in the respective year
• Yearly % Change - Percentage Yearly Change in Population
• Yearly Change - Yearly Change in Population
• Migrants (net) - Total number of migrants
• Median Age - Median age of the population
• Fertility Rate - Fertility rate
• Density (P/Km²)- Population density (population per square km)
• Urban Pop %- Percentage of urban population
• Urban Population- Urban population
• Country's Share of World Pop - Population share
• World Population - World Population in the respective year
• India Global Rank - Global Rank in Population

Source

Link : https://www.worldometers.info/world-population/us-population/

Updated Covid 19 and Other Datasets

Link : https://www.kaggle.com/anandhuh/datasets

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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.

This layer shows the types and combinations of modeled change to the World Terrestrial Ecosystems (WTEs) from 2015 to 2050 using the CMIP6 SSP5-8.5 emissions scenario. One of the most extreme SSPs is scenario SSP5-8.5, which assumes nearly doubling the current CO2 levels by 2050, with an accompanying average global temperature increase of 4.4 deg C by the end of the 21st century. 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 SSP8-8.5 scenario indicates 38.6% 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 SSP3-7.0 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.

Annual Average number of people affected by floods in Projected Climate Conditions taking into considerations Socio-Economic Projections. Future climate conditions for the years 2050-2100 are estimated from the output of the EC‐EARTH3‐HR global model (Hazeleger et al., 2012) with RCP 8.5 and grid resolution of 0.5° (~55 km at the Equator). Demographic projections for the year 2050 are estimated on the basis of the World Population Prospects 2019 ,United Nations, Department of Economic and Social Affairs, Population Division (2019). This estimation is part of the results of a probabilistic regional flood risk assessment developed for the Horn of Africa Partnership for Early Warning and Early Action (developed by CIMA Foundation in cooperation with ICPAC, WFP, UNDRR, March 2021)

This comprehensive dataset, derived from the United Nations World Urbanization Prospects 2018, provides detailed insights into the global demographic shifts from 1950 to 2050. It covers a wide range of data points including total, urban, and rural populations, alongside growth rates and urbanization trends across different regions, subregions, and countries.

Dataset Files WUP2018-F01-Total_Urban_Rural.xls: Population counts for urban and rural areas as of mid-2018, including percentages. WUP2018-F02-Proportion_Urban.xls: Historical and projected percentages of urban populations from 1950 to 2050. WUP2018-F03-Urban_Population.xls: Urban population figures from 1950 to 2050. WUP2018-F04-Rural_Population.xls: Rural population figures from 1950 to 2050. WUP2018-F05-Total_Population.xls: Total population figures from 1950 to 2050. WUP2018-F06-Urban_Growth_Rate.xls: Annual urban population growth rates from 1950 to 2050. WUP2018-F07-Rural_Growth_Rate.xls: Annual rural population growth rates from 1950 to 2050. WUP2018-F08-Total_Growth_Rate.xls: Total population growth rates from 1950 to 2000. WUP2018-F09-Urbanization_Rate.xls: Changes in the rate of urbanization from 1950 to 2050. WUP2018-F10-Rate_Proportion_Rural.xls: Changes in the proportion of rural populations from 1950 to 2050. WUP2018-F18-Total_Population_Annual.xls: Detailed annual total population data from 1950 to 2050. WUP2018-F19-Urban_Population_Annual.xls: Detailed annual urban population data from 1950 to 2050. WUP2018-F20-Rural_Population_Annual.xls: Detailed annual rural population data from 1950 to 2050. WUP2018-F21-Proportion_Urban_Annual.xls: Detailed annual urban population percentages from 1950 to 2050. Potential Uses This dataset is invaluable for researchers, policy makers, urban planners, and sociologists interested in understanding the dynamics of urbanization and its impacts on global development. The data can be used for:

Analyzing trends in urban and rural growth. Forecasting future demographic shifts. Planning for infrastructure, services, and resources in rapidly urbanizing regions. Studying regional differences in development and urbanization.

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.307.2 for complete metadata about this dataset.

