Seasonal and interannual influences of the terrestrial ecosystems on atmospheric CO2: a model study

Pergamon

Phys. Chem. Earth, Vol. 21, No. 5--6, pp. 537-544, 1996 © 1997 Elsevier Science Ltd All rights reserved. Printed in Great Britain 0079-1946/96 $15.00 + 0.00 PII: S0079-1946(97)00061-X

Seasonal and Interannual Influences of the Terrestrial Ecosystems on Atmospheric CO2: a Model Study L. Francois, B. Nemry, P. Warnant and J.-C. G6rard Laboratoire de Physique Atmosph6rique et Plan6taire, Institut d'Astrophysique, Universit6 de Liege, Avenue de Cointe 5, B-4000 Liege, Belgium Received 3 June 1996; accepted 20 December 1996

Abstract. The prognostic CARAIB (Carbon Assimilation In the Biosphere) model has been used in conjunction with the Max-Planck Institut TM2 atmospheric transport model to calculate the atmospheric CO2 fluctuations at the global scale. Two applications are briefly described. In the first one, the seasonal CO2 variation is calculated and a Fourier analysis is performed to determine the relative contributions of the various vegetation types. It is found that the seasonal signal is dominated by the grasslands and needle leaf forests in the northern boreal and temperate zones. In the southern hemisphere, tropical deciduous forests and grasslands make the primary contribution. In the second application, the net primary productivity (NPP), soil heterotrophic respiration (SHR) and net ecosystem productivity (NEP) are calculated for years 1987 and 1988 with the model driven by observed climatic variables. Preliminary results indicate that the NEP variations between these two years are strongly dominated by tropical ecosystems. However, it is shown that the results are strongly dependent on the dataset used for the 1987-88 temperature record, raising the question of reliability of sudl modelling studies of the interannual variability of the biosphere. 01997 ElsevierScienceLtd

1

Introduction

Atmospheric COs measurements taken at various monitoring stations (Boden et al., 1994; Conway et al., 1994) reveal that the concentration of CO2 in the air has increased from N315 ppm in 1958 at the beginning of the measurements to almost 360 ppm today, in response to CO2 releases into the atmosphere from fossil fuel combustion and deforestation. Superimposed to this general increase, clear seasonal and interannual fluctuations can be observed. The seasonal cycle is largely dominated Correspondence to: L. Franqois

by the continental biosphere, although the contribution of the ocean is not negligible, especially in the southern hemisphere. The amplitude of this seasonal signal is largest at high latitudes in the northern hemisphere, where the land vegetation is highly seasonal, and decreases southward in response to a progressively less seasonal biosphere combined to smaller land areas in the southern hemisphere. The interannual fluctuations, on the other hand, would have contributions of comparable magnitudes from the land biosphere and the ocean, as suggested by recent analyses of the atmospheric signal using both CO 2 and 613C measurements (Francey et al., 1995; Keeling et al., 1995; Cials et al., 1995). These interannual CO2 variations show a significant correlation with those of the global mean air temperature. On the short term, the fluctuations are largely associated with E1 Nifio events which tend to warm the planetary surface and to increase the growth rate of atmospheric CO2 above the long-term average imposed by the anthropogenic releases. At this timescale, the CO2 fluctuations somewhat lag those of temperature, suggesting that the rate of change in CO2 is influenced by temperature. Decadal fluctuations around the expected industrial trend are also visible, suda as abnormally high CO2 concentrations from 1980 to the early nineties. To better understand these atmospheric CO 2 fluctuations both at the seasonal and interannual timescales, it is important to evaluate separately the role played by the various land ecosystems. Such a study should allow to identify more easily the causes of the biospheric contribution to the CO2 fluctuations. In this paper, we use the CARAIB (CARbon Assimilation In the Biosphere) model of the land biosphere in this purpose. The present analysis of the interannual change focuses on the years 1987 and 1988 whida belong to the ENSO warming event 1986-1988 and to the cooling La Nifia episode 1988-1989 (Folland et al., 1990). The study will be extended to the whole 1980-1995 period in future works, as gridded climatic data become available. 537

