Simulating effects of climate change on boreal ecosystem ... et al _JB_1999a.pdf · Simulating effects of climate change on boreal ecosystem carbon pools in ... 1235 Queen Street

Journal of Biogeography, 26, 1237–1248

Simulating effects of climate change on borealecosystem carbon pools in central CanadaD. T. Price, C. H. Peng∗, M. J. Apps and D. H. Halliwell Natural Resources Canada,

Canadian Forest Service, Northern Forestry Centre, 5320-122 Street, Edmonton, Alberta

T6H 3S5, Canada

AbstractAim Possible effects of current and future climates on boreal vegetation dynamics and

carbon (C) cycling were investigated using the CENTURY 4.0 soil process model and a

modified version of the FORSKA2 forest patch model.

Location Eleven climate station locations distributed along a transect across the boreal

zone of central Canada.

Methods Both models were driven by detrended long-term monthly climate data. Using

a climate change signal derived from the GISS general circulation model (GCM) 2×CO2

equilibrium climate scenario, the output from the two models was then used to compare

simulated current and possible future total ecosystem C storage at the climate station

locations.

Results After allowing for their different underlying structures, comparison of output from

both models showed good agreement with local field data under current climate conditions.

CENTURY 4.0 was able to reproduce spatial variation in soil and litter C densities

satisfactorily but tended to overestimate biomass productivity. FORSKA2 reproduced

aboveground biomass productivity and spatially averaged biomass densities relatively well.

Under the GISS 2×CO2 scenario, both models generally predicted small increases in

aboveground biomass C density for forest and tundra locations, but CENTURY 4.0

predicted greater decreases in soil and litter pools, for overall decreases in ecosystem C

storage in the range 16–19%.

Main conclusions With some caveats, results imply that effects of increased precipitation

(as simulated by the GISS GCM) would more than compensate for any negative effects

of increased temperature on forest growth. Increased temperature would also increase

decomposition rates of soil and litter organic matter, however, for a net overall decrease

in total ecosystem C storage.

KeywordsEcosystem model, FORSKA2, CENTURY, climatic change, boreal forest, vegetation

dynamics, carbon density.

INTRODUCTION temperature (Houghton et al., 1995). Particularly large increases

are predicted for mid-continental high-altitude regions –Results of recent simulations using general circulation models

including the tundra and boreal biomes of North America,(GCMs) suggest that increasing atmospheric concentrations of

Europe and Asia. The ecosystems in these regions collectivelygreenhouse gases will cause significant changes in global mean

form a significant terrestrial reservoir of organic carbon (C)

estimated in the range 800–900 Pg C (Apps et al., 1993), with

much of the stored C residing in soils and peats, including∗Now at: Ontario Forest Research Institute, Ministry of NaturalResources, 1235 Queen Street East, Sault Ste. Marie, Ontario P6A 2E5, permafrost regions and wetlands. Because these C reservoirsCanada.

are vast, and potentially vulnerable, there is an obvious needCorrespondence: David T. Price, Natural Resources Canada, Ca-

to predict and assess the possible responses of these ecosystemsnadian Forest Service, Northern Forestry Centre, 5320-122 Street,Edmonton AB, T6H 3S5, Canada. E-mail: [email protected] to a warmer climate.

1999 Blackwell Science Ltd

1238 D. T. Price et al.

In an earlier paper, Peng et al. (1998) demonstrated that the (Table 1). Simulations were performed using CENTURY 4.0

as validated by Peng et al. (1998) and FORSKA2 as modifiedCENTURY 4.0 model of Parton et al. (1987, 1993) and Parton,

Stewart & Cole (1988), suitably parameterised, could produce for use with variable climate data by Price et al. (1999).

very robust predictions of surface soil C accumulations along

a transect across central boreal Canada. When driven by long-

term records of monthly time-series data, CENTURY 4.0 Climate change scenarioproved relatively insensitive to interannnual variation,

