Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

About this dataset

Context

The dataset tabulates the Earth population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Earth across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Earth was 897, a 0.44% decrease year-by-year from 2022. Previously, in 2022, Earth population was 901, a decline of 0.55% compared to a population of 906 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Earth decreased by 204. In this period, the peak population was 1,101 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Earth is shown in this column.
• Year on Year Change: This column displays the change in Earth population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Earth Population by Year. You can refer the same here

Land degradation is the persistent reduction in the capacity of the land to support human and other life on Earth (IPBES, 2018). This process jeopardizes the provision of ecosystem services. The Sustainable Development Goal (SDG) 15, ‘Life on Land’, includes efforts to sustainably manage and recover natural ecosystems and restore degraded land and soil. Under the umbrella of SDG 15, the United Nations Convention to Combat Desertification (UNCCD) has defined an indicator framework to monitor progress toward ‘land degradation neutrality’. We evaluated the performance of SDG 15.3.1, focusing on “the proportion of land that is degraded over the total land area” for the European Union (EU) using the TRENDS.EARTH software. We assessed the impact of alternative datasets at different spatial resolutions and policy-relevant data sources for land cover (CORINE) and soil organic carbon (SOC) stock (LUCAS). Our hypothesis was that higher spatial resolution sub-indicators would better identify the total share of degraded land and provide a clearer picture of the extent of degraded land for the target period. Land productivity trajectories were adjusted using the Water Use Efficiency index that revealed the high share of improving land reported by the NDVI trends. Therefore, it is advisable to use always a climate correction to assess land productivity trends. Replacing default datasets with alternative sub-indicators allowed the detection of 25–40% more degraded areas. Additionally, the integration with a combined proxy of land degradation (soil erosion >10 Mg ha-1 yr-1, and SOC concentration <1%) identified an additional 50% land degradation and revealed that a large extent of the EU needs restoration measures. The data presented here correspond to some of the maps that are present in the peer-reviewed publication entitled "Evaluation of the United Nations Sustainable Development Goal 15.3.1 indicator of land degradation in the European Union" in Land Degradation & Development, Volume 34, Issue 1Jan 2023Pagesi, 1-295, https://onlinelibrary.wiley.com/doi/full/10.1002/ldr.4457 There are 9 maps. The SOCERO1.tif map (figure 2c in the paper) represents the convergence of evidence between areas affected by extreme soil loss (>10 Mg ha-1 yr-1) and extremely low SOC content (<1%). It can be considered as the main outcome of the study. The tiff map presented has a 100m resolution. Pixel values are: 0: for soil loss <10 Mg ha-1 yr-1 and SOC content >1% 1: where soil loss >10 Mg ha-1 yr-1 or SOC content <1% 2: where soil loss >10 Mg ha-1 yr-1 and SOC content <1% Soil erosion by water can heavily alter soils in agricultural ecosystems, and it is recognized as a major land degradation pathway , therefore we used as source of soil erosion the Revised Universal Soil Loss Equation (RUSLE) modelled soil erosion rates, source https://esdac.jrc.ec.europa.eu/content/soil-erosion-water-rusle2015 and for the Soil Organic Carbon (SOC) content (<1%) was derived from the SOC concentration of Land Use and Coverage Area frame Survey LUCAS https://esdac.jrc.ec.europa.eu/content/chemical-properties-european-scale-based-lucas-topsoil-data, the two layers were combined in a single layer defining thus the indicator “SOC + Erosion” as a proxy for degraded land. No data due to urban and inland water are left blank. MODIS WUE data are masked for urban areas and water bodies; CORINE CLC land cover 2000 and 2018 are reclassified into the Trends.earth software. This operation can generate 'no data'. LUCAS topsoil organic carbon stock were obtained by using the original raster file distributed by ESDAC, multiplied for the bulk density . The data can have small amount of "no data" for high altitude and near water bodies areas. All sub-indicators displayed a good spatial coverage and the SDG 15.3.1 calculated has also a good spatial coverage and is suitable for global scale analysis, but might be not indicated for detail scale assessment The dataset includes the two maps on which the SOCERO1.tif map is based (figures 2a and 2b in the paper) Erosion_10ha.tif (at 100m resolution) with values 1 where erosion > 10 Mg ha-1 yr-1 SOC_less1%.tif (at 500m resolution) with values 1 where SOC content <1% The dataset contains also 6 tiff maps corresponding to the 6 figures (a to f) of the figure-7. The possible values are: -1 where 'degraded', 0 where 'stable', and 1 where 'improving'. The resolution of these 6 maps is 130m Some additional metadata: Geographical coverage: EU27 Reference period for the data: both 2000-2015, 2000-2018 Spatial reference: ETRS89_Lambert_Azimutal_Equal_Area Status of the dataset: final Keywords; separated by; : SDG 15.3.1;land degradation; land cover changes; drylands; erosion; soil organic carbon degradation Disclaimer: Due to post-processing of the datasets, there might be slight differences in the area coverage at national scales between these data and the published version. In addition, as the Trends.earth tool delivered recently a newer version of the AGIS plugin, it might be that this led to slightly different results. For any issue please get in touch with ec-esdac@ec.europa.eu. Important Note: this is a only first attempt to assess Land Degradation for the EU-27, in the context of the UN SDG 15.3.1. As new data and new knowledge will become available, updates could be made to this current output.