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The Biosphere Model

AS mentioned above, the CARAIB (Warnant et al., 1994; Nemry et al., 1996) biosphere model is used for this study. It is made of a chain of various submodels: a weather generator transforming the input monthly climatic datasets into daily weather, a soil hydrological model calculating the amount of soil water and snow, a physiological model estimating the rates of photosynthesis, dark respiration and stomatal exchange of CO2 at the leaf level, a canopy model integrating net photosynthesis over the canopy, a plant level module calculating wood respiration and biomass and a soil carbon module describing the seasonal change of soil heterotrophic respiration. The physiological submodel calculates photosynthetic rates using the models proposed by Farquhar et al. (1980) for Ca plants and Collatz et al. (1992) for C4 species. The soil heterotrophic respiration rate is a function of soil carbon density, air temperature and soil moisture. The temperature dependence is exponential with a Qlo of 2.03 for grasslands and croplands and of 1.74 for forests. The soil moisture dependence is taken from Raich et al. (1991). The model uses as input a map of actual vegetation in which the dataset from Wilson and Henderson-Sellers (1985) is combined with Food and Agricultural Organization data for crops (FAO, 1993). Each 1° x 1° grid cell of the model is covered by a combination of 8 different vegetation types: Ca and C4 grasslands, Ca and C4 croplands, evergreen and deciduous needle leaf forests, evergreen and deciduous broadleaf forests. Deserts are not considered as a vegetation class, but are a prior~ cow ered by grasslands and broadleaf deciduous shrubs (i.e. forests). They are located a p o s t e r i o r i as regions of low productivity. In the present study, a slightly different classification is used to separate the effects of tropical and temperate ecosystems: grasslands are subdivided into tropical (latitudes lower than 30°), temperate (between 30 ° and 60°) and boreal types (poleward of 60°) and broadleaf deciduous forests into tropical (below 30°) and temperate (poleward of 30 °) types. Evergreen and deciduous needle leaf forests are merged into a single cla~ and the distinction between Ca and Ca species is not made. Note that this latter classification is usedonly for the analysis of the results, but not in the calculation itself. A standard run is obtained by forcing CARAIB with the climatic data of Cramer and Leemans (personal communication, 1995) representing average conditions for several decades while the biosphere is assumed to be at steady state on an annual timescale: the soil carbon density is sudl that the annual mean soil heterotrophic respiration (SHR) rate at each grid cell is equal to the annual mean net primary productivity (NPP) of vegetation.

