1
Published in
Global Biogeochemical Cycles
, Vol. 15(1), 227-246, March 2001.
Uncertainties in Global Terrestrial Biosphere Modeling, Part II:
Global Constraints for a Process-Based Vegetation Model
Wolfgang Knorr and Martin Heimann
Max Planck Institute for Biogeochemistry, Jena, Germany
Abstract
The terrestrial biosphere is one of several key components of the global carbon cycle. Because
the mechanisms by which climate determines terrestrial biosphere carbon uxes are not well
understood, signicant uncertainties concerning model results exist even for the current state of
the system, with important consequences for our ability to predict changes under future climate
change scenarios. We assess how far this uncertainty can be reduced by constraining a global
mechanistic model of vegetation activity, either with global satellite-derived vegetation index
data or with measurements of the seasonal CO cycle in the atmosphere. We rst show how
constraining the model with satellite data from the National Oceanic and Atmospheric
Administration advanced very high resolution radiometer reduces the sensitivity to estimated
uncertainties in model parameters, and thus the estimated error range of net primary
productivity. Regionally, the satellite data deliver the largest constraint for vegetation activity in
boreal and arctic as well as in tropical water-limited environments. In a second analysis through
an atmospheric tracer transport model, we check the consistency of those results with the
measured seasonal cycle of CO at various remote monitoring sites. While before including the
satellite data into model calculations, some simulations within the error range lead to a CO
seasonal cycle outside the observations, there is a good agreement with the additional constraint.
The conclusion is that the constraint delivered by the satellite data is at least as signicant as
that delivered by atmospheric CO measurements. We also show that the CO data mainly
reect the activity of northern vegetation, in particular conifers and C3 grasses. This suggests
that satellite measurements provide the most useful global data currently available for checking
and improving terrestrial vegetation models and that consistency with CO measurements is a
necessary but not a sucient requirement for their realism.
2
2
2
2
2
2
1. Introduction
Verstraete,
Projections of future levels of greenhouse gases in
the atmosphere need to take into account changes in
activity and carbon storage of the terrestrial biosphere
induced by changes in the earth's climate [
1996]. Today, a substantial part of the most
important anthropogenically emitted greenhouse gas,
carbon dioxide, is removed from the atmosphere by
both the oceans and the land biosphere [
1997]. It is also suspected that rather subtle changes
in vegetation carbon uptake (net primary productivity, NPP) and soil respiration (SR) are responsible for most of the interannual uctuations in the
atmospheric CO2 content [
1994;
1995;
1995;
1996]. On longer time scales, such
trends could substantially inuence levels of CO2 in
the atmosphere.
Terrestrial vegetation models are considered the
main tools for assessing the possibility of such a development. They are, however, extremely dicult
to validate on the basis of eld measurements alone
[
this issue]. Moreover, no consensus on how NPP and SR should be modeled has
yet been reached, so that there is substantial variation
among model results, both for annual NPP [
1999], and for interannual uctuations of the
net ecosystem ux NPP minus SR [
1998]. It is, therefore, essential to increase condence
in the results of those vegetation model simulations
before predictions of future carbon uxes are to be
made. One possible route is to require such models to
be consistent with various sets of global observations.
Two such data sets that are now easily available are
global satellite reectances in the optical domain, and
the seasonal cycle of atmospheric CO2 concentration.
Several organizations have carried out extensive
global archiving of satellite data derived from National Oceanic and Atmospheric Administration(NOAA)
advanced very high resolution radiometer (AVHRR)
instruments, for example, the NOAA Global Vegetation Index [
1990], the NASA Pathnder
project [
1994], and the 1 km data
product of the International Geosphere-Biosphere Program Data and Information System (IGBP-DIS) [
1994;
1994]. Capturing vegetation
distribution with satellites is made possible by the
unique contrast between low reectance in the red
and high reectance in the near-infrared spectral domain that is characteristic of photosynthetically acSchimel
at al.,
Heimann,
Kaduk and Heimann,
Francey et al.,
Friedlingstein et al.,
Kin-
dermann et al.,
Knorr and Heimann,
Cramer
et al.,
Heimann et al.,
Kidwell,
Agbu and James,
IGBP,
Townshend et al.,
2
tive plant parts [see
1994]. Although the
satellite signal is also inuenced by view and sun
angles [
1991], soil background
color [
1988], and atmospheric
conditions [
1989], the use of vegetation indices can help reduce those disturbing eects [see
1996; 2000]. The retrieved elds
can then be used to validate and constrain global vegetation and land surface models [
1995;
2000].
In the present study, we rst asses the value of
global vegetation index data for constraining vegetation models by performing an error analysis summarizing the largest uncertainties of NPP calculations
without and with the additional constraint. The calculation is performed with a global process-based vegetation model [
1997], the Biosphere EnergyTransfer Hydrology scheme (BETHY), with an error
assessment based on a previous uncertainty analysis
with the same model [
this issue]. The results are then checked against the observed seasonal cycle of atmospheric carbon dioxide,
which has been shown to document mainly terrestrialvegetation activity [
1989;
1993]. This is done by feeding predicted net
biosphere-atmosphere exchange uxes of CO2 into
an atmosperic tracer transport model and comparing
simulated seasonal cycles against detrended measurements from various remote monitoring sites.
In a similar analysis,
[1995]
have shown that a simple biosphere model can be
constrained by this method, leading to some useful
insights into vegetation functioning at large scales.
Complex vegetation models have also been validated
this way [
1998]. It is, however, not
known yet to which extent such a test is also a sufcient criterion for the realism of global vegetation
models; in other words, how large the range of model
predictions can be that is consistent with the CO2
data still has to be assessed. This question will be
discussed in section 5.
Pinty and Verstraete,
Huete and Jackson,
Kaufman,
Ver-
straete and Pinty,
Knorr et al.
Knorr and Schulz,
Knorr,
Knorr and Heimann,
Heimann et al.,
Nakazawa
et al.,
Knorr and Heimann
Heimann et al.,
2. Methods
As described in section 1 the purpose of this study
is to assess the value of global satellite observations
of vegetation cover for checking and improving global
models of terrestrial-vegetation activity and to check
the results with atmospheric CO2 data. The method
used here consists of a systematic uncertainty analysis
through which it becomes possible to quantify error
ranges of model predicted carbon uxes. This error
analysis is performed both before and after constraining the vegetation model with the satellite data.
2.1. Denition of Model Uncertainties
The vegetation model BETHY simulates CO2 uptake by vegetation as a process that is simultaneously
limited by light, heat, soil water, and nitrogen. Light
limitation is considered by the hourly computation
of incoming and absorbed photosynthetically active
radiation (PAR) and heat limitation through the energy and water balance at the vegetated surface. Further, water limitation is calculated with a soil water model, whereas nitrogen limitation is treated as
a long-term process with xed values of photosynthetic capacity, closely related to leaf nitrogen content [
1986]. The model uses an
equal area grid with a 1 2 1 resolution at the equator (with 11,069 land points outside of Antarctica)
and calculates both energy balance and photosynthesis every hour, and the balance of soil water, snow,
and leaf interception water at daily time steps. Input data are temperature as daily average and diurnal range [W. Cramer, personal communication,
1994], precipitation [W. Cramer, personal communication, 1994;
1991], number of wet days [A. Friend, personal communication,
1995;
1982], PAR [
1992],
maps of soil texture [
1996],
soil brightness [
1985]
and vegetation type [
1985]. Daily precipitation is simulated stochastically
with a weather generator [
1986]. More
details of the model are given by
[1997; 2000]
and
[this issue].
In a previous analysis [
this
issue], approximate global error ranges have been assigned to a large number of parameters used with the
vegetation model BETHY. A sensitivity analysis of
annual NPP has then been used to rank uncertainties by their importance for modeled carbon uxes.
Here, we restrict ourselves to the ve most important
factors, listed in Table 1.
The rst uncertainty concerns the choice of the
photosynthesis submodel, which, in the standard case
(denoted \0") is that of
[1980] (C3
plants) or
[1992] (C4 plants) with
parameters from [
1995]. This
submodel computes the gross primary productivity
(GPP), which is the carbon uptake rate by leaves
without taking plant respiration into account. The
Field and Mooney,
Leemans and Cramer,
M
uller,
Pinker and Laszlo,
Dunne and Willmott,
Wilson and Henderson-Sellers,
Wilson and Henderson-Sellers,
Geng et al.,
Knorr
Knorr and Heimann
Knorr and Heimann,
Farquhar et al.
Collatz et al.
Beerling and Quick,
3
other mode, denoted \Pm," is based on the light-use
eciency approach by
[1965] with data by
[1994], modied to remove the possible
inuence of water stress and to compute GPP instead
of NPP. In this scheme, incoming PAR is multiplied
with the fraction of absorbed PAR (FPAR) and an
eciency parameter to yield GPP.
Plant respiration has also been found to lead to
rather large ranges in simulated global net primary
productivity (NPP). NPP is calculated as GPP minus
two respiration terms: maintenance respiration, RM ,
and growth respiration, RG. Growth respiration is
assumed to be a xed portion of NPP, i.e.,
RG = fR;G NPP = fR;G (GPP 0 RM 0 RG): (1)
The value of fR;G is varied between 0.2 for \R-" (low
respiration cost) to 0.3 for \R+" (high respiration
cost) following
[1991a], with 0.25 as the standard (denoted \0"). Further, leaf respiration, Rd, calculated in the photosynthetic scheme, is assumed to
account for a constant portion of maintenance respiration, so that
(2)
RM = Rd;c=fR;leaf :
Values for fR;leaf are taken from
[1991b]: 0.5
(\R-"), 0.4 (\0"), and 0.3 (\R+").