This dataset provides a detailed record of global sea level changes, offering valuable insights into historical trends and future projections. Useful for climate scientists, oceanographers, policymakers, and anyone interested in understanding and mitigating the impacts of climate change.

   Open Sourced from: https://ourworldindata.org/climate-change: Citation: Hannah Ritchie, Pablo Rosado and Veronika Samborska (2024) - “Climate Change” Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/climate-change' [Online Resource]

The RCMAP (Rangeland Condition Monitoring Assessment and Projection) dataset quantifies the percent cover of rangeland components across western North America using Landsat imagery from 1985-2023. The RCMAP product suite consists of ten fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, sagebrush, shrub, tree, and shrub height …

This dataset provides the summer NDVI trend and trend significance for the period 1984-2012 over Alaska and Canada. The NDVI were calculated per-pixel from all available peak-summer 30-m Landsat 5 and 7 surface reflectance data for the period. NDVI time series were assembled for each 30-m land location (i.e., non-water, non-snow), from observations that were unaffected by clouds as indicated by data-quality masks and following additional processing to remove anomalous NDVI values. A simple linear regression via ordinary least squares was applied to the per-pixel NDVI time series. The slope of the regression was taken as the annual NDVI trend (unit NDVI change per year) and is reported in the "trend" data files. A Student's t-test was used to assess the significance of the trend and the per-pixel significance is reported in the "trend_sig" data files. A significant positive slope indicates a greening trend, and a significant negative slope indicates a browning trend.

About this dataset

Context

The dataset tabulates the Black Earth population by year. The dataset can be utilized to understand the population trend of Black Earth.

Content

The dataset constitues the following datasets

• Black Earth, WI Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The United States Geological Survey (USGS) Land Cover Trends field photography collection is a national-scale, ground-reference dataset which initially served as a research tool to aid in Landsat-derived land-use/land-cover (LULC) change analyses and assessments. Between 1999 and 2007, Land Cover Trends scientists collected over 33,000 geographically referenced field photos with associated keywords capturing existing LULC and regional change process taking place. The field photography collection represents the most comprehensive national database of geo-referenced USGS field photography in the United States. In a project funded by the USGS Climate and Land Use Change and Core Science Systems Mission Areas, 13,000 photos distributed across 44 Level III Environmental Protection Agency (EPA) ecoregions are currently being disseminated to the public. As the remaining field photographs are geotagged, the online collection will grow to include photos within 40 additional ecoregions.