Detrended monthly climate data sets were derived from long-compared to results obtained using long-term averages derivedterm records for each of the eleven climate stations using thefrom the same climate records. The FORSKA2 model ofprocedure described in Price et al. (1999). Briefly, any linearPrentice, Sykes & Cramer (1993), was investigated using thetrend in the observed monthly time-series was removed. Thesesame data sets of monthly actual and averaged climate recordsdetrended time-series were then combined with data derivedin the preceding companion paper by Price et al. (1999). Thefrom the Goddard Institute for Space Studies (GISS) GCMlatter study found that simulated climatic variation significantlyscenario (Hansen et al., 1988), taken from the 0.5° global dataaffected the model’s prediction of species composition. Abase compiled by Smith, Leemans & Shugart (1992). Thismodified version of FORSKA2 was found to give generallyparticular GCM scenario was selected as the medianimproved estimates of latitudinal trends in boreal forestrepresentative of the four compared previously by Price &composition and aboveground biomass density along theApps (1996) for the same study area.transect, compared to those predicted using average climate

Changes in mean monthly climate variables at the stationdata (see also Price et al., 1993; Price & Apps, 1996). The greatercoordinates were estimated using bilinear interpolation betweensensitivity to climatic variation observed with FORSKA2 wasthe GISS 2×CO2 scenario values at the four nearest surroundingattributed to interannual differences in precipitation and0.5° grid nodes. The interpolated mean temperature differencestemperature which led to significant variation in the annualand precipitation ratios were then combined with the detrendedsoil water balance, with important consequences for bothtime-series data to simulate the effects of the climate-changeaccumulatd biomass and species composition.signal on monthly minimum and maximum temperatures andCENTURY 4.0 simulates aboveground litter inputs using aprecipitation (Tmin, Tmax and Pt) while preserving the variationsimple growth response function as a precursor to simulatingobserved in the actual station record. Following Lauenrothdecomposition and other nutrient cycling processes. Because(1996) and Bugmann et al. (1996), current climate conditionsCENTURY differs greatly from most forest patch models inwere assumed for the first 800 stimulated years, i.e. theits structure and focus, it was seen to offer a means of simulatingdetrended data set was repeated, with no added climatebelowground C dynamics not represented in FORSKA2. Awarming signal. The GCM-predicted changes were then addedworking hypothesis is that a more complete understanding ofto these synthetic climate data for each month. Linear changespresent and possible future ecosystem C dynamics can bein temperature and precipitation regimes were assumed, fromobtained by linking the strengths of these two dissimilar modelszero in year 800, and continuing for 100 simulated years, suchof key processes. If this hypothesis can be supported, thenthat the changes from the 2×CO2 scenario were achieved atphysical merging of the two models might provide a means ofyear 899 (see Price & Apps, 1996, for the values used) with asimulating total ecosystem C dynamics at the landscape level.stable (but varying) 2×CO2 climate assumed thereafter. CarbonThe main objective of this paper is therefore to explore thisdioxide fertilization effects were not considered in the changedpossibility by using both models to simulate whole ecosystemclimate scenario.C dynamics. Estimates of present-day total ecosystem C

(expressed as C density, Mg C ha−1) at climate station locations

along the transect were made using the output from bothSimulationsmodels and compared with field data. The simulation was

repeated for a 2×CO2 climate change scenario derived from the

Goddard Institute for Space Studies (GISS) general circulation FORSKA2

Ecosystem indicators of aboveground forest growth (includingmodel. The results of these simulations are compared and their

implications discussed below. aboveground biomass and stem density for each of 19 tree

species) were generated as the averages of 200 simulated 0.1 ha

patches, for the duration of the 1800-year synthetic monthly

time-series data, on a 1-year timestep, following LauenrothMETHODS(1996), Bugmann et al. (1996) and Price & Apps (1996). Based

on field observations, the soil water holding capacity, hmax, wasThis study is one of several field and modelling investigations

carried out in the area of the Boreal Forest Transect Case Study assumed to be 200 mm at the northern-most sites (Churchill

to Wabowden), 150 mm at the four mid-transect sites (Flin(BFTCS) (Price & Apps, 1995. Descriptions of the transect and

modelling approaches are also provded in the earlier papers Flon to Prince Albert) and 100 mm at the three southern-most

sites. The climate driving variables were monthly means ofby Price et al. (1999) and Peng et al. (1998), so only a brief

summary will be provided here. The eleven simulation sites daily maximum and minimum temperature, Tmax and Tmin,

monthly precipitation, Pt, and monthly mean sunshine fractionare located at Atmospheric Environment Service (AES) climate

stations (AES, 1983), at positions scattered along the BFTCS estimated as the ratio of mean hours of sunshine reported by

Blackwell Science Ltd 1999, Journal of Biogeography, 26, 1237–1248

Simulating effects of climate change on C pools in the Canadian boreal 1239

Table 1. Climate station locations, and summary climate statistics derived from long term station records used in the model simulations.