3

T h e S e a s o n a l A t m o s p h e r i c Signal

Nemryet al. (1996) have used the seasonal change of net ecosystem productivity (NEP=NPP-SHR) estimated by CARAIB on all the 1° × 1° grid cells of the continents to calculate the seasonal fluctuations of atmospheric CO2 b y using the TM2 atmospheric transport model of the Max-Planck Institut fdr Meteorologie (Heimann, 1995). They showed that the seasonal variations of atmospheric CO 2 observed at the various existing monitoring stations could be reasonably well reproduced by the model, at least in the northern hemisphere where the signal is clearly of biospheric origin. They performed a Fourier analysis of the model results to determine the origin (NEP zone) of the seasonal oscillation in a given latitude zone (test zone). In the method, the world is divided into six 30°-wide latitude zones. The signal in a given test zone is then decomposed into its fundamental and first harmonic modes. The amplitude A and phase ~bof each mode can then be expressed as a vector sum of the amplitudes Ai and phases ¢i of the individual contributions from the various NEP zones. The amplitude of earl1 mode of the total signal can also be expressed as the algebraic sum of the individual amplitudes of each contribution projected on the phase direction of the total signal (Nemry et al., 1996): A = ~ A~ cos(¢~ - ¢ ) . The same method is applied here, except that the NEP zones are replaced by the NEP of the various vegetation types. The contribution of the ocean is taken into account using monthly air-sea CO 2 fluxes from the HAMOCC3 model (Maier-Reimer, 1993). Some results are illustrated in figure 1. The contributions from the various vegetation types and the ocean to the amplitude of the seasonal (fundamental mode) signal are reported in each test zone as A i cos(¢i - ¢) expressed in ppmv or as the percentage of the amplitude A of the total signal. In the northern boreal (60°N to 90°N) and temperate (30°N to 60°N) test zones, where the amplitude is the largest, the seasonal signal is dominated by temperate grasslands and needle leaf forests. Boreal grasslands, temperate deciduous forests and crops also contribute significantly at these latitudes. In the northern tropical zone (0 ° to 30°N), the ecosystems dominating the signal are crops, tropical grasslands and tropical deciduous forests. At these latitudes, the contribution from temperate and boreal ecosystems remains, however, non negligible. Throughout the southern hemisphere, the signal is dominated by tropical deciduous forests and tropical grasslands. As expected, the seasonal contribution of the ocean is important only in the southern hemisphere.

4

T h e Inter~nnual Biospheric Changes

As stated in the introduction, the interannual variability of the atmospheric CO2 concentration is affected by E1

Terrestrial Ecosystems on Atmospheric CO 2

539

[30°N - 60*N] : 2.82 ppmv

[60*N - 90~N] : 3.62 ppmv

[Eq - 30*N] : 2.57 ppmv

21.2 %

14.9 %

5.1%

23.0 %

21.5 %

9.8 %

27.2 %

22.4 %

8.2 %

Temperate Deciduous Forests

15.6 %

15.8 %

7.5 %

Croplands

13.7 %

15.9 %

24.4 %

Boreal Grasslands Needle Leaf Forests Temperate Grasslands

Tropical Grasslands

-2.7 %

1.8 %

21.7 %

Tropical Deciduous Forests

-1.0 %

4.9 %

17.4 %

Broadleaf Evergreen Forests

1.5 %

1.7%

1.0 %

Global Ocean

1.4 %

0.9 %

4.9 %

1.0

-0.4-0.2 0.0 0.2 0.4 0.6 0.8

ppmv

-0.4-0,2 0.0 0.2 0.4 0.6 0,8

Needle Leaf Forests Tempera~ Grasslands Temperate Deciduous Forests Croplands Tropical Grasslands Tropical Deciduous Forests Broadle~f Evergreen Forests Global Ocean

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1.3 %

-1A%

-0.7 %

,

,

,

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,

,

,

-~4.2%

-25.4 %

-2.3%

-7.8 %

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-8.7 %

:::

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iii ! i i ! i i i i i

-1.8%

::::::II~i::::

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ii:: l i i

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[60"S - 300S] : 0.56 ppmv

[30"S - Eq] : 0.56 ppmv Boreal Grasslands

1,0

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20.1% 5o.1% L -67.3 % -0.4-0.20.0 0.2 0.4 0.6 0.8 1.0 -0.4-0,2 0.0 0.2 0.4 0.6 0.8 1.0 -0.4-0.20.0 0.2 0.4 0.6 0.8 1.0 ppmv ppmv

Fig. 1. Contributions of the ocean and the various vegetation types to the seasonality of the atmospheric CO2, as obtained by a Fourier analysis (fundamental mode) of the results of the TM2 tracer model forced with CARAIB NEP fluxes. The signal is averaged over the troposphere within 30°-wide test zones. Each contribution is reported as the amplitude A~ cos(~b~- ~) projected on the phase direction of the total signal (see text), expressed in ppmv or as the percentage of the amplitude A of the total signal (A is reported at the top of each plot). Negative numbers indicate a contribution with a phase shift; larger than 90 ° with respect to the total signal.

