1.1. Land use, land-cover change and global change research
The last 10 years have seen dramatic advances in the ability of scientific communities to simulate important interactions in the Earth system. An important factor in further understanding global change and the role of both human drivers and human interaction of natural systems is demonstrated via land use and land-cover change. Based on this recognition, and also in view of the role of land in providing goods and environmental services, attention to land use and land-cover change is sharply increasing. In this issue, several studies report integrative analyses that incorporate global climate, remote sensing and observations (Lawrence and Chase, 2010; Kvalevåg et al., 2009) while others discuss statistical, weather and/or observations to evaluate regional impacts of land use/land-cover change with climate or biophysical, hydrological processes (Sertel et al., 2009; Costa and Pires, 2009; Blanken et al., 2010; Mishra et al., 2010; Takahashi et al., 2010; Xiou et al., 2009). The strength of these analyses lies in the integrative nature and approach for understanding both the drivers and impacts of land cover and land use with human systems with an emphasis on physical processes. None of these analyses, however, were conducted to address the issues raised from questions that are driven by integrative biogeophysical, socio-economic and human decision-making perspectives.
The Earth System Modeling (ESM) and the Integrated Assessment Modeling (IAM) communities play an important role in understanding and quantifying Earth system analysis and, specifically, understanding the role of land use and land-cover change. These two groups come from very different perspectives, which result in distinctly different modelling strategies between the groups. Thus, although there is a significant overlap in the systems modelled, there are also components that are unique to each group (Figure 1(a)). The ESM approach is derived from a tradition of using models to analyse the different components and interactions of the Earth's physical system, with a significant emphasis on historical simulations and model evaluation. Although the focus was originally on the physical climate system, more recently the carbon cycle and dynamic vegetation have been added. By extending its focus, the ESM approach is increasingly adding land-use and land-cover change, hydrology, agriculture and urban systems as integral components of the Earth system. (For purposes of clarification in this work, ESMs represent both the highly computationally expensive coupled atmosphere–ocean general circulation models that incorporate coupled biogeochemistry, atmospheric chemistry and vegetation components as well as the Earth System Models of Intermediate Complexity (EMIC). For further information, see Claussen et al., 2002 or McGuffie and Henderson-Sellers, 2005.) The IAM approach comes largely from a tradition of modelling the interaction of human activities, decision making and the environment. In this work, the focus has been mostly on economic production and consumption, energy systems, greenhouse gas (GHG) emissions and climate change. Land use (timber, agriculture, pasture or grazing) was often also included and increasing attention was focused on improving its representation. The focus in the way ESMs and IAMs represent the systems they study is strongly related to the nature of these systems. In ESMs, more often historical data can be used to equilibrate the model to contemporary time. Given the inherent uncertainty in human systems, in IAMs all kinds of assumptions on future development of factors related to land use such as socio-economic, energy and demographic processes are made. These assumptions are partly based on historical evidence. Often the focus of analysis is not the baseline, but alternative policy scenarios that explore the implications of limited alterations to this set of assumptions (e.g. a climate target). The different focus of the ESM and IAM communities has led to significant differences in emphasis in describing land use and land cover (see also Section 1.3). Although there is a substantial overlap in the systems modelled, there are also components that are unique to each group (Figure 1). Additionally, as both communities acknowledge the need for increased complexity and begin to incorporate additional biogeochemical or socially relevant components in their models, there is an increasing overlap in simulated domain with both the groups including aspects of other modelling strategies (Figure 1(b)).
Improving the description of land use and land-cover change dynamics is the current focus of both groups (Foley et al., 2005; Piao et al., 2008; Rose et al., 2008). This is driven by the role of land system dynamics in global environmental change, and the role of land endowments in providing economic services and that in energy and climate policy and response (Figure 2). In this context, it should be noted that the focus of climate modelling is now shifting from finding an answer to the question ‘how will the climate look if we carry-on the way we are going?’ to ‘what do we need to do to minimize the risk of dangerous climate change, and to adapt to the changes that are now inevitable?’ Addressing the latter question relies heavily on behavioural modelling of economic, land use and energy systems, which is the bailiwick of IAMs. In addition, behavioural decisions are defined by the biophysical environment, the strength of the climate and the observational and ecosystem modelling communities.
