Map projections are essential tools in climate change studies. They help visualize data related to global warming and its impacts across different regions of the world. Choosing the right projection ensures accurate representation and interpretation of spatial information. As climate science increasingly relies on geospatial data to communicate risks and inform policy, the choice of map projection can significantly influence how trends are understood by researchers, decision-makers, and the public. This article examines the role of map projections in climate research, the types most commonly used, their strengths and weaknesses, and best practices for selection.

The Role of Map Projections in Communicating Climate Data

Climate data is inherently spatial. Temperature anomalies, precipitation changes, ice loss, and sea-level rise are measured at specific locations or across grids that span the globe. To visualize these datasets in a two-dimensional format—whether on paper or a screen—cartographers and scientists must transform the Earth’s curved surface into a flat map. This transformation is a map projection. No projection can preserve all spatial properties (area, shape, distance, direction) simultaneously; each introduces some distortion. The choice of projection therefore determines which aspects of the data are represented faithfully and which are compromised.

In climate change communication, accuracy matters. A map that exaggerates the size of polar regions might overstate the extent of Arctic sea ice loss, while one that distorts area could misrepresent the geographic distribution of vulnerable populations. Conversely, a well-chosen projection can highlight critical patterns, such as the poleward shift of temperature belts or the concentration of extreme weather events in specific latitudes. As NASA’s Earth Observatory notes, effective climate visualization helps audiences grasp the scale and urgency of global changes without introducing unintended biases.

Fundamentals of Map Projections

What is a Map Projection?

A map projection is a mathematical method of transforming the three-dimensional coordinates of the Earth (latitude and longitude) into two-dimensional x,y coordinates on a plane. The Earth is an oblate spheroid, but it is often approximated as a sphere for global mapping. Projections can be classified by the developable surface (cylinder, cone, plane) onto which the globe is projected, or by the property they preserve (equal-area, conformal, equidistant, or azimuthal). Understanding these families helps in choosing a projection suited to the analytical goal.

Common Distortion Types

Every projection distorts at least one of four properties:

  • Area – Equal-area (equivalent) projections preserve the relative size of regions, making them essential for comparing geographic distributions of phenomena like population, land cover, or emissions.
  • Shape – Conformal projections preserve local angles and shapes, important for navigation, weather maps, and displaying features like coastlines accurately at small scales.
  • Distance – Equidistant projections maintain true distances from one or two points, useful for analyzing proximity to a climate event or measuring impacts along a transect.
  • Direction – Azimuthal projections preserve directions from a central point, valuable for studying atmospheric circulation patterns or storm tracks relative to a specific location.

No projection can preserve all four properties over a large area. The trade-offs require careful consideration based on the intended use of the visualization.

Key Map Projections Used in Climate Research

Climate scientists and cartographers employ a range of projections to address specific analytical needs. Below are some of the most common projections used in climate studies, along with their typical applications and limitations.

Mercator Projection – Strengths and Limitations

Developed in 1569 for maritime navigation, the Mercator projection is conformal, preserving angles and shapes locally. It became the default in many web mapping applications, including early versions of Google Maps. However, its severe area distortion—especially at high latitudes—makes Greenland appear larger than South America, when in fact South America is nearly nine times bigger. For climate studies that involve polar regions (Arctic sea ice, ice-sheet mass balance), the Mercator projection dramatically exaggerates the perceived scale of change. Despite its convenience for interactive maps, using Mercator for global climate data can mislead viewers. Many climate data portals now avoid Mercator in favor of equal-area projections for global overviews. The Intergovernmental Panel on Climate Change (IPCC) typically uses equal-area projections for its summary figures.

Robinson Projection – A Compromise for Global Views

The Robinson projection, created in 1963, is a pseudocylindrical compromise that balances distortions of area, shape, distance, and direction. It is neither equal-area nor conformal, but its visual appeal and moderate distortions make it popular for general-purpose world maps in textbooks and media. In climate communication, the Robinson projection is often used for thematic maps that show broad patterns, such as global temperature anomalies or ocean acidification trends. Its main drawback is that it is not equal-area, so comparing the extent of phenomena across different latitudes can be problematic. For rigorous spatial analysis, equal-area alternatives are preferred.