Average bright sunshine percentage is derived from McKay & Morris (1985) as the ratio of observed (or interpolated) hours of bright sunshine

to simulated total daylength for each month. Remaining data are monthly means and standard deviations (SD) of the detrended synthetic time-

series, and therefore will differ slightly from official AES thirty-year climate normals (cf. Price & Apps, 1995). See Price et al. (1999) for details

of the detrending procedure.

Jan. mean July mean Annual Est. bright Max. years

Location temp. (SD) temp. (SD) precip. SD) sunshine used from

Climate station (lat., long.) (°C) (°C) (mm) (%) record

Churchill, Man 58°45′N, 94°04′W −27.1±4.5 11.9±2.0 411±90 39.8 49

Gillam, Man 56°21′N, 94°42′W −26.3±4.2 15.0±2.0 465±94 43.2 43

Thompson, Man 55°48′N, 97°52′W −25.2±4.9 15.6±2.1 530±69 44.0 26

Wabowden, Man 54°55′N, 98°38′W −24.1±4.5 16.8±1.7 466±64 45.5 28

Flin Flon, Man 54°46′N, 101°51′W −21.7±5.2 18.3±1.7 461±69 45.7 41

Waskesiu Lake, Sask 53°55′N, 106°05′W −19.0±5.3 16.4±2.0 457±95 47.4 35

Nipawin, Sask 53°20′N, 104°00′W −19.9±7.0 18.4±2.0 413±77 50.8 65

Prince Albert, Sask 53°13′N, 105°41′W −19.4±7.1 17.8±2.1 407±85 47.3 93

Rosthern, Sask 52°40′N, 106°20′W −18.6±7.1 18.2±2.1 381±69 50.5 47

Saskatoon, Sask 52°10′N, 106°41′W −18.0±7.1 18.5±2.2 360±68 53.4 93

Medicine Hat, Alta 50°01′N, 110°43′W −11.0±8.1 20.4±2.3 338±94 51.2 108

McKay & Morris (1985) to the calculated monthly total daylight were obtained from Canadian Soil Survey data (Clayton et al.,

1977). Atmospheric N deposition and fixation inputs, whichhours.

Disturbance events were assumed to completely destroy all were not available from the literature, were estimated using a

simple linear function of Pt based on data from the U.S. Greataboveground biomass in the affected patch, whenever they

occurred. The intervals between individual events for each Plains (Metherell et al., 1993). In the simulations reported here,

the parameters governing the proportions of live biomass killedpatch were calculated using the Weibull function, assuming a

root mean square (r.m.s.) return interval of 100 years (a by natural disturbances such as fire were adjusted from values

used elsewhere, based on observations of boreal fire sites inreasonably approximation for the average fire return frequency

in the area of the transect), and that the probability of the BFTCS area. Of total live biomass, 100% of fine branches

and 90% of stems and coarse branches were assumed to bedisturbance increased linearly with patch age (time elapsed since

the previous disturbance); i.e., the Weibull shape parameter was either oxidised directly, or become available for consumption

by decomposers following disturbance.set to 2.0. Average aboveground biomass values (Mg ha−1)

predicted for years 601–800 (1×CO2 scenario), and for years In order to assess the distribution and ecosystem responses

of forest vegetation to current and possible future climates,1601–1800 (2×CO2 scenario) were used to estimate

aboveground C densities, assuming the C content to be 50% climate data were included for sites north and south of the

present boreal forest. The two southern-most sites (Saskatoonof dry mass.

and Medicine Hat) are presently grassland ecosystems and

were parameterized accordingly. Churchill was parameterizedCENTURY 4.0

Above- and belowground processes were simulated for each of as a forest site, even though it is actually in the northern

transition between forest and tundra. The vegetation in thisthe eleven climate statiion locations, driven by the same monthly

values of Tmax, Tmin and Pt used for the FORSKA2 simulations. region is properly termed ‘woodland tundra’, dominated by

northern boreal tree species such as black spruce and aspen.Non-site-specific parameters for two biomes (boreal forest and

grassland) used in other studies (Parton et al., 1987, 1993; Using CENTURY’s repeated mean monthly temperature and

stochastic precipitation generator, 5000-year simulations wereMetherell et al., 1993; Peng et al., 1998) were left unchanged.