JP~

21:5/|..G

540

L. Francois e t al.

Nifio events. This variability would be the consequence of both the oceanic and the biospheric exchanges. However, the distribution over the continents of the interannual changes of the biospheric exchange is not known. In this section, making use of the CARAIB model, we propose a preliminary study of the biospheric variability on interannual timescales. A t this stage, the study is restricted to 1987 and 1988. As a sensitivity test, two different datasets are used for monthly air temperatures: the 2.5 ° x 2.5 ° gridded d a t a of the Global Upper Air Climatic Atlas (1993) and the 5 ° × 5 ° d a t a from the Hadley Center, UK (Jones, 1994). Missing d a t a were found in the Hadley Center dataset over ~10% of the continents, especially in Africa, Amazonia and the boreal zone. The air temperature in the corresponding 5° x 5 ° grid cells with missing d a t a was evaluated from the values in the neighbouring grid cells by using a distance weighted average procedure. For each set, the monthly anomalies were then interpolated into the 1°× 1° CARAIB grid and added to Cramer and Leemans' d a t a (assumed to be a long term average). The necessary corrections for elevation are thus implicitly considered in the interpolation. Below, the simulation performed with the first temperature set will be referred to as G U A C A and the other one as HADLEY. The precipitation d a t a are from the Global Precipitation Climatology Center (GPCC, 1992; Adler et al., 1996; Janowiak and Arkin, 1996; Legates, 1987; Rudolfet al., 1992; Wilheit et al., 1991; Willmott et al., 1985; W M O / I C S U , 1990) and the solar irradiance (or more precisely the relative number of sunshine hours compared to total daylight hours) is from the International Satellite Cloud Climatology Project (Darnell et al., 1995). For these latter two datasets, the interpolation scheme is applied to the d a t a themselves, and not to the anomalies, without any attempt to correct for elevation effects. All other inputs to the CARAIB model are the same as in the standard run of the previous section and are thus common to both years (mean amplitude of the diurnal temperature oscillation Tmax-T,~n, relative air humidity, vegetation types and soil texture). Soil carbon (litter + humus) densities are fixed to the steady state distribution of the standard run, so that the model NEP has the ability to vary from one year to the other in response to changing climatic conditions. This hypothesis of constant soil carbon made in this preliminary study rests on the rather long characteristic times associated with soil carbon oxidation. However, it is stressed that the soil litter component contributes to the respired CO2 flux with shorter characteristic times. This approximation may lead to an overestimation of the amplitude of the change in soil respiration between the two years, but should not largely affect its sign and its relative distribution among the various ecosystems. The annual continental mean air temperature (fig. 2) is 0.69°C colder in 1988 compared to 1987 in the GUACA dataset, whereas in the HADLEY record 1988 is warmer by 0.13°C. In the model, these air temperature differ-

