Identifying and quantifying the sources of climate impacts from land use and
land cover change (LULCC) is necessary to optimize policies regarding LULCC
for climate change mitigation. These climate impacts are typically defined
relative to emissions of CO
Global land use and land cover change (LULCC) is recognized as an important element of past and future anthropogenic climate changes (Feddema et al., 2005; van der Werf and Peterson, 2009; Foley et al., 2011). Decision makers are faced with the major challenge of meeting increasing global demands for food products (Godfray et al., 2010) while simultaneously minimizing the climate costs of expanding or intensifying agriculture. The Reducing Emissions from Deforestation and Forest Degradation (REDD) program is a one such effort that seeks to lower anthropogenic greenhouse gas emissions from deforestation using financial incentives to maintain or increase forest area (Lubowski and Rose, 2013).
Estimating the costs to climate from LULCC activities is necessary for
developing policies like REDD, yet these costs are difficult to define. The
total CO
The general approach to identifying sources of anthropogenic impacts on
climate has been to divide the impacts by the forcing agent (e.g. Forster et
al., 2007; Myhre et al., 2013). However, as pointed out by Unger et al. (2010),
it is more useful for policy making to break impacts down into
contributions by economic sectors. Specific sectors can be regulated more
easily than an individual forcing agent, such as CH
In this study, we use previously compiled estimates of the global LULCC radiative forcing (Ward et al., 2014) for six future scenarios, including the four representative concentration pathways (RCPs; Moss et al., 2010), and compute the contributions of three major LULCC sectors to the total radiative forcing (RF): agriculture, direct modifications to the land surface (e.g. deforestation, reforestation, wood harvesting), and the wildfire feedback. The first of the two non-RCP scenarios projects business-as-usual deforestation activity in the tropics through year 2100 and the second is a theoretical extreme case in which all arable and pasturable land is cultivated or converted to pasture by the year 2100. The extreme case is intended to be a worst-case scenario that is not likely to be realized but is instructive as an upper bound of LULCC impacts. These pessimistic scenarios are added to expand the range of projected future land use because the RCP scenarios are optimistic in their estimates of current and future land use conversion compared to current census- and satellite-based estimates (see Fig. 5 in Ward et al., 2014; FAO, 2010; Hansen et al., 2013; Kim et al., 2015). The global total and sector-specific forcings are ascribed to their source locations on a latitude/longitude grid basis for historical LULCC and for the projected LULCC of the future scenarios. With these methods, our objectives are to (1) identify where the RF of specific LULCC activities will likely come from in the future, and, based on this information, (2) to assess the relative importance of land use location and type of activity for future mitigation of global RF.
The methodology employed in this study is explained in this section in four steps. First, a brief summary is given of the computation of global RFs due to LULCC from Ward et al. (2014) that are used in this study (Sect. 2.1) with additional details given in Appendix A. This is followed by a description of the future LULCC scenarios used by Ward et al. (2014) and in this study, and also the development of an additional scenario (Sect. 2.2). In Sect. 2.3, the methods for attributing the global LULCC RFs for each scenario to three major sectors of land use activities are explained, supplemented by Appendix B. Finally, our approach for ascribing the sector and agent-specific RFs to individual source locations is described in Sect. 2.4.
We use the adjusted radiative forcing (RF), as defined by Forster et al. (2007), and relative to a preindustrial state (year 1850), to measure the impacts of LULCC activities. RF has several advantages as a metric for this kind of study in which different forcing agents are assessed together. The RF is defined the same way for short-lived and long-lived forcing agents allowing for their direct comparison. Also, this metric has been used in many studies, including the Intergovernmental Panel on Climate Change assessment reports, to compute the total anthropogenic contribution to climate change, providing substantial context within which to place our results (Forster et al., 2007; Myhre et al., 2013).
