BOSTON UNIVERSITY

GRADUATE SCHOOL OF ARTS AND SCIENCES

Thesis

EVALUATING AND IMPROVING TERRESTRIAL

CARBON EXCHANGE IN THE NCAR CCM3 LAND

SURFACE MODEL

by

RUI GONG

B.E., Tsinghua University, 1997

Submitted in partial ful�llment of the

requirements for the degree of

Master of Arts

1999

Approved by

First ReaderRanga B. Myneni, Ph.D.Associate Professor of Geography

Second ReaderCurtis E. Woodcock, Ph.D.Professor of Geography

Third ReaderMark Friedl, Ph.D.Assistant Professor of Geography

Fourth ReaderDennis Dye, Ph.D.Assistant Professor of Geography

Acknowledgments

First, I want to express my deep thanks for Professor Ranga Myneni, my major

academic advisor. His relentless e�orts lead me to this exciting research �eld. His

enthusiasm, high standard and persistence for science set a good example for me.

I bene�ted hugely from the enormous amount of academic interactions with him.

Professor Curtis Woodcock has actively advised me, both on academic and broader

issues, as professor in class and as former graduate advisor. I also thank Mark Friedl

for encouragements in my times of di�culty and for his academic instructions as

professor of two courses in my �rst semester. Those courses are crucial for this thesis

work. I thank Dennis Dye and Guido Salvucci for academic guidance that contributed

to this work.

Second, I thank my fellow graduate students in the department of Geography.

They make the department a dynamic and exciting place to be, a nice environment to

work, and a place for active academic discussions. I thank my o�cemates, especially

Rongqian Yang, for helping me with computer problems at times.

My work is based upon a substantial amount of work done by other scientists. I

thank the National Center for Atmospheric Research, especially Gordon Bonan for

developing and distributing the CCM and LSM, and for providing useful information

through the ccm user group discussions, Christopher Potter for the CASA model, and

the CENTURY model group at Colorado State University for CENTURY model.

This research is funded by NOAA project NA76GP0481.

EVALUATING AND IMPROVING THE TERRESTRIAL

CARBON EXCHANGE IN NCAR CCM3 LAND

SURFACE MODEL

Abstract

I include a simple dynamic soil respiration model in the Land Surface Model (LSM),

in a manner similar to several global biogeochemical models (CENTURY, TEM and

CASA). The improved model, LSM BU, is simulated for about 100 years, driven by

typical atmospheric forcings extracted from CCM3. An equilibrium scenario is de-

rived with stable carbon pool sizes attuned to the CCM3 climatology and vegetation

distribution. Carbon pool sizes are reasonable and show good correlation with Leaf

Area Index (LAI) derived from satellite observation. Soil respiration estimates gen-

erally agree with �eld data, but are underestimated for shrublands and needleleaf

forests. Simulated Net Ecosystem Exchange (NEE) successfully reproduced seasonal

and diurnal cycles of observations from Harvard Forest, the First ISLSCP Field Exper-

iment (FIFE) and the Boreal Ecosystem-Atmosphere Study (BOREAS). Compared

to a diagnostic carbon model based on satellite measurement of Normalized Di�er-

ence Vegetation Index (NDVI), the model simulation of NEE has one month early

phasing and large amplitudes. This can probably be attributed to LSM's vegetation

phenology which di�ers signi�cantly from satellite observations.

Key words: climate change, terrestrial carbon exchange, remote sensing, biogeo-

chemical model, 'missing' carbon sink, global carbon cycle, land surface model

CONTENTS

1 Introduction 1

2 Background 2

2.1 Mechanisms of the Terrestrial Carbon Sink 2

2.2 Previous Approaches to Terrestrial Carbon Study 4

2.3 The Approach of LSM BU 7

3 Methods 8

3.1 Model Description 8

3.1.1 Land Surface Model 8

3.1.2 Soil Carbon Model 9

3.2 Simulation Scheme 12

4 Results 14

4.1 Net Primary Production 14

4.2 Net Ecosystem Exchange 17

4.3 Soil Carbon Pools and Respiration 21

5 Discussions 22

5.1 Land Cover and Vegetation Phenology 23

5.2 Applications of LSM BU 25

6 Conclusions 26

References 28

LIST OF TABLES

Table 1 Maximum turnover rates of carbon pools in LSM BU 37

Table 2 Lignin content and carbon-to-nitrogen ratios in LSM BU 37

Table 3 Photosynthesis, autotrophic respiration and Net Primary Production

from LSM BU simulation, by vegetation types 38

Table 4 Comparison of Net Primary Production between LSM BU, CASA, SiB2

(in GCM), TEM and Lieth's result (unit: gC=m2=yr) 39

Table 5 Field data of Net Primary Production by LSM vegetation types for

calibration purposes 40

Table 6 Stable soil carbon pool sizes simulated by LSM BU, according to veg-

etation and pool types 41

Table 7 Comparison of LSM BU simulated soil respiration with observations 42

LIST OF FIGURES

Fig. 1 Schematic carbon uxes between di�erent pools modelled by LSM BU 43

Fig. 2 Di�erence of mean precipitation and land surface temperature between

observations, CCM3 simulation and those used in LSM BU 44

Fig. 3 Global distribution of Photosynthesis, Net Primary Production (NPP),

Net Ecosystem Exchange (NEE), autotrophic and heterotrophic respi-

ration, simulated by LSM BU 45

Fig. 4 Seasonal distribution of Gross Primary Production (GPP), Net Pri-

mary Production, Net Ecosystem Exchange (NEE), autotrophic and

heterotrophic respiration simulated by LSM BU, according to broad

latitudinal bands 46

Fig. 5 Seasonal distribution of Net Ecosystem Exchange (NEE) from LSM,

LSM BU and Fung et al. (1987), according to broad latitudinal bands 47

Fig. 6 Comparison of NEE seasonality between LSM, LSM BU and Fung et

al. (1987) 48

Fig. 7 Comparison of global Net Ecosystem Exchange (NEE) distribution for

annual, January and July averages, between LSM, LSM BU and Fung

et al. (1987) 49

Fig. 8 Comparison of monthly Net Primary Production (NPP), soil respira-

tion and net carbon ux between LSM BU simulation and observations

at Harvard Forest, a FIFE site and a BOREAS site 50

Fig. 9 Comparison of diurnal cycle of net carbon ux between LSM BU sim-

ulation and observations at Harvard Forest, a FIFE site, a tropical

evergreen forest and three BOREAS sites during di�erent periods of

the year 51

Fig. 10 Comparison of diurnal cycle of net carbon ux between LSM BU sim-

ulation and observations at Harvard Forest, a FIFE site, a tropical ev-

ergreen forest and three BOREAS sites during di�erent periods of the

year. The dates for simulation are one month earlier than observations,

to show the e�ects of shifting phenology. 52

Fig. 11 Global distribution of soil carbon pool size simulated by LSM BU and

maximum Leaf Area Index (LAI) derived from AVHRR data, both

scaled to their global maxima 53

Fig. 12 Leaf Area Index (LAI) comparison between the LSM pro�le and a pro-

�le based on AVHRR data. 54

1

1 Introduction

Industrial emission of greenhouse gases, especially CO2, may be contributing to one

of the most important forms of climate change { global warming (Schimel et al.,

1995). International initiatives are underway to deal with the problem: 174 coun-

tries are signatories to the United Nations Framework Convention on Climate Change

(UNFCCC), which aims at the stabilization of greenhouse gases in the atmosphere at

a level that will prevent dangerous anthropogenic interference with the climate sys-

tem. Some of the countries (Annex I countries) agreed to the Kyoto Protocol, thus

promising reductions of fossil fuel emissions and sequestration of atmospheric CO2 in

terrestrial ecosystems through human intervention.

Terrestrial ecosystems play a crucial role in the complex feedback processes be-

tween the rising concentration of atmospheric CO2 and the changing climate. By

a�ecting the radiative balance of the atmosphere, increases in CO2 concentration can

induce changes in climate, such as global warming, and in uence vegetation activities

(Shine et al., 1995; Myneni et al., 1997a); responding to the changing climate the ter-

restrial ecosystems may act as sources or sinks of carbon on the land surface, thereby

in uence the distribution of CO2 in the atmosphere through transport by the winds.