enees are directly reflected in the SHR. In the GUACA simulation, SHR globally decreases by 2.21 Gt C in 1988 compared to 1987, while it increases by 0.52 Gt C in the HADLEY simulation (fig. 2). By contrast, the response of the NPP is much more uniform among the two simulations: N P P increases by 0.99 and 0.98 Gt C respectively in GUACA and HADLEY. The net result is thus an increase of NEP by 3.20 GtC from 1987 to 1988 for G U A C A compared to only 0.46 Gt C for HADLEY. These results may be compared to the terrestrial biospherie exchange d e r i w d b y Keeling et al. (1995) for the period 1978-1994, using a deconvolution method combining atmospheric CO2 and 6xaC measurements. From the plot published by these authors, we estimate that the NEP increased by 0.9-1.0 Gt C from 1987 to 1988, a value intermediate betv~en the two simulations performed here. Figures 3a and 3b show the seasonal change of the global mean model NEP in 1987 and 1988 respectively for the GUACA and HADLEY simulations. The much larger NEP difference between 1988 and 1987 in the G U A C A simulation compared to HADLEY is mainly due to a very large difference from January to April, a period when the GUACA temperature dataset also shows the largest temperature difference. The latitude distribution of the zonal mean NEP in 1987 and 1988 is presented in figures 3c and 3d. In both simulations, the NEP change between the two years mostly occurs in the tropical zone, between 30°S and 30°N. The tropical ecosystems are thus responsible for the increased NEP in 1988 compared to 1987. The other latitude zones have a rather negligible influence, except between 40°N and 60°N, where a change of the opposite sign is o b s e r ~ i (NEP lower in 1988). The distribution of the NPP, SHR. and NEP changes from 1987 to 1988 among the various ecosystems is presented in figure 2, together with the changes in air temperature, sunshine hours and soil water averaged over the areas of each ecosystem. Tropical vegetation has experienced a cooling (the amplitude of which varies from one dataset to the other) and a reduction of solar Jrradiance from 1987 to 1988. The hydrological model then predicts an increase of soil water in the corresponding ecosystems, in response to lower evapotranspiration rates. Since water is often limiting in these ecosystems, the NPP increases. On the other hand, the behaviour of the SHR. depends on the amplitude of the temperature decrease: for GUACA, the SHR. sharply decreases from 1987 to 1988, while much smaller changes are obtained for HADLEY. As a result, the NEP of tropical ecosysterm increases from 1987 to 1988 in both simulations, but the trend is much less marked for HADLEY. In the temperate zone, the sign of the temperature change depends on the datuset used, while the solar radiation increases and soil water decreases from 1987 to 1988. The combined effect of these changes is a decrease of NPP in both simulations. The response of the SHR largely depends on the temperature dataset used. The NEP

Terrestrial Ecosystems on Atmospheric CO 2

ASH

541

AT

ASM

Boreal Grasslands ( 12.2 I0 e krn= ) Needle LeafFerests ( 10.5 10~ kan2) Temperate Grasslands ( 25.8 l0 s krn2 ) Temperate Deciduous Forests ( 9.4 10e kan2 ) Croplands ( 14.2 10e kan= ) Tropical Grasslands ( 31.6 106 km 2 ) Tropical Deciduous Forests ( 16.7 10s kan2 ) Broadleaf Evergreen Forests ( 11.6 l0 s krn2 ) Global Vegetation ( 132.0 10s kan2 ) -2

-I 0 I Z gt of daylighthorn

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Boreal Grasslands ( 12.2 10e km 2 ) Needle Leaf Forests ( 10.5 10e km 2 ) Temperate Grasslands ( 25.8 10ekm2 ) Temperate Deciduous Forests ( 9.4 10ekm 2) Croplands( 14.2 10~km=)

I J

r--I

r----!

Tropical Gr~__~nds ( 31.6 10e km 2 ) Tropical Deciduous Forests ( 16.7 10e lan 2) Broadleaf Ev~s/een F ~ t s

!

( 11.6 106 km a )

Global Vegetation ( 132.0 10~ km a ) -0.2 0.0 0.2 0.4 0.60JI 1.0

OtCyr "l

°:~

-1.5 -1,0 ~.$ 0,0

~Cyr ~

0.5

0

1

otc~-,

2

3

Fig. 2. Change of annual mean climatic variables and biospheric carbon fluxes from 1987 to 1988 in the major continental biomes. ASH, AT, A S M , A N P P , A S H R and A N E P stand for differences between 1988 and 1987 of respectively the number of sunshine hours, the air temperature, the soil wster amount, the net primary productivity, the soil heterotrophic respiration and the net ecosystem productivity. The white and black bars indicate differences respectively for the GUACA and HADLEY simulations. The number of sunshine hours is expressed as the percentage of daylight hours and soil water is in units of available water with respect to field capacity, i.e. as ( S M - P W P ) / ( F C - P W P ) where SM, FC and P W P are respectively soil water amount, field capacity, and permanent wilting point. Antarctica, urban areas and inland waters are not included in the global continental area.