Rooting depth, together with soil texture, determines the amount of plant-extractable soil moisture
that can be stored in the soil to be used as a buer
during dry periods. It thus can have a strong impact on NPP in water limited environments. Since
rather little is known about rooting depth globally,
uncertainties in NPP coming from parameterization
of root depth can be substantial [
this issue]. Here, rooting depth is set to 1 m in most
cases, in savannas to 2 m for grasses and 3 m for trees,
and for evergreen broadleaf trees, rooting depth is increased by steps of 1 m until the leaf area index (LAI)
is at least 0.93^ (see equation (3)), with an upper limit
of 10 m. In this way, the model takes account of the
fact that deep roots account for much of the water
supply of rainforests during the dry season as found
by
[1994] and rst modeled by
[1997]. To dene a global uncertainty
range we set the initial values of root depth to half
those values for model version \D-" and double for
\D+".
As the last source of uncertainty for calculating
NPP, we vary the parameterization of the temperature limited phenology, i.e. the desciption of leaf onset
Monteith
Ruimy et al.
Ryan
Ryan
Knorr and Heimann,
Nepstad et al.
and Heimann
Kleidon
and shedding. The LAI is simulated every 10 days as
the minimum of a temperature, 3T , water, and carbon limited value. The temperature-limited LAI is
assumed as in [
1993] from T0:5, the
temperature at 0.5 m soil depth:
8
0
if T0:5 < T
>
>
< ^
T 0T : 2
^
if T < T0:5 < T^
3T = > 3 1 0 T^ 0T
>
: 3^
if T0:5 > T^
(3)
The standard values chosen are 3^ = 5, T = 5C and
T^ = 15C, and correspond to the observed phenology
of central Europe [
1994]. For
evergreen vegetation, T0:5 is set to the value of the
warmest month, while for arable crops, T = 12C
is assumed. To dene model uncertainties, we vary
T by 2 C, dening \T-" (shorter) and \T+" (longer
vegetation period), and 3^ by 1, dening \L-" and
\L+."
For calculating the net exchange of CO2, which is
NPP minus SR, it is also necessary to consider some
uncertainty in the parameterization of SR, which is
calculated in a way combining results of
[1978] and
[1995]:
R
NPP(t0)dt0
SR(t) = R1 year 0 T (t )=10 0 fe(t)QT10 (t)=10 (4)
fe(t )Q10 dt
1 year
with Q10 = 1:5 and fe, the ratio of actual over potential evapotranspiration. The integral assures that
over 1 year, the carbon budget of each pixel is in balance. Several test variants for SR are listed in Table 2.
They take into account the range of values given by
[1995] (\SR-," \SRw"), earlier reported higher values of Q10 [
1992] (\SR+"), ndings of high CO2 ux in the winter [
1983;
1997] (\SRw"), and
resultes with earlier models with no water constraint
[cf.
1995] (\SR0").
Dickinson et al.,
05
Walter and Breckle,
Meentemeyer
Raich and Potter
0
Raich and Potter
Raich and Schlesinger,
Moore,
Winston et al.,
Knorr and Heimann,
2.2. Computation of Uncertainty Ranges
The senstivity tests dened before are summarized
to a single uncertainty range by an error analysis
method. This is carried out both for annual NPP
and for the simulated seasonal cycle of CO2. Two
important assumptions are used with it: the ve
sources of uncertainty are independent of each other
and their eect is additive when deviations from the
4
standard congurations are considered. In this case,
the squared error ranges can simply be added up.
To illustrate the method, let f (~x) be the model
simulated quantity of interest (NPP or atmospheric
CO2), and ~x = (x1; x2; 1 1 1 ; xn) the n = 5 sets of
parameters that are varied in the present analysis.
The assumption is now that
n
X
(5)
f (~x) = f (~x0 ) +
fi (xi 0 x0i )
i=1
where ~x0 = (x01; x02; 1 1 1 ; x0n) is the standard conguration and fi is some function of single-parameter variations. The expected error range 1f of the quantity
f is then determined by
[1f ]2 =
n
X
i=1
[1fi]2
(6)
It is thus possible to avoid a Monte Carlo exploration
of the parameter space, which would require a large
number of model runs. As shown in the appendix, the
assumption of additive contributions for deviations
from the standard conguration (eqation (5)) is well
fullled for both NPP and CO2 concentrations.
To estimate the single contributions, we take
[1fi]2 12 [fi(1xi)]2 + 12 [fi(01xi)]2
= 12 (fi+ 0 f0)2 + 12 (fi0 0 f0)2; (7)
where fi0 and fi+ stand for the results of the model
simulations at one standard deviation away from the
standard run with a value denoted f0 (e.g. fR0 and
fR+ for sensitivity runs concerning respiration costs,
versions \R-/+"). For the variation of the photosynthesis scheme
(\0" vs. \Pm"), we take [1fp]2 =
2
(fPm 0f0 ) as an estimate of the uncertainty resulting
from a change in modeling concept.
In general, we should expect some degree of mutual
saturation for the dierent parameters when NPP (or
any other variable f ) approaches very large or very
small values, which should lead to some overestimation of the error range. This eect will, however, be
compensated by the restriction to only the ve most
important sets of parameters, neglecting minor sensitivities. In general, it should be borne in mind that
the present analysis only constitutes a rst systematic assessment of global modeling uncertainties for
process-orientated vegetation models.
2.3. The Method of Model Constraint
The method used here to constrain the vegetation model with satellite data is described in detail
by
[1997]. It relies on an estimate of FPAR,
the fraction of photosynthetically active radiation absorbed by the vegetation, based on the Global Environmental Monitoring Index (GEMI) [
1992]. Compared to more traditional vegetation indices, such as the Normalized Dierence Vegetation Index (NDVI) [e.g.,
1994;
1995] or the simple ratio of channel
2 (near-infrared) over channel 1 (red) reectances
[e.g.,
1989;
1993],
GEMI has been shown to be less sensitive to changes
in soil color or atmospheric composition [
1994;
1994], and to achieve
a higher signal-to-noise ratio than the other indices
[
1994;
1995]. It is therefore chosen as an indicator of FPAR (denoted fPAR )
using
8
0 if GEMI 0:380;
>
>
>
>
< 1
if GEMI 0:720;
(8)
fPAR =
>
0
:122 0 1:061GEMI
0
>
>
>
: +3:639GEMI2
else.
GEMI is computed from channels 1 and 2 reectances
of the AVHRR instrument on board the NOAA-11
satellite. We use the weekly Global Vegetation Index
(GVI) data set [
1995] with 1/7 resolution, calibrated and corrected for atmospheric eects
by a group from the Centre des Etudes Spatiales de
la Biosphere (CESBIO) [
1994]. The
original data are from the NOAA GVI archive and
comprise the years 1989 and 1990. Further processing envolves removing data with sun or view angles
above 60, or under hot-spot condiditions (G 0:25
as dened in [
1990]) and cloud identication (GEMI more than 0.2 below a time ltered
curve with a Gaussian width of 2 = 2:8 weeks), then
averaging to a monthly resolution. The data are further averaged to 1 spatial resolution, and grid points
with residual cloud contamination are identied by a
ratio of the monthly GEMI maximum to the monthly
screened average of > 1:1. Averaging the remaining data of both years and lling in gaps of only one
month by linear interpolation results in a global areaweighted coverage of 74.8% of land areas.
An illustration of the method of model constraint
is given by Figure 1. It consists of the following steps:
(1) The vegetation model BETHY computes carKnorr
Pinty and Ver-
straete,
Ruimy et al.,
Knorr
and Heimann,
Heimann and Keeling,
Potter et al.,
Leprieur et
al.,
Flasse and Verstraete,
Goel and Qin,
Meyer et al.,
Gutman et al.,
Berthelot et al.,
Verstraete et al.,
5
bon exchanges and vegetation distribution expressed
as FPAR. (2) Atmospherically corrected satellite reectance data are screened for reliability and translated into FPAR. (3) The model-predicted value of
FPAR is compared to its satellite-derived analog. (4)
Parameters of BETHY are corrected and FPAR is
recomputed as described below. This and the preceding steps are repeated until an agreement within
a pre-dened degree of accuracy is reached. The parameters adjusted are fc, a constant vegetation cover
fraction, dr , the rooting depth, T, and 3T . The simulations without adjustment to the satellite data will
be called \prognostic," those after adjustment will be
called \diagnostic" throughout the remainder of this
text.
The following strategy is used for modifyingBETHY
model parameters. It is based on the requirement
that fractional cover and phenology parameters are
set such that at least one time per year, FPAR reaches
the highest monthly FPAR value observed, denoted
max :
fPAR
1. The revised fractional cover, fc0 , is computed
from the maximum
of the monthly measured
max , according to
FPAR values, fPAR
fc0 =
max
fPAR
1 0 e00:53^=fc;max
(9)
max is nonexistent. Its value is further
unless fPAR
limited to fc;max = 0:9. T is also modied such
that for the warmest month, 3T reaches 3^ (this
applies mainly to arctic regions).