Land surface ecosystems �x atmospheric CO2 through photosynthesis and release

CO2 through plant respiration and microbial respiration. The di�erence between

gross photosynthesis (GPP) and autotrophic respiration is de�ned as Net Primary

Production (NPP). The di�erence between heterotrophic respiration and NPP, de-

�ned as Net Ecosystem Exchange (NEE), is the net carbon ux between the land

surface and the atmosphere. Photosynthesis and respiration are nearly balanced on

an annual basis. However, evidence from various sources indicates the existence of a

missing terrestrial carbon sink, with an estimated size of 1 to 3 petagram of carbon

(PgC) per year, located mainly in northern middle and high latitudes (Francey et

2

al., 1995; Tans et al., 1990; Wofsy et al., 1993; Houghton et al., 1998; Schimel 1995;

1998).

The sequestration of atmospheric CO2 by the land surface is important because

it will determine the reasonable goal of reducing fossil fuel emissions, which is at

high economic and political cost. However, the mechanisms, magnitudes and spatial

distribution of the terrestrial carbon sink remain puzzling to date, which is often

referred to as the missing carbon sink.

Scienti�c priorities have been set with respect to the global carbon cycle regard-

ing questions such as: Where is the sequestration taking place? Which ecosystems

are responsible for the sink? How is the sequestration partitioned between soil and

vegetation? What are the underlying mechanisms? Will the sequestration continue or

will it be transient? How will the sink respond to environmental changes? To answer

these questions, many important links are missing in current observational data, and

mechanistic models validated by observations have the potential to reconstruct those

missing links.

2 Background

2.1 Mechanisms of the Terrestrial Carbon Sink

Over the long term, NPP is either retained in standing biomass which will increase au-

totrophic respiration, or transfered to the soil carbon pool, increasing heterotrophic

respiration. This means that increasing NPP will gradually be matched by conse-

quently increasing respiration, and only a continuously increasing NPP will maintain

a constant sink (Thompson et al., 1996). Current terrestrial carbon sequestration is

mainly due to increased vegetation productivity, in response to environmental changes

or natural variabilities. What are the changes responsible for current sink and whether

3

those changes will continue are critical to the future of terrestrial carbon sink.

Di�erent mechanisms, including CO2 fertilization (Gi�ord, 1994), nitrogen de-

position (Schindler & Bayley, 1993), forest regrowth (Dixon et al., 1994), climate

variability (Dai & Fung, 1993) and a lengthening growing season (Myneni et al.,

1997a), have been suggested as possible explanations for the terrestrial carbon sink.

It is well known that increased ambient CO2 concentration will stimulate vege-

tation productivity by providing more substrate for photosynthesis (Idso & Kimball,

1992). Numerous laboratory experiment results support this mechanism; however,

there are no evidences to show that ecosystem-level vegetation response to increased

CO2 concentration signi�cantly. Generally, vegetation productivity is determined by

the most limiting environmental stresses. Initial increases in atmospheric CO2 will

relieve CO2 stress, but with further increases in CO2 abundance, other environmental

stresses such as temperature, water or nutrient stress dominate, reducing the e�ects

of further increasing CO2. Grulke et al. (1990) concludes that there is 'little if any

long term stimulation of ecosystem carbon acquisition by increases in atmospheric

CO2'.

Nitrogen content of the atmosphere is greatly enhanced by industrial emissions

and widespread use of fertilizers in agriculture. Through precipitation deposition

the nitrogen becomes available to forests, whose growth is most limited by nitrogen

availability. Some studies suggest that globally nitrogen fertilization can contribute

to 0.2 PgC the missing carbon sink (Peterson & Mellilo, 1985).

Forest regrowth following past disturbances is another possible sink mechanism

for atmospheric CO2 (Dixon et al., 1994). Much of North America's forest has been

heavily harvested since late 19th century and is now at a regrowing stage, seques-

tering carbon from the atmosphere (Hart, 1968). Most European forests are actively

managed and continuous regrowth is present (Kauppi et al., 1992). Kauppi et al.

(1992) estimated European forest as a net carbon sink of about 0.2 PgC per year.

4

Forest regrowth at abandoned agricultural land after deforestation is also drawing at-

mospheric carbon (Houghton et al., 1983). However, forest regrowth can not be sep-

arated from other mechanisms, e.g., nitrogen deposition or CO2 fertilization, which

may contribute to forest regrowth as well.

Climatic factors exert direct controls on vegetation productivity. Warming and

high precipitation can enhance vegetation productivity. A study by Dai & Fung (1993)

found that climate variability during the 1950-1984 period could have contributed to

half of the terrestrial carbon sink from deconvolution studies.

Keeling et al. (1996) analysed atmospheric CO2 data and found that for the

1980s, the northern high latitude growing season has lengthened, progressing toward

earlier in the spring and later in the fall. This study corroborated an earlier discovery

of reduced snow cover extent and temperature rise in the northern high latitudes

(Groisman et al., 1994), which was later con�rmed by analysis of satellite observations

from 1981 to 1991 (Myneni et al., 1997a). Recently, analysis of over 30 years of

observations from a large observational network in Europe indicate that the growing

season has advanced by about 6 days in the spring and delayed 4.8 days in the fall

since 1960s, most of which happens in the 1980s (Menzel & Fabian, 1999). Using an

atmospheric transport model and a biogeochemical model, Randerson et al. (1998)

found that major observational constraints can be satis�ed only by an early season

NPP increase, as suggested by Myneni et al. (1997a), possibly from changes in

species composition and vegetation phenology caused by disturbance through human

activities.

2.2 Previous Approaches to Terrestrial Carbon Study

Observations of atmospheric CO2 concentration have been used to constrain the dis-

tribution of the terrestrial carbon sink in di�erent ways. The missing terrestrial

carbon sink can be estimated from the carbon equation, in which industrial emis-

5

sions, emissions from land use change, ocean and terrestrial carbon exchanges add

up to the increments of CO2 in the atmosphere (Schimel, 1998). The latitudinal

gradient of observed CO2 concentrations, combined with other information, suggests

that the carbon sink is located in the Northern Hemisphere terrestrial ecosystems

(Tans et al., 1990). Measurements of C13=C12 isotopic ratios of atmospheric CO2,

which provide information about the partitioning of CO2 uptake between ocean and

land surface, also con�rm the presence of a large carbon sink in the northern middle

and high latitude terrestrial ecosystems (Ciais et al., 1995a, 1995b). Observations of

increases in the amplitude of the seasonal cycle of atmospheric CO2 concentration

suggest increased activity of vegetation at the northern middle and high latitudes,

which is further supported by satellite observations (Keeling et al., 1996; Myneni et

al., 1997a).

Modelling the 'missing' terrestrial carbon sink is a challenging task, because in

most previous studies of terrestrial carbon exchange, soil respiration is assumed to

balance NPP annually, a fairly reasonable assumption based on the fact that NEE is

more than one order of magnitude smaller than NPP and soil respiration, well within

the range of uncertainty of both estimates. Since photosynthesis is directly in uenced

by climate, terrestrial carbon exchange studies were �rst made by correlating �eld

measurements with climatic parameters such as temperature and precipitation (Lieth,

1975). Correlations between climate and vegetation productivity provide a broad

picture of biospheric photosynthetic activities.

Later, satellite observations were applied to model terrestrial carbon processes

(Fung et al., 1983, 1987; Heimann & Keeling, 1989; Maisongrande et al., 1995). The

Normalized Di�erence Vegetation Index (NDVI), de�ned as the ratio of di�erence

between near infrared band re ectance and red band re ectance over the sum of the

two, was found to be correlated to vegetation activity (Tucker et al., 1986), and was

used in many studies of terrestrial productivity. Fung et al. (1983; 1987) used NDVI

6

to prescribe seasonality of NPP. More recent studies directly simulate NPP, using

empirical relationship between NDVI and Fraction of absorbed Photosynthetically

Active Radiation (FPAR) to describe energy input, and light use e�ciency factor to

account for other environmental stresses (Potter et al., 1993; Maisongrande et al.,

1995).

Global biogeochemical models like the Terrestrial Ecosystem Model (TEM), CEN-

TURY and Carnegie-Ames-Stanford Approach model (CASA) have considered more

physiology and biogeochemistry factors at the ecosystem level, to model terrestrial

carbon dynamics (Melillo et al., 1993; Parton et al., 1987; Parton et al., 1993; Potter

et al., 1993). They use historical climate data (monthly temperature and precip-

itation) and soil attributes as model inputs, and simulate the processes of carbon

�xation, biomass and nutrient allocation, litterfall, soil nitrogen mineralization and

microbial CO2 production at monthly time steps.

