SUPPORTING ONLINE MATERIAL
1 The GCAM Integrated Assessment Model
GCAM1 (Kim et al., 2006, Clarke, et al., 2007, Edmonds and Reilly, 1985) is an integrated
assessment (IA) model that links a global energy-economy-agricultural-land-use model
with a climate model of intermediate complexity. 2 GCAM has 14 global regions defined on
geopolitical boundaries: the United States, Canada, Western Europe, Japan, Australia & New
Zealand, Former Soviet Union, Eastern Europe, Latin America, Africa, Middle East, China
and the Asian Reforming Economies, India, South Korea, and Rest of South & East Asia.
GCAM is a long-term model, typically operating in five-year time steps through the year
2095 (though the code is written to accommodate any arbitrary time-step or end year). As
part of GCAM’s modeling of human activities and physical systems, GCAM tracks emissions
and concentrations of the important greenhouse gases and short-lived species (including
CO2, CH4, N2O, NOx, VOCs, CO, SO2, BC, OC, HFCs, PFCs, and SF6).
GCAM version 3.0 is marked by a new approach for modeling agriculture and land use at a
finer level of spatial resolution, as well as the general ability to run the model at shorter
time intervals. In the core version of GCAM 3.0, the modeling and data representing
agriculture and land is specified at a resolution of 151 land use subregions around the
globe. These land use subregions are based on a division of the extant agro-ecological zones
(AEZs), which we derived from work performed for the GTAP project (Monfreda et al,
2009), within each of GCAM’s 14 global geo-political regions. These changes provide a
substantial enhancement to GCAM’s ability to model crops and land use decisions and
implications in much more physical, technological, and spatial detail while maintaining
tight integration with the rest of the GCAM.
GCAM models the energy, agriculture, and land use in an economically and physically
consistent global framework. In each model period, GCAM explicitly models markets and
solves for equilibrium prices in energy, agriculture and other land uses, and emissions; that
is, the set of prices that ensures that supplies are equal to demands in all markets. GCAM is
a dynamic-recursive model, which means that it solves for each period’s market
equilibrium sequentially.3 GCAM models energy and agriculture technologies as discrete
and linear rather than by abstracting them with economic production functions. However,
economic choices and allocations among factors such as energy resources, technologies,
and uses of land are based on nonlinear functions such as logit choice models. In contrast to
linear optimization models, which are often characterized by winner-take-all solutions that
must be limited by explicitly imposed constraints, the logit choice approach used in GCAM
ensures some degree of heterogeneity in economic decisions.
1
Note that GCAM was formerly known as MiniCAM.
Documentation for GCAM can be found at http://www.globalchange.umd.edu/models/MiniCAM.pdf/.
3
In contrast, an intertemporal optimization model would solve for all periods simultaneously.
2
The size and composition of the global population and the flow of GDP are the principle
terms shaping the scale of energy, agriculture, and land-use systems. The modeling of the
future regional economies and populations is highly aggregated. GDP is calculated as the
product of labor force and average labor productivity modified by an energy-service cost
feedback elasticity that captures the effect of mitigation on GDP. The labor force and labor
productivity are both exogenous inputs to GCAM, developed offline from detailed
demographic analyses.
1.1 The Energy System in GCAM
GCAM was originally designed to answer questions about the role of the energy system and
energy technologies in mitigation, and energy remains an area of. The GCAM energy system
models energy from its point of origin to its final end-use. This includes primary energy
resources, production, energy transformation, and energy consumption. GCAM includes
both depletable (coal, gas, oil, uranium) and renewable (wind, solar, geothermal)
resources, which are represented through graded resource curves. As more energy is
extracted and used, costs rise, though those cost increases can be ameliorated by
technological improvement. The supply of bioenergy is determined by the agriculture and
land-use submodel within GCAM, which is discussed below.
