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2 Hydrology, Biogeochemistry, and Ecosystems in Climate Models

2 Hydrology, Biogeochemistry, and Ecosystems in Climate Models

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Forests and Global Change



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Building upon the pioneering work of Deardorff (1978), model development in

the mid-1980s expanded this geophysical representation of the land surface to a

biogeophysical paradigm by addressing the full hydrologic cycle and the effects of

vegetation on energy and water fluxes (Dickinson et al. 1986, 1993; Sellers et al.

1986). These models represented plant canopies, including radiative transfer, turbulent processes above and within the canopy, and the physical and biological

controls of evapotranspiration. The models partitioned evapotranspiration into the

separate fluxes of evaporation of intercepted water, soil evaporation, and transpiration. The hydrologic cycle was represented as interception, throughfall, stemflow,

infiltration, runoff, soil water, snow, evaporation, and transpiration. Model experiments demonstrated biogeophysical regulation of climate by vegetation, for example, through studies of tropical deforestation (Dickinson and Henderson-Sellers

1988).

In the mid-1990s, representation of the biological control of evapotranspiration

was further advanced in a third generation of models that used the theoretical

developments of Collatz et al. (1991) to link the biochemistry of photosynthesis

with the biophysics of stomatal conductance (Bonan 1995; Sellers et al. 1996a).

These models scaled leaf processes to the plant canopy using concepts of sunlit and

shaded leaves and optimal allocation of photosynthetic resources. Model experimentation identified the importance of stomata for climate simulations (Sellers

et al. 1996b), and the models provided the framework to simulate the effects of

the biosphere on atmospheric CO2 (Denning et al. 1996; Craig et al. 1998).

Formalization of dynamic global vegetation models in the late-1990s (Foley

et al. 1996; Sitch et al. 2003), and their coupling to land surface parameterizations

(Foley et al. 2000; Bonan et al. 2003), incorporated theoretical advances in biogeochemistry, vegetation dynamics, and biogeography into models of the coupled

biosphere–atmosphere system. These models simulate the terrestrial carbon cycle,

plant community composition, and vegetation dynamics in relation to climate.

Model experiments demonstrated biogeophysical feedbacks from coupled climate–vegetation dynamics in the Arctic, where expansion of trees into tundra

decreases surface albedo (Levis et al. 1999, 2000), and in North Africa, where

expansion of vegetation into desert similarly lowers albedo (Levis et al. 2004).

Other studies demonstrated biogeochemical feedbacks from the carbon cycle (Cox

et al. 2000; Friedlingstein et al. 2006).

Development of the current generation of biosphere models for climate simulations continues to incorporate theoretical advances in hydrology, biogeochemistry,

and ecology. For example, some carbon cycle parameterizations are built from a

biogeochemical modeling heritage and do not represent individual plants, excluding key ecological principles of allometric constraints and age – structure or size –

structure that are important determinants of vegetation dynamics. A new class of

models better integrates long-term demographic and ecosystem processes with

short-term biogeophysical, biogeochemical, and hydrologic processes (Medvigy

et al. 2009). Global crop models simulate managed agroecosystems in addition to

natural ecosystems (Gervois et al. 2004; Bondeau et al. 2007). New ecological

processes added to the models have identified feedbacks from ozone and stomata



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G.B. Bonan



250



Soil moisture (–)



–2



Latent heat flux (W m )



0.8



0.6



0.4



0.2



Observations

CLM 3.0

CLM 3.5



0.0



J F M A M J J A S O N D

2003



300



b



250

–2



300



a



Sensible heat flux (W m )



1.0



200

150

100

50



200

150

100

50



0



0



–50



–50



J F M A M J J A S O N D

2003



c



J F M A M J J A S O N D

2003



Fig. 35.2 Model simulations (grey lines) compared with observations (black lines) for Morgan

Monroe State Forest, Indiana, during 2003 (a) soil moisture relative to saturation at 30 cm depth;

(b) monthly latent heat flux; and (c) monthly sensible heat flux. Error bars show estimated

uncertainties of observed turbulent fluxes. The grey lines show simulations using version 3.0

and version 3.5 of the Community Land Model (from St€

ockli et al. 2008)



(Sitch et al. 2007), photosynthetic enhancement by diffuse radiation (Mercado et al.