Notes for the 13.10.2019 update- The months of January 2019 to July 2019 were added to the ERA5 reconstructions- The robustness of the ERA5 reconstruction was improved for a few Greenland and Antarctica mascons by better handling a special case occuring when air temperature is always lower than 0°C during the calibration period.- The updated ERA5 time series might differ from the previous version (especially individual ensemble members). With the exception of the special case mentioned above, these differences are not significant.List of all filesReadme file 00_readme.txtMonthly grids - ensemble means 01_monthly_grids_ensemble_means_allmodels.zipMonthly grids - ensembles, model 1 to 6 02_monthly_grids_ensemble_JPL_MSWEP_1979_2016.zip 02_monthly_grids_ensemble_JPL_GSWP3_1901_2014.zip 02_monthly_grids_ensemble_JPL_ERA5_1979_201907.zip 02_monthly_grids_ensemble_GSFC_MSWEP_1979_2016.zip 02_monthly_grids_ensemble_GSFC_GSWP3_1901_2014.zip 02_monthly_grids_ensemble_GSFC_ERA5_1979_201907.zipDaily grids - ensemble means, model 1 to 6 03_daily_grids_ensemble_means_JPL_MSWEP_1979_2016.zip 03_daily_grids_ensemble_means_JPL_GSWP3_1901_2014.zip 03_daily_grids_ensemble_means_JPL_ERA5_1979_201907.zip 03_daily_grids_ensemble_means_GSFC_MSWEP_1979_2016.zip 03_daily_grids_ensemble_means_GSFC_GSWP3_1901_2014.zip 03_daily_grids_ensemble_means_GSFC_ERA5_1979_201907.zipGlobal averages - daily and monthly time series 04_global_averages_allmodels.zipContent of readmeGRACE TWS Reconstruction (GRACE_REC_v03)The dataset contains reconstructed time series of daily and monthly anomalies of terrestrial water storage (TWS) based on two different GRACE solutions and three different meteorological forcing datasets. There is a total of 6 different models:JPL_MSWEP - trained with GRACE JPL mascons, forced with MSWEP forcing (1979-2016)JPL_GSWP3 - trained with GRACE JPL mascons, forced with GSWP3 forcing (1901-2014)JPL_ERA5 - trained with GRACE JPL mascons, forced with ERA5 forcing (1979-present)GSFC_MSWEP - trained with GRACE GSFC mascons, forced with MSWEP forcing (1979-2016)GSFC_GSWP3 - trained with GRACE GSFC mascons, forced with GSWP3 forcing (1901-2014)GSFC_ERA5 - trained with GRACE GSFC mascons, forced with ERA5 forcing (1979-present)The reconstruction aims at reproducing the sub-decadal climate-driven variability observed in the GRACE data. Seasonal cycle and human impacts on TWS are not reconstructed. A GRACE-based seasonal cycle is provided for convenience. Long-term signals (trends over a period >15 years) are removed during the model calibration procedure but are still present in the final dataset and mainly represent precipitation-driven trends. The interpretation of the reconstructed long-term trends should be done with the awareness that there can be some uncertainty in the reconstructed trends.For most applications, uncertainty ranges can be derived from the 100 ensemble members available for each model.The grids are stored in NetCDFv4 files in units of mm (kg m^-2). Although the data is provided on a 0.5 degrees grid, the effective spatial resolution should be considered to be 3 degrees, similar to the original resolution of the GRACE datasets. This might need to be taken into account when comparing this dataset against other sources.The global means are stored as csv files in units of Gt of water. To convert back to mm of water, use the land area values given in the reference paper below.When using this dataset, please cite:Humphrey, V., & Gudmundsson, L. (2019). GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data, 11(3), 1153-1170.Vincent Humphrey, October 2019California Institute of TechnologyYour feedback is always welcome:vincent.humphrey[-a-t-]caltech.edu (vincent.humphrey[-a-t-]bluewin.ch) Abstract