Some site-specific parameters for boreal forest ecosystems were performed initially for each site, assuming no disturbances, to

allow equilibrium soil C density to be achieved under presentunavailable, requiring modification of existing values for

temperate coniferous forest – a procedure discussed in greater climatic conditions. This was unrealistic in the particular case

of the Churchill site – because in this region, much of thedetail in Peng et al. (1998). These parameters include: the

maximum specific decomposition rate for each pool; constants land surface was submerged approximately 2000 yr BP (I. D.

Campbell, 1996, Canadian Forest Service, Edmonton, pers.for partitioning flows of decomposition products; parameters

representing the effects of soil texture, lignin/N ratios, comm.). This was considered a minor problem, because the

assumption of a 5000-year period without disturbance was alsotemperature, and moisture on decomposition rate; and the

allocation of decomposition products. Site-specific parameters unrealistic: when simulated disturbances were applied to the

equilibrated soil C densities, the differences between usingand initial conditions, such as soil texture (clay, silt and sand

content), bulk density, soil pH, and drainage characteristics, 2000- and 5000-year initialization periods were small. The



Figure 1 Above- and belowground biomass,

litter and soil carbon density simulated by

CENTURY 4.0 at Waskesiu Lake,

Saskatchewan (53°55′N, 106°55′W), using a

detrended monthly climate record from the

period 1958–1992, replayed to create a

synthetic 1800-year time-series. A simulated

change in climate derived from the GISS

general circulation model was applied

beginning at year 801, continuing until year

900, followed by 900 years assuming a stable

2×CO2 climate.

5000 yr equilibrium levels of soil and biomass C (including fine spatially-averaged over all patches, resulting in a lower (but

and coarse root components) were then used as initial more correct) estimate. Hence the biomass C density data

conditions for the 1800-year simulation of above- and shown in Fig. 1 are not directly comparable with the spatial

belowground processes (using the synthetic time-series data to averages in Fig. 2. Spatially-averaged estimates of forest biomassrepresent current climate, followed by the climate change C density in the area of the transect are not readily available,scenario described above). Because of the different model although some representative values were reported by Price etstructures, however, disturbance events were not generated al. (1993), derived from the Canadian Forest Biomass Inventorystochastically as in FORSKA2, but at regular 100-year intervals. data compiled by Bonnor (1985). Price et al. (1993) estimated

average aboveground C density to be approximately 17.3 Mg

C ha−1 for the western boreal forest ecoregion, compared toRESULTS AND DISCUSSION16.4 Mg C ha−1 simulated using FORSKA2. In the present

For ease of comparison between the two models, all biomass study, the comparable averages of FORSKA2’s output fordensities are expressed in Mg C ha−1. Typical simulation results climate stations located within the boreal forest ecoregion werefrom both models are shown for Waskesiu Lake climate station approximately 20.0 Mg C ha−1 when driven by a varyinglocated at 53°13′N, 105°41′W, in the southern boreal forest climate record (see Table 2) and 16.0 Mg C ha−1 when driven(Figs 1 and 2). by the averaged data. Note that results for Rosthern are not

included in these averages, because it is considered to be a

parkland site (scattered outcrops of aspen in a prairieModel comparisonlandscape). Unlike CENTURY, FORSKA2 does not simulate

the dynamics of the agricultural and grassland components.Biomass C densityIn the preceding paper, Price et al. (1999) reported very clearAt first sight, the output of the two models shown in Figs 1

sensitivity of FORSKA2 to changes in precipitation, but it wasand 2 appears rather different, but this can be explained withalso apparent from these simulation results – including thosereference to field data reported by Halliwell, Apps & Pricebased on averaged climate records – that the climate warming(1995). CENTURY performs a one-dimensional simulationprojected under the GISS 2×CO2 climate scenario does notof potential biomass C accumulation as a function of age,have a major impact on biomass production. Indeed, theinterrupted by disturbances occurring at predetermined regularGISS scenario causes simulated biomass to increase slightly,intervals. These above ground biomass C density estimatesparticularly at the more water-limited southern sites. Hence,can be compared with field measurements of forest biomassthese sensitivity tests demonstrate the probable response ofobtained at different times following disturbance, specificallyFORSKA2’s projections to changes in seasonal water balance,those reported for the BFTCS area by Halliwell, Apps & Priceand support the hypothesis that species distribution and(1995 Figs. 4 and 5). Initial comparison of these data suggestsstructure of the boreal forest are more closely related to thisthat CENTURY tends to overestimate the range of biomassvariable than to the effects of temperature alone.densities reported in the field.