2009), peatlands and methane (Wania et al. 2009), and carbon–nitrogen biogeochemistry (Sokolov et al. 2008; Thornton et al. 2009; Zaehle et al. 2010).

Scientists have a diverse array of methodologies spanning many spatial and temporal scales with which to test and inform these models. Such data include ecosystem and

watershed monitoring (e.g., eddy covariance flux towers, long-term ecological

research) and experimental manipulation (e.g., soil warming, free-air CO2 enrichment),

as well as continental- to global-scale coverage from satellite sensors and atmospheric

monitoring of CO2. Such data are routinely used to diagnose and improve deficiencies

in the models (Oleson et al. 2008; St€

ockli et al. 2008; Randerson et al. 2009).

For example, eddy covariance measurements of sensible and latent heat fluxes

identify deficiencies in soil water and surface fluxes, illustrated in Fig. 35.2 for

simulations of a temperate deciduous forest using the Community Land Model.

Version 3.0 of the model has low soil moisture compared to observations, resulting

in low latent heat flux and high sensible heat flux, especially during the growing

season. Improvements to the parameterization of infiltration, runoff, soil evaporation, and groundwater in version 3.5 of the model produce wetter soil, higher latent

heat flux, and lower sensible heat flux, which better matches observations. Stand–

level synthesis of net primary production can be used to test the simulated carbon

cycle. For example, Fig. 35.3 compares the simulated net primary production of two

different biogeochemical models coupled to the Community Land Model.



35.3



Carbon Cycle–Climate Feedbacks



Undisturbed terrestrial ecosystems absorbed 2.6 Gt C y–1 during the 1990s, approximately one-third of the anthropogenic carbon emission from fossil fuel combustion

and land use change during the same period (Denman et al. 2007). This carbon sink



Forests and Global Change



Net primary production (g C m–2 yr–1)



35



1500



715



Observations

CASA’

CN



1000



500



0

0



500



1000



1500



2000



–1



Precipitation (mm yr )



Fig. 35.3 Simulated net primary production for two biogeochemical models (CASA’ and CN)

coupled to the Community Land Model compared with observations. Net primary production is

shown in relation to annual precipitation. Vertical bars show observational uncertainty (from

Randerson et al. 2009)



is expected to weaken with climate warming. Many climate models now include

terrestrial and oceanic carbon fluxes so that atmospheric CO2 concentration is

simulated in response to anthropogenic CO2 emissions. Coupled carbon cycle–

climate simulations find that the capacity of the terrestrial biosphere to store anthropogenic CO2 emissions decreases over the twenty-first century, providing a positive

feedback whereby warming further increases atmospheric CO2 concentration (Friedlingstein et al. 2006; Plattner et al. 2008).

The overall carbon cycle–climate feedback consists of two distinctly different

responses of the terrestrial biosphere to global environmental change. Plants respond

to increasing atmospheric CO2 through photosynthetic enhancement, and increased

land carbon uptake through this “CO2 fertilization” is a negative feedback to higher

atmospheric CO2 concentration. This carbon gain is diminished by net global carbon

loss with warming. Respiration loss increases with warming in a positive climate

feedback. Warming can enhance photosynthesis (negative feedback) in cold

regions, but decrease photosynthesis (positive feedback) in warm regions, where

greater evaporative demand dries soil. Model intercomparison finds an overall

positive feedback in which carbon cycle processes increase atmospheric CO2 at

the end of the twenty-first century (Friedlingstein et al. 2006; Plattner et al. 2008).

This interpretation of the carbon cycle is formed from models that do not include

carbon–nitrogen biogeochemistry. Decomposition of plant litter and soil organic

matter produces much of the nitrogen to support plant growth. Biological nitrogen



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G.B. Bonan



fixation and anthropogenic nitrogen deposition provide additional inputs. Nitrogen

availability limits plant productivity in many ecosystems, and there is insufficient

nitrogen available to sustain the CO2 fertilization simulated by the carbon cycle–climate models (Wang and Houlton 2009). Experimental studies confirm that low

nitrogen availability restricts plant productivity gain with CO2 enrichment (de

Graaff et al. 2006). However, soil warming can increase decomposition of organic

material and thus nitrogen mineralization, thereby reducing nitrogen limitation

(Melillo et al. 2002). These carbon–nitrogen interactions, long included by ecologists in their ecosystem models, are key regulators of ecosystem response to

warming and CO2 enrichment. Two carbon cycle–climate model simulations of

future climate change that include carbon–nitrogen biogeochemistry do indeed find

that inclusion of the nitrogen cycle decreases carbon uptake from CO2 fertilization

and changes the sign of the warming feedback so that terrestrial ecosystems gain

carbon as climate warms (Sokolov et al. 2008; Thornton et al. 2009).