Processed monthly variables for members 1-10 of World Avoided rcp4.5 members

Group members: Katrin Chapkis, Ava Kamali, Simranjit Kang, Jennifer Mariatha This lab was conducted on September 24th 2015 from 2.30 PM to 5.30 PM, at York University. The dataset was collected from Grasslands located next to Danby woodlot. The weather was cloudy and partially sunny with a temperature of 21 degrees Celsius. There was hardly any wind to interfere with field training. The purpose of this lab was to teach how to collect field data for a very first time and familiarize us with sampling tools, such as traits, transects and quadrats. These tools make it easier to understand the relation between several variables and their effects on each other. All group members contributed to collecting data for all of four datasets, but each member only submits one. Late Purple Aster (Aster prenathoides) was selected as the target species for Grassland data set 2 because it was easily recognizable in the field. This flower was identified according to information provided in the Practical ecology website. Each group was provided with two transect tapes, one of which was used for measuring distances and the other one was used for measuring the length of each target plant. To begin with, the transect tape was tied to a tree branch, which was chosen randomly. Since the measuring tape started at 3in dut to the tied knot, we subtracted that portion from each measured distance to record the actual value. From the starting point, we chose a random direction and walked in a straight line. Every time the line touched the target plant we observed, read, and recorded the distance shown on the transect tape. Then with our second transect tape, we measured the length of the plant. If the plant was bent we held it up (without pulling it) and measured its length from its highest point to the ground level. For every Late Purple Aster, three other data were recorded: the number of leaves, the number of flowers, and the level of crowding (0-3). We first started to count each flower and leaf, but as we got familiar to it we were able to estimate their number by observing the plant. Crowding level refers to the number of plants which surround the target plant. There are three levels to it: 0= no plants, 1= one plant, 2= two to three plants, 3= more than three plants. We limited the last observation within a 30 centimeters radius of the target plant to check its surrounding easier (an estimation made by vision). These assumptions are specific to this dataset only. Finally, we repeated this process until we gathered enough data for 20 individuals.

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 exploratory extended time-mean sea-level projections to 2300 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 risk 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 2300, 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). These exploratory projections show sea levels continue to increase beyond 2100 even with large reductions in greenhouse gas emissions. It should be noted that these projections have a greater degree of uncertainty than the 21st Century Projections and should therefore be treated as illustrative of the potential future changes. They are designed to be used alongside the 21st Century projections for those interested in exploring post-2100 changes.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/a077f4058cda4cd4b37ccfbdf1a6bd29Data has been extracted from the v20221219 version (downloaded 17/04/2023) of three files:seaLevelAnom_marine-sim_rcp26_ann_2007-2300.ncseaLevelAnom_marine-sim_rcp45_ann_2007-2300.ncseaLevelAnom_marine-sim_rcp85_ann_2007-2300.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.pdf.Further information in the Met Office Climate Data Portal Understanding Climate Data ESRI Storymap.

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

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 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. The A2 world sees more international tensions and less cooperation than in A1 or B1. People, ideas and capital are less mobile so that technology diffuses slowly. International disparities in productivity, and hence income per capita, are maintained or increased. With the emphasis on family and community life, fertility rates decline only slowly, although they vary among regions. Henc... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.154.2 for complete metadata about this dataset.

BackgroundSelf-harm has become a major public health problem globally. Data on the burden of self-harm in this study were taken from the GBD 2021. This study aimed to quantify historical trends (1990–2021) in the global burden of self-harm across genders, age groups, and regions, and project future changes (2022–2050) through Bayesian forecasting models.MethodsBased on the seven GBD super-regions, the burden of self-harm was analyzed by region, age, and gender from 1990 to 2021. Hierarchical statistical approach was used to predict trends in global and regional changes in the burden of self-harm, 2022-2050.ResultIn 2021, the global DALYs and death counts from self-harm were 33.5 million (95% UI: 31.3-35.8) and 746.4 thousand (95% UI: 691.8-799.8). The region with the highest number of DALYs and deaths is South Asia and the highest age-standardized rates of DALYs and mortality were in central Europe, eastern Europe, and central Asia. Globally, the burden of self-harm was higher for males than for females. DALYs rates were highest among adolescents and young adults (20-29 years), whereas mortality rates showed a predominantly age-progressive pattern with the highest burden observed in middle-aged and older populations, albeit with a modest decline in the oldest age groups. Forecasting models showed a sustained decline in the global burden of self-harm from 2022-2050.ConclusionThe results highlight the need for policymakers to allocate resources to high-burden regions (e.g., South Asia and Eastern Europe), to implement gender- and age-specific prevention programs, and to strengthen cross-sectoral collaboration to address the underlying social determinants of self-harm. The findings call for strengthened mental health services and targeted interventions to effectively respond to and reduce the devastating impact of self-harm on individuals and the global community.