Greenland

This page offers a comprehensive overview of Greenland's climate zones of temperature and precipitation with reflection of their climatological seasonal cycle, drawing on the Köppen-Geiger classification system and recent historical data from the Climatic Research Unit (CRU). ​

To deepen understanding of regional climate dynamics, the page includes visual context layers that highlight key aspects of exposure and vulnerability to climate change—such as population density, topography, and the frequency of natural hazards. These layers help users assess how environmental and social factors intersect with climate hazards to shape climate-related risks.​

The page also presents historical and future climate projections based on the latest CMIP6 models. These projections offer insights into how temperature and precipitation patterns may evolve under different climate scenarios, providing essential information for planning, adaptation, and resilience-building efforts across .

What you can see in this figure
The Köppen-Geiger climate classification system is widely used as a simple, yet effective way for categorizing the world's climates based on temperature and precipitation conditions. 

Understanding the Data: Implications and Utility
This classification is useful to help users understand and describe what the climate is like in different regions of the world. The Köppen system is based on defined temperature and precipitation thresholds and changes in these variables over time can cause a region to shift from one climate classification to another. This visual presentation offers a spatial understanding of climate zones and sub-classifications globally. These shifts are strong indicators of changing climates and aid understanding of changes in specific areas of interest, for example:

  • Areas of southern Europe classified as Csa (Hot-summer, Mediterranean climate) may be shifting toward BSh (Hot, semi-arid climate) as rainfall decreases and temperatures rise.
  • Regions in the Arctic once classified as ET (Tundra) are seeing warming trends that may eventually reclassify them as Dfc (Subarctic climate), reflecting potential for a longer growing season.

This map applies this classification system to the latest climatology (1991-2020) of CRU data. 

  • Tropical Climates
    • Af – Tropical rainforest climate
    • Am – Tropical monsoon climate
    • As/Aw – Tropical savanna climate
  • Dry Climates
    • BWh – Hot desert climate
    • BWk – Cold desert climate
    • BSh – Hot semi-arid climate
    • BSk – Cold semi-arid climate
  • Temperate Climates
    • Csa – Hot-summer Mediterranean climate
    • Csb – Warm-summer Mediterranean climate
    • Csc – Cold-summer Mediterranean climate
    • Cwa – Monsoon-influenced humid tropical climate
    • Cwb – Monsoon-influenced temperate oceanic climate / or subtropical highland climate
    • Cwc – Monsoon-influenced subpolar oceanic climate / or subtropical highland climate
    • Cfa – Humid subtropical climate
    • Cfb – Temperate oceanic climate
    • Cfc – Subpolar oceanic climate
  • Continental Climates
    • Dsa – Mediterranean-influenced hot-summer humid continental climate
    • Dsb – Mediterranean-influenced warm-summer humid continental climate
    • Dsc – Mediterranean-influenced subarctic climate
    • Dsd – Mediterranean-influenced.extremely cold subarctic climate
    • Dwa – Monsoon-influenced hot-summer humid continental climate
    • Dwb – Monsoon-influneced warm-summer humid continental climate
    • Dwc – Monsoon-influenced subarctic climate
    • Dwd – Monsoon-influenced extremely cold subarctic climate
    • Dfa – Hot-summer humid continental climate
    • Dfb – Warm-summer humid continental climate
    • Dfc – Subarctic climate
    • Dfd – Extremely cold subacrtic climate
  • Polar Climates
    • ET – Tundra
    • EF – Ice cap climate

Full classifications and corresponding calculations can be reviewed here.
The original Köppen Classification system can be reviewed here. 

What you can see in this figure
Temperature, precipitation, and other expressions of climate change throughout a 12 month period. This basic climatology chart provides the fundamentals of seasonality for the selected area of focus. The chart is dynamic and elements can be turned on/off by selecting variables in legend. 

Understanding the Data: Implications and Utility
Understanding these patterns can help to identify local climate behavior and the potential for changing seasons over time. 

Users should use this chart to understand:

  • When is the wet season? Are there multiple rainy seasons?
  • How intense is the dry season and does it overlap with times of the year with highest temperatures? What might this mean for risk and need for adaptation/ resilience efforts?
  • Are there significant temperature swings - throughout the year and/or between minimum and maximum temperatures?

What you can see in this figure
The Intergovernmental Panel on Climate Change (IPCC) defines risk as the result of interactions between three key components: hazards (such as climate change), exposure (e.g., population, infrastructure), and vulnerability (e.g., weak governance or limited resources). Importantly, this framework also considers adaptive capacity—the ability of systems and communities to respond and adjust to climate impacts.