Process-based land surface-atmosphere models such as the Biosphere-Atmosphere

Transfer Scheme (BATS; Dickinson et al., 1986), the Simple Biospheric Model (SiB;

Randall et al., 1996; Denning et al., 1996), the Land Surface Model (LSM; Bonan,

1995; 1996) and the Integrated Biosphere Simulator (IBIS; Foley et al., 1996) repre-

sent a more physical approach to the study of terrestrial carbon exchange because the

representations of hydrology and surface energy balance are more sophisticated and

can be coupled to Atmospheric General Circulation Models (AGCM). Denning et al.

(1995) demonstrated the importance of considering short-time-scale terrestrial carbon

exchanges in resolving atmospheric CO2 distribution. Using climatology derived from

AGCMs, these models are able to resolve the seasonal, synoptic and diurnal cycles in

carbon metabolism with time steps of minutes, and to simulate the short-time-scale

interactions of carbon exchange and atmospheric transport.

The photosynthetic process modelled in LSM and SiB2 is based on the work of

Collatz et al. (1991; 1992), with stomatal conductance playing a key role in the sur-

7

face energy balance. Evapotranpiration is regulated by leaf stomotal opening which

achieves an optimal state between the competing needs of maximizing carbon uptake

and minimizing water loss. LSM has an autotrophic and heterotrophic respiration

scheme that is not linked to the carbon �xation during photosynthesis, and thus cre-

ates large local carbon sources or sinks because of inaccurate initial size of soil carbon

pools. Acknowledging the initial carbon pool problem as intractable at this stage,

SiB2 adopts a simple diagnostic model of soil respiration, making it inherently un-

able to directly simulate the terrestrial carbon sink arising from climate change and

human activities (Denning et al., 1996).

2.3 The Approach of LSM BU

A dynamic terrestrial carbon exchange model (LSM BU) based on version 1.0 of the

Land Surface Model (LSM; Bonan, 1996) is developed to address the initial soil carbon

pool problem. Stable carbon pool sizes are derived according to a typical stable

CCM climatology representing current climatic conditions. Soil carbon pools can

dynamically respond to climate or vegetation changes, and interannul imbalances in

carbon exchanges can be simulated later when interannual variations are introduced as

inputs. The pool sizes derived here are speci�cally attuned to the CCM climatology,

and later when used in standard CCM will not produce signi�cant carbon sources

or sinks. The equilibrium scenario is thus very valuable in evaluating the possible

mechanisms for the 'missing' carbon sink, all of which are essentially continuous

disturbances resulting from natural variations or human interactions.

In this thesis, LSM BU is described with emphasis on the soil respiration model

and results of 100 years' LSM BU simulation is presented. Simulated carbon pool sizes

are analysed, compared with observations and related to global Leaf Area Index (LAI)

distribution derived from satellite observations. Latitudinal distributions of carbon

exchanges (GPP, plant respiration, NPP, soil respiration and NEE) are analysed with

8

emphasis on the seasonality. LSM's LAI pro�le is compared with satellite-derive LAI

to explain the di�erence in seasonality as revealed in comparison with a diagnostic

carbon model based on satellite NDVI. Seasonal and diurnal NEE simulations are

compared with �eld data from Harvard Forest, FIFE and BOREAS. Suggestions on

further improving LSM BU are discussed and conclusions are drawn in the end.

3 Methods

3.1 Model Description

3.1.1 Land Surface Model

The Land Surface Model (LSM1.0) is an integral part of NCAR CCM3, a General

Circulation Model (GCM). LSM is an one-dimensional model of energy, momentum,

water and CO2 exchange between the atmosphere and land, accounting for ecological

di�erences among vegetation types, hydraulic and thermal di�erences among soil

types, and allowing for multiple surface types including lakes and wetlands within a

single grid cell. The model can be run at various resolutions; for this study I used

temporal resolution of 20 minutes and the T31 spatial resolution, which is 3:75o �3:75o. For each grid cell, the soil has 6 layers vertically, with depths of 100, 200,

400, 800, 1600, 3200 mm. Thirteen vegetation types are prescribed with di�erent

phenological, biophysical and biogeochemical properties. Each grid cell is a mixture

of no more than 5 subgrid cells, with lake, wetland and three di�erent vegetation

types. The model is simulated for each subgrid cell independently, with the same grid-

averaged atmospheric forcing, and grid-averaged surface variables are obtained using

the subgrid cell fractional areas. A detailed description of the processes simulated by

LSM1.0 is presented in Bonan (1996), and the source codes for CCM and LSM are

currently available from http://www.cgd.ucar.edu/cms/ccm3.

9

LSM simulates photosynthesis, autotrophic respiration and heterotrophic respira-

tion. The modelling of photosynthesis, similar to Collatz et al. (1991) and Sellers

et al. (1992a), is coupled to stomatal conductance that controls latent heat ux and

hence is an integral part of the surface energy balance. Autotrophic respiration is

broken into growth respiration, which is assumed to be 25% of photosynthesis, and

maintenance respiration, which is dependent on vegetation-speci�c parameters and

vegetation temperature. Bonan (1995) shows that the NPP scheme works well as

compared to previous estimates, while large local sources and sinks of carbon are

created because LSM's soil respiration scheme is not dynamically linked to the NPP

scheme. For example, a standard CCM3 control run simulates a global net sink of

14.0 PgC per year, which is inconsistent with current understanding of the global

carbon budget (Schimel et al., 1995; Houghton et al., 1998).

3.1.2 Soil Carbon Model

The soil respiration model is based on the CENTURY model and the CASA model

(Parton et al., 1985, 1993; Potter et al., 1993; Randerson et al., 1996; Potter &

Klooster, 1997). It partitions soil carbon into three categories: litter pools, microbial

pools and soil organic pools. Carbon in the organic form is present in either the

SLOW pool, which contains mainly chemically protected C and has residence time of

several decades, or the OLD pool, which is mainly composed of physically protected

C and has a residence time of a century or more.

Fig. 1 shows the detailed pathways for carbon transfers and soil respiration. At

each time step, each ux in Fig. 1 is characterized by the following equation:

Tr(i; j; t) = C(i; t) �K(i) �Ws(t) � Ts(t) � Ft(i; j); (1)

where Tr(i,j,t) is the carbon transferred from pool i to pool j at time step t; C(i,t)

is the carbon storage in pool i at time step t; K(i) is the maximum turnover rate of

10

carbon pool i; Ws(t) is the soil moisture scaling factor at timestep t; Ts(t) is the soil

temperature scaling factor at time step t; Ft(i,j) is the fraction of carbon transferred

from pool i to pool j; i ranges from 1 to 9, representing the 9 soil carbon pool types;

j ranges from 0 to 9, representing atmosphere and soil carbon pools.

The model assumes that NPP is allocated to surface litter pools on an annual

basis. The assumption is made that carbon is allocated to leaf, root and wood pools

by the ratio of 1:1:1 for forests, and to leaf and root pools by the ratio of 1:1 for grass-

lands (Potter et al., 1993). LAI is used to describe the vegetation phenology, which

determines the timing of when plant detritus becomes available to decomposition.

The litterfall for leaf, root and wood pools are allocated each month, then scaled to

the time step for the model simulation. The seasonality of litterfall allocation can be

described by the following equations (Potter et al., 1993; Randerson et al., 1996):

LL(m) = maxf[0:5 �LAI(m�2)+LAI(m�1)]� [LAI(m+1)+0:5 �LAI(m+2)]; 0g;(2)

LTleaf (m) = [LAImin

LAIave� 1

12+

LL(m)P12

t=1 LL(m)(1� LAImin

LAIave)]; (3)

LTroot(m) = A �LAI(m)LAImax

+ LL(m)LLmax

P12t=1 (

LAI(t)LAImax

+ LL(m)LLmax

)+B; (4)

LTwood(m) =1

12; (5)

where LAI(m) is LAI of month m; LTleaf (m), LTroot(m), LTwood(m) are fractions

of month m's litterfall for leaf, root and wood carbon pools respectively; LAImax,

LAImin and LAIave are maximum, minimum and average LAI over the 12 months of

a year; A, B are constants.

11

Similar to LSM, the scaling e�ect of soil temperature and soil moisture content

on the transfer of carbon is given by

Ts = QT�30

10

10 ; (6)

Ws =�

� + a1� a2� + a2

� (pa1 +

pa2p

a2)2; (7)

where T, � are temperature and moisture of soil surface layer from LSM, Q10, a1 and

a2 are constant parameters.