2. To adapt the water balance to the satellite data,
the rooting depth, dr , is increased in steps of
2 m until the maximum monthly LAI,0 3max
starts to ll up the fractional cover, fc. This
is expressed by the condition
3max=fc0 0:83^ =fc;max:
(10)
There is a limit of 10 m for tropical and evergreentemperate trees (same as in prognostic mode),
0.5 m for eld crops and tundra vegetation, and
4 m for other types. Here dr is set uniformly to
0.5 m at program start and to 2 m in savannas.
3. For grasses and dry-seasonal vegetation, if there
are valid FPAR measurements and the monthly
mean temperature is greater than 5C (to avoid
the inuence of a snow background on the satellite signal), the temperature limited LAI, 3T , is
replaced by a diagnostic LAI limit, 3D . For
that purpose, the assumed evergreen portion of
FPAR, i.e. fPAR;e = ev fc[10exp(00:53^ =fc;max)]
is rst subtracted from the measured value fPAR
(eqation (8), ev is the evergreen vegetation fraction as derived from the vegetation map). If the
remainder is less or equal to zero, 3D = 0 is
assumed, otherwise 3D is set according to the
condition
3
D
(1 0 ev )fc 1 0 exp 00:5 (1 0 ev )fc =
fPAR 0 fPAR;e:
(11)
It is important to note that the satellite data
are not used as a direct input, which has been the
most widely used approach (e.g., CASA [
1993], SIB2 [
1996], TURC [
1996], GLO-PEM [
1996]).
One advantage is that, by using the data for model
constraint, it is possible to exclude unreliable measurements. More important, however, is the fact that
only in this way the information content of the satellite data can be assessed. For example, if the position
of dense evergreen forest is already accurately represented within the vegetation map, the satellite data
do not contain any additional information for those
particular areas because FPAR of dense vegetation is
known a priori. This second advantage is essential for
the approach of this study.
Potter et
al.,
et al.,
Sellers et al.,
Ruimy
Prince and Goward,
3. Eect of Model Constraint on
Simulated NPP
We rst evaluate the eect of constaining the
BETHY model by comparing annual NPP before
(prognostic) and after inclusion of satellite data (diagnostic) for the two standard model versions \0" and
\Pm". This more conventional method is here included for completeness. Since it does not account
for the inherent uncertainties of the model parameterization, a second analysis is added that evaluates
the sensitivity of the model between the two modes,
which is then summarized by an error range in the
way described in section 2.
3.1. Prognostic and Diagnostic Simulations
Latitudinal averages for the versions \0" and \Pm"
are shown in Figure 2 for the prognostic (\p") and
diagnostic (\d") versions. For the standard version
(\0"), the dierence between the prognostic and diagnostic versions is rather small for all latitudes. By
6
contrast, there is often a pronounced decrease after
inclusion of the satellite data with the Monteith photosynthesis (\Pm"). Globally, NPP changes only little for \0" and considerably for \Pm". Some of this
can be explained by a general increase in FPAR after accounting for the satellite-derived information,
which for the Monteith scheme always leads to an increase in NPP. For the Farquhar scheme, an increase
in FPAR, through an increase in LAI and higher respiration costs, can also lead to a decrease in NPP.
This eect is most pronounced in the tropics.
The spatial distribution of the diagnostic NPP of
version \d0" and its dierence to the prognostic version, \p0," is shown in Figure 3. The largest dierences are again found in the tropics, that also have the
highest NPP values, although the direction can vary
quite signicantly in space. Tropical semiarid areas
appear to increase somewhat, and high latitudes to
decrease. Some parts of North Africa and the Middle
East show a clear NPP decrease which is probably
attributable to heavy land use; in fact, it has to be
considered that the prognostic simulations predict a
potential vegetation amount that is not limited by
human impact.
3.2. Sensitivity to Global Uncertainties
So far, it has been dicult to assess the impact of
the model constraint because the dierences shown
seem to depend strongly on region and model version.
Instead of comparing single model versions, we now
asses the impact by comparing sensitivities to global
parameter uncertainties dened in section 2.1. The
results are shown in Figure 4 for global NPP and mean
NPPfor four climatic regions: high latitudes (north
of 60 N), moist-temperate (coldest month colder than
15.5 C, moisture index (MI) 1), arid (MI<1), and
moist-tropical (coldest month warmer than 15.5C,
MI1; MI is annual precipitation divided by annual
potential evapotranspiration).
As expected, the variation coming from the temperature controlled phenology (\T+/-") and the rooting depth (\D+/-") is reduced most by remote sensing, as far as the global integrals are concerned. It is
remarkable, though, that the largest source of uncertainty, which comes from the parametrization of respiration costs, is also reduced. The reduction ranges
from 1/10 for cold to 1/3 for warm climates. By contrast, the sensitivity to changing the photosynthesis
scheme (\P") does not change on the global scale,
while this dierence can vary considerably between
climatic regions.
Since phenology is directly related to the sensor signal, it is not surprising that the sensitivity to \T" can
generally be well reduced. However, the sensitivity to
the saturated LAI (\L") is only reduced signicantly
at high latidudes where vegetation is sparse; for high
LAI values, FPAR rapidly saturates so that it does
not contain any information for constraining LAI.
3.3. Calculated Error Ranges
The sensitivities of annual NPP to global parameter uncertainties are now summarized according to
the method described in section 2.2:
"Z
#2
9
X
2
[1NPP] = wv
(f (r)
~ 0 f0 (r))
~ dr~ :
region v
v=1
(12)
Here v runs over the nine model versions (Table 1),
and the weights wv are set to 1 for \Pm" and 0.5 for
all other versions. Table 3 shows the results for the
spatial integrals over the four climate zones dened
before, and for the globe. This error range turns out
to be large in all cases, and, with 100 GtC, the upper bound lies above most previously cited values. For
example, at the 1995 NPP intercomparison workshop
in Potsdam [
1999], results were presented ranging from 39.9 to 80.5 GtC.
This generally large error range is reduced considerably by inclusion of the satellite data. The most
signicant reduction in the absolute error comes from
the arid zone (by 4.7 GtC yr01), whereas in the moist
tropics, the reduction is insignicant, with no change
in the relative error (40%).
A spatially resolved picture is oered by Figure 5.
Here, the total range from the lower to the upper
bound is displayed, which in some cases, as in Table 4
below, is larger than the mean. The reduction in the
error is largest for the temperature limited areas of
the north and for some water limited regions, mostly
in Brazil (northeast and south), southern Africa, and
Australia. By comparison, the eect is small for the
evergreen tropical rainforest because their value of
FPAR is already known rather accurately without the
satellite data.
The same is also found when the error ranges are
computed by vegetation type (see Table 4): the satellite data neither reduce uncertainties in NPP calculations for tropical nor for temperate evergreen trees.
The largest reduction of uncertainty in absolute terms
is found for C4
grasses (by 3 GtC yr01), and shrubs
1
0
(2.5 GtC yr ), despite of their low productivity per
area, which to some extent also applies to the tunKicklighter et al.,
7
dra (0.8 GtC yr01). A signicant eect
is also found
for evergreen conifers (0.7 GtC yr01). The largest
reduction in the relative error range is found again
for shrubs, temperate deciduous trees, C4 grasses,
and the tundra. (In some cases where the number
of pixels is small, error ranges can exceed mean values because stochastic rainfall simulation adds some
statistical \noise"; this is not a problem for larger
averages, however.) The error range for agriculture
remains high because of high nitrogen content, leading to large uncertainties of respiration costs [
this issue].
Knorr
and Heimann,
4. Consistency Check With CO
Measurements
2
After having demonstrated the usefulness of optical satellite-based measurements to constrain a global
vegetation model, the task is now to check whether
this has in fact lead to an improved consistency of
model calculations with independent global observations. After discussing additional uncertainties coming from soil respiration and other sources, we compare both the standard simulations and the complete
error ranges to actual measurements of CO2 at a number of remote monitoring sites.
4.1. Method
As explained in section 1, the seasonal cycle of CO2
in the atmosphere as measured at remote monitoring
sites mainly reects the activity of the land biosphere.
Therefore such measurements are an ideal data source
to check simulated CO2 uxes. This method will here
be applied to the complete error range of NPP calculations as estimated in section 3. The method used is
illustrated by Figure 6 and consists of the following
four steps:
1. The vegetation model BETHY computes soil
respiration (SR) and net primary productivity
(NPP).
2. The tracer transport model of the atmosphere
TM2 [
1995] uses SR-NPP and additional sources and sinks from oceans and fossilfuel emissions to compute the transport of CO2
from the source/sink areas to the model grid
boxes corresponding to the positions of the
monitoring stations. The additional oceanic
sources, including plankton activity, are taken
from
[1996], and the fossil fuel emissions are assumed as in
Heimann,
Six and Maier-Reimer
Heimann
[1989], scaled to the annual emission of 1987 (5.66 GtC) [
1991].