Results of carbon–nitrogen models question the conclusions of carbon-only

simulations, but raise new uncertainties. The influence of nitrogen on CO2 fertilization varies greatly among models (Sokolov et al. 2008; Thornton et al. 2009;

Zaehle et al. 2010). Anthropogenic nitrogen deposition can further stimulate plant

productivity (Thomas et al. 2009), but progressive nitrogen limitation caused by

accumulation of nitrogen in plant biomass and soil organic matter may diminish

productivity (de Graaff et al. 2006). Redistribution of plant species in response to

climate change alters patterns of nitrogen uptake and mineralization (Pastor and

Post 1988). The different results found with carbon–nitrogen models will motivate expansion of carbon cycle–climate models to include the nitrogen cycle, as

well as other biogeochemical cycles. However, better understanding of the carbon

cycle–climate feedback requires an expansion of model capabilities. The representation of the terrestrial biosphere in climate models has expanded from an

initial biogeophysical focus on energy and water to include biogeochemical

cycles. The models must be further expanded to represent biogeographical processes such as land use, fire, and postdisturbance vegetation succession. For

example, few models currently account for the land use carbon flux directly

(Shevliakova et al. 2009).



35.4



Land Cover Change



Human activities have converted large regions of the world from natural forest,

grassland, and savanna ecosystems to managed cropland and pastureland (Klein

Goldewijk et al. 2007; Ramankutty et al. 2008). Between 1850 and 2005, cropland

area increased in much of the world while forest cover decreased (Fig. 35.4). Farm

abandonment resulted in an increase in tree cover in eastern United States and

Europe. This land cover change produced a net release of carbon to the atmosphere

(Denman et al. 2007). It also altered climate through biogeophysical processes at

the land surface, including surface albedo, surface roughness, and the partitioning



35



Forests and Global Change



a



717



2005 - 1850 Crop



(percent of grid cell)



b



2005 - 1850 Tree



(percent of grid cell)



c



2005 - 1850 Grass



(percent of grid cell)



–50



–25



–10



–2.5



–1



1



2.5



10



25



50



Fig. 35.4 Land cover change represented in the Community Land Model version 4 (CLM4).

Shown are the difference in (a) crop, (b) tree, and (c) grass cover (percent of model grid cell) in

2005 compared with 1850



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G.B. Bonan



of net radiation into sensible and latent heat fluxes. The biogeophysical effects of

historical land cover change are an important climate forcing, especially at regional

scales (Pitman et al. 2009).

The climate forcing from land cover change varies among the world’s ecosystems (Bonan 2008). Forests generally have a lower albedo than croplands or

pasturelands, especially in snow-covered lands. Forests can also sustain high rates

of evapotranspiration. Climate model simulations indicate that tropical forests have

high rates of evapotranspiration, decrease surface air temperature, and increase

precipitation compared with pastureland. Consequently, tropical deforestation is

generally accepted to warm climate, because the warming associated with reduced

evapotranspiration offsets the cooling from the higher surface albedo of pasturelands. In high latitudes, climate model simulations indicate that the low surface

albedo of forests during the snow season warms climate compared to an absence of

trees. Consequently, deforestation in northern latitudes is thought to cool climate

primarily because of higher surface albedo. In mid-latitudes, higher albedo following conversion of forest to cropland or pastureland leads to cooling, but changes in

evapotranspiration can enhance or mitigate this cooling.

The greatest uncertainty in the land cover change forcing is in mid-latitudes and

is associated with evapotranspiration (Bonan 2008). Model intercomparison suggests that historical land cover change in mid-latitudes has cooled climate, but the

magnitude, and even the sign, of this simulated climate change varies due to modelspecific parameterization of albedo, evapotranspiration, and crop phenology (Pitman et al. 2009). Croplands have a higher albedo than forests, and parameterization

of surface albedo is a key determinant of the climate forcing. Albedo parameterizations span a range from detailed plant canopy radiative transfer algorithms utilizing

leaf optical properties to semiempirical algorithms utilizing prescribed land coverdependent albedo. Comparison with satellite observations can constrain simulated

albedo.