The average soil temperature of the �rst layer (0-100mm) and average soil moisture

of 1m depth soils are used in Equations (6) and (7). A Q10 factor of 2.0 is used, and

a1, a2 have values of 0.20 and 0.23 (Bonan, 1996). Compared with soil respiration

representation in CENTURY and CASA, which use observed surface air temperature

as surrogate for soil temperature and very simple hydrological models to simulate soil

moisture, LSM has the potential to capture a wider range of hydrological processes

and use soil temperature and moisture directly from model simulation.

The leaf and �ne root litter pools are partitioned into structural and metabolic

fractions, according to Parton et al. (1987):

MTf = 0:85� 0:018 � LN; (8)

where MTf is the fraction of metabolic litter and LN is lignin-to-nitrogen ratio. I

have derived the lignin percentage and carbon-to-nitrogen ratio from Thompson et

al. (1996; Table 1). For maximum turnover rates of di�erent carbon pools, I use

those of CASA (Potter et al., 1993) and Parton et al. (1987; Table 2).

As shown in Fig. 1, there are di�erent transfer paths out of each carbon pool. In

most situations Ft(i,0) (the fraction of carbon transfer associated with respiration) is

0.55 for carbon transfer and 0.30 for lignin transfer. There a few exceptions. Ft(i,j)

12

is 0.55, 0.42 and 0.03 for carbon transferred from SLOW pool to the atmosphere, soil

microbial pool and OLD pool. When carbon is transferred out of the soil microbial

pool, 0.004 is to OLD pool, f(SC) is to atmosphere, and 1-0.004-f(SC) is transferred

to the SLOW pool. With SC representing the soil silt plus clay fraction, f(SC) is

speci�ed as:

f(SC) = 0:85� 0:68 � SC: (9)

Soil texture also plays a role in decreasing the soil microbial turnover rate in �ne

textured soils. The scalar ETX is described by the following equation:

ETX = 1:0� 0:75 � SC: (10)

Human impact on agricultural land is considered by two modi�cations. First,

plant residue lignin concentration is set to 5%; second, the turnover rates of soil mi-

crobial pool, SLOW pool and OLD pool are increased by 25%, 50%, 50% respectively

(Potter & Klooster, 1997).

3.2 Simulation Scheme

LSM can be simulated either coupled to CCM (online), or o�ine with atmospheric

forcings at appropriate spatial and temporal resolution. The di�erence is that an

o�ine LSM is decoupled from CCM and will not in uence CCM climatology by

feedback processes. Online CCM simulation is too time-consuming for a study of

carbon pool sizes in which at least hundreds of years of integration are required for the

soil carbon pools to stabilize. Since LSM simulation is much faster o�ine than online,

for this study LSM was simulated o�ine where radiation (visible and near infrared,

beam and di�use), precipitation (convective and large scale), near surface wind speed,

humidity, pressure and temperature were provided as atmospheric forcings.

13

These forcings were derived from standard CCM3.2 control run with 355 parts per

million (p.p.m.) atmospheric CO2 concentration, representing the current concentra-

tion. After a period of model spin-up (5 years), each forcing variable was averaged

over the next 5 years on the 15th day of each month. The forcings thus obtained

resolve the seasonal and diurnal cycle, and are provided for each subgrid cell. The

synoptic temporal resolution of atmospheric forcings (variations of each day of the

same month) is sacri�ced to make up for the enormous demand of data storage and

processing entailed by the temporal resolution and global coverage of CCM3. I then

use this same typical daily forcing of each month (at 20 minutes time step) to drive

every day of that month, and use the same set of forcing to drive every year.

Since atmospheric forcings have profound impact on the land surface model perfor-

mance, the precipitation and land surface temperature di�erences between observa-

tion, standard CCM3.2 output and those used in LSM BU experiment are compared

in Fig. 2. Observations of precipitation and temperature are from the compilation

of Leemans & Cramer (1990). Bonan (1998) also has discussed CCM3 climatology

as compared to observations. The di�erence between CCM3 and LSM BU dataset

is small compared to its variations from observations. Land surface temperature is

generally lower than that of CCM3, except for the desert areas in North Africa and

Australia, where it is higher; this will have some in uence on simulated NPP and soil

respiration. Since the forcings are similar to those of the online CCM3 simulation,

it is expected that the stable soil carbon pools derived here will create small carbon

sources or sinks when coupled with CCM3.

It is di�cult to simulate LSM BU for hundreds of years to derive stable carbon

pool sizes using current computational facilities; hence a scheme is employed to adjust

the pool sizes, in order to accelerate the convergence toward stable soil carbon pool

sizes. Soil respiration is proportional to soil carbon sizes, and convergence time is

related to the turnover time of di�erent pools. From the ratio of soil respiration to

14

NPP, it can be roughly deduced how much of an increase or decrease of soil carbon

sizes will balance NEE (especially for the SLOW pool, which has relatively long

turnover time). The pool sizes can be adjusted and the model re-simulated, to allow

further allocation of carbon among di�erent types of pools. For the results presented

below, a 30-year simulation was performed, the pool sizes were adjusted, another 30-

year simulation was then performed, the pool sizes were readjusted, and another 60

years of simulation was performed. Although NEE is not in perfect equilibrium, soil

pool sizes are relatively stable at this stage and NPP balances soil respiration fairly

well over the land surface.

4 Results

4.1 Net Primary Production

Global NPP from LSM BU simulation is 42.6 PgC per year, within the lower range of

previous estimates. Melillo et al. (1993) reported 13 such estimates ranging from 40.5

PgC per year to 78.0 PgC per year, with a mean of 53.1 PgC per year. Fig. 3 shows

the global distribution of annual photosynthesis, NPP, NEE, autotrophic and het-

erotrophic respiration. The general pattern of NPP is consistent with observations

and other estimates, which peaks in tropical evergreen forests and reaches relative

maxima in broad areas of temperate and boreal forests. Major unvegetated areas

are indicated by low NPP on the map. Generally the tropics are photosynthesiz-

ing too much and not respiring enough, resulting in NPP values higher than previous

estimates. Because the photosynthetic and autotrophic respiration processes are mod-

elled independently in LSM, negative annual NPP is simulated over small patches of

land, which is not realistic. NPP is well balanced by soil respiration over most of land

surface, leaving minimal surface sources and sinks of carbon.

15

Table 3 shows photosynthesis, autotrophic respiration and NPP aggregated by

vegetation types prescribed in LSM. Compared to SiB2, which assumes NPP to be

0.4 times GPP, here the autotrophic respiration for needleleaf evergreen tree and

arctic deciduous shrub is much higher (87%, 77% of GPP) and for crops and irrigated

crops it is too low (36%, 38% of GPP). Globally, growth respiration, leaf, stem and

root maintenance respiration consume about 25%, 12%, 9% and 12% of the Gross

Primary Production, repectively, and NPP is about 42% of GPP.

Fig. 4 shows the seasonal distribution of GPP, plant respiration, NPP, soil mi-

crobial respiration and NEE by broad latitudinal bands. The seasonality of GPP is

clearly re ected in NPP and NEE, for the northern mid and high (15N-45N; 45N-

90N) latitudes and global average, indicating maximum vegetation activity in June

and July. This arises from the fact that most of the world's vegetation is distributed

in the Northern Hemisphere and is seasonal. Soil respiration has a di�erent season-

ality from that of NPP, and is mainly in uenced by the seasonality in the tropics

(15S-15N), which peaks in September, when the soil is warm and moist.

Simulated NPP for major vegetation types are compared with results from CASA,

TEM, SiB2 (coupled with AGCM) and Lieth's results in Table 4. Broadleaf evergreen

forests and crops have large NPP values, while NPP for shrublands and needleleaf

evergreen trees is low, compared to other estimates. As mentioned above, LSM's

autotrophic respiration scheme is responsible for the NPP discrepancy of crops and

needleleaf evergreen forests. LSM prescribes leaf area index for each pure vegetation

type empirically, the errors of which may have contributed to the errors in NPP es-

timates for shrublands. Also, the surface temperatures used are much higher than

observations on deserts where evergreen and deciduous shrublands are mainly dis-

tributed, and it can be limiting photosynthesize through water stress resulting from

high surface temperatures (Fig. 2).