GCAM models the transformation of primary energy resources into final energy forms
(electricity, hydrogen, refined liquids, refined gas, coal, and solid bioenergy) through a set
of conversion sectors, each of which may include a range of conversion technologies. For
example, GCAM includes multiple technologies for producing electricity from coal, natural
gas, oil, bioenergy, wind energy, nuclear power, solar energy, hydropower, and geothermal
energy. Final energy forms are consumed by three end-use sectors (buildings, industry,
and transportation). GCAM includes detailed representations of each of these demand
sectors in the U.S. It also includes a globally detailed representation of transportation
demands. Consumption of energy in these sectors is determined by the demand for final
energy services, as well as the characteristics of the technologies used to provide those
services.
1.2 The Agriculture, Forest, and Land Use Systems in GCAM
As integrated assessment research has evolved, understanding agriculture and land use
systems has become increasingly important. Land use interacts with mitigation both as a
supplier of bioenergy and as a source or sink of terrestrial emissions. For this reason,
GCAM includes a spatially-disaggregated land use model that models land cover, land use,
and production of agricultural and forest products, as well as ecosystem types. Energy,
agriculture, forestry, and land markets are integrated in GCAM, along with unmanaged
ecosystems and the terrestrial carbon cycle. GCAM determines the demands for and
production of products originating on the land and the carbon stocks and flows associated
with land use. GCAM 3.0 divides the globe into 151 land use subregions based on a
mapping of up to 18 AEZs (Monfreda et al, 2009) within each of GCAM’s 14 global geopolitical regions, as shown in Figure 1. These AEZs are defined as zones with similar
temperature and precipitation levels, and as such are a useful division of land for modeling
agriculture and other land use. A complete description of GCAM 3.0 modeling of agriculture
and land use is provided in Wise and Calvin (2011) and Kyle et al (2011).
Figure 1. GCAM 3.0 Geopolitical and Land Use Region Map
Within each of these 151 subregions, GCAM categorizes land into approximately a dozen
types based on cover and use. Some of the land types in the subregions, such as tundra and
desert, are not considered arable. Among arable land types, further divisions are made for
lands historically in non-commercial uses such as forests and grasslands as well as
commercial forestlands and croplands. Within each subregion, arable land is allocated
across a variety of uses based on expected profitability, which depends on the productivity
of the land, the non-land costs of production (labor, capital, fertilizer, etc.), and the price of
the product. Production of approximately fifteen crops and commercial forest product is
currently modeled, with yields of each specific to each of the 151 subregions. The model is
designed to allow specification of different options for future crop management for each
crop in each subregion, and the model structure itself allows for other regional
breakdowns besides the AEZs.
Because it is an IA model, land use modeling in GCAM is not limited to agriculture but is
instead comprehensive in scope of modeling land use and land cover. Land in each of each
subregions is divided into one of several land use and land cover categories, with the soil
and vegetative terrestrial carbon in each category in each subregion modeled (Kyle et al,
2011). These land categories include lands for commercial uses such as cropland,
commercial pasture, forest products, and bioenergy crops, as well as non-commercial but
arable lands such as non-commercial forestlands, grasslands, and shrublands. For both
commercial and unmanaged forestlands, the GCAM models the temporal accumulation of
terrestrial carbon based on growth profiles specific to each subregion. Non-arable lands
such as tundra, desert, and urban land are also tracked but considered fixed for this study.
The amounts of land in each of the arable land categories, including the distinction between
commercial and non-commercial land coverage, are not rigid in GCAM. As GCAM models
future periods, the amount of land devoted to each of these categories and uses changes in
response to socioeconomic, policy, and technology drivers, and the net terrestrial carbon
emission or net carbon uptake from these land use changes are computed.
As with the GCAM energy system, the economic modeling approach for GCAM agriculture,
forest, and land is that of an integrated economic equilibrium in the products, sectors, and
factors that are modeled. Markets, for products such as corn, wheat, wood, or bioenergy
crops must be cleared so that supplies are equal to demands in each model period.