542

L. Franfois et al.

= NPP-SHR

NEP ,

,

,

,

,

,

-1

GUACA

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Fig. 3. Seasonalvariationof the globalmean NEP calculatedby CARAIB for 1987 and 1988 using (a) G U A C A and (b) H A D L E Y temperature datasets.Latitudinaldistributionofthe annual NEP from CARAIB for 1987and 1988 using (c) G U A C A and (d)H A D L E Y temperature dataaeta.

Terrestrial Ecosystems on Atmospheric CO 2 difference r e m a i n s , however, m i n o r in b o t h eases: t h e only significant c h a n g e is o b s e r v e d in t e m p e r a t e grasslands for H A D L E Y . In t h e b o r e a l zone, air t e m p e r a t u r e t e n d s to increase in b o t h d a t a s e t s , s u n s h i n e h o u r s are reduced a n d soil w a t e r increases. T h e changes in N P P a n d S H R are s u b s t a n t i a l only for H A D L E Y , b u t t h e y a p p r o x i m a t e l y c o m p e n s a t e for each o t h e r , so t h a t t h e N E P c h a n g e is negligible.

5

Conclusion

In this s t u d y , we h a v e a n a l y s e d t h e c o n t r i b u t i o n of t h e t e r r e s t r i a l ecosystems to t h e seasonal a t m o s p h e r i c CO2 changes a n d t o t h e i n t e r a n n u a l v a r i a t i o n s in NEP. T h e seasonal cycle of a t m o s p h e r i c C O 2 is d o m i n a t e d by temp e r a t e a n d b o r e a l ecosystems in t h e n o r t h e r n h e m i s p h e r e , a n d by t r o p i c a l e c o s y s t e m s in t h e s o u t h e r n hemisphere. According to t h i s p r e l i m i n a r y s t u d y of t h e 1987-1988 variability, t h e i n t e r a n n u a l v a r i a t i o n s of N E P are largely d o m i n a t e d by t h e t r o p i c a l ecosystems. However, t h e a m p l i t u d e of t h i s N E P c h a n g e s t r o n g l y d e p e n d s o n t h e d a t a s e t used for air t e m p e r a t u r e . T h e difference between t h e two d a t a s e t s used here is so large t h a t it s t r o n g l y influences t h e model N E P response. Moreover t h e tropical ecosystems which are s h o w n here to play a m a j o r role in t h e i n t e r a n n u a l variability are also t h o s e where t h e t e m p e r a t u r e record is very sparse leading for i n s t a n c e to m i s s i n g d a t a in t h e H A D L E Y 5 ° × 5 ° grid. Similar a n d possibly larger u n c e r t a i n t i e s exist o n t h e n u m b e r of s u n s h i n e h o u r s a n d t h e p r e c i p i t a t i o n a m o u n t . In t h i s context, t h e r e c o n s t r u c t i o n of t h e i n t e r a n n u a l v a r i a t i o n of t h e CO2 fluxes w i t h a biosphere m o d e l m a y b e questioned. Acknoudedgemcnts. LMF and JCG are supported by the Belgian National Fund for Scientific Research (F.N.R.S.), and BN and PW by the Belgian Federal Office for Scientific, Technical and Cultural Affairs under contract GC/12/017, within the "Global Change" Impulse Programme. Additional funding was provided by the Commission of the European Union under contracts ENV4-CT950111 and ENV4-CT95-0116 within the "Environment and Climete" Programme. The authors gratefullythank Martin Heimann and Ernst Maler-Reimer (Max-Pland¢ Iastltut fiir Meteorologic, Hamburg, German~.) for supplying the TM2 Atmospheric Transport Model and the HAMOCC3 air-sen CO 2 fluxes. They also thank Phil Jones for supplying the air temperature dataast from the Hadley Center.

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