3. By an appropriate data preparation, samples
from the ask sampling program of the NOAA
Climate Monitoring and Diagnostic Laboratory
[
1990;
1991]
are converted to mean seasonal cycle, cobs
s;m , and
obs .
its standard deviation, s;m
4. Measured CO2 concentrations at the monitoring stations are compared to simulated values
with model version v, cvs;m, in the corresponding TM2 grid boxes. The following value is used
to measure similarity:
nst X
12 (cobs 0 cv )2
1 X
s;m
s;m
jv2 =
;
(13)
obs ]2
12nst s=1 m=1 [s;m
where m runs over 12 months and s runs over
the number of selected stations, nst.
(5) The additional possibility of adjusting the vegetation model is discussed in section 4.2.
The stations used are listed in Table 5. TM2 is driven
with wind data from ECMWF analyses of either 1986
or 1987
and is used in its coarse resolution mode
(7.83 latitude 2 10 longitude, nine vertical layers).
Computation of the transport is carried out through
a matrix representation developed by
[1997].
and Keeling
Boden et al.,
Conway and Tans,
Boden et al.,
Kaminski et al.
4.2. Soil Respiration
Some of the model variants concerning soil respiration (SR, Table 2) are rst tested with CO2 data
from a tropical station (ASC, Ascension Island, tropical Atlantic), and the South Pole (SPO). As Figure 7
shows, without moisture control on SR (\SR0"), the
amplitude at ASC is clearly larger than observed. A
similar result has been found in an analysis by
[1995] with a simple diagnostic vegetation model, with the conclusion that the seasonal
amplitude of CO2 uxes in the tropics is small because moisture has a similar eect on both SR and
NPP. Figure 7 also shows that applying the precipitation driven model by
[1995] can
also be excluded as not consistent with the CO2 data
(\SRp"). In this case, the temporal lag of soil moisture, controlling NPP, against precipitation, controlling SR, creates an unrealistic peak in the simulated
CO2 concentrations.
Knorr
and Heimann
Raich and Potter
8
A test of all variants v of SR, expressed by the
mean squared normalized deviation, jv2, is given in
Table 6. The best agreement is found for the standard model (\0"), for a smaller Q10 (\SR-"), and for
the assumption of increased winter soil temperatures
under snow (\SRw"). There is an inferior agreement
both for an increased Q10 (\SR+"), and for a precipitation dependent ux (\SRp"). By comparison, the
dierences in transport between 1986 and 1987 are
generally smaller than those between model variants
and are rather evenly distributed across stations [cf.
1995]. Therefore, as a general
result of this comparison, a value of up to jv2 5 will
be considered to be in good agreement with observations of atmospheric CO2.
Knorr and Heimann,
4.3. Additional Uncertainties
Additional sources of uncertainty concern the representation of atmospheric transport in TM2, the selection of wind direction during measurements, and
the seasonality of oceanic uxes. For example, the
February-March peak at Seychelles (SEY), caused by
the monsoon circulation, is somewhat too small in the
simulations [
1989]. Further, a recent
intercomparison among atmospheric transport models has revealed some rather pronounced dierences
in the simulated concentrations from the same source
elds [
1995;
1996]. However, if the mean concentration is subtracted as it is
done here, most of the simulated seasonal cycles fall
into a rather narrow range. The remaining modeling
uncertainty is then of a similar size as that coming
from soil respiration (see section 4.2).
Preferred wind directions during measurement have
been found to be signicant at Cape Grimm, Tasmania (CGO) [
1996]. Because
samples are taken only when the air comes from the
open ocean, there is a signicant discrepancy of the
simulated transport statistics with that during ask
samplings, leading mainly to a change in the phase
of the seasonal cycle. However, the above conclusions concerning soil respiration formulations are not
eected because they largely rely on a comparison of
amplitudes.
Eventually, roughly half of the seasonal amplitude
comes from oceanic sources at the southern stations
[
1989;
1995], so
that some uncertainty of the oceanic contribution has
to be allowed for. The advantage of the uxes generated by
[1996] is that the corresponding oxygen uxes, for which the oceans domHeimann et al.,
Rayner and Law,
Law et al.,
Ramonet and Monfray,
Heimann et al.,
Knorr and Heimann,
Six and Maier-Reimer
inate in the Southern Hemisphere, agree well with
measurements of the seasonal cycle of the atmospheric
O2/N2 ratio [
1992].
Keeling and Shertz,
4.4. Prognostic and Diagnostic Error Ranges
In this section, we carry out the error analysis described in section 2 for the simulated annual cycles of
the standard version, c0s;m, and of the nine sensitivity tests, cvs;m, as listed in Table 1. The error range
of0 the simulated
monthly CO2 concentrations is then
sim , with
cs;m 6 s;m
9
X
sim
2
[s;m] = wv(cvs;m 0 c0s;m)2:
v=1
(14)
This equation corresponds to (6) and (7) of section 2.2. The index, v, runs over the nine model versions, with weights wv set again to 1 for \Pm" and
0.5 for the others (with opposite pairs). Here m and
s indicate month and station number, respectively.
To average the deviation across the complete error range from the measured CO2 concentrations, we
assume normal distribution around the standard simulations, ci;0, and calculate the averaged deviation,
J 2:
12 Z +1 (cobs 0 c )2
nst X
X
s;m
s;m
p1 sim
J2 =
obs
12nst 2s;m s=1 m=1 01 [s;m]2
!
(
cs;m 0 c0s;m )2
exp 0 [sim ]2
dcs;m
s;m
12 [sim ]2
nst X
X
s;m
= j02 + 121n
(15)
obs ]2 :
st s=1 m=1 [s;m
Both deviations, for the standard and the error range,
are listed in Table 7. It turns out that on average, the
range of the diagnostic simulations is more consistent
with observations
than the range of prognostic simulations (J 2). This is an expression of the fact that
the width of the simulated error range has decreased
but also that it has decreased into the \right" direction, i.e. toward more consistency with the CO2 data.
This is also underlined by0the closer consistency of the
standard
seasonal cycle, cs;m, with observations (compare j02). While after including the satellite data, the
simulations fall rather well into the above-mentioned
range of up to 5, without that additional source
of information, some of the model congurations lie
clearly above that value when compared to CO2 observations.
9
A station-by-station comparison is shown in Figures 8 and 9. The agreement is generally very good,
with the largest uncertainties and the largest deviations (several ppm) at the arctic stations, i.e., where
the largest amplitudes are found. It is mainly at those
northern stations where a clear deviation from the
measurements is found for the prognostic case and
the most signicant reduction of this deviation after
inclusion of the satellite data. Such a reduction in
the uncertainty range, however, can be found at most
stations.
An exception to this trend is the station Amsterdam Island (AMS) in the southern Indian Ocean, receiving its signal largely from southern South America, with some contributions from southern Australia
and New Zealand [
1996]. We suspect that the seasonal character of NPP of the warmtemperate southern grasslands is overestimated, so
that the upper bound of the simulations is too large
in both cases.
There is also one minor source of CO2 that can
contribute to a seasonal cycle but has not been taken
into account here. It is caused by savanna res, for
example in Africa, for which
[1994]
estimate an amplitude of 1 ppm near the station
Ascension (ASC), with a peak in August and a low
point in April. Such an additional source would increase the agreement between the standard diagnostic
simulation (\d0") and observations (see Figure 7).
As a general result, this consistency check of the
vegetation model can be considered successful in its
form constrained by satellite data. Without those
data, some model congurations have been found to
conict with observed CO2 measurements.
Kaminski et al.,
Iacobellis et al.
5. CO data for model constraint
2
In the section 4, it has turned out that the additional information contained in the satellite data can
be used to constrain the vegetation model BETHY
just far enough to make simulations within the range
of general uncertainties consistent with CO2 measurements and this despite of remaining large uncertainties in the global NPP. If, instead of the satellite
data, the CO2 data were now taken as a constraint
for the vegetation model, then all calculated diagnostic model versions of section 4 would be possible realizations of this condition. It should therefore be
asked what additional constraint measurements of the
atmospheric CO2 content can deliver.
From Table 4 it can be gathered for which vege-
tation types the information content of the satellite
data leads to a renement of the NPP calculations.
Figure 10 allows a similar evaluation of the CO2 data,
showing the relative contributions of dierent vegetation types to the simulated seasonal cycle, ci. Calculations are with version \d0" and the transport matrix
for 1987 with 26 stations taken from [
1997] as listed in Table 5. The contributions, at, are
dened as the normalized
scalar product of the sin;t (month m, station s) of type
gle contributions, c0s;m
t with the simulated concentration from the uxes of
all vegetation, c0s;m:
Kaminski et al.,
P12 0;t 0
=1 cs;m cs;m :
0
;t
as = Pm12
0 2
m=1 [cs;m]
(16)
It turns out that in the Northern Hemisphere, the
signal comes primarily from C3 grasses and conifers,
while the smaller signal in the Southern Hemisphere is
caused mainly by C3 and C4 grasses and to a smaller
degree by evergreen tropical trees. Because there is
a strong seasonal cycle only at the northern stations,
the consistency check of section 4 mainly tconcerns
C3 grasses and conifers with a total of as 0:55
for most stations. Errors in the modeling of, for example, broadleaf trees cannot be checked with this
method. Also, if signals of opposite phase compensate
each other, such as at the important station Acension
(ASC) for tropical evergreen and deciduous trees, it
could mean that two errors actually cancel. Hence
it must be concluded that a more accurate determination of the net ux SR-NPP and through it of the
NPP of those two important vegetation types is possible neither with CO2 nor with satellite data.
