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Thematic Guide to Integrated Assessment Modeling

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USE OF EOS AND OTHER SATELLITE DATA IN INTEGRATED ASSESSMENT MODELS OF CLIMATE CHANGE

The 1996 issue of Our Changing Planet underscores the continuing commitment of the U.S. Global Change Research Program (USGCRP) for supporting and furthering efforts at integrated assessments of climate change. Integrated assessment models (IAMs) of climate change are a key method by which the wide range of global change data and information, including data from satellites, can be translated into a form useful for decision making (see SEDAC's Thematic Guide essay In Search of Integrated Assessment and the MVA Usage Guide). Indeed, promoting effective use of IAMs by analysts and decision makers is a key way to improve the use of EOS and other satellite data in decision making.

Because of the ability of IAMs to rapidly integrated very diverse sets of data both within and between the natural and social sciences, they have gained widespread use among researches interested in the climate change issue. Detailed descriptions of nineteen climate change integrated assessment models can be found in the Thematic Guide essay In Search of Integrated Assessment. These models are developed by researchers at universities, government agencies, and research institutions. Many of these models have been used to support research by the Intergovernmental Panel on Climate Change (IPCC), the USGCRP, and other national and international organizations. The recent findings of IPCC Working Group III on issues related to economics and decision making are based partly on the work of IAMs.

A particular strength of climate change IAMs are their ability to assimilate or integrate data on how different parts of the climate system work, and how the climate system interacts with ecosystems and social systems. This integration can involve the use of satellite data. For example,the IMAGE 2.0 model developed by RIVM in The Netherlands uses TOMS data to calibrate the relationship between column ozone and chlorine in the atmospheric composition model (Krol and van der Woerd 1994). The terrestrial vegetation model and land cover model in IMAGE 2.0 were designed to take advantage of remotely sensed data that are being developed through efforts supported by the IGBP (International Geosphere-Biosphere Programme); however, they do not yet make direct use of these data (Leemans and van den Born 1994).

The ability of IAMs to assimilate diverse sets of data leads to a richer understanding of how the climate system interacts with ecosystems, and how human activity influences both the climate and the environment, and provides a powerful tool for assessing the merits of different options for responding to global climate change. In addition, IAMs allow for the examination of uncertainty surrounding various geophysical as well as socioeconomic parameters in a consistent framework, permitting the prioritization of efforts to reduce uncertainty.

Several of the IAMs currently available or under development make either direct or indirect use of the types of data that will be made available as a result of NASA's Mission to Planet Earth program. As noted above, the IMAGE 2.0 model directly draws on satellite data, and is designed to take advantage of new remotely sensed land cover data sets. Other IAMs draw indirectly on remote sensing data by relying on the results of other climate change research and assessment activities such as the development of the general circulation models (GCMs) or the scientific findings of the IPCC. Satellite and remote sensing data, for example, are used in determining the concentration of greenhouse gases in the atmosphere, sources and sinks for these gases, and their radiative forcing potential (see Houghton, Callander, and Varney 1992). This is all information that is used in one form or another in IAMs.

Satellite and remote sensing data can also contribute greatly to our understanding of the potential effect of climate change on the biosphere; however, less effort has been directed at using remote sensing data for research in this area than has been directed and understanding climate and atmospheric processes. Understanding the potential effect of climate change on the bioshpere is an area of research were much work still needs to be done. A recent Office of Technology Assessment (OTA) report identified some key areas were remote sensing can already contribute to research on impacts of climate change and areas where it is likely to contribute in the future with improvements in remote sensing technology (OTA 1993):

Potential Uses of Current Remote-Sensing Data for Biosphere Study

  • Classify land-surface cover
  • Detect vegetation-climate relationships
  • Detect frequency and extent of fire
  • Detect inundation extent
  • Detect surface soil moisture in areas of low vegetation cover
  • Detect land and ocean surface temperature
  • Calculate ocean color indices
  • Calculate vegetation indices
  • Estimate global net primary production
  • Estimate ranges of evapotranspiration
  • Measure horizontal canopy structural characteristics
  • Measure canopy biochemical constituents
  • Measure vegetation water content

Potential Uses of Future Remote-Sensing Data

  • Classify vegetation cover by community types or species assembladges
  • Detect and monitor margins of ecosystems
  • Detect successional stages in forests
  • Characterize vegetation stress (in natural communities as well as in crops)
  • Estimate contaminant concentrations in water and snow
  • Estimate biochemical composition of vegetation canopies in more detail
  • Estimate canopy structural characteristics with independent methods
  • Estimate biomass
  • Estimate extent of deforestation
  • Measure soil moisture in vegetated areas
  • Measure vertical canopy structural characteristics
  • Measure canopy biochemical constituents in more detail
  • Measure canopy moisture content
  • Measure canopy height

As the above two lists illustrate, there are a great many potential contributions of satellite and remote senesing data to the assessment of climate change impacts on the biosphere. Much of this information could contribute greatly to the imporvement of IAMs. Indeed, as more remote sensing data on the bioshpere become available through efforts such as EOS and the International Geosphere-Biosphere Programme, the next generation of IAMs will make direct use of these data to improve both the climate/atmosphere and bioshpere components of the models.

References

Houghton, J.T., B.A. Callander, and S.K. Varney, eds. 1992. Intergovernmental Panel on Climate Change 1992: The Supplementary Report to the IPCC Scientific Assessment Report. Cambridge: Cambridge University Press.

Krol, M.S. and H.J. van der Woerd. 1994. Atmospheric compostion calculations for evaluation of climate scenarios. In IMAGE 2.0: Integrated Modeling of Global Climate Change, ed. J. Alcamo, 259-281. Dordrecht: Kluwer.

Leemans, R. and G.J. van den Born. 1994. Determining the potential distribution of vegetation, crops and agricultural productivity. In IMAGE 2.0: Integrated Modeling of Global Climate Change, ed. J. Alcamo, 133-161. Dordrecht: Kluwer.

Office of Technology Assessment. 1993. Preparing for an Uncertain Climate. 2 vols. GPO: Washington, D.C.

 

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Sources

Parson, E.A. and K. Fisher-Vanden, Searching for Integrated Assessment: A Preliminary Investigation of Methods, Models, and Projects in the Integrated Assessment of Global Climatic Change. Consortium for International Earth Science Information Network (CIESIN). University Center, Mich. 1995.

 

Suggested Citation

Center for International Earth Science Information Network (CIESIN). 1995. Thematic Guide to Integrated Assessment Modeling of Climate Change [online]. Palisades, NY: CIESIN. Available at http://sedac.ciesin.columbia.edu/mva/iamcc.tg/TGHP.html [accessed DATE].

 

 

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