Currently various sets of NPP measurements are available and many ongoing

16

projects include measurements of NPP. The Oak Ridge National Laboratory (ORNL)

Distributed Active Archive Center (DAAC) Net Primary Production (NPP) Database

includes NPP measurements from 44 intensively-studied and well-documented �eld

study sites, including various types of grasslands, boreal forests and tropical forests

(http: //www-eosdis.ornl.gov/npp/npp home.html). The International Biological

Programme (IBP) Woodlands Data Set consists of contributions from 117 inter-

national forest research sites, all but a few associated with projects committed to

the IBP, collected in the 1960s and early 1970s (DeAngelis et al., 1981; http://www-

eosdis.ornl.gov/npp/ibp /ibp des.html). More than 700 single point estimates of NPP

or biomass of natural and agricultural ecosystems worldwide were synthesized in the

1970s and early 1980s by Lieth, Esser and others, which comprises the Osnabruck NPP

Dataset (Esser et al. 1997; http://www-eosdis.ornl.gov/npp/ods/ods des.html). The

Terrestrial Ecosystem Model (TEM) calibration dataset includes data on pool sizes

and uxes of carbon and nitrogen from 16 �eld study sites ranging from tundra to

tropical forest excluding wetlands (McGuire et al., 1992). Bazilevich et al.'s (1986,

1992, 1994) compilation of literature data on phytomass and productivity of Eurasia

and other regions at 145 sites is supplemented by a number of new records in Russia,

North America and North European sites to describe the arctic ecosystems. How-

ever, validation of NPP by �eld data is a di�cult task, because di�erent measurement

techniques and conditions, which makes the measured NPP often inconsistent with

model estimates, and with each other as well. For example, many measurements of

NPP only include aboveground NPP, not underground NPP, for which estimates has

to be made. In the Osnabruck NPP dataset, NPP for crops can reach very high values

because of irrigation, fertilization and other human interventions, which makes them

inappropriate for direct validation of NPP estimates according to general vegetation

types used in global NPP models.

From the various sources cited above, typical �eld data of NPP have been selected

17

for each vegetation type prescribed in LSM, for the purpose of calibrating NPP in

future versions of LSM BU (Table 5). This is similar to the TEM NPP calibration

dataset, but for the vegetation types used in LSM. A careful calibration of NPP with

the experimental conditions and some generalization is needed before more realistic

validations or calibrations with NPP estimates can be carried out.

4.2 Net Ecosystem Exchange

Seasonal patterns of NEE from Fung et al. (1987) that employed �eld data and

satellite seasonality and the unmodi�ed version of LSM are compared with results

from LSM BU in Fig. 5. All results show that northern high and mid latitudes act

as carbon sinks in summer and as sources in winter, while the tropics and southern

hemisphere show much weaker and opposite seasonality of carbon exchange through

the year. In the LSM simulation, the tropics act as strong carbon sinks. The net sink

is removed in LSM BU result, but strong seasonality still appears compared to Fung

et al. (1987).

The overall discrepancy is clearly shown in Fig. 6. LSM simulates the land

surface as a net sink of 14 PgC per year, which is unrealistic. LSM BU removes

this net sink, but retains the seasonal pattern of carbon exchange. The maximum

drawdown of carbon is close to that of Fung et al., but the timing is about one

month earlier. This probably arises from LSM's prescription of vegetation phenology.

The empirically prescribed Leaf Area Index (LAI) in LSM peaks in June and July

for dominant vegetation types, while NDVI data from various satellite observations

indicate maximum vegetation activity in July and August (Fung et al., 1987; Myneni

et al., 1997a). Carbon released from the land surface peaks in both late spring and

autumn in Fung et al.'s result, but in LSM BU the spring peak is almost absent and

in autumn the release is much greater. This indicates increased vegetation growth in

spring or increased soil microbial activities in the fall.

18

In Fig. 7, NEE simulated from Fung et al. (1987), LSM, and LSM BU are

compared for annual, January, and July averages. LSM's strong local annual carbon

sources and sinks are not seen in Fung et al.'s result, and in LSM BU it is greatly

weakened. In January, all results indicate the Northern Hemisphere as a carbon

source and the Southern Hemisphere as a sink, but both the sources and sinks are

much stronger in LSM and LSM BU as compared to the results from Fung et al.'s. In

July, the patterns are similar to each other. This is due to the di�erence in phasing

of NEE seasonality demonstrated in Fig. 6.

NEE measurements at several sites are compared with model results on seasonal

and diurnal cycles. These observations are generally on spatial scales smaller than

a GCM grid cell (� 1 m2 to � 1 km2) and last for short periods (several months

to years). The eddy correlation technique is a well-developed method for measur-

ing trace gas ux densities between the biosphere and atmosphere. More continuous

measurements of NEE are available with eddy ux measurement techniques and in-

ternational initiatives to coordinate the ux towers currently set up around the world

are underway (FluxNet; Running et al., 1998). At Harvard Forest, a temperate forest

in Massachusetts, eddy correlation measurements provide CO2 exchange data consis-

tently throughout the year, which resolves the diurnal cycle, seasonality and some

interanuual variations (Wofsy et al., 1993; Goulden et al., 1996). The First ISLSCP

Field Experiment (FIFE, Sellers et al., 1992b) measurements of CO2 ux from tem-

perate grasslands in Kansas during four di�erent periods of the summer are also

available. Measurements of CO2 exchange from the Boreal Ecosystem-Atmosphere

Study (BOREAS, Sellers et al., 1997) are also compared with the model output at

the corresponding location. Fig. 8 compares the seasonality of net CO2 ux between

model output and �eld measurements. Despite the di�erence between the scales, the

model simulation shows reasonably good agreement with the measurements.

Harvard Forest has been shown to be a net sink of about 370 gCm�2yr�1 for at-

19

mospheric CO2 (Wofsy et al. 1993), while in LSM BU simulation, NEE is forced to

be zero. The plan is to simulate the sink later with climate and vegetation changes

after the equilibrium scenario. A simple adjustment is made to LSM BU simulation

for another comparison (Fig. 8). The assumption is that the changes in environment

will adjust the simulated photosynthesis to measured value while keeping the season-

ality (the shape), and short-term respiration will not change because of the longer

turnover time of carbon pools. Revised model results show better agreement, but the

growing season seems shorter compared to observations because simulated photosyn-

thesis peaks more abruptly 9. One possible explanation is that the LSM-prescribed

LAI of broadleaf deciduous tree peaks more abruptly than the actual LAI.

LSM's land cover scheme prescribes the vegetation of the grid cell containing the

FIFE site (38:9oN , 93:7oW ) to be cropland; the vegetation type of an adjacent cell

(35:2oN , 101:2oW ) is de�ned as warm grass. Simulated CO2 ux for both grid cells are

compared with the observations (Fig. 8). The result shows that CO2 ux of the grid

cell corresponding to the FIFE site agrees much better with the measurement than

that of the grid cell corresponding to the correct vegetation type. The result suggests

that for the FIFE site, the climate has larger in uence on the seasonal pattern of CO2

exchange than the vegetation type.

Measured carbon dioxide exchange of an aspen, a spruce and an old jack pine forest

from May to September 1994 at forested sites in an area west of Thompson, Manitoba

(55:7oN; 97:9oW ) are compared with the simulated CO2 ux at the corresponding grid

cell (Fig.8). More details about the site and vegetation can be found in Savage et

al. (1997). The CO2 ux comparison suggests that the growing season of the boreal

forests in model simulation ends too early as compared to the observations.

The diurnal cycles of CO2 ux from model simulation are compared with obser-

vations at the Harvard Forest, FIFE site, a tropical broadleaf evergreen forest site

(Wofsy et al., 1988; Fan et al., 1990) and BOREAS sites in Fig. 9. The diurnal

20

cycles of Harvard Forest is the mean from July 21 to 30, 1991 (Wofsy et al., 1993).

The FIFE data was averaged over late July and early August of 1987. The tropical

forest data was collected between April 22 and May 8 of 1987. The BOREAS data

was from tower uxes measured at the southern study area old aspen (SSA-OA),

southern study area old jack pine (SSA-OJP), and northern study area old jack pine

(NSA-OJP) sites during the three intensive �eld campaigns in 1994 (May 24 to June

16 (IFC-1), July 19 to August 10 (IFC-2), August 30 to September 19 (IFC-3), Bonan

et al., 1997).