To better understand these dynamics, we present a series of context layers—visual tools that help explore how climate-related risks manifest across different regions. These layers fall into two main categories:

  • Geospatial raster data, such as topography, land cover, or population density
  • Geocoded point data, such as the locations of cities, ports, or energy infrastructure

Understanding the Data: Implications and Utility
By overlaying these layers with climate data, we can begin to explore critical questions, such as:

  • Which cities are most exposed to extreme heat?
  • Where do heat stress and pollution risks overlap?
  • How vulnerable is the energy sector to climate-related hazards?

What are some caveats and potential limitations to consider?
These visualizations serve as a foundation for deeper analysis, helping identify priority areas for adaptation, resilience planning, and policy intervention. This is just a first stop. There are plenty more layers and indicators that contribute to risk. 

Night Lights: Our analysis incorporates annual nighttime lights data from the Earth Observation Group (EOG) at the Colorado School of Mines. Specifically, we utilize the VIIRS Nighttime Lights Version 2 (VNL V2) product for 2024. This dataset represents the median observed light intensity, effectively filtering out transient events like fires and capturing stable illumination. While originally at a 15 arcsecond resolution, it has been downsampled to 0.25 degrees for enhanced visualization. The units for light radiance are nanoweatts per square centimeter per steradian (nW/cm2/sr). 

While logarithmic scaling of VIIRS night lights data enhances visibility in dimmer regions, it also amplifies inherent biases. Key sources of error, particularly affecting high latitudes and oceans, include cloud cover (obscuring or scattering light), high-albedo surfaces like snow and ice (overestimating brightness), varying day-night hours and polar night conditions (limiting observations), and natural phenomena like auroras (confused with artificial light). Additionally, stray light from the sun, moonlight reflections, lights from fishing vessels, and thermal sources like gas flares or fires further contaminate the data. 

For analyses aiming to represent high population concentration / high human activity, we recommend focusing on highly clustered lights rather than diffuse illumination, as these are more likely to reflect genuine population density.
Source: https://eogdata.mines.edu/products/vnl/   

Particle Contamination: Here we show the annual average for 2022. PM2.5 refers to particulate matter smaller than 2.5 micrometers in diameter. It is significant due to its harmful health effects, such as causing respiratory and cardiovascular issues, especially in vulnerable populations.  It can influence climate by either reflecting (aerosol) or absorbing sunlight (e.g. black carbon). Most importantly, contamination represents a multiplier to the effects of climate change. Global and regional PM2.5 concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the variance in ground-based PM2.5 measurements. A subsequent statistical fusion incorporates additional information from PM2.5 measurements. The World Health Organization sets acceptable PM2. 5 levels at 10 µg/m³ annually and at 25 µg/m³ in the 24-hour window.  
Source: Satellite-derived PM2.5 from the Atmospheric Composition Analysis Group (ACAG) version: V6.GL.02.02 https://sites.wustl.edu/acag/datasets/surface-pm2-5/  

Population: The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. Here we show the year 2020.  
Source: Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 – 2.5 arc-minute resolution https://cmr.earthdata.nasa.gov/search/concepts/C1597159135-SEDAC.html 

Topography(elevation): The ETOPO Global Relief Model combines topography, bathymetry, and shoreline data from both regional and global sources to provide a detailed, high-resolution representation of Earth’s surface features. In this study, we use the bedrock elevation layer—representing surface height with ice sheets and vegetation removed—at a resolution of 60 arcseconds (approximately 1.8 km). At this coarse resolution, sharp peaks are smoothed out, so even the highest mountain ranges appear with reduced elevations, typically around 6,000 meters. 
Source: NOAA https://data.noaa.gov/metaview/page?xml=NOAA/NESDIS/NGDC/MGG/DEM//iso/xml/etopo_2022.xml&view=getDataView&header=none   
Data download: https://www.ncei.noaa.gov/products/etopo-global-relief-model and https://www.ncei.noaa.gov/maps/grid-extract/ 

Cities: The dataset GHS-UCDB R2024A contains statistics on urban centres based on data from the Global Human Settlement Layer (GHSL) produced at the Joint Research Centre of the European Commission, unit E.1 (Disaster Risk Management). This release is based on the GHSL Data Package 2023, the Degree of Urbanisation to delineate spatial entities, and geospatial data integration from a variety of open source datasets to characterise them. The result is the most complete information system on cities to date with data for 11,422 quality-controlled urban centres across 15 thematic domains, 471 indicators, and 2600 attributes. The UCDB has two data streams, one based on a fixed delineation of urban centres in 2025, and a second version based on multi-temporal delineation of urban centres, traceable over time. The UCDB integrates data from Copernicus Services (including Emergency, Land Monitoring, Marine and Climate), peer-reviewed datasets (i.e. from the scientific literature), and institutional information systems (i.e. from the United Nations). 
Source - https://human-settlement.emergency.copernicus.eu/download.php?ds=ucdb and https://ec.europa.eu/regional_policy/rest/cms/upload/16102020_113348_ghs_ucdb_ewrc.pdf 