It has been demonstrated that the biophysical effects of LULCC have a
different climate sensitivity compared to identical forcing from CO
The RFs attributed to LULCC by Ward et al. (2014) from changes to greenhouse
gas concentrations, including CO
RFs were estimated by Ward et al. (2014) for the year 2100 (relative to 1850) given historical LULCC (Hurtt et al., 2011) and five projections of future LULCC including four developed as part of the Coupled Model Intercomparison Project phase 5 (CMIP5) (Taylor et al., 2012) corresponding to each of the four representative concentration pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5) (Hurtt et al., 2011; Lawrence et al., 2012). The fifth projection represents a theoretical extreme case (TEC) in which all arable land is converted to crops at a linear rate between years 2010 and 2100, and remaining pasturable land (defined as land for which the climate would support crops but where the soil is too nutrient-poor) is converted to grasses (Ward et al., 2014). The TEC leads to a near complete deforestation of the tropics and more than 2.5 times the present-day crop area. Since the land use included in the RCPs is thought to be smaller than is likely in reality based on historical land use change (e.g. Ward et al., 2014), the TEC allows us to have a higher-than-likely estimate in order to bound the probable impacts of land use on climate.
All projections represent LULCC as changes in plant functional type (PFT) coverage over time, with redistribution of carbon by wood harvesting also included (Lawrence et al., 2012). Recent work has demonstrated that changing agricultural practices, even something as simple as improving livestock feeding, can also reduce greenhouse gas emissions (Bryan et al., 2012). Here we assume changes in agricultural practices are consistent with the LULCC projections created to accompany the representative concentration pathways.
Forest area projections for all four RCP scenarios assume reductions in the rate of global deforestation during the 21st century (Lawrence et al., 2012). It is also important to understand the impacts of LULCC and the sources of these impacts under a scenario in which current land use practices are continued. To address this knowledge gap we introduce a sixth projection in which tropical forest area changes for years 2010 to 2100 follow the year 2000 to 2010 rates published by the FAO (2010). Together with the RCPs, this creates a more comprehensive range in possible outcomes for the 21st century. In this tropical business-as-usual (Trop-BAU) scenario, the forest area change reported for each country is gridded. Only grid points with past forest area loss were allowed to experience future loss, although in the case of completely deforested grid points the forest loss spilled into adjacent points. Forest PFTs are converted to cropland and pasture (grasses) at proportions of 80 and 20 %, respectively, as reported by Houghton (2012) for the tropics. Global wood harvesting rates and extra-tropical land cover changes in the Trop-BAU scenario are from RCP8.5. Some reforestation was reported in southeast Asia between 2000 and 2010 (FAO, 2010), but we assume only tropical forest area loss in Trop-BAU, citing an increase in net forest loss in this region between 2005 and 2010 (FAO, 2010). Recent studies suggest that deforestation rates are higher than reported in census data (Hansen et al., 2013; Margono et al., 2014), especially in the tropics (Kim et al., 2015). Therefore, the Trop-BAU scenario may underestimate global forest area loss if current rates were to continue during this century.
We divide RFs attributed to LULCC into three groups of anthropogenic
activities and feedbacks (Fig. 1). The first group, direct modifications,
includes land cover changes with associated deforestation fires, and wood
harvesting. We define land cover changes as the replacement of a biome, such
as grassland or forests, with a different biome through anthropogenic activity.
The agricultural emissions group contains N
We take a simple approach to apportioning the global LULCC RF into these
three categories. Forcing is assigned to a category in proportion to the
fraction of global LULCC emissions of the forcing agent, or agent precursor
gases, that are associated with the category. For example, roughly 90 % of
LULCC NO
To ascribe the global RF to each point on a 1.9
Breakdown of anthropogenic activities into categories associated with land use and land cover change, and fossil fuel burning. Note that “wildfires” refers only to the change in wildfire activity (non-deforestation and non-agricultural fires) that results from anthropogenic land use and land cover change.
RF values (W m
In the year 2010, the LULCC RF consists of two large positive contributions
from direct modifications to the land cover and from agricultural
activities, and a smaller negative contribution from changes to wildfire
activity (Table 1; Fig. 2). The major source of positive forcing from direct
modifications to the land cover is from CO
The fraction of radiative forcing from each main sector as defined in Fig. 1. The forcings are reported for the year 2010 (2010) or in year 2100 (RCP2.6, RCP4.5, RCP6.0, RCP8.5, Trop-BAU, TEC).