Land cover changes in evapotranspiration are less well known. Our understanding of the effect of land clearing on evapotranspiration is based on the conceptualization that tall, deep-rooted trees have greater rates of evapotranspiration than

short, shallow-rooted crops and grasses because of their larger surface roughness

and greater pool of soil water to sustain evapotranspiration. Indeed, many land

surface components of climate models utilize this paradigm. Such a paradigm is

evident in observations. For example, eddy covariance flux tower measurements at

the Duke Forest show forests have greater rates of evapotranspiration than adjacent

pastureland (Juang et al. 2007). The surface cooling from this higher evapotranspiration offsets the warming due to the lower albedo of forests. In Europe, remote

sensing measurements of surface temperature show little difference between forests

and crops during a wet summer, but forests are significantly greener (higher

normalized difference vegetation index) and cooler than crops during a severe

drought (Zaitchik et al. 2006). However, evapotranspiration is the sum of canopy

interception, transpiration, and soil evaporation, each of which responds differently

to land cover change. Differences among trees, grasses, and crops in stomatal

conductance also affect evapotranspiration, as does variation in leaf area index.



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The representation of land cover change can affect the climate change signal

(Pitman et al. 2009). Some models include parameterizations of crop growth and

management to simulate leaf area index; others use prescribed monthly varying leaf

area index. Some models allow multiple plant functional types within a model grid

cell; others allow only a single dominant land cover type in the grid cell.



35.5



Climate Change Mitigation



An understanding of the combined biogeophysical (albedo and evapotranspiration)

and biogeochemical (carbon cycle) effects of ecosystems remains an elusive goal.

Through these and other processes, forests can amplify or dampen climate change

arising from anthropogenic greenhouse gas emission (Bonan 2008). In tropical

forests, the negative climate forcing from strong evaporative cooling augments

the negative forcing from high rates of carbon accumulation. The climate forcing of

boreal forests is less well known. The positive climate forcing (warming) due to low

surface albedo may counter the negative forcing from carbon sequestration so that

boreal forests warm global climate. The climate benefit of temperate forests is

poorly understood. Reforestation and afforestation may sequester carbon, but the

effects of albedo and evapotranspiration are moderate compared with other forests,

and forest influences on evapotranspiration are unclear. Warming from the low

albedo of forests could offset cooling from carbon sequestration so that the net

climatic effect of temperate reforestation and afforestation is negligible, or greater

evapotranspiration by trees could augment biogeochemical cooling.

The net climate forcing through historical land use and land cover change is not

clear. The dominant competing signals from historical deforestation are an increase

in surface albedo countered by carbon emission to the atmosphere. For example,

loss of forest increases surface albedo in the eastern United States through the midtwentieth century until warming reduces snow cover and decreases albedo in the

late-twentieth century (Fig. 35.5). Globally, however, loss of forest releases carbon

to the atmosphere (Fig. 35.6). Climate warming over the twentieth-century may be

less than that expected from greenhouse gases alone, primarily from increased

albedo with loss of extratropical forests (Brovkin et al. 2006). Carbon emission

from land use dampens this biogeophysical cooling. Biogeophysical cooling may

outweigh biogeochemical warming at the global scale (Brovkin et al. 2004) or may

only partially offset warming (Matthews et al. 2004; Pongratz et al. 2010). The net

effect of these competing processes is small globally, but is large in temperate and

high northern latitudes where the cooling due to an increase in surface albedo

outweighs the warming due to land use CO2 emission.

Future trajectories of land use and land cover change over the twenty-first

century driven by socioeconomic needs, societal responses to climate change, and

policy implementation will also affect climate. The biogeophysical land use forcing

of climate may in some regions be of similar magnitude to greenhouse gas climate

change (Feddema et al. 2005). For example, one possible socioeconomic trajectory



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G.B. Bonan



Annual albedo, eastern United States



0.180



0.175



0.170



0.165



0.160



0.155



0.150



1860



1880



1900



1920



1940



1960



1980



2000



Fig. 35.5 Annual albedo for eastern United States simulated by CLM4 (uncoupled from a climate

model) for the period 1850–2005



Global land use carbon flux (Pg C yr–1)



2.5



2.0



1.5



1.0



0.5



0



1860



1880



1900



1920



1940



1960



1980



2000



Fig. 35.6 As in Fig. 35.5, but for annual global land use carbon flux simulated by CLM4 for the

period 1850–2005



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entails high greenhouse gas emission and widespread agricultural expansion with

most suitable land used for farming by 2100 to support a large global population.