The two groups have historically worked together in an ‘offline’ manner, i.e. scenarios that produce radiative GHG and other climate forcings (such as the time evolution of land-use-induced land-cover changes) have been transferred from the IAMs to the climate models without much interaction. More detailed information on the underlying drivers (e.g. land use change) or specific focus on how results of ESMs may impact the underlying drivers of the IAMs (global feedbacks and carbon cycle dynamics) has received very little attention. The observation that interactions between the ESM approach and the IAM approach have been limited to date can also be illustrated by describing the interaction between these communities in the context of the assessments of the Intergovernmental panel on climate change (IPCC). From the onset of IPCC, the climate modelling community has contributed in understanding the physical aspects of climate change, now the focus of Working Group I. The focus of the IAM community has been directed at how land use and emissions may develop and at determining the costs of reducing them, now the focus of Working Group III (Fisher et al., 2007). The two approaches therefore contributed to different working groups. In this context, the interaction between the communities was essentially only a few years away: first for IS92 scenarios (Leggett et al., 1992) and second for the scenarios of the Special Report on Emission Scenarios (SRES) (Nakicenovic et al., 2000). In fact, for the fourth assessment report (AR4) (IPCC 2007), there was no interaction at all between the groups. One may therefore conclude that contact between the integrated assessment and climate modelling communities has been extremely limited. Outside IPCC, however, there have been collaborations in the research domain; but here also one may see this as an exception to a general rule (Sitch et al., 2005; Voldoire et al., 2007). A third engagement occurred recently as the IAM community has provided the climate community with representative concentration and radiative forcing pathways—a group of datasets, including land use and land cover, that together describe a broad range of radiative forcing pathways to facilitate a more expansive characterization of potential climate outcomes than ever before (Moss et al., 2008; van Vuuren et al., 2008).
It should be noted that the results of both the climate and IAM simulations have been made broadly available for analysis by other communities as well. The primary groups to utilize modelled results are those that study climate interactions with ecosystems, water resources, biodiversity, agriculture, human settlements and ultimately for understanding the potential for human adaptation to climatic changes, as well as for finer resolution analysis of potential socio-economic and energy transformations associated with long-run climate objectives.
It is important to realize that the ESM and IAM communities do share a common goal, i.e. to understand the continual evolution of the Earth system and all the components that drive that evolution—human decision-making and intrinsic natural variability alike. Although these drivers may be of very different origin, both must be understood to improve our understanding of both why the Earth's system behaves as it does now, and what impact future decisions about the human driving forces might hold. There are important interactions between human economic decision making about energy, land use and GHGs, and potential feedbacks in the physical climate system. In this context, these communities should be exploring new research strategies to identify the most important of these interactions, and develop ways to explore them, their consequences and ultimately the consequences of the overall evolution of the Earth system in a more comprehensive and sophisticated way than previously imagined. To some degree, the realization that a major link between human and physical systems is through the carbon cycle is compelling both communities to examine each other's strategies for missing components in their modelled systems.
1.2. Land use and land-cover change
A variety of approaches to address land use and land-cover change have been considered by both the modelling communities. The modellers of ESM have taken an approach that stems from a combination of basic ecosystem (e.g. carbon cycle) and dynamic global vegetation models (DGVMs), and have begun to incorporate different plant functional types (PFTs) into their model structures. These aspects of ESMs are increasingly being used for understanding the ecosystem and the impacts on hydrology which are modified by ecosystem responses (Betts et al.2007). Traditionally, information to create an explicit geography of land cover and land surface properties is derived from snapshots of satellite data and often do not acknowledge temporal transitions. However, as land-cover change is also driven by human land use and decision-making processes, ESMs are increasingly adding scenarios of land-use change to their analysis. Historical land cover is typically produced at coarse spatial resolution (e.g. 50–200 km2). These reconstructions of land use and land cover are often used to estimate a baseline or reference for current and future carbon stocks and fluxes (e.g. the influence of fire suppression on forest carbon or the influence of past agriculture on soil carbon) and the extent to which land-use change modifies the impacts of climate change such as hydrological changes (Piao et al., 2008).
The IAM community also recognizes the importance of land use as a critical factor in socio-economic decision making, e.g. for food and timber production, valuation of the state of ecosystems and their services, and increasingly, as a response to demand for biofuels in the electricity and transportation sectors. Although many IAMs have focused strongly on energy-economy systems and only included land-use emissions as exogenous factors, this is now changing with the development and implementation of increasingly coupled socio-economic and climate modelling strategies (Rose et al., 2008). How land use is included differs strongly across the IAM community; some IAMs have included land as an additional production factor and relate its use to associated GHG emissions. Such models describe land use at the level of large global regions and would, for instance, differentiate between agricultural land, forested areas and pasture/grazing lands to understand and simulate the economic consequences of changes in supply and demand for these services. The basis of the models is socio-economic and the models rely on census data at national or regional levels of aggregation, with relatively limited specification of land-cover characteristics. A small number of models have more detail and, in particular, describe land use and land cover with greater geographical specificity. However, all the models must contend with the fact that economic statistics [such as United Nations Food and Agriculture Organization (FAO) statistics] are national and regional (not gridded or by water sheds); and therefore, land-use decision making must be modelled at this coarser level.
Although a variety of modelling methodologies have benefits in terms of estimating global uncertainties, further collaboration both within and across the two communities can help to advance science, to make use of the best of both worlds (and avoid unnecessary duplication), to enhance integration (also for future climate assessments) and to better understand the role of both human and climate-based uncertainties and their feedbacks. The research issues that have the most potential for productive collaboration between the two modelling communities are identified in this work. In this context, there are three major areas of research priorities where both the ESM and IAM communities may play a role with regard to land-cover and land-use change: deforestation, agriculture and bioenergy. We first highlight ongoing and new model development and how these new model components are being applied through cooperation and evaluation. We finish with a proposed strategy and research priorities for moving forward with an integrative approach for improved understanding of energy, agriculture and forest management.