It also follows that it is mainly conifers and C3
grasses for which productivity can be checked and
constrained by the CO2 data. At the same time, those
are just two of the three major vegetation types for
which, according to Table 4, satellite data contain
the most information for the vegetation model simulations. This might explain why constraining the
vegetation model to the satellite measurements leads
to such a clear improvement for the CO2 simulations.
In most other cases, however, the signicance of the
satellite data is clearly higher: for example, while the
productivity of C4 grasses can be constrained most,
this eect cannot be checked with the CO2 signal because of the small amplitude at southern latitudes.
For that reason, the satellite data have a clearly
higher information content for determining global
NPP than measurements of the atmospheric CO2 content. That means that despite or just because of large
10
remaining inaccuracies, they constitute the most important global data set for validating and improving
global vegetation models. Nonetheless, the importance of the CO2 data lies in the fact that they allow
an additional check of the dierence between NPP
and SR that cannot be captured with optical remotesensing data.
6. Summary and Outlook
In this contribution it has been shown that the
range of possible model congurations of a complex,
process-based model of global vegetation activity can
be constrained further with the help of satellite data.
Model parameters could thus be modied for most areas in such a way that model predictions and satellite
measurements match within their inherent degree of
accuracy.
The most signicant result of this analysis is that
the inclusion of satellite data has reduced the sensitivity of the model against uncertainties in the most
important parameters. This eect is less important
globally than regionally. An error analysis shows that
the possible range of NPP estimates, on the basis of
climate, soils, and vegetation data, is still large even
when agreement with the satellite data is assured. For
that reason, the greatest signicance of those data
lies in the more regional validation and improvement
of vegetation models, in particular for arid environments. As far as the simulation of NPP is concerned,
we nd that the net primary production01 of land vegetation amounts to 70 6 35 GtC yr , taking the
satellite data into account.
A consistency check with measurements of the seasonal cycle of CO2 in the free atmosphere delivers
good agreement with those observations. It is then
shown that by inclusion of the satellite data, the complete estimated error range is consistent with those
observations. The consequence is that if the CO2
data were used as a constraint, they could not reduce the uncertainties in NPP any further. In other
words, simulations consistent with standard knowledge on plant activity with an NPP ranging from 35
to 105 GtC are all consistent with the CO2 data.
As a further analysis shows, the CO2 seasonal cycle
is mainly a test for the activity of northern temperate
vegetation, notably conifers and C3 grasses. Because
of the reduced seasonality in the tropics and the relatively large oceanic source at tropical and southern
latitudes, the contribution from the important tropical vegetation is dicult to capture. By comparison,
the satellite data are able to reduce uncertainties both
for northern vegetation and for tropical vegetation in
more arid areas. Therefore, as far as our ability to
model NPP is concerned, the satellite data constitute
the most important constraint currently available.
There are certainly numerous possibilities for extending the method of vegetation model constraint
outlined here. Conceptually easy is an application to
other types of satellite data, such as measurements
of the thermal outgoing radiation [
1995], active or passive microwaves [e.g.,
1990], or other optical data from newer satellites [e.g.,
1999]. Further applications can also be
found for certain diagnostic techniques of the terrestrial biosphere with the help of isotopic trace gas measurements, e.g. C18O16O [
1993;
1996].
An important extension of this approach concerns
interannual variations in atmospheric CO2. On the
one hand, there are indications of an increase of the
seasonal cycle's amplitude by 20% since the early
1970s [
1996]; on the other hand,
[1997] report similar changes in the vegetation index NDVI between 1981 and 1991, accompanied by a clear rise in spring temperatures. It would
be particularly insightful to bring those three observations together by using a vegetation model to simulate
both changes in the CO2 signal and in the satellite
data on the basis of measured changes in the climate.
Norman et al.,
Ulaby et al.,
Diner et al.,
Farquhar et al.,
Ciais
et al.,
Keeling et al.,
My-
neni et al.
Appendix: Test of the Additive Error
Model
An important assumption used in the error analysis is that deviations from the standard version \0"
add up in terms of the model variable f . Here, we
test this assumption for both NPP and the seasonal
cycle of atmospheric CO2 by running two versions
that combine the variants \R+," \D-" and \T-," and
\R-," \D+" and \T+," respectively. The results are
compared with those values predicted by (5), i.e., by
testing the approximation
fR+D0T 0 f0 + (fR+ 0 f0 ) + 1 1 1
1 1 1 + (fD0 0 f0 ) + 1 1 1
(A1)
1 1 1 + (fT 0 0 f0 )
and vice versa, where fv stands for the result with
model version \v."
Table A1 shows the results in the case where f
stands for annual NPP, i.e. for the combined versions
11
(denoted fR+D0T 0 , fR0D+T + ) as well as the additive deviations from f0. Spatial integrals are formed
over the four climatic regions dened in section 3.2.
As the comparison shows, the assumption of additive
deviations for variations along the dierent axes of
the parameter space considered is well fullled, even
for rather large variations.
We also tested the linear error model for the simulated seasonal cycle of CO2 at the stations Alert
(ALT) and Mauno Loa (). The resulting combined
and additive seasonal cycles, not shown here, turned
out to dier by less than 0.2 ppmv at ALT, i.e., by
less than 1%, and by a similar amount at MLO.
Acknowledgments.
The authors would like to thank Gerard Dedieu for providing the satellite data, and Thomas Kaminski for help
with using his matrix representation of atmospheric tracer
transport, as well as for many helpful suggestions. This
work has also proted immensely from numerous stimulating discussions with Michel Verstraete and Bernard
Pinty, in particular on vegetation observation from satellites. This work has been funded in part by the European
Union's ESCOBA/Atmospheres project, under Contract
No. ENV4-CT95-0116. Support for numerical computations was provided by the German Climate Computing
Centre (DKRZ).
References
Agbu, P. A., and M. E. James, The NOAA/NASA Pathnder
AVHRR Land Data Set User's Manual, The Goddard
Distributed Active Archiving Center, NASA, Goddard
Space Flight Cent., Greenbelt, Md., 1994.
Beerling, D. J., and W. P. Quick, A new technique for estimating rates of carboxylation and electron transport in
leaves of C3 plants for use in dynamic global vegetation
models, Global Change Biol., 1, 289-294, 1995.
Berthelot, B., Dedieu, G., Cabot, F. and Adam, S. Estimation of surface reectance and vegetation index
using NOAA/AVHRR: Methods and results at global
scale, paper presented at the 6th International Symposium on Physical Measurements and Signatures in
Remote Sensing, Int. Soc. for Photogramm. and Remote Sens., Val d'Isere, France, Jan. 17 to 21, 1994.
Boden, T. A., R. J. Serpanski, and F. W. Stoss (Eds.),
Trends 1991: A compendium of data on global change,
ORNL/CDIAC-46, Carbon Dioxide Inf. Anal. Cent.,
Oak Ridge Nat. Lab., Oak Ridge, Tenn., 1991.
Ciais, Ph., et al., A three dimensional synthesis study of
18 O in atmospheric CO2 , 1, Surface uxes, J. Geophys. Res., 102, 5857-5872, 1996.
Collatz, G. J., M. Ribas-Carbo, and J. A. Berry, Coupled
photosynthesis-stomatal conductance model for leaves
of C4 plants, Aust. J. Plant Physiol., 19, 519-538, 1992.
12
Conway, T. J., and P. Tans, Atmospheric CO2 concentrations: the NOAA/GMCC ask sampling network,
NDP-005/R1, Carbon Dioxide Inf. Anal. Cent., Oak
Ridge Nat. Lab., Oak Ridge, Tenn., 1990.
Cramer, W., D. W. Kicklighter, A. Bondeau, B. Moore III,
G. Churkina, B. Nemry, A. Ruimy, A. Schloss, and the
participants of Potsdam NPP intercomparison, Comparing global models of terrestrial net primary productivity (NPP): Overview and key results, Global Change
Biol., 5, Suppl. 1, 1-15, 1999.
Dickinson, R. E., A. Henderson-Sellers, and P. J. Kennedy,
Biosphere-atmosphere transfer scheme (BATS) version
1e as coupled to the NCAR community climate model,
Tech. Note NCAR/TN-387+STR, Nat. Cent. for Atmos. Res., Boulder, Colorado, 1993.
Diner, D. J, et al., MISR: A multi-angle imaging spectroradiometer for geophysical and climatological research
from EOS, IEEE Trans. Geosc. Remote Sens., 27,
200-214, 1989.
Dunne, K. A., and C. J. Willmott, Global distribution of
plant-extractable water capacity of soil, Int. J. Climatol., 16, 841-859, 1996.
Farquhar, G. D., S. von Caemmerer, and J. A. Berry,
A biochemical model of photosynthesis in leaves of C3
species, Planta, 149, 78-90, 1980.
Farquhar, G. D., J. Lloyd, J. A. Taylor, L. B. Flanagan,
J. P. Syvertsen, K. T. Hubick, S. C. Wong, and R.
Ehleringer, Vegetation eects on the isotope composition of oxygen in atmospheric CO2, Nature, 363, 439443, 1993.