The biogeophysical effects of this forest loss yield warming in Amazonia, but

cooling in mid-latitudes. An alternative storyline is low greenhouse gas emission,

with temperate reforestation (farm abandonment) and reduced tropical deforestation because of increases in agricultural efficiency and declining global population.

Temperate reforestation provides biogeophysical warming, as does tropical deforestation. When the carbon cycle is included, the different storylines yield similar

twenty-first century climates despite their different socioeconomic trajectories

(Sitch et al. 2005). In both storylines, net carbon loss from deforestation causes

biogeochemical warming, greatest in the high growth trajectory with widespread

agricultural expansion and weaker in the low growth trajectory with temperate

reforestation and reduced tropical deforestation. In the high growth trajectory,

widespread agricultural expansion produces strong biogeochemical warming that

offsets strong biogeophysical cooling to provide net global warming. The low

growth pathway has similar net warming because weak biogeochemical warming

augments moderate biogeophysical warming.

As climate models evolve into Earth system models with representation of

terrestrial ecosystems and their regulation of hydrological and biogeochemical

cycles, they can inform land management practices to mitigate climate change.

Reforestation, afforestation, and avoided deforestation are possible such practices.

For example, tropical afforestation may help mitigate global warming, while the

influence of temperate and boreal afforestation is more complex (Bala et al. 2007).

However, the interplay among albedo, evapotranspiration, and the carbon cycle is

not well known. These, and other, climate influences of ecosystems need to be

better understood to craft strong climate change mitigation science (Bonan 2008).

Land use policies must recognize the many forest influences, their competing

biogeophysical and biogeochemical effects on climate, and their long-term effectiveness and sustainability in a changing climate.



35.6



Conclusions and Research Needs



The world’s forests influence climate through a variety of hydrological, biogeochemical, and ecosystem processes (Bonan 2008). These forest–atmosphere interactions can dampen or amplify anthropogenic climate change. The effects of

deforestation on biogeophysical processes (albedo, evapotranspiration) are generally thought to cool mid-latitude climate and warm tropical climate, while biogeochemical processes (carbon) are thought to warm climate globally. The net effect of

these and other processes is uncertain and varies among boreal, temperate, and

tropical forests. As the climate benefits of forests become better understood, land

use policies can be crafted to mitigate climate change. These policies must recognize the many ways in which forests affect climate and their long-term effectiveness and sustainability in a changing climate.



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G.B. Bonan



Global models of the biosphere–atmosphere system are still in their infancy, and

processes not yet fully understood may initiate unforeseen feedbacks. Key model

uncertainties include the interactions of the carbon cycle with other biogeochemical

cycles, especially nitrogen and phosphorus; the response to CO2 fertilization, nitrogen

fertilization, and soil warming; and human management of the carbon cycle through

land use and land cover change. The overall responses of the hydrologic cycle and

biogeochemical cycles to land use and land cover change, and their climate effects are

also poorly understood in the models.

Much of our knowledge of forest influences on climate, and our ability to inform

climate change mitigation policy, comes from models. Models of climate and the

biosphere are abstractions of complex physical, chemical, and biological processes.

Extrapolation of process-level understanding of ecosystem functioning gained from

laboratory experiments or field studies to large-scale Earth system models remains a

challenge. Monitoring studies at the ecosystem and watershed scales and large-scale

monitoring from satellite sensors provide important data to test and inform model

development. However, model development and validation must better utilize the

results of experimental manipulation studies such as soil warming and free-air CO2

enrichment to test the response of the models to perturbations. In addition, novel modeldata fusion techniques allow estimation of model parameters, identify model errors,

provide optimal sampling strategies, and improve model simulations (Wang et al.

2009). Comprehensive model-data comparisons are the keys to gaining confidence

in the model simulations and their utility for climate change mitigation policy.

Acknowledgments The National Center for Atmospheric Research is sponsored by the National

Science Foundation.



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