FORSKA2 appears to predict greater overall productivity,On the other hand, FORSKA2’s stochatically-generatedparticularly in the south under the GISS 2×CO2 scenario,disturbance history simulates a negative exponential age-class

because the negative effects of warmer temperatures (increasedstructure, i.e., the largest proportion of patches are in the

youngest age classes. The reported biomass C densities are respiration and evapotranspiration) are outweighed by the



Figure 2 Species composition and aboveground (A/G) biomass simulated by FORSKA2 before (year 0–800), during (801–900) and following

(901–1800) a simulated change in climate at Waskesiu Lake, Saskatchewan (53°55′N, 106°55′W), derived from the GISS general circulation model

2×CO2 scenario. Unlike the CENTURY 4.0 results shown in Fig. 1, these data are spatially averaged biomass C densities. Note that the legend

vertical order is the same as that used in the graphs, with full species names as follows: Abies balsamea (L.) Mill; Betula papyrifera Marsh; Larix

lariccina (Du Roi) K. Koch; Picea glauca (Moench) Voss.; Picea mariana (Mill.) B.S.P.; Pinus banksiana Lamb., Pinus contorta Dougl. ex. Loud.;

Populus balsamifera L.; Populus tremuloides Michx.

Figure 3 Model predictions of average

aboveground (A/G) biomass C density (lines)

related to stand age, compared to field data

observed in the boreal forest zone

surrounding the indicated climate stations

(symbols). Stand age was determined as the

age of the oldest tree found at the site,

generally measured by counting annual rings

in cores extracted at breast height diameter,

plus 5 years to allow for growth to breast

height. (a) Northern Manitoba, represented

by model simulations for Thompson; (b)

southern Saskatchewan, represented by model

simulations for Waskesui Lake, Nipawin and

Prince Albert. The three lines plotted for

CENTURY in (b) are indistinguishable.



benefit of increased precipitation (leading to lower seasonal tends to follow an exponential growth function, initialized by

the live biomass assumed to survive the previous disturbancesoil water deficits). Such effects may not be unrealistic for the

given scenario, particularly when greater biomass is tied to event. On the other hand, FORSKA2 predicts relatively slow

establishment compared to reality, leading it to underestimatechanges in species composition. In reality, however, climate-

induced changes in fire, insect outbreaks and other disturbances biomass production, at least in the juvenile growth phase. This

can be explained by its establishment routine which ‘plants’ a(Kurz et al., 1995) may introduce offsetting alterations not

accounted for in the present model simulations. small number of new saplings each year. In fact, the model

used here was modified to increase the initial establishmentIn qualitative terms, Fig. 1 shows that CENTURY’s estimates

of biomass C density are greatly reduced by the effects of rate in the first year following patch disturbance (see Price et

al., 1999).disturbances (first appearing in year 100 of the simulation).

CENTURY further predicts a slight decrease in average total In spite of these limitations, the comparison indicates that

both models are performing reasonably well in absolute terms,C density under the GISS 2×CO2 climate change scenario, due

to steady decreases in soil and litter C densities, which override although FORSKA2 produced more credible variation in

vegetation biomass density in response to differences amongthe slight increases in biomass C. In general, FORSKA2 also

predicts slight increases in average aboveground biomass C the climate stations, as discussed above. Reconciliation of

specific data points in Fig. 3b to particular climate stations hasdensity with a warmer climate, although Waskesui Lake is

atypical in this respect because no obvious change in biomass not been attempted, however, and the apparently superior

performance of FORSKA2 should not be considered definitive.occurs (Fig. 2).

Fig. 3 compares field measurements of aboveground biomass Moreover, CENTURY 4.0 is primarily a model of belowground

processes, which requires an aboveground component toreported in Halliwell & Apps (1997a; see also Halliwell et

al., 1995) with aboveground biomass growth simulated by generate litter inputs to the soil system. In this regard,

CENTURY’s estimates of aboveground biomass densities areCENTURY 4.0 and averages of ‘biomass inventories’ simulated

by FORSKA2. The latter data were obtained from FORSKA2 almost certainly acceptable.