Field, C. B., and H. A. Mooney, The photosynthesisnitrogen relationship in wild plants, in On the Economy
of Plant Form and Function, edited by T. J. Givnish,
pp. 25-55, Cambridge Univ. Press, New York, U.K.,
1986.
Flasse, S., and M. M. Verstraete, Monitoring the environment with vegetation indices: comparison of NDVI
and GEMI using AVHRR data over Africa, in Vegetation, Modelling and Climate Change Eects, edited
by F. Veroustraete and R. Ceulemans, pp. 107-135,
Academic, San Diego, Calif., 1994.
Francey, R. J., P. P. Tans, C. E. Allison, I. G. Enting,
J. W. C. White, and M. Trolier, Changes in oceanic
and terrestrial carbon uptake since 1982, Nature, 373,
326-330, 1995.
Friedlingstein, P., I. Fung, E. Holland, J. John, G. Brasseur,
D. Erickson, and D. Schimel, On the contribution
of CO2 fertilization to the missing sink, Global Biogeochem. Cycles, 9, 541-556, 1995.
Geng, S., F. Penning de Vries, and I. Supit, A simple
method for generating daily rainfall data, Agric. For.
Meteorol., 36, 363-376, 1986.
Goel, N.S., and W. Qin, Inuences of canopy architecture
on relationships between various vegetation indices and
LAI and FPAR: A computer simulation, Remote Sens.
Rev., 10, 309-347, 1994.
Gutman, G., D. Tarpley, A. Ignatov, and S. Olson, The
enhanced NOAA global land dataset from the Advanced Very High Resolution Radiometer, Bull. Am.
Meteorol. Soc., 76, 1141-1156, 1995.
Heimann, M., The TM2 atmospheric transport model,
Tech. Rep. 10, Deutsches Klimarechenzentrum, Hamburg, Germany, 1995.
Heimann, M., A review of the contemporary global carbon cycle and as seen a century ago by Arrhenius and
Hgbom, Ambio, 26, 17-24, 1997.
Heimann, M., and C. D. Keeling, A three-dimensional
model of atmospheric CO2 transport based on observed
winds, 2, Model description and simulated tracer experiments, in Aspects of Climate Variability in the Pacic
and Western Americas, Geophys. Monogr. Ser., vol.
55, pp. 237-275, American Geophys. Union, Washington, D.C., 1989.
Heimann, M., C.D. Keeling, and C.J. Tucker, A threedimensional model of atmospheric CO2 transport based
on observed winds: 3. Seasonal cycle and synoptic time
scale variations, in Aspects of Climate Variability in
the Pacic and Western Americas, Geophys. Monogr.
Ser., vol. 55, pp. 277-303, American Geophys. Union,
Washington, D.C., 1989.
Heimann, M., et al., Evaluation of terrestrial carbon cycle models through simulations of the seasonal cycle of
atmospheric CO2 : First results of a model intercomparison study, Global Biogeochem. Cycles, 12, 1-24,
1998.
Huete, A. R., and R. D. Jackson, Soil and atmosphere
inuences on the spectra of partial canopies, Remote
Sens. Environ., 25, 89-105, 1988.
Iacobellis, S. F., R. Frouin, H. Razampanilo, R. C. J.
Somerville, and S. C. Piper, North African savanna res
and atmospheric carbon dioxide, J. Geophys. Res. 99,
8321-8334, 1994.
International Geosphere-Biosphere Programme (IGBP),
IGBP Global Modelling and Data Activities 1994-1998,
Rep. 30, Stockholm, 1994.
Kaduk, J., and M. Heimann, The climate sensitivity of the
Osnabrck Biosphere Model on the ENSO time scale,
Ecol. Modell., 75/76, 239-256, 1994.
Kaminski, T., R. Giering, and M. Heimann, Sensitivity
of the seasonal cycle of CO2 at remote monitoring stations with respect to seasonal surface exchange uxes
determined with the adjoint of an atmospheric transport model, Phys. Chem. Earth, 21, 457-462, 1996.
Kaminski, T., M. Heimann, and R. Giering, A global
scale inversion of the transport of CO2 based on a matrix representation of an atmospheric transport model
derived by its adjoint, paper presented at the XXI
NATO/CCMS International Technical Meeting on Air
Pollution Modelling and its Application, North Atlantic Treaty Organization, Clermont-Ferrand, France,
June 2-6, 1997.
Kaufman, Y. J., The atmospheric eect on remote sensing
13
and its correction, in Theory and Applications of Optical Remote Sensing, edited by G. Asrar, pp. 336-428,
John Wiley, New York, 1989.
Keeling, R. F., and S. R. Shertz, Seasonal and interannual
variations in atmospheric oxygen and implications for
the global carbon cycle, Nature, 358, 723-727, 1992.
Keeling, C. D, J. F. S. Chin, and T. P. Whorf, Increased
activity of northern vegetation inferred from atmospheric CO2 data, Nature, 382, 146-149, 1996.
Kicklighter, D. W., A. Bondeau, A. Schloss, J. Kaduk, A.
D. McGuire, and the participants of Potsdam NPP intercomparison, Comparing global models of terrestrial
net primary productivity (NPP): Global pattern and
dierentiation by major biomes, Global Change Biol.,
5, Suppl. 1, 16-24, 1999.
Kidwell, K.B. (Ed.), Global Vegetation Index User's Guide,
Nat.l Oceanic and Atmos. Admin., Washington, D.C.,
1990.
Kindermann, J., G. Wurth, G.H. Kohlmaier, and F.
W. Badeck, Interannual variation of carbon exchange
uxes in terrestrial ecosystems, Global Biogeochem.
Cycles, 10, 737-755, 1996.
Kleidon, A., and M. Heimann, A method of determining
rooting depth from a terrestrial biosphere model and its
impacts on the global water- and carbon cycle, Global
Change Biol., 4, 275-286, 1997
Knorr, W., Satellitengestutzte Fernerkundung und Modellierung des globalen CO2 -Austauschs der Landvegetation: Eine Synthese, Examensarbeit Nr. 49, http://www.bgcjena.mpg.de/ wolfgang.knorr (in English), Max-PlanckInstitut fur Meteorologie, Hamburg, Germany, 1997.
Knorr, W., Annual and interannual CO2 exchanges of the
terrestrial biosphere: process-based simulations and
uncertainties, Global Ecol. Biogeogr., 9, 225-252, 2000.
Knorr, W., and M. Heimann, Impact of drought stress
and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model, Tellus, Ser. B, 47, 471-489, 1995.
Knorr, W., and M. Heimann, Uncertainties in global terrestrial biosphere modeling, Part I: A comprehensive
sensitivity analysis with a new photosynthesis and energy balance scheme, Global Biogeochem. Cycles, this
issue.
Knorr, W., and J.-P. Schulz, Using Satellite Data Assimilation to Infer Global Soil Moisture Status and Vegetation Feedback to Climate, in Remote Sensing and
Climate Modeling: Synergies and Limitations, edited
by M. Beniston and M. M. Verstraete, Advances in
Global Change Research series, Kluwer, Boston, Mass.,
in press.
Knorr, W., N. Gobron, Ph. Martin, B. Pinty, M. M. Verstraete and G. Dedieu, Constraining a climate driven
vegetation model with satellite data, in Proceedings
Photosynthesis and Remote Sensing, pp. 269-279, Europ. Ass. Remote Sens. Lab., Paris, 1995.
Law, R. M., et al., Variations in modeled atmospheric
transport of carbon dioxide and the consequences for
CO2 inversion, Global Biogeochem. Cycles, 10, 783796, 1996.
Leemans, R. and W. Cramer, The IIASA climate database
for mean monthly values of temperature, precipitation
and cloudiness on a terrestrial grid, Rep. RR-91-18, Institute of Applied Systems Analysis, Laxenburg, Austria, 1991.
Leprieur, C., M. M. Verstraete, and B. Pinty, Evaluation of the performance of various vegetation indices to
retrieve vegetation cover from AVHRR data, Remote
Sens. Rev., 10, 265-284, 1994.
Meentemeyer, V., Macroclimate and lignin control of litter
decomposition rates, Ecology, 59, 465-472, 1978.
Meyer, D., M. M. Verstraete, and B. Pinty, The eect
of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR, Remote Sens. Rev.,
12, 3-27, 1995.
Monteith, J. L., Light distribution and photosynthesis in
eld crops, Ann. Bot., 29, 17-37, 1965.
Moore, T. R., Winter-time decomposition in a subarctic
woodland, Arct. Alp. Res., 15, 413-418, 1983.
Muller, M. J., Selected Climatic Data for a Global Set
of Standard Stations for Vegetation Science, Dr. Junk,
Norwell, Mass., 1982.
Myneni, R. B., C. D. Keeling, C. J. Tucker, G. Asrar, and
R. R. Nemani, Increased plant growth in the northern
high latitudes from 1981 to 1991, Nature, 386, 698-702,
1997.
Nakazawa, T., S. Morimoto, S. Aoki, and T. Masayuki,
Time and space variations of the carbon isotopic ratio
of tropospheric carbon dioxide over Japan, Tellus, Ser.
B, 45, 258-274, 1993.
Nepstad, D. C., C. R. de Carvalho, E. A. Davidson, P. H.