The future scenarios show considerable variation in the breakdown of forcing
between LULCC sectors (Table 1). The RCP2.6 scenario is characterized by
widespread proliferation of biofuel crops, largely at the expense of forests
(Vuuren et al., 2007; Hurtt et al., 2011). This storyline is expressed
in the RF as high positive forcing from direct modifications to land cover
(0.94 W m
While RCP2.6 projects proliferation of biofuels, RCP4.5 includes widespread
afforestation in response to a global carbon tax policy. The afforestation
is reflected in the RF of direct modifications to land cover for RCP4.5,
which is the only scenario that leads to a decrease in the RF from this
sector between 2010 and 2100 (Fig. 2). Wildfire emissions of CO
Time series of the historical change in global terrestrial carbon storage for Community Land Model (CLM) simulations with and without LULCC (green and blue, respectively), and with and without fires (solid and dashed, respectively), relative to the year 1850. Changes in carbon storage due to increased land cover conversion carbon emissions when fires are removed are shaded in light green. The time series is smoothed with a 25-year running average.
While agricultural emissions and land cover change projections for each RCP were developed jointly by an integrated assessment model (IAM), the land cover change projections were modified during harmonization for terrestrial model use (Di Vittorio et al., 2014). This means that the sector RFs calculated in this study may be in conflict with the original LULCC storylines of the IAMs, and, therefore, it may be more informative to consider the RF from each sector as a range of possible outcomes, separately from their respective RCPs.
Non-deforestation fires are often considered carbon-neutral, meaning the
carbon sequestered during post-fire regrowth roughly balances the carbon
emitted. But this is not the case for periods of trending global fire
activity, as during rapid climate change (Prentice et al., 2011) or
ecosystem shifts (Runyan et al., 2012), when the fire carbon source and sink
are out of balance and atmospheric CO
Time series of the projected change in global terrestrial carbon storage for CLM simulations with RCP4.5, with RCP8.5, and without LULCC (red, green, and blue, respectively), and with and without fires (solid and dashed, respectively), relative to the year 2000. The time series is smoothed with a 25-year running average.
Spatial distribution of the sources of LULCC RF for
Spatial distribution of the sources of LULCC RF by sector
for
Local effects such as those that occur in the Amazon are generally not well
represented by global-scale fire models that do not capture ecosystem edge
effects or small-scale variations in surface hydrology. Area burned by fires
in the Kloster et al. (2010) model used here responds to changes in biomass
availability, meaning a decrease in vegetation, such as that following
deforestation, leads to a decrease in area burned. Therefore, global-scale
conversion of forests to grassland or crops, a source of carbon to the
atmosphere, leads to a decrease in fire emissions of carbon to the
atmosphere. From 1850 to 2004, fires were responsible for a greater than
50 PgC decrease in total carbon emissions from LULCC (Fig. 3, difference
between dashed and solid green lines). About half of this decrease can be
attributed to an artifact of our experimental setup that results from the
removal of fires from the Community Land
Model (CLM) simulations. Fires are a substantial loss term
for terrestrial carbon and when they are excluded from the CLM simulations,
terrestrial carbon storage increases everywhere fires normally occur (Ward
et al., 2012). As a result, in the “no-fire” simulations, carbon emissions
from land cover conversions are enhanced because there is more aboveground
carbon available to be released. We calculate the difference in carbon
emissions from land cover conversions in the simulations with and without
fire and plot this as the shaded area in Fig. 3. We do not include this
reduction in terrestrial carbon emissions from fires when computing the
CO
The sources of the LULCC sector RFs are spatially heterogeneous and depend strongly on the LULCC projection (Fig. 5). Major present-day agricultural regions that are projected to remain productive during this century, in particular India, eastern China, and the central United States (Hurtt et al., 2011), contribute 70–80 % of the global LULCC RF in 2010 as well as in the RCP4.5 and RCP6.0 scenarios (Fig. 5). In contrast, the remaining scenarios all exhibit a substantial tropical source of positive RF from LULCC. Direct modifications to land cover dominate the RF from the tropics, although there are subtropical areas where agriculture contributes the most of all sectors, especially for RCP8.5 LULCC (Fig. 6). Similarly, in 2010, direct modification to land cover is the dominant tropical source of RF (Fig. 6). In all cases, there are regions of negative forcing from LULCC, particularly in northern China and Mongolia, although these are smaller in magnitude than the positive forcings.