Equal-Area Projections – Eckert IV, Mollweide, and Others

Equal-area projections preserve the relative size of regions, making them indispensable for quantitative comparisons. The Mollweide projection (1805) uses a sinusoidal distortion to maintain area while showing the entire globe in an ellipse. It is commonly used by climate scientists for visualizing global datasets like land-surface temperature, vegetation indices, and carbon flux maps. The Eckert IV projection offers a different shape, with less distortion at the poles and more at the equator, and is also frequently employed in climate research publications. The Gall-Peters projection, while controversial for its shape distortion, is equal-area and has been used by some organizations to emphasize equitable representation of landmasses. For climate data that must reflect actual proportions—such as the area of land affected by drought or the extent of permafrost thaw—equal-area projections are the gold standard.

Lambert Conformal Conic for Regional Studies

Many climate impact assessments focus on specific regions—continents, countries, or watersheds. For such regional work, the Lambert Conformal Conic (LCC) projection is widely used. It is conformal (preserves shapes well) and yields minimal distortion along two standard parallels. The LCC projection is the standard for many national weather services and for the National Oceanic and Atmospheric Administration (NOAA) in displaying temperature and precipitation maps over the contiguous United States. It allows detailed analysis of storm tracks, heatwaves, and regional climate trends without the shape distortion that would occur with a global projection.

Azimuthal Projections for Polar Regions

Polar regions are critically important in climate studies because of their role in ice-albedo feedback, sea-level rise, and atmospheric circulation. Azimuthal projections, such as the Azimuthal Equidistant and Lambert Azimuthal Equal-Area (LAEA), center on a pole and provide accurate distances or areas around that point. The LAEA is especially useful for mapping Arctic sea ice concentration, permafrost distribution, and Antarctic ice sheet dynamics because it preserves area relationships within the polar region. These projections minimize distortion near the center while allowing the entire hemisphere to be displayed. Climate scientists often use polar stereographic projections (which are conformal) to represent ice-sheet topography and glacier flow lines.

Impact of Projection Choice on Climate Visualizations

The choice of projection can fundamentally alter the message conveyed by a climate map. Below are specific examples showing how different projections affect the interpretation of common climate data types.

Visualizing Temperature Anomalies

Global temperature anomaly maps, showing deviations from a baseline period, are a staple of climate reporting. When plotted on an equal-area projection, the map accurately represents the proportion of each region’s surface area where temperatures are higher or lower than average. Using a Mercator projection, however, overemphasizes high-latitude warming because Greenland, Canada, and Siberia appear much larger than they are. This can create a visual impression that polar amplification (the faster warming in high latitudes) is even more dominant than the data indicate. While polar amplification is real, the map should not exaggerate its apparent spatial extent. Reports from organizations like the NOAA National Centers for Environmental Information often use equal-area or modified Robinson projections to avoid this bias.

Mapping Sea-Level Rise

Sea-level rise projections are frequently illustrated with global maps of coastal inundation risk. An equal-area projection ensures that the relative land area threatened in different regions is correctly scaled. For instance, the low-lying delta regions of South and Southeast Asia, which house millions of people, should not be visually minimized compared to smaller but larger-looking landmasses in high latitudes. Using a projection that distorts area could downplay the hazard in densely populated areas or exaggerate risks in sparsely populated polar regions. Best practice is to use an equal-area projection for any map that emphasizes the geographic extent of a risk.

Displaying Precipitation Patterns

Precipitation change maps often show projected increases or decreases in rainfall under different emission scenarios. Again, area distortion can mislead viewers into thinking that changes over large high-latitude areas are more significant than those over smaller equatorial regions. In addition, conformal projections like Mercator preserve shape at the expense of area, which can make storm tracks appear more uniform in scale than they actually are. For regional precipitation studies within the tropics, a simple cylindrical equal-area projection (such as the Lambert cylindrical equal-area) is often effective because it preserves area and aligns with the latitudinal bands of rainfall patterns.