Jipp, P. A. Lefebvre, G. H. Negeiros, E. D. da Silva,
T. A. Stone, S. E. Trumbore, and S. Vieira, The role
of deep roots in the hydrological and carbon cycles of
Amazonian forests and pastures, Nature, 372, 666-669,
1994.
Norman, J. M., M. Divakarla, and N. S. Goel, Algorithms
for extracting information from remote thermal-IR observations of the Earth's surface, Remote Sens. Environ., 51, 157-168, 1995.
Pinker, R. T., and I. Laszlo, Global distribution of photosynthetically active radiaton as observed from satellites, J. Clim., 5, 56-65, 1992.
Pinty, B., and M. M. Verstraete, Extracting information on surface properties from bidirectional reectance
measurements, J. Geophys. Res., 96, 2865-2874, 1991.
Pinty, B., and M. M. Verstraete, GEMI: A non-linear index to monitor global vegetation from satellites, Vegetatio, 101, 1335-1372, 1992.
Potter, S. C., J. T. Randerson, C. B. Field, P. A. Matson,
P. M. Vitousek, H. A. Mooney, and S. A. Klooster, Terrestrial ecosystem production: a process model based
on global satellite and surface data, Global Biogeochem.
14
Cycles, 7, 811-841, 1993.
Prince, S. D., and S. N. Goward, Global net primary production: The remote sensing approach, J. Biogeogr., 6,
1212-1224, 1996.
Raich, J. W., and C. S. Potter, Global patterns of carbon dioxide emissions from soils. Global Biogeochem.
Cycles, 9, 23-36, 1995.
Raich, J.W., and W. H. Schlesinger, The global carbon
dioxide ux in soil respiration and its relationship to
vegetation and climate, Tellus 44B, 81-99, 1992.
Ramonet, M., and P. Monfray, CO2 baseline concept in
3-D atmospheric transport models, Tellus, Ser. B, 48,
502-520, 1996.
Rayner, P.J., and R. M. Law, A comparison of modelled
responses to prescribed CO2 sources, Tech. Pap. 36,
p. 1-82, Commonwealth Sci. and Indust. Res. Org.,
Aust. Div. Atmos. Res., Aspendale, Australia, 1995.
Ruimy, A., B. Saugier, and G. Dedieu, Methodology for
the estimation of terrestrial net primary production
from remotely sensed data, J. Geophys. Res., 99, 52635283, 1994.
Ruimy, A., G. Dedieu, and B. Saugier, TURC: A diagnostic model of continental gross primary productivity and
net primary productivity, Global Biogeochem. Cycles,
10, 269-285, 1996.
Ryan, M. G., Eects of climate change on plant respiration, Ecol. Appl., 1, 157-167, 1991a.
Ryan, M. G., A simple method for estimating gross carbon budgets of vegetation in forest ecosystems. Tree
Physiol., 9, 255-266, 1991b.
Schimel D., D. Alves, I. Enting, M. Heimann, F. Joos,
D. Raynaud, T. Wigley, Radiative forcing of climate
change: CO2 and the carbon cycle, in Climate Change
1995, edited by J.T. Houghton et al., pp. 76-86, Cambridge Univ. Press, New York, 1996.
Sellers, P. J., D. A. Randall, G. J. Collatz, J. A. Berry, C.
B. Field, D.A. Dazlich, C. Zhang, and G. D. Collelo, A
revised land surface parameterization (SiB2) for atmospheric GCMs, Part 1, Model formulation, J. Clim., 9,
676-705, 1996.
Six, K. D. and E. Maier-Reimer, Eects of plankton dynamics on seasonal carbon uxes in an ocean general
circulation model, Global Biogeochem. Cycles, 10, 559583, 1996.
Townshend, J. R. G., C. O. Justice, D. Skole, J.-P. Malingreau, J. Cihlar, P. Teillet, F. Sadowski, and S. Ruttenberg, The 1 km resolution global data set: Needs
of the International Geosphere Biosphere Programme,
Int. J. Remote Sens., 15, 3417-3441, 1994.
Ulaby, F. T., K. Sarabandi, K. McDonald, M. Whitt, and
M.C. Dobson, Michigan microwave canopy scattering
model, Int. J. Remote Sensing, 11, 1223-1253, 1990.
Verstraete, M. M, Retrieving canopy properties from remote sensing measurements, in Imaging Spectrometry:
A Tool for Environmental Observations, edited by J.
Hill and J. Megier, pp. 109-123, Europ. Coal and Steel
Comm., Euop. Econom. Comm., Europ. Atom. Energy Comm., Brussels and Luxemburg, 1994.
Verstraete, M. M., and B. Pinty, Designing optimal spectral vegetation indices for remote sensing applications.
IEEE Trans. Geosc. Remote Sensing, 34, 1254-1265,
1996.
Verstraete, M. M. and B. Pinty, Environmental information extraction from satellite remote sending data,
in Inverse Methods in Global Biogeochemical Cycles,
edited by P. Kasibhatla et al., Geophysical Monograph
114, pp. 125-137, American Geophys. Union, Washington D. C., 2000.
Verstraete, M. M., Pinty, B., and Dickinson, R. E., A
physical model of the bidirectional reectance of vegetation canopies, 1, Theory, J. Geophys. Res., 95,
11775-11765, 1990.

Walter, H., and S.-W. Breckle, Okologie
der Erde, Band

3: Spezielle Okologie
der gemaigten Zonen EuroNordasiens, 586 pp., Gustav Fischer Verlag, Stuttgart,
Germany, 1994.
Wilson, M.F., and A. Henderson-Sellers, A global archive
of land cover and soils data for use in general circulation
models, J. Clim., 5, 119-143, 1985.
Winston, G. C., E. T. Sundquist, B. B. Stephens, and S.
E. Trumbore, Winter CO2 uxes in a boreal forest, J.
Geophys. Res., 102, 28,795-28,804, 1997.
W. Knorr and M. Heimann, Max Planck Institute
for Biogeochemistry, Carl-Zeiss-Promenade 10, 07745
Jena, Germany. (e-mail: [email protected])
Received June 24, 1998; revised October 19, 1999; accepted
January 13,
2000.
This preprint was
prepared with AGU's
ATEX
L
v5.01. File paper_pp formatted February 16, 2001.
macros
15
calibrated &
atm. corrected
reflectances
data
screening
(2)
FPAR=f (GEMI)
dates
FPAR
(3)
compare
(4)
FPAR
climate
soils
adjust
a priori
parameters
(1)
vegetation
model
vegetation type
NPP, SR
A diagram illustrating the method of using remote-sensing data as a constraint for vegetation models.
The numbers refer to the points in the text. FPAR is the fraction of photosynthetically active radiation absorbed
by the vegetation.
Figure 1.
75oN
p0 (75.8 GtC yr -1)
d0 (73.6 GtC yr -1)
pPm (82.9 GtC yr -1)
dPm (67.1 GtC yr -1)
o
60 N
o
45 N
30oN
15oN
0o
75oS
30oN
45oS
60oS
0
400
800
-2
1200
-1
NPP [gC m yr ]
Latitudinal average of the NPP of the prognostic (\p") and diagnostic (\d") model versions with
photosynthesis according to Farquhar (\0") and Monteith (\Pm").
Figure 2.
16
Version "d0"
30°N
0°
diagnostic
NPP
30°S
180°W
90°W
0
0°
500
90°E
1000
1500
180°E
gC m -2 yr -1
Version "d0"
Version "p0"
30°N
0°
diagnostic
minus
prognostic
30°S
180°W
90°W
0°
-500
0
90°E
+500
180°E
gC m -2 yr -1
Diagnostically computed annual NPP according to version \d0" of the vegetation model BETHY and
the dierence to the corresponding prognostic version \p0."
Figure 3.
17
NPP difference [GtC yr -1]
global sum
20
prognostic
10
diagnostic
0
-10
-20
Pm
R+
R–
D+
D–
T+
T–
L+
L–
version
high latitudes
moist-temperate
200
60
40
100
NPP difference [gC m-2 yr -1]
20
0
0
-20
-100
-40
-60
-200
Pm R+ R– D+ D– T+ T– L+
L–
arid
600
50
400
200
0
0
-200
-100
-150
parameters.
L–
moist-tropical
100
-50
Figure 4.
Pm R+ R– D+ D– T+ T– L+
-400
Pm R+ R– D+ D– T+ T– L+
L–
-600
Pm R+ R– D+ D– T+ T– L+
L–
Sensitivity of the prognostic and diagnostic model versions against various uncertainties in model
18
prognostic
diagnostic
0
Figure 5.
400
800
1200
gC m -2 yr -1
Estimated error range of annual NPP of the prognostic and diagnostic model versions per 1 grid point.
19
CO2
(3)
concentrations
at the monitoring sites
data
preparation
seasonal CO 2 cycle at
the monitoring sites
(4)
compare
seasonal CO2 cycle
in the corresponding
model grid boxes
(2)
winds
convection
additional
sources
and sinks
transport
model
RES-NPP
climate
soils
(1)
vegetation type
(ocean,
fossil-fuel
emissions)
(adjust)
(5)
vegetation
model
Diagram illustrating information ow during model validation with measurements of the atmospheric
CO2 concentration. A possible extension to a method of model constraint is shown by the dashed arrow. The
numbers refer to the points in the text.