The grid cell of the Harvard Forest location is a cool mixed forest, and consists of

broadleaf deciduous and needleleaf evergreen trees by LSM's speci�cation. Simulated

CO2 uxes of both types capture the shape of the observed diurnal cycle, but both

amplitudes are smaller than the observations, because the modelled scenario does

not simulate the observed large carbon sink at the Harvard Forest location at this

stage. Also, the phasing of simulated diurnal CO2 exchange cycle lags behind the

observations. The tropical forest site shows good agreements between modelled and

observed NEE diurnal cycles.

Bonan et al.'s (1997) results from a LSM simulation are included in the comparison

between LSM BU simulation result and the observed diurnal cycles of the BOREAS

sites (Fig. 9). For the SSA-OJP site, Bonan's results agree well with the observations,

while LSM BU tends to overestimate the amplitude of CO2 exchange. Both LSM and

LSM BU simulate the diurnal cycle of CO2 ux at the SSA-OA site well. For the

NSA-OJP site, LSM BU performs better than LSM in simulating the amplitude of

the diurnal cycle of CO2 ux, but the phasings are similar, and both are earlier than

observations for the IFC-2 period.

LSM BU does not capture the observed diurnal cycle, either at the FIFE site or

the adjacent grid cell which has the correct vegetation type. Considering the one-

month-early phasing of terrestrial CO2 ux simulated by LSM BU, as compared to

21

Fung et al. (1987; Fig. 6), another comparison was performed, in which the period

of simulated NEE diurnal cycle is one month earlier than that of observations (Fig.

10).

The simulated CO2 uxes of late June and early July at the FIFE site are similar

to the observed diurnal cycle of CO2 exchange in late July and early August. This

con�rms the general inference from Fig. 5, that the vegetation phenology of LSM is

about one month earlier than reality, at least for the FIFE site. It also indicates that

for the FIFE site in summer, vegetation type has more in uence on the diurnal cycle

of CO2 exchange than the climate. The LAI of tropical broadleaf evergreen trees does

not vary much throughout the year. In comparison, the seasonality of precipitation

has more in uence on the seasonal and diurnal cycles of CO2 exchange. This may

explain the poor correspondence between the simulated and observed diurnal cycles

for tropical forests in Fig. 10.

4.3 Soil Carbon Pools and Respiration

In Table 6 the model results for soil carbon pool sizes (in the top 1 meter soil layer)

are presented according to vegetation and soil carbon pool types. The OLD soil car-

bon pool requires thousands of years to reach equilibrium. The OLD soil carbon pool

does not converge well and is largely dependent on initialization, because the simu-

lation time is not that long. Therefore, the OLD carbon pool type was excluded and

only modern soil carbon pools which have turnover times from days to decades was

discussed. By vegetation type, the needleleaf evergreen forest has the largest carbon

pool sizes, because the climate determines that the soil turnover rate is low. Broadleaf

evergreen forests also have relatively high active soil carbon content, because of its

high NPP. Grasslands and crop �elds generally have low soil carbon storage because

of their high turnover rates. By soil carbon pool type, the SLOW pool size is 502.3

PgC, accounting for 89% of the modern soil carbon pools. Microbial pool size (the

22

sum of surface and soil microbial pools) is 58.30 PgC. Woody litter pool size is 38.66

PgC, lower than that reported by Potter & Klooster (1997; 54.15 PgC).

The distribution of maximum LAI is compared with the distribution of the modern

soil carbon pool size in Fig. 11, both scaled to the maximum value over the land

surface. The LAI distribution is obtained from the 1981-1991 AVHRR Path�nder

NDVI data using an algorithm described in Myneni et al. (1997b). A close correlation

between current vegetation activity and active soil carbon pool size can be seen.

This correlation exists because soil carbon pools are converted from past vegetation

senescence at the same site, which is connected with vegetation activity, and LAI is

a good indicator of current vegetation activity. Although tropical forests have higher

greenness and NPP, the carbon pools are lower than those of the boreal forests,

because the decomposition rates are very high and NPP can not accumulate in the

soil.

Table 7 compares soil respiration simulated by LSM BU with observations for sev-

eral ecosystems (Raich & Schleisinger, 1992). For most land cover types the model

predictions fall within the range of observations, except for needleleaf forests and

shrublands where the simulated values are too low. These discrepancies can be at-

tributed to poor simulation of NPP, because in LSM BU simulation soil respiration

balances NPP on annual average. Fig. 2 reveals that the atmospheric forcings used

have higher temperature and reduced precipitation over most of shrublands, which

may be partly responsible for this underestimate of NPP and soil respiration for

shrublands. The low estimate of soil respiration in temperate forests is mainly due to

the disproportionally high autotrophic respiration of needleleaf evergreen trees (1096

gC=m2 per year, about 87% of its GPP).

23

5 Discussions

5.1 Land Cover and Vegetation Phenology

Several comparisons presented above suggest that the LAI pro�le used by LSMmay be

inaccurate in describing vegetation phenology and causing incorrect phasing of NEE

simulation. A new generation of remote sensors in the Earth Observing System (EOS)

era, prominently the Moderate Resolution Imaging Spectrometer (MODIS) and the

Multi-angle Imaging Spectrodiometer (MISR), will have high spatial resolution (250m

to 1km), specially designed spectral bands and multi-angle views providing a wealth

of information about land cover and vegetation phenology.

To compare LSM's LAI pro�le with LAI based on satellite observations, a monthly

LAI map at 0:25o resolution is produced, using a MODIS LAI prototype algorithm

with NOAA AVHRR Path�nder NDVI data from 1981 to 1991 (Myneni et al., 1997).

The LAI algorithm used a global map of vegetation which classi�ed world vegeta-

tion into six types: grasses/cereal crops, shrubs, broadleaf crops, savannas, broadleaf

forests and needleleaf forests. I then converted the 6-type vegetation map into LSM's

14 vegetation types, together with latitudinal information and Olson et al.'s land

cover map (1993), aggregated the LAI map by LSM's vegetation type, compensated

for hemispheric e�ect by shifting LAI six months in the Southern Hemisphere (Bonan,

1996), and compared the �nal results with LSM's LAI pro�le in Fig. 12.

Generally, the LAI pro�les derived from AVHRR data show less variations and

lagging of phases compared to that of LSM. For major vegetation types such as

needleleaf and broadleaf deciduous trees and warm and cool grass, the LAI pro�les

based on satellite observation indicate maximum vegetation activity in July/August,

as compared June/July of LSM, which o�ers an explanation for the discrepancies

demonstrated in Fig. 6. LSM-prescribed LAI of needleleaf deciduous trees is low

compared to the LAI derived from satellite observations, and is re ected in its low

24

NPP, shown in Table 3 of Bonan (1998). For broadleaf evergreen trees, a higher

value of LAI in LSM may have resulted in higher NPP (Table 5; Bonan 1995). In

LSM, the growing season of broadleaf deciduous trees is shorter and changes more

abruptly compared to that observed from satellite, and this may have led to some

discrepancies in the seasonal and diurnal CO2 exchanges between observations and

model simulations (Figs. 8 & 9). For croplands, satellite-derived LAI has more

gradual seasonal transition compared LSM.

In comparison with LSM's LAI, satellite-derived LAI is lower for grasslands, crops

and shrublands. This is because it is an average over the whole area of observation

which often contains a fraction of bare ground, while in LSM it is averaged over the

fraction of vegetated land. The bare ground fraction can be very high for semidesert

areas, where most evergreen shrubs are distributed. As a result, the satellite-derived

LAI for evergreen shrubland is very low. The LAI pro�le of needleleaf evergreen

trees derived from satellite NDVI shows large seasonal variation, which appears to

be unrealistic. Both the snow burying e�ect that occurs during the winter and the

quality of AVHRR data can contribute to the strong seasonality shown in the satellite

LAI. The satellite visits the northern high latitude pixels in the late afternoon; in the

winters the sun angle is quite low at that time and this in uence the observations.

Little data is available for regions north of 60o in the winter, and when available they

are often of poor quality. This e�ect gives an arti�cial seasonality to the NDVI, and

therefore, the LAI pro�le of the needleleaf evergreen trees. The large seasonality of

evergreen needleleaf trees also shows in the LAI data produced from the FASIR NDVI

dataset, which uses a simpler NDVI-LAI relationship (Sellers et al., 1994; Dickinson

et al., 1998). Misclassi�cation of land cover type may also contribute to the arti�cial

seasonality, for those pixels classi�ed as needleleaf evergreen trees often contain some

broadleaf deciduous trees, which have higher seasonality of LAI.