Depending on the product or on user specifications, markets can be cleared globally,
regionally, or across groups of regions.
GCAM models the production of several types of bioenergy: traditional bioenergy (straw,
dung, fuel wood, etc.), bioenergy from waste products (including crop residues, municipal
solid waste, and black liquor from the pulp and paper industry), and purpose-grown
bioenergy crops. Purpose grown bioenergy crops, including perennial grasses like
switchgrass, and woody crops such as willow, are modeled as economically competing for
land with all other agriculture, forestry, and other uses of land. Food crops, such as corn,
soybeans, and sugar, can also be used as energy feedstocks to be supplied to GCAM’s energy
transformation and use sectors.
1.3 The Climate System in GCAM
All integrated assessment models must include some meaningful representation of global
bio-geophysical processes that govern the fate of greenhouse gas and other anthropogenic
emissions. GCAM uses the MAGICC model (Wigley and Raper 2001) as its default
biophysical component. MAGICC provides a representation of important physical Earth
system elements: carbon cycle, atmospheric chemistry, ocean systems, and climate
systems.
MAGICC operates by taking anthropogenic emissions from the other GCAM components,
converting these to global average concentrations (for gaseous emissions), then
determining anthropogenic radiative forcing relative to preindustrial conditions, and
finally computing global mean temperature changes. The MAGICC climate system model is
an energy-balance climate model that simulates the energy inputs and outputs of key
components of the climate system (sun, atmosphere, land surface, and ocean) with
parameterizations of dynamic processes such as ocean circulations.
The carbon cycle in MAGICC is modeled with both terrestrial and ocean components. The
terrestrial component includes CO2 fertilization and temperature feedbacks; the ocean
component is a modified version of the Maier-Reimer and Hasselmann (1987) model that
also includes temperature effects on the terrestrial biosphere. Reactive gases and their
interactions are modeled on a global-mean basis using equations derived from results of
global atmospheric chemistry models (Wigley et al. 2002).
Global mean radiative forcings for CO2, CH4, and N2O are determined from GHG
concentrations using analytic approximations. Radiative forcing for other GHGs is
proportional to concentrations. Radiative forcing for aerosols (for sulfur dioxide and for
black and organic carbon) is taken to be proportional to emissions. Indirect forcing effects,
such as the effect of CH4 on stratospheric water vapor, are also included. Given radiative
forcing, global mean temperature changes are determined by a multiple box model with an
upwelling-diffusion ocean component. Climate sensitivity is specified as an exogenous
parameter.
2 Estimation of Climate Impacts on Crop Yields
The data available from Easterling et al. (2007) is disaggregated by crop type but
aggregated over large geospatial regions, dividing the world into two regions (mid-to-high
and low latitude). Crop yield impacts depend on many factors including soil and terrain
characteristics as well as the regional impacts of changes associated with global climate
means. Crop yields can be expected to vary significantly within latitudinal bands and
across crop models. In addition, estimates of climate change can be expected to vary across
climate models. For this reason, we have opted to use the edges of the distribution.
The effect of climate change is quite different along each of these impact pathways. Figure
1 summarizes wheat yield studies of climate change impacts. The envelope of the high
damage (labeled “Most Decrease” in Figure 1) studies shows a monotonic decline in crop
yields beginning in the present. In contrast, the envelope of the lowest damage (labeled
“Most Increase” in Figure 1) studies show an increase in crop yields rising until climate
change reaches almost two degrees Centigrade beyond which further increases in global
mean surface temperature has little effect until global mean surface temperature change
reaches 3.5 degrees Centigrade. When temperature rises above this level, yields decline
from their peaks, but do not return to present crop yield levels until temperature rise more
than 4.5 degrees Centigrade.
Similar relationships were extracted for each of the three crops and both latitudinal bands
from (Easterling et al, 2007, p286).
