Figure 1 displays mapped results of global crop and economic models estimating temperature and precipitation changes on country-level agricultural productivity. Figure 2 highlights the eographic distribution of areas where current climatic conditions could lead to greater susceptibility to malaria in Africa. Figure 3 shows relative risk from sea-level rise by highlighting elevation differences along the coastline of Bangladesh. Figure 4 consists of a portion of a nautical chart from the Gulf of Maine used in a participatory mapping process aimed at understanding how local fishing communities respond
to environmental change. Finally, Figure 5 illustrates differences between two global climate models predicting changes in annual water runoff at the grid-cell level across the world.
It is important to note that while each of these maps illustrates an aspect of the potential impacts of climate change, no single one provides a comprehensive view of the overall global climate problem. The maps also vary in how they represent past and projected environmental changes across scales, scenarios, and scientific disciplines. Understanding the strengths and limitations of such different studies and map displays is an essential window into the big picture view of climate science and policy.
Map Reading 101
Now let’s take a closer look at these figures to see how three particular primary map elements mentioned above—scale, scenario, and baseline—can vary and influence the interpretation of climate change research.
Scale and Resolution: Perhaps the most visible differences between climate maps lie in the scale at which data are presented. Maps can be displayed at the global scale, representing the entire world (Figures 1 and 5) or be displayed at the regional, national, and local scales (Figures 2–4 respectively). While many studies focusing on the impacts of climate change on natural systems are large scale, other studies focusing on social and natural impacts of climate change can reach down to the small scale of a single community or ecosystem. Maps at any scale can also vary in the resolution, or level of detail, at which data are displayed. Some spatial data are based on an assembly of grid cells that are consistent across a given scale but whose resolution can vary between datasets, from a coarse 50-kilometer grid resolution to a fine 1-kilometer grid resolution, for example. While some spatial climate data are mapped on a grid, other spatial data are displayed as polygons representing administrative, ecoregional, or watershed units, to name a few.
Scenario and Forecast:A second major element of interpreting climate change maps lies in understanding the scenario, or alternative future, represented by a map. The Intergovernmental Panel on Climate Change (IPCC) has developed several scenarios portraying how climate change could occur under different pathways of human development. Most mapped global climate model results are presented based on these different scenarios and predicted ranges of global average temperature changes. For example, Figure 1 combines the results of several studies that encompass temperature change scenarios with increases ranging from 2.6 to 3.7 degrees Celsius. In contrast, Figure 5 shows the extent to which estimated climate impacts on water systems vary using an older and newer version of a major global climate model for the same scenario.
Baseline and Assumptions: The third essential component of evaluating climate maps is understanding the baseline from which any environmental change is forecasted. Projections of climate changes and the uncertainty surrounding the projections can vary depending on which baseline year is selected and how far into the future a study goes. Figures 1 and 5 show how baselines can vary across scientific studies and maps. Figure 1 illustrates changes in agricultural productivity from 2003–2080, while Figure 5 assumes a baseline period from 1961–1990 with forecasted changes extending to the year 2050. Similarly, many studies contain certain assumptions that determine which variables are included in a modeled climate projection, and which ones are not. Understanding the differences in assumptions across different climate studies and their spatial models and maps is key, as these assumptions often strongly influence a study’s results. For example, some climate studies that predict impacts on agricultural systems do not take into account future changes in water systems. Other climate studies consider multiple stressors or feedback loops between simultaneous impacts on more than one sector.
The differences illustrated above highlight the extent to which modeling and mapmaking decisions can change how maps should be interpreted and if, or to what extent, data and results from different studies might be aggregated. Despite the focus on variations between maps here, mapping has the potential to play a unifying role across the many fields of climate science, policy, and practice. Climate change is inherently a spatial problem, where the locations of impacts, people, and resources are critical. As climate scientists around the world are redrawing familiar landscapes, we need to remind ourselves that maps are limited by the quality and accuracy of the underlying studies and data that they represent. The necessary simplifications that are often an implicit part of making maps can obscure the uncertainty surrounding many studies and cloud the ways in which different maps are interpreted, compared, and combined.
Because climate change is a global problem, climate information needs to be accessible to a global audience. Making good use of this information will require scientists, policymakers and increasingly all of us to be aware of seemingly small variations in scales, scenarios, and baselines in the maps that are created and interpreted. With greater map literacy in these particular areas related to climate change, we can develop a more common understanding of the problem and further harness the power of maps to communicate the ideas that shape the many lenses through which we view both climate science and policy.
Figure 1 (click to expand)
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Figure 5 (click to expand)