Satellite observations provide global coverage and details of land surface prop-

25

erties which can not be obtained through ground measurements. However, data

products from satellite observations often di�er from the requirement of land surface

and vegetation models. For example, the LAI products from satellite observations is

pixel-averaged e�ective green LAI. It does not resolve the bare ground fraction, the

snow burying e�ects, or non-green LAI such as brown leaves, all of which may be of

some importance to land surface models. For the LAI algorithm, a 6-type vegetation

map is used, which di�ers a lot from the LSM vegetation map in vegetation types

and spatial coverage. These issues have to be appropriately addressed when applying

satellite-derived LAI in land surface and vegetation models.

5.2 Applications of LSM BU

Dynamic vegetation models such as IBIS currently simulate the vegetation distribu-

tion and carbon and nutrient dynamics in more detail than LSM BU. Future LSM BU

modelling will include vegetation distribution and phenology derived from satellite

observations, instead of simulating these variables. A full consideration of carbon

dynamics requires that future LSM BU e�orts should include the allocation of GPP

into vegetation carbon pools, which are dynamically converted to litter pools and thus

determine rates of autotrophic respiration. Nutrient cycles (nitrogen, phosphorous,

calcium, etc.) are closely coupled to the carbon cycle and in uence carbon transfer in

nonlinear ways. Although poorly understood and hard to quantify, it is an important

issue that a dynamic carbon cycle model should address (Schimel, 1998; Dickinson et

al., 1998; Houghton et al., 1998).

The coupling of LSM with a GCM (CCM) provides many advantages in studying

the global carbon cycle. For example, one can separate di�erent carbon sources and

sinks and study their individual in uence on the geographical and temporal patterns

of atmospheric CO2 concentration and climate. The atmospheric forcing used in this

study is simpli�ed CCM3 climatology that resolves the seasonal and diurnal cycle,

26

which resembles CCM3 climatology at annual scales. It is averaged over 5 years but

the diurnal cycle can be not perfectly representative of each month. Speci�cally, day-

to-day variations within a month are not represented. This can lead to carbon pool

sizes which are not perfectly attuned to CCM3 climatology, because carbon dynamics

respond to climatology in nonlinear ways. These in uences can be addressed when

LSM BU is coupled to CCM, by allowing some simulation time for the pools to

integrate. Later a LSM simulation fully coupled to CCM will be performed, with the

soil respiration model and stable carbon pool sizes derived here.

Direct validation of terrestrial NPP and NEE is rather di�cult because of insuf-

�cient observations and mismatching scales. Instead, validations are often made by

comparing simulated atmospheric CO2 concentration with observations collected from

ask sampling network (Randerson et al., 1997; Heimann et al., 1998). In order to

do this, an atmospheric transport model and scenarios of ocean exchange, industrial

emission and land use changes are needed. LSM BU can be easily coupled to CCM3

and use its atmospheric transport scheme to do this validation, and if successful, to

further predict the CO2 concentration in the following decades with projected CO2

emissions.

6 Conclusions

A simple dynamic soil respiration model is developed in LSM, in a manner similar

to CASA and CENTURY. The improved model, LSM BU, is simulated for about

100 years, driven by a set of atmospheric forcing extracted from CCM3 representing

current climate. An NEE equilibrium scenario is derived, with stable carbon pool

sizes attuned to the CCM climatology and vegetation distribution.

Carbon pool sizes are reasonable and show a close correlation with maximum

LAI derived from satellite observation. Soil respiration estimates generally agree

27

with �eld data, but are underestimated for shrublands and boreal forests because

their NPP estimates are low. Simulated NEE is validated against the seasonal and

diurnal cycles of observations from Harvard Forest, FIFE and BOREAS. They agree

reasonably well but show discrepancies which can be attributed to the LAI pro�le

used in LSM. Compared to estimates of a diagnostic model based on satellite NDVI,

LSM BU has an early phasing and larger seasonal amplitude of NEE. This is possibly

due to LSM's phenology which di�ers signi�cantly from satellite observations. A

LAI dataset derived from AVHRR data is compared with LSM's LAI, which reveals

signi�cant di�erences and suggests the incorporation of satellite LAI to improve NEE

simulation.

28

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37

Table 1: Maximum turnover rates of carbon pools used in LSM BU

Pool Type Maximum turnover rate (month�1)

Structural Leaf Litter Pool 0.330Metabolic Leaf Litter Pool 1.217Structural Root Litter Pool 0.408Metabolic Root Litter Pool 1.521Wooden Litter Pool 0.04Leaf Microbial Pool 0.455Soil Microbial Pool 0.600SLOW Organic Pool 0.0163OLD Organic Pool 0.000056

Table 2: Lignin content and Carbon-to-Nitrogen (C/N) ratios used in LSM BU

vegetation type (LSM) vegetation type (Thompson et al. 97) Lignin% C/N1. needleleaf evergreen tree 4. needleleaf evergreen tree 25 802. needleleaf deciduous tree 5. needleleaf deciduous tree 20 503. broadleaf evergreen tree 1. broadleaf evergreen tree 20 404. broadleaf deciduous tree 2. broadleaf deciduous tree 20 505. tropical seasonal tree 8. broadleaf shrub 20 656. cool C3 grass 9. tundra 15 507. evergreen shrub 8. broadleaf shrub 20 658. deciduous shrub 8. broadleaf shrub 20 659. arctic deciduous shrub 9. tundra 15 5010. arctic grass 9. tundra 15 5011. crop 11. Agriculture 10 4012. irrigated crop 11. Agriculture 10 4013. warm C4 grass 7. perrenial grasslands 10 5014. bare 10. hot and cold desert 15 50

38

Table 3: Photosynthesis, autotrophic respiration and Net Primary Production fromLSM_BU simulation, by vegetation type (unit: gC/m2/yr)

vegetation type Photosynthesis GrowthRespiration

LeafRespiration

StemRespiration

RootRespiration

NPP

needleleaf evergreen tree 1217 304 99 368 285 160needleleaf deciduous tree 383 96 23 24 18 222broadleaf evergreen tree 3433 858 529 453 254 1340broadleaf deciduous tree 811 203 94 24 22 468tropical seasonal tree 829 207 145 45 84 348cool grass (C3) 471 118 60 0 49 245evergreen shrub 40 10 11 0 0 20deciduous shrub 46 12 12 0 0 22arctic deciduous shrub 153 38 11 7 60 36arctic grass 415 104 29 5 44 233crop 793 198 89 0 0 506irrigated crop 1563 391 180 0 0 992warm grass (C4) 1166 292 108 0 281 486baregroud 0 0 0 0 0 0all vegetation 720 180 89 67 90 294

39

Table 4: Comparison of Net Primary Production between LSM_BU, CASA, SiB2(in GCM), TEM and Lieth’s result (unit: gC/m 2/yr)

vegetation/landcover LSM_BU CASA SiB2(in GCM)

TEM Lieth*

broadleaf evergreen forest 1340 1027 657 1098 946broadleaf deciduous forest 467 315 251 620* 649mixed forest 301 316 310 669 325needleleaf evergreen forest 160 226 273 228* 286needleleaf deciduous forest 222 153 201 --- 153grassland 234 180 233 266 363crop 506 288 202 --- ---tundra 77 80 66 120 144shrubland 18 184 250 129* 384desert/semidesert 1.4 28 4.4 53 ---savanna 583 --- --- 393 669

* NPP data from Lieth is converted assuming per mol C produces 28.5 g biomass. Approximationswere made when TEM vegetation type ‘boreal forest’ is mapped to ‘needleleaf evergreen forest’, ‘temperatedeciduous forest’ to ‘broadleaf deciduous forest’, ‘arid shrubland’ to ‘shrubland’.

