Towards a GIS-Based Framework for Climate Change Studies

“…a better world is the common goal all of us—geographers, planners, scientists, and others—have been striving for.  Although we’ve made a lot of progress in building the (technological) infrastructure to help us accomplish this monumental task, we’re still not quite there yet.”
–Jack Dangermond (Dangermond 2009)

A GIS-based approach to climate change studies provides a framework for understanding and addressing the entire breadth of climate change science issues in a holistic manner.  Scientists have long classified various phenomena into logical groupings or “systems.”  These classifications have helped greatly to advance the understanding of component physical, biological, and social systems, yet often create artificial boundaries between disciplines that can be detrimental to the understanding of larger issues.  While advancing the understanding of each of these individual systems is vitally important, ultimately we need to bring all of these systems together, to understand how they are interrelated and dependent upon one other.

Such a framework provides a base enablement system for global data management, visualization, analysis, modeling, and ultimately design.  In order to move climate change studies from a massive collection of unrelated or loosely linked endeavors towards an open, integrated framework, there are four areas we need to change: data, models, organization, and mindset.

Various frameworks and programs already address a number of the issues and challenges in establishing such a framework.  Careful review of the approaches to data, models, organization and mindset in these frameworks and programs will help us to identify concepts and components that can be leveraged—as well as gaps that can be filled—by a GIS-based framework for climate change studies.

The review below presents some representative examples, and is not meant to present a comprehensive inventory of such frameworks and programs.


OpenStreetMap.  The OpenStreetMap project leverages volunteers to perform on-the-ground surveys with their personal GPS and other equipment to create a global base map that is freely distributed and can be edited by anyone.  The non-profit OpenStreetMap Foundation provides support for the project, but does not “control” the project per se. 

OpenStreetMap is a model for creating a global data set by citizen volunteers.  Organizationally it provides a good example of a successful structure for managing the creation and distribution of the data, as well as maintaining quality standards.

Global Earth Observation System of Systems (GEOSS).  The notion of a system of systems for geospatial information was first suggested by the National Academy of Sciences Mapping Science Committee and was referred to as the National Spatial Data Infrastructure. More recently, this architecture has been adopted by the National Oceanic and Atmospheric Administration and others as part of their architecture for the Global Earth Observation System of Systems (GEOSS). GEOSS serves as a global framework for integrating the large number of global remote-sensing systems into a loosely coupled network available to many participants, providing decision-support tools to a wide range of users.

GSDI.  GIS has proven to be an important and reliable tool for management of spatial information at all geographic levels, from local to global. Over the past 15 years, a number of national, regional, and international organizations have moved towards a vision of building a Global Spatial Data Infrastructure (GSDI) for the sharing of spatial data. The GSDI Association and its membership are responsible for promoting this framework, with a goal of mapping the globe at a resolution of 1 km or better, and including information on a wide variety of geographic features.


Standalone and GIS-based Models.  An ever-growing number of models currently exist for abstracting, simulating, and understanding complex details of physical, biological, and social systems and subsystems (Goodchild 2005).   The domains of the individual modeling packages vary widely, from soils to hydrology, from socioeconomics to land-use transportation (Wegner 2005, Batty 2005, Maidment 2005).  While much progress has been made in recent years to develop models to help us to better understand our world, there is still much more to be done—especially in the area of integration.  As we gain more detailed understanding of different granular systems and their components, the challenge in addressing complex issues such as global climate change is coupling these models together to gain a more complete picture.  The combination of powerful hardware, sophisticated software, and increased human knowledge have all contributed to better models and more accurate simulations, but a GIS-based framework for integrating these disparate representations of past, present, and future states is key to understanding the whole earth (Maguire 2005).

Earth System Modeling Framework (ESMF) .  The Earth System Modeling Framework (ESMF) is an open source collaborative project co-sponsored by the U.S. Department of Defense, NASA, the National Science Foundation, and the National Oceanic and Atmospheric Administration (NOAA).  The goal of the ESMF project is to build “…high-performance, flexible software infrastructure to increase ease of use, performance portability, interoperability, and reuse in climate, numerical weather prediction, data assimilation, and other Earth science applications.”  (UCAR ND)

A key component is definition of an architecture for coupling together of disparate modeling systems, as well as providing support of new, framework-complaint models.  A core principle of the ESMF framework is the deconstruction of complex models into small components defined by standards such that they can be quickly and easily assembled in different ways to create new models.  However, ESMF is primarily focused on sharing of code and models, not data and workflows.