The amount of water stored on continents is an important constraint for water mass and energy exchanges in the Earth system and exhibits large inter-annual variability at both local and continental scales. From 2002 to 2017, the satellites of the Gravity Recovery and Climate Experiment mission (GRACE) have observed changes in terrestrial water storage (TWS) with an unprecedented level of accuracy. In this paper, we use a statistical model trained with GRACE observations to reconstruct past climate-driven changes in TWS from historical and near real time meteorological datasets at daily and monthly scales. Unlike most hydrological models which represent water reservoirs individually (e.g. snow, soil moisture, etc.) and usually provide a single model run, the presented approach directly reconstructs total TWS changes and includes hundreds of ensemble members which can be used to quantify predictive uncertainty. We compare these data-driven TWS estimates with other independent evaluation datasets such as the sea level budget, large-scale water balance from atmospheric reanalysis and in-situ streamflow measurements. We find that the presented approach performs overall as well or better than a set of state-of-the-art global hydrological models (Water Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a spatial resolution of 0.5°, at both daily and monthly scales over the period 1901 to present, based on two different GRACE products and three different meteorological forcing datasets, resulting in 6 reconstructed TWS datasets of 100 ensemble members each. Possible user groups and applications include hydrological modelling and model benchmarking, sea level budget studies, assessments of long-term changes in the frequency of droughts, the analysis of climate signals in geodetic time series and the interpretation of the data gap between the GRACE and the GRACE Follow-On mission.Check reference for additional details and caveats.ReferenceHumphrey, V., & Gudmundsson, L. (2019). GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data, 11(3), 1153-1170.

We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. To characterize the climate conditions across the study period, we use the Standardized Precipitation Evapotranspiration Index (SPEI). The SPEI is a water balance index which includes both precipitation and evapotranspiration in its calculation. Conditions from the prior n months, generally ranging from 1 to 60, are compared to the same respective period over the prior years to identify the index value (Vicente-Serrano et al., 2010). Values generally range from -3 to 3, where values less than 0 suggest drought conditions while values greater than 0 suggest wetter than normal conditions. For this study, we are using the 12-month, or 1-year, SPEI to compare annual conditions within the larger Upper Gila River watershed. The SPEI data was extracted into a CSV spreadsheet using data from the Gridded Surface Meteorological (GRIDMET) dataset, which provides a spatially explicit SPEI product in Google Earth Engine (GEE) at a 5-day interval and a spatial resolution of 4-km (Abatzoglou, 2013). In GEE, we quantify overall mean values of SPEI across each 5-day period for the watershed from January 1980 to December 2021. Using R software, we reduced the 5-day values to represent monthly mean values and constrained the analysis to water year 1980 (i.e., October 1980) through water year 2021 (i.e., October 2021). Using the monthly timeseries, we completed the breakpoint analysis in R to identify breaks within the SPEI time series. The algorithm identifies a seasonal pattern within the timeseries. When the seasonal pattern deviates, a breakpoint is then detected. These breaks can be used to pinpoint unique climate periods in the time series. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. The spreadsheet attached to this Child Item consists of 5 columns, including the (i) month from January 1985 through October 2021, (ii) the 1-year SPEI monthly time series, (iii) the dates identified as breaks within the breakpoint algorithm, (iv) the breakpoint trend identified within the breakpoint algorithm, and (v) the dates that were used as the climate period breaks in this study. The climate periods identified in this spreadsheet using the SPEI data were used as the climate periods in our riparian study.

Ship-based measurements of sea surface wind speed displays a spurious upward trend due to increases in anemometer height. To correct this bias, we construct a new sea surface wind dataset from ship observations of wind speed and wind wave height archived in the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The Wave and Anemometer-based Sea-surface Wind (WASWind) dataset is available at monthly resolution on a 4 degrees by 4 degrees. longitude-latitude grid from 1950 to 2011. It substantially reduces the upward trend in wind speed through height-correction for anemometer-measured winds, rejection of spurious Beaufort winds, and use of estimated winds from wind wave height. The reduced upward trend is smallest among the existing global datasets of in situ observations and comparable with those of reanalysis products. Despite the significant reduction of globally-averaged wind speed trend, WASWind features rich spatial structures in trend pattern, making it a valuable dataset for studies of climate changes on regional scales. Not only does the combination of ship winds and wind wave height successfully reproduce major modes of seasonal-to-decadal variability, but its trend patterns are also physically consistent with sea level pressure (SLP) measurements. WASWind is in close agreement with wind changes in satellite measurements by the Special Sensor Microwave Imager (SSM/I) for the recent two decades. The agreement in trend pattern with such independent observations illustrates the utility of WASWind for climate trend analysis.