3 Estimation of Climate Impacts on Agricultural Systems
Climate impacts on agricultural systems depend on the effects of climate change on crop
yields, as discussed above, as well as the response of human agricultural and economic
systems to those changes in crop yields through management and policy actions. We
estimate the consequences of climate change for agricultural and land-use systems for two
cases—Reference and 550 ppm stabilization.
The concentrations of greenhouse gases in the atmosphere for each scenario were
estimated using MAGICC, which in turn computed radiative forcing and the transient global
mean surface temperature. The associated change in crop yields for the two different
latitude classes were then calculated from the above data and these values were used to
adjust crop yields relative to the case without climate change impacts.
Because GCAM is a model of human and natural Earth systems crop yield changes trigger
changes in the choices that farmer and consumers make. As crop yields decline, farmers
produce less per unit land area. The reduced production results in higher crop prices,
which in turn increases the value of crop land relative to land in other uses. The land area
used to produce crops therefore expands at the expense of other activities. Thus, GCAM
incorporates endogenous adaptation to climate impacts driven by the market equilibrium
process. That is, it includes shifts in both where crops are grown and the composition of
the crops that are grown, as well as shifts in the total demand for crops. GCAM does not,
however, include other adaptive responses. For example, it does not consider the
development of new crop strains. It does not consider shifts in management practices
beyond those included in the underlying source literature assembled by Easterling, et al.
Importantly, GCAM does not include the effects of changing water supplies. Thus, a major
response to climate change could be to change the extent to which crops are irrigated.
However, since water is not explicitly incorporated in GCAM, this line of accommodation is
not currently available. Similarly, the effects of reduced water availability would not be
incorporated.
4 References
Clarke, L., J. Edmonds, H. Jacoby, H. Pitcher, J. Reilly, R. Richels, 2007. Scenarios of
Greenhouse Gas Emissions and Atmospheric Concentrations. Sub-report 2.1A of Synthesis
and Assessment Product 2.1 by the U.S. Climate Change Science Program and the
Subcommittee on Global Change Research. Department of Energy, Office of Biological &
Environmental Research, Washington, D.C., USA, 154 pp.
Edmonds, J. and J. Reilly. 1985. Global Energy: Assessing the Future, Oxford University
Press, New York. 1985.
Kim, SH, JA Edmonds, SJ Smith, M Wise, J Lurz. 2006. The Object-oriented Energy Climate
Technology Systems (ObjECTS) Framework and Hybrid Modeling of Transportation The
Energy Journal Special Issue 2.
Kyle, G. Page, Patrick Luckow, Katherine Calvin, William Emanuel, Mayda Nathan, and Yuyu
Zhou. 2011. GCAM 3.0 Agriculture and Land Use: Data Sources and Methods. Pacific
Northwest National Laboratory. PNNL-21025.
http://wiki.umd.edu/gcam/images/2/25/GCAM_AgLU_Data_Documentation.pdf
Maier-Reimer, E., and Hasselmann, K. 1987. "Transport and Storage of CO2 in the Ocean:
An Inorganic Ocean-Circulation Carbon Cycle Model," Climate Dynamics, 2:63-90.
Monfreda, C., N. Ramankutty and T. W. Hertel (2009). “Global Agricultural Land Use Data
for Climate Change Analysis.” in Economic Analysis of Land Use in Global Climate Change
Policy. T. W. Hertel, S. Rose and R. S. J. Tol, Routledge.
Wigley, T.M.L. and Raper, S.C.B., 2001: Interpretation of high projections for global-mean
warming. Science 293, 451-454.
Wigley, T.M.L., Steven J. Smith, and M.J. Prather (2002) Radiative Forcing due to Reactive
Gas Emissions. Journal of Climate 15(18), pp. 2690–2696.
Wise, Marshall and Kate Calvin. 2011. GCAM 3.0 Agriculture and Land Use: Technical
Description of Modeling Approach. Pacific Northwest National Laboratory. PNNL-20971.
https://wiki.umd.edu/gcam/images/8/87/GCAM3AGTechDescript12_5_11.pdf