Climate Collaboratorium.  The Climate Collaboratorium is a project of the Massachusetts Institute of Technology (MIT) Center for Collective Intelligence in the Sloan School of Management.  The Climate Collaboratorium project aims to leverage new information technology and social media to bring together large numbers of like-minded yet geographically and socially dispersed individuals to collaborate on issues surrounding the global climate change debate.  Using what they term collective intelligence, Malone and Klein hope that this framework will “focus … on a possible use of such a system with a particularly high social return: drawing on the best human and computational resources available to develop government policies about climate change.” (Malone and Klein 2007)  The Climate Collaboratorium project hopes to show that adopting a framework that is decentralized yet carefully managed can be an effective method to approach large, resource-intensive problems such as global climate change. (Malone 2009)

Planet Action.  Planet Action is a not-for-profit collaborative initiative launched in June 2007 by Spot Image. Its purpose is to encourage the earth observation industry and professional GIS communities to address climate change by supporting projects that investigate and assess climate change environmental impacts in five areas of focus: human dimensions and habitation, drought and water resources, vegetation and ecosystems, oceans, and ice and snow cover. By assisting in and funding projects that will support understanding and action on environmental impacts, the Planet Action initiative hopes to strengthen international cooperation and response to climate change problems.

Planet Action projects must meet certain criteria before qualifying for support. Each project must assess climate change-related impacts and issues and initiate a course of action. Accepted projects must also incorporate good scientific understanding, resources, and methods. The Planet Action project is an example of private industry leaders coming together to tackle global issues usually associated with the realm of governments and NGOs.

CPDN and APS@home .  Citizen scientists are people who have a strong interest in some facet of science, but pursue this interest outside of mainstream academic, research, and industrial organizations.  These self-directed individuals might very well be using their own resources, working in their garages to develop “the next big thing.” But more often they are networked, working together with fellow citizen scientists. And this is where they become a powerful force to be taken seriously within the scientific community. Scientists, and “professionals doing science,” often are the ones organizing these citizen science networks; they realize the great value a group of eager volunteers can bring to a project.

A good, although somewhat controversial (depending on your belief in intelligent extraterrestrial life) example of a mass of volunteers carefully organized to work on an overwhelmingly humongous project is SETI@home.  As a volunteer, you download some software that utilizes the “idle time” on your home computer to scan through reams of radio telescope data and search for signs of extraterrestrial intelligence. If nothing else, it has served as a model for bringing large numbers of volunteers (more than five million participants worldwide) together to work collectively on a massive task.

Closer to home, CPDN and APS@home are two distributed computing projects with an earth science spin. CPDN is investigating how small changes affect climate models. APS@home is looking at atmospheric components of climate change. Although public participation in both CPDN and APS@home is not nearly at the same scale as SETI@home, the potential is certainly there.

Is there an opportunity for the citizen scientist to leverage geospatial technologies in their quest for knowledge, entertainment, and contributing to society? Absolutely. With the relatively recent arrival of powerful (and free!) geospatial visualization tools such as Google Earth, ArcGIS Explorer, and NASA World Wind, it is now easier than ever for the citizen scientist to have some fun with maps while making a potentially important scientific contribution.

Amassing large numbers of volunteers to work on geospatial problems such as climate change is already taking place as shown by the CPDN and APS@home examples. What is needed next is something at a much larger scale, where not just physical, but also biological, social, cultural, economic, and political data and models are integrated to give a more accurate depiction of the complexities inherent in the anthropogenic Earth.

First we need to create an environment that successfully brings together a plethora of data sources and modeling systems—a noble vision for GIS, but not something to be tackled by citizen scientists. Once the data and technology is in place, and a clear framework is established, then comes the opportunity to organize a large group of volunteers who would do the “grunt work” of tackling one of the biggest challenges facing us.

Imagine a framework where tens or even hundreds of thousands of citizen scientists log in to a web site and download geospatial data sets and work task lists, then using a focused desktop geospatial application they also downloaded, they run different analysis and modeling scenarios as defined in the task list…then upload the results of their analysis back to the main data repository.

If properly structured and managed, such a project could significantly advance our understanding of the planet. At this scale, it would be difficult if not impossible to pull off without the participation of citizen scientists. They are out there, anxious to help… just waiting for us to create the framework.