For the transect as a whole, the output from the two modelsoutput by capturing total biomass estimates for 200 individual

patches at simulated 10-year intervals and sorting them into is quite similar in relative terms. Table 2 shows firstly, that

under the simulated present-day climate, both models predict10-year age classes (i.e., simulated time elapsed after the patch

was last disturbed). The averages of all plots in each age-class maximum growth towards the mid-southern part of the transect

area (Waskesiu Lake and Nipawin), conforming to reality.were then used to generate the curves shown in Fig. 3. The

results for northern Manitoba (Fig. 3a), based on Thompson Secondly, decreased rainfall reduces simulated biomass with

both models, particularly in the south, although FORSKA2as the most representative climate station, suggest good

estimation of average growth rates by FORSKA2, and slight exhibits greater sensitivity and a less consistent trend than

CENTURY (see Price et al., 1999; Peng et al., 1998).overestimation by CENTURY 4.0. The results for southern

Saskatchewan (using Prince Albert as a reference) are at first

sight less satisfactory. Whereas CENTURY still tends to Litter and soil C density

Fig. 1 also shows the estimates of C densities for soil and litteroverestimate, FORSKA2 appears to underestimate significantly.

The main explanation for this apparent contradiction may pools simulated by CENTURY at Waskesiu Lake. Both pools

fluctuate in synchrony with changes in biomass, as stand growthbe that Prince Albert is not a representative climate station for

the area from which most of the biomass measurements were is interrupted periodically by the prescribed disturbance events.

Not surprisingly, the amplitudes of these fluctuations are muchobtained. The models are driven by climate data observed at

Prince Albert airport, located several kilometres south of the smaller, however, because litter inputs are a function of annual

turnover and the residues of dead material remaining after liveboreal forest edge, and suject to strong prairie influences.

Prevailing south-westerly airflows advect warm dry air to biomass is killed. The fluctuations in the soil pool are even

smaller, due to the fact that decomposed litter C entering thethe forest edge, enhancing evapotranspiration, and causing

increased drought stresses. FORSKA2 therefore predicts soil pool is only a relatively small fraction of the litter input.

Because the fluctuations in these C pools are small comparedrelatively low biomass accumulations (not much greater than

those obtained for Rosthern). When driven by climate data to the average pool sizes, their time-averages should be relatively

good indicators of the spatial averages. This contrasts with thefrom stations at Nipawin and Waskesui Lake, however, both

models predict higher productivities: CENTURY’s response is simulations of biomass C storage which vary greatly over

the disturbance interval, and which in reality are particularlyvery conservative with insignificant differences among the three

stations whereas FORSKA2 responds strongly, with the growth affected by age-class structure as discussed earlier. The results

summarised in Table 2 indicate latitudinal trends in the soilcurve generated for Waskesiu Lake agreeing particularly well

with the observed data (Fig. 3b). and litter C pools which conform very well with reality; the C

densities simulated by CENTURY 4.0 decrease as sites becomeA second contributing explanation is that CENTURY

estimates total above-ground biomass accumulations, including warmer and drier (i.e. with decreasing latitude).

Comparison of Tables 2 and 3 shows that both modelsshrub, herb and moss layers, whereas the field data are for

woody biomass only. Thirdly, CENTURY takes no account of generally predict small increases in average biomass C densities

for the tundra and forests sites in response to the GISS 2×CO2factors causing regeneration delays following disturbance such

as inadequate seed dispersal and seedling mortality. Instead it climate scenario, although the southern grassland sites oppose



Table 2. Carbon densities (Mg C ha−1) for

soil, aboveground (A/G) and belowground (B/

G) biomasses, simulated by CENTURY 4.0,

and spatially averaged aboveground biomass

C densities simulated by FORSKA2 for

current climatic conditions using a varying

climate record (monthly time-series). The

data presented are averaged over the last 200

years of an 800-year simulation.