40

Table 5: Field data of Net Primary Production by LSM vegetation types forcalibration purposes

LSM vegetation type NPP(gC/m2/s)

datasource

Sitelongitude

Sitelatitude

location vegetation description

broadleaf evergreen tree 1050 [1] 59.95W 2.83S Brazil tropical evergreenbroadleaf deciduous tree 650 [1] 72.17W 42.53N USA temperate deciduousneedleleaf evergreen tree 535 [1] 122.33W 44.25N USA temperate coniferousneedleleaf deciduous tree 186 [2] 68.33E 67.17N Yamaldwarf shrub/larch woodlandtropical seasonal tree 435 [1] 28.70E 24.65S S. Africa savannawarm grass (C4) 425 [1] 96.53W 36.95N USA tall grasscool grass (C3) 190 [3] 51.70E 51.70N USSR grassland/meadow steppecrop 585 [3] 111.78W 43.82N USA triticum aestivumirrigated crop 814 [3] 141.0E 39.75N Japan plantation/larix, morusarctic grass 239 [2] 45.0W 60.0S Signi I. moss turfarctic deciduous shrub 255 [2] 173.00E 67.00N Chuckchi shrub (betula/pinus)deciduous shrub 110 [1] 67.96W 34.03S Argentina arid shrublandevergreen shrub 76 [3] 41.08E 34.50N Syria desert

[1] from TEM Model calibration data, Raich et al. [1991][2] from ‘Global Change and Arctic Terrestrial Systems’, Oechel et al. [1996], assuming biomass is 50% carbon[3] from Osnabruck NPP dataset, Esser et al. [1997]

41

Table 6: Stable soil carbon pool sizes simulated by LSM_BU, according tovegetation and pool types (unit: Pg C)

land cover type Non-woodyLitter Pool

WoodyLitter Pool

MicrobialPool

SLOWPool

Total Area(m2)

needleleaf evergreen tree 1.261 7.804 12.71 158.0 179.8 9.26e12needleleaf deciduous tree 0.328 1.991 2.626 32.07 37.02 2.28e12broadleaf evergreen tree 3.280 14.31 16.27 124.8 158.7 1.32e13broadleaf deciduous tree 0.659 4.561 5.226 46.02 56.47 5.05e12

tropical seasonal tree 0.125 0.812 7.354 46.07 54.36 3.84e12cool grass (C3) 0.513 0.000 2.635 18.88 22.03 6.27e12

evergreen shrub 0.007 0.047 0.039 0.199 0.292 2.96e12deciduous shrub 0.014 0.096 0.075 0.363 0.548 5.09e12

arctic deciduous shrub 0.102 0.559 0.729 7.823 9.213 2.71e12arctic grass 0.818 0.000 3.784 34.56 39.16 4.21e12

crop 0.994 8.173 7.477 42.50 59.14 1.20e13irrigated crop 0.036 0.300 0.262 1.409 2.007 2.51e11

warm grass (C4) 1.421 0.000 5.760 31.12 38.30 2.09e13baregroud 0.000 0.000 0.000 0.000 0.000 5.71e13

all vegetation 8.200 38.66 58.30 502.3 607.5 1.45e14

42

Table 7: Comparison of LSM_BU simulation soil respiration with observations(unit: g C/m2/year)

Simulated Soil Respiration Observed Soil Respirationvegetation type Mean vegetation type Mean ± s.d.tundra 88 tundra 65 ± 21needleleaf forest 200 boreal forest 348 ± 134grassland 225 grassland 477 ± 253broadleaf deciduousand mixed forest 310

temperate forest 700 ± 297

shrubland 56 shrubland 770 ± 343cropland 426 cropland 588 ± 441savanna 590 savanna 679 ± 172broadleaf evergreen forest 1238 tropical moist forest 1362 ± 195

* observed data assumes 30% of soil respiration is from roots, as assumed in Raich and Schleisinger, [1992].

43

LITTER S_LEAF M_LEAF M_ROOT S_ROOT WOOD

LEAF_MIC SOIL_MIC

SLOW

OLD

MICROBE

ORGANIC

NPP

Non_Lignin Non_Lignin

LigninLignin

start or end is not specified are connected to the atmospheric pool. S_ means structural, M_ means metabolic, _MIC means microbial.

Figure 1: Schematic carbon fluxes between different pools modelled by LSM_BU. Arrows for which theBoxes represent pools and arrows represent fluxes.

44

Figure 2: Di�erence of precipitation and land surface temperature between observations, CCM3 simu-lation, and those used in LSM BU on annual average. Observations are from the International Institutefor Applied Systems Analysis (IIASA) dataset, created by Leemans and Cramer [1990], consisting ofover 6000 records from at least �ve di�erent sources. The CCM3 precipitation and surface temper-ature is the average of 5-9 years of a standard simulation at T31 resolution. For use in LSM BU,precipitation and surface temperature from the 15th day of each month were extracted and averagedover 5-9 years of a standard CCM3 simulation. Observations are not available for areas south of 60oS.

45

Annual photosynthesis Annual Autotrophic Respiration

Annual Net Primary Production Annual Heterotrophic Respiration

Annual Net Ecosystem Exchange

Figure 3: Global distribution of Photosynthesis, Net Primary Production (NPP), Net Ecosystem Exchange(NEE), autotrophic and hetorotrophic respiration, simulated by LSM BU.

46

Figure 4: Seasonal distribution of Gross Primary Production (GPP), Net Primary Production (NPP), au-totrophic and heterotrophic respiration and Net Ecosystem Exchange (NEE) simulated by LSM BU, accordingto broad latitudinal bands. Sums from band ranges of 45N-90N, 15N-45N, 15S-15N, 90S-15S and global areplotted respectively. The coordinates on the left are for 45N-90N, 15S-15N and global band, on the right for15N-45N and 90S-15S bands respectively. Numbers on the panels indicate net exchanges of each latitudinalband.

47

Figure 5: Seasonal distribution of Net Ecosystem Exchange (NEE) from LSM, LSM BU and Fung et al. (1987),according to broad latitudinal bands. Sums from latitudinal ranges of 45N-90N, 15N-45N, 15S-15N, 90S-15Sand global are plotted respectively. The coordinates on the left are for 45N-90N, 15S-15N and global band, onthe right for 15N-45N and 90S-15S bands respectively. Numbers on the panels indicate NEE of each latitudinalband.

48

Figure 6: Comparison of NEE seasonality between LSM, LSM BU, and Fung et al.[1987]. Numbers on the panel indicate annual NEE value.

49

NEE yearly average by Fung NEE yearly average by LSM NEE yearly average by LSM BU

NEE January average by Fung NEE January average by LSM NEE January average by LSM BU

NEE July average by Fung NEE July average by LSM NEE July average by LSM BU

Figure 7: Comparison of global Net Ecosystem Exchange (NEE )distribution by annual, January and Julyaverages ,between LSM, LSM BU and Fung et al.

50

Figure 8: Comparison of monthly Net Primary Production (NPP), soil respiration and net carbon uxbetween LSM BU simulation and observations at Harvard Forest, a FIFE site and a BOREAS site. Forthe Harvard Forest site, both unrevised and revised results of NPP, soil respiration and NEE are comparedwith observations. Simulated CO2 ux at FIFE site and an adjacent grid cell that contains warm grassvegetation type are compared with observations. Observed net carbon uxes of a spruce, an aspen and apine site of BOREAS are compared with the simulated ux.

51

Figure 9: Comparison of diurnal cycle of net carbon ux between LSM BU simulation andobservations at Harvard Forest, a FIFE site, a tropical evergreen forest and three BOREAS sitesduring di�erent periods of the year. Simulated net carbon ux for both broadleaf deciduoustrees and needleleaf evergreen trees are compared with observations at the Harvard Forest site.Observations at the FIFE site are compared with simulations for the FIFE site grid cell and anadjacent grid cell that contains warm grass. For the BOREAS sites, simulations from Bonanet al. (1997) are compared with LSM BU simulation and observations. The uncertainties ofmeasurements at the BOREAS sites are indicated by error bars.

52

Figure 10: Comparison of diurnal cycle of net carbon ux between LSM BU simulation andobservations at Harvard Forest, a FIFE site, a tropical evergreen forest and three BOREAS sitesduring di�erent periods of the year. The dates for simulation are one month earlier than obser-vations, to show the e�ect of shifting phenology. Simulated net carbon ux for both broadleafdeciduous trees and needleleaf evergreen trees are compared with observations at the HarvardForest site. Observations at the FIFE site are compared with simulations for the FIFE site gridcell and an adjacent grid cell that contains warm grass. For the BOREAS sites, simulations fromBonan et al. (1997) are compared with LSM BU simulation and observations. The uncertaintiesof measurements at the BOREAS sites are indicated by error bars.

53

Figure 11: Global distribution of modern soil carbon pool size simulatedby LSM BU and maximum Leaf Area Index (LAI) derived from AVHRRdata, both scaled to their global maxima.

54

Figure 12: Leaf Area Index (LAI) comparison between the LSM pro�le and a pro�le based on AVHRRdata (Myneni et al., 1997). Numbers in the �gure indicate the area occupied by each pure vegetationtype according to the two schemes. The LAI pro�le is for Northern Hemisphere vegetation; to do thatsatellite-derived LAI are shifted six months for Southern Hemisphere.