Earth Systems Engineering & Management (ESEM).  The relatively new field of earth systems engineering and management (ESEM) concerns itself with the design, engineering, analysis, and management of complex earth systems. ESEM takes a holistic view of multiple issues affecting our earth—not only taking environmental, social, and other considerations into account up front in the design process, but also looking at challenges from an adaptive systems approach, where ongoing analysis feeds back in to the continual management of the system.  (Dangermond 2009)

Braden Allenby, professor of civil and environmental engineering at Arizona State University and one of ESEM’s founders, often emphasizes the undeniably dominant role humans have in earth systems. “We live in a world that is fundamentally different from anything that we have known in the past,” says Allenby. “It is a world dominated by one species, its activities and technologies, its cultures, and the integrated effects of its historical evolution.” (Allenby 2009)  Ian McHarg was already moving in this direction in the 1960s, and today we understand that it is even more important to emphasize the anthropogenic elements of earth systems. (McHarg 1969)  In other words, at this stage of ecological evolution, humans are a significant, if not dominating, component of the natural environment, and all problems need to be addressed and decisions made with anthropogenic elements in the forefront.

Allenby sees reasoned design and management in the age of the anthropogenic earth as our moral imperative, but the biggest obstacle to our success is that we are not set up to work, or even think, in this way. “We lack solid data and analytical frameworks to make assertions about the costs, benefits, and normative assessments of different … practices” (Allenby 2005). And this is why GIS integrated with design is critical to the success of approaches such as ESEM and other logical and rational models for dealing with the environmental and planning problems of ours and future generations.  (Dangermond 2009)

Anthropogenic Biomes.  Biomes are geographic areas sharing similar biological characteristics.  Anthropogenic factors are now a major, if not primary, contributor to biomes and other methods for classifying features and functions of earth systems.

The concept of anthropogenic biomes “offer(s) a new way to understand our living planet by describing the way humans have reshaped its ecolog(y)” (Ellis and Ramankutty 2007).  Conventional methods of representing biomes on maps are no longer applicable in an the age of the Anthropocene, and Ellis and Ramankutty note that ”[b]iomes derived from global patterns of human interaction with ecosystems may be a stronger model of global ecological patterns & processes.”

Anthropogenic biomes provide us with a framework for seamlessly integrating human factors in to natural systems, a necessary feature of an all-inclusive modeling framework for our planet.


Allenby, Braden, 2005.  Biomass Management Systems.  In Reconstructing Earth, 2005.

Batty, Michael , 2005. Socioeconomic Applications. In D.J. Maguire, M. Batty, and M.F. Goodchild, editors, GIS, Spatial Analysis, and Modeling. Redlands, CA: ESRI Press, pp. 147–149.

Dangermond, Jack, 2009.  GIS: Designing Our Future.  ArcNews, Summer 2009.

Ellis, Erle C., and Ramankutty, Navin.  Anthropogenic Biomes: A Framework for Earth Science and Ecology in the 21st Century.  American Geophysical Union Fall Meeting, December 10-14, 2007, San Francisco, California.

Goodchild, Michael F., 2005. GIS and Modeling Overview. In D.J. Maguire, M. Batty, and M.F. Goodchild, editors, GIS, Spatial Analysis, and Modeling. Redlands, CA: ESRI Press, pp. 1–18.

Maguire, David J., 2005. Towards a GIS Platform for Spatial Analysis and Modeling. In D.J. Maguire, M. Batty, and M.F. Goodchild, editors, GIS, Spatial Analysis, and Modeling. Redlands, CA: ESRI Press, pp. 19–39.

Maidment, David R., 2005. Hydrologic Modeling. In D.J. Maguire, M. Batty, and M.F. Goodchild, editors, GIS, Spatial Analysis, and Modeling. Redlands, CA: ESRI Press, pp. 319–332.

Malone, Thomas W., 2009.  Can Collective Intelligence Save the Planet? May 5, 2009

Malone, Thomas W., and Klein, Mark, 2007. Harnessing Collective Intelligence to Address Global Climate Change.  In Innovations.  Summer 2007, Vol. 2, No. 3, Pages 15-26

McHarg, Ian, 1969.  Design with Nature.

UCAR ND.  About ESMF.  University Corporation for Atmospheric Research (

Wegner, Michael, 2005. Urban Land-Use transportation Models. In D.J. Maguire, M. Batty, and M.F. Goodchild, editors, GIS, Spatial Analysis, and Modeling. Redlands, CA: ESRI Press, pp. 203–220.