Dams: The Global Dam Watch consensus global database brings together the foundational global datasets GOODD, GRAND and FHReD, complemented with data from various other sources, into a single, globally consistent dam and instream barrier data product for global-scale analyses. Degree of Regulation (DOR) in percent; equivalent to “residence time” of water in the reservoir; calculated as ratio between storage capacity and total annual flow. A DOR < 100% means the reservoir cannot hold an entire year’s worth of flow — limited regulation capacity. A DOR = 100% means the reservoir can store one full year’s worth of river flow. A DOR > 100% indicates strong regulatory capacity, where water can be stored over multiple years, providing greater flexibility for water supply, flood control, or hydropower. DOR is a proxy for human intervention: high values often reflect dams for irrigation, hydropower, flood control, or water supply.  
Source: https://www.globaldamwatch.org/ 

Natural Disasters: GDIS presents a new open-source extension to the Emergency Events Database (EM-DAT) that allows researchers to explore and make use of subnational, geocoded data on major disasters triggered by natural hazards since 1960 until 2018. EM-DAT, maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the Catholic University of Louvain in Belgium, constitutes a comprehensive and widely used multi-disaster catalogue. The EM-DAT database records disaster events by country and “location” (i.e., a string variable providing the names of affected provinces, districts, towns, etc.), but the database contains no geographical information that allows easy integration into a geospatial analysis framework.“  
Source: GDIS, a global dataset of geocoded disaster locations, Rosvold and Buhaug, 2021 Data: https://sedac.ciesin.columbia.edu/data/set/pend-gdis-1960-2018/data-download 

Ports: The World Port Index (Pub 150) is an on-line database of world-wide maritime port information which serves as a general reference and navigational planning tool for mariners. The principal sources of information in the WPI are the Sailing Directions and charts published by the National Geospatial-Intelligence Agency (NGA), but where information from those sources is lacking or incomplete, other authoritative sources, both domestic and foreign, are used. The WPI in no way replaces the charts and related publications which cover in detail the ports that are summarized herein. For detailed operational planning, reference should always be made to the latest charts and publications. 
Source: World Port Index https://msi.nga.mil/Publications/WPI (downloaded on March 20th 2025) 

Power Plants: A comprehensive, global, open-source database of power plants by type of fuel.  Last update - 2024 
Source: Global power plant database, World Research Institute (WRI) https://datasets.wri.org/datasets/global-power-plant-database  

What you can see in this figure
A Warming Stripes graph provides a visual representation of the change in selected temperature or precipitation variable. Each bar represents the the annual average for a year. Data is derived from CMIP6 multi-model ensemble median: historical simulation, 1950-2014 and per each SSP, 2015-2100. 

Relatively warmer is represented as yellow to red colors, while relative cooling is represented as blue. For rain, bluer means wetter. Hover over the graph to view the exact annual values for each year, allowing for more detailed exploration of the data.

Understanding the Data: Implications and Utility
This visualization makes it easy to spot both gradual trends and abrupt shifts in climate, and to compare the changes projected for each scenario into the future.
 

SSP1-1.9: Sustainable Path (Very Low Emissions)

  • World: Focuses on sustainability, global cooperation, and reduced inequality.
  • Emissions: Very low; aims for warming well below 2°C (e.g., 1.5°C).

SSP1-2.6: Sustainable Path (Low Emissions)

  • World: Similar to SSP1-1.9 in its sustainable societal development, but with slightly less aggressive mitigation.
  • Emissions: It aims to keep global warming below 2°C. This still requires significant global cooperation and substantial emissions reductions, but not as rapid or deep as SSP1-1.9.

SSP2-4.5: Middle of the Road (Intermediate Emissions)

  • World: Continues historical social and economic trends, with some progress but no dramatic shifts towards sustainability.
  • Emissions: Intermediate; current trends continue, leading to moderate warming.

SSP3-7.0: Regional Rivalry (High Emissions)

  • World: Characterized by fragmentation, nationalism, and declining cooperation, leading to slow development and high inequality.
  • Emissions: High; limited climate action results in significant warming.

SSP5-8.5: Fossil-Fueled Development (Very High Emissions)

  • World: Rapid, energy-intensive economic growth reliant on fossil fuels, with high technological advancement.
  • Emissions: Very high; leads to severe warming due to continued fossil fuel reliance.

The number (e.g., 1.9, 4.5, 7.0, 8.5) refers to the radiative forcing (in Watts per square meter) by 2100, indicating the projected warming level. A higher number means more warming.