CENTURY 4.0 FORSKA2

Biomass

Biomass

Climate station Soil Litter A/G B/G Ratio A/G

Churchill 81.3 54.1 24.2 6.9 3.50 1.6

Gillam 79.2 43.6 32.9 9.3 3.53 17.5

Thompson 76.8 40.9 35.9 10.1 3.54 20.4

Wabowden 69.0 37.7 37.5 10.6 3.54 22.8

Flin Flon 48.4 34.6 39.7 11.2 3.54 18.7

Waskesiu Lake 50.2 36.0 40.0 11.3 3.54 25.5

Nipawin 54.8 32.1 40.0 11.3 3.55 22.4

Prince Albert 48.8 32.0 40.4 11.4 3.55 12.5

Rosthern 57.3 36.1 37.6 10.7 3.53 10.9

Saskatoon 44.5 1.4 5.58 0.48 11.6 2.9

Medicine Hat 29.8 1.36 4.07 0.39 10.4 0.1

Table 3. Carbon densities (Mg C ha−1) for

soil, aboveground (A/G) and belowground (B/

G) biomasses and the ratio (aboveground/

belowground), simulated by CENTURY 4.0,

and aboveground biomass C densities

simulated by FORSKA2 for the GISS 2×CO2

scenario with a varying climate. All data

presented are averaged over the last 200 years

of the 900 years of the simulated 2×CO2

climate forcing.

CENTURY 4.0 FORSKA2

Biomass

Biomass

Climate station Soil Litter A/G B/G Ratio A/G

Churchill 47.9 25.4 41.2 11.7 3.54 1.6

Gillam 64.4 32.7 38.2 10.8 3.53 19.0

Thompson 61.0 30.5 40.5 11.4 3.54 23.1

Wabowden 55.1 28.8 41.1 11.6 3.54 22.8

Flin Flon 36.3 25.1 41.5 11.7 3.54 20.5

Waskesiu Lake 37.5 25.8 43.4 12.2 3.55 25.6

Nipawin 41.8 23.0 40.4 11.4 3.55 24.8

Prince Albert 36.7 22.8 40.6 11.5 3.54 14.6

Rosthern 42.4 23.8 38.8 11.0 3.53 18.9

Saskatoon 43.3 1.66 5.15 0.55 9.36 7.8

Medicine Hat 28.6 1.51 3.39 0.39 8.69 0.1

this trend. FORSKA2, however, predicts quite large increases between CENTURY and FORSKA2 shows that for one crucial

indicator, aboveground biomass C density, the models agreenear the southern boundary of the boreal forest (Prince Albert,

Rosthern and Saskatoon). Evidently, any effects of increased fairly well, even though their underlying algorithms are very

different. Of the two models, FORSKA2 appears more accuratetemperature on seasonal water deficits are outweighed by the

GCM forecasts of increased annual rainfall leading to greater in predicting aboveground biomass density, both in absolute

terms and in its response to a latitudinal climatic gradient.biomass productivity. Whether this is realistic is a matter for

debate and requires further examination (below). Conversely, Undoubtedly part of the reason for this is that CENTURY’s

method of simulating aboveground biomass production isCENTURY predicts that soil and litter C densities will decrease

in response to a warmer climate. These reductions in litter and simplistic, and may not be realistically sensitive to changes in

soil water deficits. Nevertheless, when using these estimates ofsoil C occur because the simulated decomposition rates are

strongly temperature-dependent, but not particularly sensitive biomass production, CENTURY is able to simulate

belowground processes and litter decompositions, to produceto small changes in soil moisture.

credible estimates of soil C densities in the transect area (Peng

et al., 1998).Combining model output

Considering these facts, is it possible to combine the strengths

of the two models to enable better assessments of totalThe strength of FORSKA2 is its ability to simulate competition

and consequences for species composition. On the other hand, ecosystem C dynamics? One approach would be to physically

combine the two models, but this is likely to be a difficult andone of FORSKA2’s critical weaknesses, at least for estimating

ecosystem C dynamics, is that it does not account for litterfall time-consuming exercise. A simpler alternative is to combine

FORSKA2’s simulations of species composition and biomassor litter and soil decomposition processes. The comparison



with CENTURY’s simulations of soil and litter C densities. composition and biomass, compared to the unmodified version

driven by averaged data. The modified version of FORSKA2Although CENTURY’s estimates of biomass C density are not

spatially averaged, its predictions of the ratio of aboveground also agrees with CENTURY 4.0 in projecting only small

increases in average forest biomass C density under a warmerto belowground biomass should be applicable to FORSKA2’s

aboveground estimates. These simulated ratios are conservative climate, but these models differ in that FORSKA2 shows much

greater sensitivity to seasonal water balances (Price et al., 1999).across all the boreal forest locations (from Table 2, the ratio

is about 3.5, in good agreement with recent estimates made by The simulated consequences of possible climate change on

boreal zone vegetation must be treated with caution and thereKurz, Beukema & Apps (1996) for Canadian forests), while