This dataset provides the Gravitational Mass Balance (GMB) product derived from gravimetry data from the GRACE satellite instrument, by DTU Space. The data consists of two products: a mass change time series for the entire Greenland Ice Sheet and different drainage basins for the period April 2002 to June 2016; and mass trend grids for different 5-year periods between 2003 and 2016. This version (1.5) is derived from GRACE monthly solutions from the CSR RL06 product.The mass change time series contains the mass change (with respect to a chosen reference month) for all of the Greenland Ice Sheet and each individual drainage basin. For each month (defined by a decimal year) a mass change in Gt and its associated error (also in Gt) is provided. The mass trend grid product is given in units of mm water equivalent per year.Mass balance is an important variable to understand glacial thinning and ablation rates to enable mapping glacier area change. The time series allows the longer term comparison of trends whereas the mass trend grids provide a yearly snapshot which can be further analysed and compared across the data set. Basin definitions and further data descriptions can be found in the Algorithm Theoretical Baseline Document (ST-DTU-ESA-GISCCI-ATBD-001_v3.1.pdf) and Product Specification Document (ST-DTU-ESA-GISCCI-PSD_v2.2.pdf) which are provided on the Greenland Ice Sheet CCI project website. Citation: Barletta, V. R., Sørensen, L. S., and Forsberg, R.: Scatter of mass changes estimates at basin scale for Greenland and Antarctica, The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013, 2013.

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Observed Climate Change Impacts Database contains observed responses to climate change across a wide range of systems as well as regions. These data were taken from the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008). It consists of responses in the the physical, terrestrial biological systems and marine-ecosystems. The observations that were selected include data that demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable, and the is for at least 20 years between 1970 and 2004, although study periods may extend earlier or later. For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing more than 29,500 data series. Observations in the database are characterized as a "change consistent with warming" or a "change not consistent with warming", based on information from the underlying studies.

This dataset provides estimates of trends in temperature, moisture, and vegetation changes over the circumpolar Arctic. Time series trends were measured by the Theil-Sen slope and associated p-values for a variety of variables including 2-meter air temperature, precipitation, soil moisture, non-frozen season days, permafrost active layer thickness, snow cover, vapor pressure deficit, land surface water fraction, normalized difference vegetation index (NDVI), and vegetation optical depth. Trends were measured annually and over specific seasons of spring (March to May), summer (June to August), autumn (September to November) and winter (December to February), and for the 1980-2020 and 1997-2020 time periods, depending on the variable and original data availability. Emerging hotspots of change were identified for the same variables and seasons, but only over the 1997-2020 period. In addition, a multivariate ranking was used to create combined hotspot layers to show areas of substantial changes in the thermal environment, moisture, and vegetation; these themes reflect landscape changes considered to be detrimental (e.g., a threat) to ecosystems and human populations. Ancillary files provide the boundaries of study regions, Brown permafrost regions, and a land cover product. The data are provided in cloud optimized GeoTIFF (COG) and shapefile formats.

Global warming vs climate change

Many people use these two terms interchangeably, but we think it’s important to acknowledge their differences. Global warming is an increase in the Earth’s average surface temperature from human-made greenhouse gas emissions. On the other hand, climate change refers to the long-term changes in the Earth’s climate, or a region on Earth, and includes more than just the average surface temperature. For example, variations in the amount of snow, sea levels, and sea ice can all be consequences of climate change.

Content

Worldwide Climate Change & Global Warming keyword / topic search in Google Search Engine from 2004 - present

Acknowledgements

Google Trends Lab

Trends of Atmospheric Carbon Dioxide measurements from the Mauna Loa Baseline Observatory, Hawaii, United States.

Regional barystatic sea-level change trend and uncertainties, from 2003-2016 and 1993-2016.
Attention: This dataset is linked to a submitted manuscript, and has not undergo peer review yet!
Barystatic sea-level change (also known as ocean mass change) is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage.
Here, we use a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic sea-level change.
We then compute the trend (rate of sea-level change), and quantify three types of uncertainties of these regional barystatic sea-level change fields:1. Intrinsic uncertainty: related to the observational error;2. Temporal uncertainty: related to the temporal variability in the time series;3. Spatial-structural: related to the location/distribution of the mass change sources;
The methods used to obtain this dataset, as well as the results, are presented in the manuscript "Trends and Uncertainties of Regional Barystatic Sea-level Change in the Satellite Altimetry Era", submitted to the journal Earth System Dynamics
Code for exploring this dataset can be found on: https://github.com/carocamargo/barystatic_SL