Figure 6.
3
CO2 [ppmv]
2
ASC
SPO
1
0
-1
-2
-3
d0
dSR0
dSRp
J FMAM J J A SOND J FMAM J FMAM J J A SOND J FMAM J
Observed (crosses) and simulated seasonal cycle for two test variants of soil respiration (\SR0," \SRp"),
and the standard run (\0"). First six months are repeated for display purposes.
Figure 7.
20
ALT
BRW
NWR
AZR
MLO
KUM
GMI
SEY
ASC
AMS
CGO
SPO
CO2 [ppmv]
CO2 [ppmv]
CO2 [ppmv]
CO2 [ppmv]
Prognostic
standard
error range
Simulated seasonal cycle of CO2 of the standard run and the error range for the prognostic model
versions of BETHY and comparison with measurements (crosses) from 12 monitoring stations.
Figure 8.
21
ALT
BRW
NWR
AZR
MLO
KUM
CO2 [ppmv]
CO2 [ppmv]
Diagnostic
SEY
ASC
AMS
CGO
SPO
CO2 [ppmv]
CO2 [ppmv]
GMI
standard
error range
Figure 9. Simulated seasonal cycle of CO2 of the standard run and the error range for the diagnostic model
versions of BETHY and comparison with measurements (crosses) for the same 12 monitoring stations.
22
The contribution of (top) various trees and other vegetation types (below) to the seasonal cycle at
dierent stations, simulated with version \d0."
Figure 10.
23
Table 1.
Description of Model Variants of BETHY Used for the Error Analysis
Code
Sensitivity Test of
Description
Pm
RR+
DD+
TT+
photosynthesis
plant respiration
"
rooting depth
"
temperature phenology
"
LL+
leaf area
"
Monteith model
low, fR;G = 0:2
and fR;leaf = 0:5
high, fR;G = 0:3
and fR;leaf = 0:3
halved
doubled
short: T = 7C (arable 14C)
and T^ = 17C
long: T = 3 C (arable 10C)
and T^ = 13C
low, 3^ = 4
high, 3^ = 6
Standard
Farquhar model
fR;G = 0:25,
fR;leaf = 0:4
1 to 10 m (see text)
T = 5 C (12 C),
T^ = 15C
3^ = 5
Monteith model, Farquhar model, see section 2.1; fR;G , see (1); fR;leaf , see (2); T , T^ , 3^ , see (3).
24
Table 2.
(SR)
Test Variants of the Vegetation Model BETHY Concerning Soil Respiration
Code
SRSR+
SRw
SRp
SR0
Description
Q10 = 1:4
Q10 = 1:8
Q10 = 1:5
warmer soil
than air temperature at freezing
(T 05C when calculating SR)
\Model B, All Data" from [
1995]
(precipitation rate as humidity multiplier)
no humidity multiplier
Raich and Potter,
For Q10 and fe , see equation (4).
Standard
SR proportional
to fe
25
Annual NPP of the Standard Version and Its Estimated Error
Range in GtC yr01
Table 3.
Zone
High-Latitude
Moist-Temperate
Arid
Moist-Tropical
Global
Prognostic
Total
Error
Diagnostic
Total
Error
3.39
17.54
29.18
25.69
75.80
2.33
16.82
29.63
24.83
73.61
61.15 (33%)
65.22 (29%)
610.74 (36%)
610.50 (40%)
624.10 (31%)
60.65 (27%)
62.75 (16%)
66.05 (20%)
610.15 (40%)
618.50 (25%)
26
Table 4.
Annual NPP of the Standard Version and the Estimated Error Range by Vegetation Type
Prognostic
Type
trop. ev. trees
trop. dec. trees
temp. ev. trees
temp. dec. trees
ev. conifers
dec. condifers
ev. shrubs
dec. shrubs
C3 short grass
C3 long grass
C4 short grass
C4 long grass
tundra
wetlands
agriculture
Diagnostic
Total NPP,
GtC yr01
Mean Error
Average NPP,
gC m02 yr01
Mean
Error
Total NPP,
GtC yr01
Mean Error
Average NPP,
gC m02 yr01
Mean
Error
16.22
7.31
0.24
2.45
3.61
0.72
1.39
1.23
8.16
4.78
5.07
15.49
1.33
0.70
7.12
1203
1005
536
632
388
359
61
355
477
845
665
800
134
533
628
15.43
9.39
0.22
2.07
2.72
0.74
0.25
0.99
8.25
4.66
5.05
15.05
0.93
0.50
7.36
1145
1291
498
534
292
367
10
287
482
825
662
777
93
383
650
68.27
62.86
60.09
60.83
62.04
60.23
62.06
60.88
62.86
61.19
62.25
64.98
61.29
60.18
67.66
6613 ( 51%)
6392 ( 39%)
6204 ( 38%)
6214 ( 33%)
6219 ( 56%)
6112 ( 31%)
6 90 (148%)
6254 ( 71%)
6167 ( 35%)
6209 ( 24%)
6295 ( 44%)
6257 ( 32%)
6130 ( 97%)
6136 ( 25%)
6677 (107%)
Abbrevations: trop., tropical; ev., evergreen; dec., deciduous.
68.97
63.98
60.09
60.27
61.37
60.19
60.08
60.31
62.31
60.97
61.04
63.12
60.51
60.14
67.38
6665 ( 58%)
6547 ( 42%)
6204 ( 40%)
6 69 ( 12%)
6147 ( 50%)
6 95 ( 25%)
6 3 ( 33%)
6 89 ( 31%)
6134 ( 27%)
6172 ( 20%)
6137 ( 20%)
6160 ( 20%)
6 51 ( 54%)
6104 ( 27%)
6652 (100%)
27
Table 5.
Code
ALT
MBC
KTL
BRW
STM
CBA
SHM
CMO
NWR
AZR
MID
KEY
MLO
KUM
AVI
GMI
CHR
SEY
ASC
SMO
AMS
CGO
PSA
SYO
HBA
SPO
a
CO2 Monitoring Sites, from North to South
Name
Country
Alert, N.W.T.
Canada
Mould Bay, N.W.T.
Canada
Kotelny Island
Russia
Point Barrow, Alaska
U.S.A.
Ocean Station \M"
Norway
Cold Bay, Alaska
United States
Shemya Island, Alaska
United States
Cape Meares, Oregon
United States
Niwot Ridge, Colorado
United States
Azores (Terceira Is.)
Portugal
Sand Island, Midway
United States
Key Biscayne, Florida
United States
Mauna Loa, Hawaii
United States
Cape Kumukahi, Hawaii United States
St. Croix, Virgin Islands United States
Guam
United States
Christmas Island
Kiribati
Seychelles (Mahe Is.)
Seychelles
Ascension Island
United Kingdom
American Samoa
United States
Amsterdam Island
France
Cape Grim, Tasmania
Australia
Palmer Station
Antarctica
Syowa Station
Antarctica
Halley Bay
Antarctica
South Pole
Antarctica
Latitude Longitude Altitude, m
8227'N
7614'N
7606'N
71 19'N
6600'N
5512'N
5243'N
4529'N
40 03'N
3845'N
2813'N
2440'N
1932'N
19 31'N
1745'N
13 26'N
1 42'N
440'S
7 55'S
1415'S
3757'S
4041'S
6455'S
6900'S
7540'S
89 59'S
6231'W
11920'W
137 54'E
15636'W
200'E
16243'W
17406'E
12400'W
105 38'W
2705'W
17722'W
8012'W
15535'W
154 49'W
64 45'W
14447'E
157 19'W
55 10'E
1425'W
170 34'W
7732'E
14441'E
6400'W
3935'E
2530'W
24 48'W
Period used here for computing the mean observed annual cycle (see Figures 8 and 9).
210
15
5
11
6
25
40
30
3749
30
4
3
3397
3
3
2
3
3
54
30
150
94
10
11
10
2810
Perioda
1986-1990
1980-1990
1980-1990
1980-1990
1980-1990
1980-1990
1980-1990
1980-1990
1980-1990
1983-1989
1985-1990
1980-1990
28
Mean Normalised Deviation, jv2, From Observations for the
Diagnostic Standard Version and the Test Variants of Soil Respiration
Table 6.
Transport
d0
dSR-
dSR+
dSRw
dSR0
dSRp
1986
1987
3.63
2.86
3.60
2.73
7.00
6.43
3.80
2.79
4.87
4.44
9.41
8.88
29
Table 7. Deviation of the Simulated Seasonal CO2
Cycle From Observations
Version
Transport
j02
J2
Prognostic
Diagnostic
1986
1987
1986
1987
6.59
5.25
4.48
3.66
10.52
9.03
6.09
5.22
j0 , deviation for prognostic and diagnostic standard
versions; J , average deviation over the estimated error
range assuming normal distribution.
30
Annual NPP in GtC by Climatic Region Obtained by
Combined Parameter Variations from
Standard Version and Inferred
Through the Additive Error Modela
Table A1.
Version
\R+," \D-," \T-"
combined
additive
\R-," \D+," \T+"
combined
additive
a
See equation (A1).
HighMoistLatitude Temperate
1.97
2.07
4.92
5.19
10.91
10.37
23.65
23.68
Arid
MoistTropical
17.32
16.82
40.74
40.27
13.82
12.40
34.58
33.41