Comparing the latitudinally averaged total RF from LULCC to the RF from other anthropogenic activities, mainly fossil fuel burning, demonstrates the role of LULCC as the major tropical source of positive anthropogenic forcing both in 2010 and in the future projections. We are only able to compare the LULCC RFs against non-LULCC RFs from RCP4.5 for which fossil fuel burning emissions were used to compute background constituent concentrations in Ward et al. (2014). Note that the contribution of non-LULCC activities to global RF would be larger if RCP6.0 or RCP8.5 were shown. In the TEC, the tropical RF from LULCC nearly surpasses the northern hemispheric extra-tropical RF from other anthropogenic activities (RCP4.5), largely due to direct modifications of the land cover (Figs. 5 and 6).
The ratio of the absolute value of LULCC RF to the absolute
value of the RF of fossil fuel burning activities computed for each country
for the year 2010 including
The ratio of the absolute value of LULCC RF to the absolute
value of the RF of fossil fuel burning activities computed for each country
for the year 2100 and the RCP4.5 scenario anthropogenic emissions and land
cover change, including
Slope of the regression of year 2100 RF from
We plot the ratios of LULCC RF to total anthropogenic RF to illustrate that
on an individual country level there is a substantial range in the
proportion of total anthropogenic RF that can be ascribed to LULCC
activities (Figs. 7 and 8). The forcing from developed countries, such as the
United States, Canada, Japan, and the European Union countries, is dominated
by fossil fuel burning in the year 2010 (Fig. 7a). This is also true for
many African countries where the total anthropogenic RF is small (Ward and
Mahowald, 2014). The important developing countries for global,
anthropogenic climate change, China, India, Brazil, and Indonesia (Ward and
Mahowald, 2014), all contribute more LULCC RF than fossil fuel burning RF.
These differences in the source of RF between developed and developing
countries were noted by Pongratz and Caldeira (2012) for LULCC CO
In this section, we address whether a simple linear regression approach
could be used to estimate the RF of future changes in forest and crop area.
We have calculated the RF from different LULCC sectors for six possible
future scenarios, providing six data points per grid cell in the tropics to
test this approach (in the extra-tropics there are only 5 data points since
the Trop-BAU and RCP8.5 emissions are the same). Here we regress the RF from
the year 2100, referenced against the year 2010, onto forest area change
over the same period for the direct modification and wildfire sectors
(increases in forest area are shown with a positive sign), and onto crop area
change for the agriculture sector (increases in crop area are shown with a
positive sign) for each country, using a 1.9
The regression coefficients for the agriculture sector are generally positive, indicating that an increase in crop area leads to a positive RF from that sector. The magnitudes of the regression coefficients are high in tropical countries but also in northern hemisphere extratropical countries with major agricultural sectors. The relationship is significant at a 95 % confidence level (two-tailed test), using the Spearman rank correlation coefficient, for most countries (Fig. 9a). Most countries also have a statistically significant regression between direct modification RF and the change in forest area, using the same significance test (Fig. 9b). Here deforestation always leads to positive RF, including in the high latitudes where negative forcings from land albedo change play a larger role. The relationship is particularly strong in tropical countries and appears to be linked to the terrestrial carbon storage such that the impact of deforestation on RF is greatest for the high carbon-storage regions of the Amazon and central African rain forests. The regression of the wildfire sector RF onto forest area change does not produce as many statistically significant regression coefficients, but does result in a positive relationship in the deep tropics of South America and Africa and a weak relationship in several subtropical and extra-tropical countries (Fig. 9c). As forest area is reduced, the wildfire emissions simulated by CLM in deforested areas are also reduced. Notably in Brazil and Bolivia, the positive relationship between RF and forest area change through the wildfire feedback is almost as strong as the negative relationship through direct modification of the land cover (Fig. 9b and c; note the different scales of these two figure panels). This result warrants further study given the possible shortcomings of the fire model used in this study for simulating LULCC-fire interactions in the Amazon (Sect. 3.2).
