7.4.2 Land-Surface Change
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Figure 7.5: Schematic showing relationships between a simulation of
the Atmospheric Boundary Layer (ABL), a Land-Surface Parametrization (LSP),
vegetation and soil properties, and anthropogenic change. Interactions
are shown by broad white arrows marked with capital letters, fluxes by
grey arrows, and dependencies by dotted lines. (A) Diurnal-seasonal interactions
between the ABL and the LSP; the ABL variables of air temperature, humidity,
downward short-wave radiation, downward long-wave radiation, wind speed
and precipitation (T, q, S
,L , u, P) are used to
force the LSP which calculates net radiation minus ground heat, sensible
heat, and latent heat fluxes ( Rn –G, H, lE),
which in turn feed back to the atmosphere. Three surface parameters in
the LSP are critical to these calculations: Albedo and surface roughness
(a,Z0 ) determine the radiative
balance and turbulent exchange regime, and in third generation LSPs, the
canopy conductance term, gc (equivalent to the summation of
all the leaf stomatal conductances) determines the vegetation evapotranspiration
rate (lE) and net photosynthetic rate (Pnet
). On time-scales of minutes to hours, gc is a direct function
of T, q, S ,CO2
concentration and soil moisture (W). Increasing CO2 concentration
can act to signifi-cantly reduce gc and hence limit lE.
The maximum value of gc is determined by parameters related
to vegetation density or leaf area index (LAI), and biochemical capacity
(Vmax ). Long-term climatic forcing (B) and land-use change
(C) can alter the vegetation type and density, soil properties and ecosystem
respiration rates, Rd , by which carbon is returned to the
atmosphere from the vegetation and soil. (D) Changes in vegetation properties
affect Vmax and LAI, and changes in soil properties affect
soil moisture (W) and runoff (R0 ).
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Climate and carbon cycle simulations extending over more
than a few decades must take account of land-surface change for two main reasons.
First, changes in the physical character of the land surface can affect land-atmosphere
exchanges of radiation, momentum, heat and water (see Figure
7.5 and the simulation studies discussed below). These effects must be allowed
for within climate simulations or analyses to avoid confusion with the effects
of global warming. Second, changes in vegetation type, density and associated
soil properties usually lead to changes in terrestrial carbon stocks and fluxes
that can then directly contribute to the evolution of atmospheric CO2
concentration. Therefore, any historical analysis of the atmospheric CO2
record must estimate these contributions to avoid inaccurate attribution of
carbon sinks or sources. Similarly, model simulations extending over the next
50 to 100 years should allow for significant perturbations to the atmospheric
carbon budget from changes in terrestrial ecosystems (Woodwell et al., 1998;
see also Chapter 3).
There are two types of land-surface change; direct anthropogenic change, such
as deforestation and agriculture; and indirect change, where changes in climate
or CO2 concentration force changes in vegetation structure and function within
biomes, or the migration of biomes themselves. With respect to direct anthropogenic
change, population growth in the developing countries and the demand for economic
development worldwide has led to regional scale changes in vegetation type,
vegetation fraction and soil properties (Henderson-Sellers et al., 1996; Ramankutty
and Foley, 1998). Such changes can now be continuously monitored from space,
and the satellite data record extends back to 1973. Large-scale deforestation
in the humid tropics (South America, Africa and Southeast Asia) has been identified
as an important ongoing process, and its possible impact on climate has been
the topic of several field campaigns (Gash et al., 1996), and modelling studies
(for example, Nobre et al., 1991; Lean et al., 1996; Xue and Shukla, 1996; Zhang
et al., 1996a; Hahmann and Dickinson, 1997; Lean and Rowntree, 1997). Some significant
extra-tropical impacts have also been identified in several model experiments
(e.g., Sud et al., 1996; Zhang et al., 1996b). Replacement of tropical forest
by degraded pasture has been observed to reduce evaporation, and increase surface
temperature; these effects are qualitatively reproduced by most models. However,
large uncertainties still persist about the impact of large-scale deforestation
on the hydrological cycle over the Amazon in particular. Some numerical studies
point to a reduction of moisture convergence while others tend to increase the
inflow of moisture into the region. This lack of agreement occurs during the
rainy season and reflects our poor understanding of the interaction of convection
and land-surface processes (Polcher, 1995; Zhang et al., 1997a), in addition
to the effects of differences between the formulations in the land-surface schemes,
their parameter fields, and the host GCMs used in the studies.
Other simulation work has indicated that the progressive cultivation of large
areas in the East and Midwest USA over the last century may have induced a regional
cooling of the order of 1 to 2°C due to enhanced evapotranspiration rates
and increased winter albedo (Bonan, 1999). Snow-vegetation albedo effects significantly
influence the near-surface climate; assignment of an open snow albedo value
to the winter boreal forest in an NWP led to the prediction of air temperatures
that were 5 to 10°C too low over large areas of Canada (Betts et al., 1998).
Work has also been done on the interaction between Sahelian vegetation and rainfall
that suggests that the persistent rainfall anomaly observed there in the 1970s
and 1980s could be related to land-surface changes (Claussen, 1997; Xue, 1997).
All these studies indicate that large-scale land-use changes can lead to significant
regional climatic impacts. However, it is unlikely that the aggregate of realistic
land-use changes over the next 50 to 100 years will contribute to global scale
climate changes comparable to those resulting from the warming associated with
the continuing increase in greenhouse gases.
Changes to the land surface resulting from climate change or increased CO2
concentration are likely to become important over the mid- to long term. For
example, the extension of the growing season in high latitudes (Myneni et al.,
1997) will probably result in increases in biomass density, biogeochemical cycling
rates, photosynthesis, respiration and fire frequency in the northern forests,
leading to significant changes in albedo, evapotranspiration, hydrology and
the carbon balance of the zone (Bonan et al., 1992; Thomas and Rowntree, 1992;
Levis et al., 1999). There have been several attempts to calculate patterns
of vegetation type and density as a function of climate (e.g., Zeng et al.,
1999); most of these have made use of climate predictions to calculate the future
steady-state distribution of terrestrial biomes but some have attempted to model
transitional cases (Ciret and Henderson-Sellers, 1998).
However, over the next 50 to 100 years, it is more likely that changes in
vegetation density and soil properties within existing biome borders will make
a greater contribution to modifying physical climate system and carbon cycle
processes than any large-scale biogeographical shifts. In some cases, soil physical
and chemical properties will limit the rate at which biomes can “migrate”;
for example, colonisation of the tundra by boreal forest species is likely to
be slowed by the lack of soil. Climate-vegetation relations are discussed further
in Chapter 8, Section 8.5.5 with
respect to past climates.
At present, only limited global data sets for LSPs are available and these
need to be further improved. A comprehensive land-use/land cover data set, providing
a global time-series of vegetation and soil parameters over the last two centuries
at GCM resolution, would be a very useful tool to separate land-use change impacts
on regional climate from global scale warming effects. Additionally, for both
historical analyses and future projections, there is a need for interactive
vegetation models that can simulate changes in vegetation parameters and carbon
cycle variables in response to climate change. These proposed fourth generation
models are just beginning to be designed and implemented within climate models.