Exploring the effect of climate change and human activities on vegetation is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long-term vegetation GIMMS NDVI data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detail human activities with some innovations. The results showed that (1) NDVI showed an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PⅠ: 1982–2000) than after it (PⅡ: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate-driven promotion had the strongest effect, followed by human-driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis showed that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PⅠ and economization during PII. Methods (1) To monitor the vegetation, the AVHRR_version 5 (Advanced Very High Resolution Radiometer) NDVI3g datasets were used because of their high quality, with 0.05° and 1-day spatial and temporal resolution, respectively (download from https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5). To reduce the effects of atmospheric and aerosol scattering, we used the maximum value composite (MVC) method to develop a monthly NDVI dataset. The dataset covers the period from 1982 to 2020. In the text, the growing season of vegetation in Inner Mongolia is defined as April–October. (2) To further explore the anthropogenic factors inhibiting vegetation growth and achieve a detailed stripping of human activities, we adopted the statistical data and the Hasse diagram technique to be combined. The directed Hasse diagram technique (HDT) is an extension of the ISM explanatory structure model, realization based on the theory of partial order, and has been widely used in several fields, such as chemical risk assessment and environmental science, as well as factor ranking of land degradation, to rank the impacts of each factor. The Hasse diagram technique can be used to reflect the correlations of all elements in an ensemble and has shown good performance in the analysis of drivers of land degradation.

The Dynamic Land Cover Dataset of Australia is the first nationally consistent and thematically comprehensive land cover reference for Australia. It is the result of a collaboration between Geoscience Australia and the Australian Bureau of Agriculture and Resource Economics and Sciences, and provides a base-line for identifying and reporting on change and trends in vegetation cover and extent.

The dataset comprises digital files of the land cover classification, three trend datasets showing the change in behaviour of land cover across Australia for the period 2000 to 2008 and a digital copy of the technical report.

This raster dataset, in Cloud Optimized GeoTIFF format (COG), provides information on land surface changes at the pan-arctic scale. Multispectral Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI imagery (cloud-cover less than 80%, months July and August) was used for detecting disturbance trends (associated with abrupt permafrost degradation) between 2003 and 2022. For each satellite image we calculated the Tasseled Cap multi-spectral index to translate the spectral reflectance signal to the semantic information Brightness, Greenness, and Wetness. In order to characterize change information, we calculated the linear trend of the Brightness, Greenness and Wetness over two decades on the individual pixel level. The final map product therefore contains information on the direction and magnitude of change for all three Tasseled Cap parameters in 30m spatial resolution across the pan-arctic permafrost domain. Features detected include coastal erosion, lake drainage, infrastructure expansion, and fires. The general processing methodology was developed by Fraser et al. 2014 and adapted and expanded by Nitze et al. 2016 and Nitze et al. 2018. Here we upscaled the processing to the circum-arctic permafrost region and the recent 20-year period from 2003 through 2022. The service covers the permafrost region up to 81° North: Alaska (USA), Canada, Greenland, Iceland, Norway, Sweden, Finland, Russia, Mongolia, and China. For Russia and China, regions not containing permafrost were excluded. The data has been processed in Google EarthEngine within the research projects ERC PETA-CARB, ESA CCI+ Permafrost, NSF Permafrost Discovery Gateway, and EU Arctic PASSION. The dataset is a contribution to the 'Panarctic requirements-driven Permafrost Service' of the Arctic PASSION project (see references). Changes in the Tasseled Cap indices Brightness, Greenness, and Wetness are displayed in the image bands red, green, and blue, respectively. Here, coastal erosion (a trend of a land surface transitioning to a water surface) is depicted in dark blue colors, while coastal accretion (a trend of a water surface transitioning to a land surface) is depicted in bright orange colors. Drained lakes appear in bright yellow or orange colors, depending on the soil conditions and vegetation regrowth. Fire scars are a further common feature, which can appear in different colors, depending on the time of the fire and pre-fire land cover. […]