Discussions of the climate impacts of LULCC activities are often limited to
the effects of deforestation (e.g. Brovkin et al., 2013; Boysen et al.,
2014; Bala et al., 2007). Here we find a substantial contribution to
anthropogenic climate forcing from agricultural activities in 2010 and in
most of the future projections. Fertilizer application drives both a
positive forcing, as N
There is now recognition of the importance of atmospheric chemistry in
determining the sum forcing of LULCC (e.g. Heald et al., 2008; Ganzeveld et
al., 2010). Unger (2014) found a global RF of
When interpreting these results it is important to note that while the set
of forcing agents considered in this study is nearly comprehensive,
feedbacks of LULCC onto the hydrological cycle and clouds were not included
in this study. These feedbacks could lead to a net cooling of global surface
temperatures from deforestation even when accounting for increased CO
However, by attributing forcing from LULCC activities to specific sectors and locations, given the set of forcing agents included in this study, we gain a better understanding of where efforts to mitigate anthropogenic climate changes could be focused. Future forcing from direct modifications to land cover is scalable to changes in forest area (Fig. 9). The potential importance and scalability of RF from the direct modifications sector lends support to the REDD strategy of valuing land based on the potential C emissions from deforestation (Lubowski and Rose, 2013). This strategy could be particularly effective in the tropics, although LULCC-related changes in wildfire activity modify the overall LULCC contribution to global RF.
In the remainder of this section we provide a summary of the different
methodologies used to compute the RFs from LULCC for all forcing agents in
Ward et al. (2014). The order of forcing agents in this summary is CO
Global CO
Nitrous oxide is emitted by livestock and by the application of fertilizer
onto crops. LULCC also has a minor impact on N
Methane concentrations are modified directly by emission of CH
LULCC impacts tropospheric O
Emissions of several aerosol species are impacted by land use and land cover change. Ward et al. (2014) considered changes in biogenic secondary organic aerosol from modified leaf area index, changes in dust emissions from cultivation, and changes in fire emissions of black carbon (BC), organic carbon (OC), and sulfate aerosols from LULCC. Changes in aerosol concentrations were computed with a set of CAM version 5 simulations with the Modal Aerosol Model (MAM3) (Liu et al., 2012), with and without the LULCC emissions. Radiative effects of the aerosols, both direct effects and indirect effects on clouds, were diagnosed online, giving values for ERFs for the LULCC aerosol emissions.
Changes to the land surface albedo from land cover change were derived
directly from the CLM simulations in Ward et al. (2014). The simulated
changes in albedo alter the fraction of incident solar radiation that is
reflected back into the atmosphere. The reflected solar radiation is
multiplied by the fraction of outgoing radiation that reaches the top of the
atmosphere at each grid point of a model climatology characteristic of the
year 2000 in which clouds and aerosol scattering are implicit. The radiative
forcing is then simply the difference in top-of-atmosphere net solar
radiative flux caused by the changes in albedo. Additional forcing from
modified albedo following fires was also included for the change in fires
due to LULCC, following the offline analysis of Ward et al. (2012).
Feedbacks of nitrogen deposition by aerosols and feedbacks of climate change
onto the carbon cycle have been identified and quantified by Mahowald (2011).
The magnitudes of these feedbacks for LULCC were estimated by Ward
et al. (2014) and included in the total CO
In this Appendix we discuss the methods for attributing forcing from
individual trace gas and aerosol agents to the three LULCC sectors defined
in Sect. 2.3. As mentioned in Sect. 2.3, apportioning of the O
With these forcings for individual aerosol species estimated, the direct ERF attributed to LULCC is apportioned into sectors by the relative emissions of each of the five species listed above. The indirect ERF attributed to LULCC is apportioned according to the fraction of aerosol number concentration emissions originating from each sector.
N
Apportioning the CO
Deforestation fires occur separately from wildfires in the Kloster et al. (2010)
model. In this scheme, after deforestation, vegetation carbon that is
normally lost to the atmosphere through decomposition may be converted to
atmospheric CO
We perform two historical simulations from 1850 to 2004 with CLM, one with LULCC and one without LULCC, and both without wildfires, branched from a preindustrial spinup without fires (year 1850 land cover). This is followed by 14 future simulations without wildfires, including two simulations for each future scenario (six LULCC scenarios and the non-LULCC case), one for each of two sets of future atmospheric forcing. The future atmospheric forcing data sets, produced by Kloster et al. (2012), are derived from the output of two coupled climate models each following the Special Report on Emissions Scenarios A1B1 future scenario. The same atmospheric forcing is used for all future simulations regardless of the LULCC scenario and in this way the impacts of the LULCC can be isolated (Ward et al., 2014).
We acknowledge support from the National Science Foundation (NSF) EaSM-1049033 and ETBC-1021613 and thank the two anonymous reviewers for their comments and suggestions. Model integrations were performed with support from the Computational & Information Systems Lab at the National Center for Atmospheric Research, which is sponsored by the NSF. Edited by: A. Kleidon