Misleading Perceptions and Examples

One of the most famous examples of projection-driven misinterpretation comes from media maps of Arctic sea ice extent. News outlets sometimes use Mercator-derived web maps, which make the ice cover appear much larger and more linear than it would on an equal-area projection. Conversely, some maps of global deforestation or land-use change have used the Gall-Peters projection to intentionally emphasize the size of tropical forests compared to temperate ones. While Gall-Peters does preserve area, its shape distortion can make regions appear stretched and unfamiliar, potentially reducing the map’s effectiveness. The key lesson is that projection choices carry implicit narratives, and climate communicators must be transparent about their methods.

Challenges and Best Practices

Despite decades of research, effectively using map projections in climate visualization remains challenging. Scientists and communicators must balance accuracy with visual clarity, especially when the audience may not be familiar with cartographic concepts.

Selecting the Right Projection for the Data

The first step is to define the analytical goal. If the objective is to compare the areal extent of a phenomenon (e.g., area of drought-affected land), an equal-area projection is mandatory. If the goal is to trace weather systems or ocean currents, a conformal projection like Mercator or Lambert Conformal Conic may be better. For general audience communication, a compromise projection like Robinson or Winkel Tripel (which also balances distortions) can produce a natural-looking map while minimizing extremes of distortion. Many climate modeling centers now provide data in a native grid (e.g., latitude-longitude), but when regridding or reprojecting, they document the projection used in their final visualizations.

Interactive web maps present additional challenges. Modern web mapping libraries (e.g., Leaflet, Mapbox) default to the Web Mercator projection for technical reasons (tiling, performance). For global climate data, this can be problematic. Fortunately, many libraries now support alternative projections via plug-ins, allowing developers to build interactive equal-area maps. The D3.js library, for instance, offers a wide range of projections that climate data journalists and researchers can use to create custom visualizations.

Combining Multiple Projections

For complex analyses covering both global and regional scales, it may be necessary to use multiple projections. A study on Arctic sea-ice loss and its effects on mid-latitude weather, for example, might use a polar stereographic projection for the Arctic and a Lambert Conformal Conic for the mid-latitudes, with careful attention to how the two maps are aligned and interpreted. Multimedia reports sometimes use a small inset globe with an equal-area projection to provide context, while the main map uses a regional projection optimized for detail.

Software and Tools

Modern GIS software and programming libraries simplify projection use. Popular open-source tools include:

  • QGIS – Offers a comprehensive set of projections and on-the-fly reprojection.
  • Python (Cartopy, Matplotlib) – Cartopy provides a large database of projections and is widely used in climate science for plotting.
  • R (ggplot2, sf) – The sf package supports coordinate reference system transformations.
  • JavaScript (D3.js, Leaflet) – Enable interactive web maps with custom projections.

When using these tools, always verify that the projected coordinate system is correctly defined and that the underlying data uses a latitude-longitude coordinate system before transformation. Metadata and provenance are crucial for reproducibility.

Future Directions – Dynamic and Interactive Projections

As climate data volumes grow and visualization technologies advance, new approaches to map projections are emerging. One trend is the use of dynamic projections that can be adjusted by the user. For example, an interactive globe can allow viewers to rotate and zoom, essentially letting them choose the projection for the portion they are viewing. While this mitigates distortion by focusing on a smaller area, global views still require a projection. Another trend is the adoption of equal-area projections in major data portals. The NASA Climate website and the World Bank Climate Knowledge Portal increasingly use equal-area projections for their flagship visualizations. There is also growing interest in projection-free methods, such as spherical maps rendered in 3D virtual globes, which completely eliminate planar distortion. These are particularly useful for interactive exploration but may not be suitable for static print reports. Ultimately, the future will likely see a hybrid approach: equal-area projections for static comparisons, conformal projections for detailed regional analyses, and 3D globes for exploratory data browsing.

Conclusion

Map projections are not neutral tools; they actively shape how we perceive climate change. A projection that exaggerates high-latitude regions can overstate the visual impact of polar warming, while one that distorts area can hide the vulnerability of equatorial nations. Climate scientists, educators, and communicators must understand the mathematics behind projections and the trade-offs they entail. By selecting projections that faithfully represent the data’s intended message—whether area, shape, distance, or direction—we can produce visualizations that inform rather than mislead. As climate change continues to reshape our world, clear and honest mapping is more important than ever.