FORSKA2’s estimates of aboveground biomass are closest to are several important caveats which must be considered. First,

the 2×CO2 scenario projections of the GISS model (or anyreality for these locations. Hence it seems reasonable to combine

the results from both models to estimate spatially-averaged other GCM) should not be treated aas predictions of the

future climate, but rather as indicators of climate sensitivity toecosystem C densities (Table 4).

Table 4 shows that the combined CENTURY–FORSKA2 anthropogenic radiative forcing (Houghton et al., 1995). As a

consequence, the results reported here must be similarlysimulations of total ecosystem C exhibit a similar latitudinal

trend to the field data, although there are some significant regarded as possible projections, rather than predictions, of

future ecosystem responses to climatic change. In particular, itdiscrepancies in the various component C densities. FORSKA2’s

estimates of aboveground woody biomass for the forested sites has been suggested that GCM projections of rainfall patterns

do not properly simulate the rain-shadow effect of the Rockyare low compared to the field data, because the latter are

simple arithmetic means of measurements taken from many Mountains, causing unreasonably high rainfalls, and hence

underestimates of future soil water deficits, in the prairiestands of different ages; these data are therefore not area-

weighted to account for the preponderance of younger stands provinces (Burn, 1994; B. Lee, 1996, Canadian Forest Service,

Edmonton, pers. comm.). Second, no attempt has been made(carrying lower biomass). FORSKA2 also greatly

underestimates vegetation biomass at the grassland and tundra in the present work to consider the likely changes in disturbance

frequency (both spatial and temporal) which would be expectedsites because it does not simulate production of non-woody

vegetation. Conversely, CENTURY’s estimates of soil and litter to accompany a warmer, drier climate (e.g. Bergeron &

Flannigan, 1995; Flannigan & Van Wagner, 1991; Stocks, LeeC are generally rather high compared to the data. Part of the

explanation for this is that the CENTURY validation performed & Martell, 1996; Volney, 1996). In particular Baxter (1995) has

shown that fire danger is strongly dependent upon the timingby Peng et al. (1998) was based on data of Siltanen et al. (1997),

which generally give higher values than those reported here, of precipitation during the season, further suggesting that

climate variability is an important factor influencing borealcompiled by Halliwell & Apps (1997b). Although the

methodologies used in both studies are very similar, it is possible ecosystem responses to climate change. Third, neither

FORSKA2 nor CENTURY 4.0 explicitly accounts for the effectsthat samples used in the Siltanen et al. (1997) database are not

as representative of the soil types found along the BFTCS. A of climate and climate change on permafrost dynamics and

their effects on local surface hydrology and soil temperaturefurther factor may be poor simulations of natural disturbance

effects on soils by both models – fires often remove substantial (cf. Bonan, 1991). Fourth, the simulation of regeneration by

FORSKA2 may not adequately represent the effects of summeramounts of organic carbon from the litter and soil surface

layers in this region (Kurz & Apps, 1996, 1999). droughts, particularly at the southern limit, which may in

turn allow deceptively high biomass accumulations under theThe overall agreement between the models and observational

data leads us to believe that even though some problems changed climate scenario. Finally, the transitional responses of

boreal forest vegetation in the immediate period of a changingstill remain, the simulation approach is essentially valid. It is

therefore appropriate to take the final step of using it to climate will almost certainly involve all of these factors – it is

unlikely whether either model can account for all of the possibleestimate ecosystem C storage under the GISS 2×CO2 scenario

(Table 5). In general, biomass C storage is predicted to increase, interactions.

It is also worth reiterating that CO2 fertilization was notbut these gains are generally exceeded by reductions in litter

and soil C. The only slight gain of ecosystem C under a changed considered in any of the simulations reported here. Preliminary

results with CENTURY 4.0 indicate that increased CO2 doesclimate occurs at Saskatoon, due primarily to gains in biomass

predicted by FORSKA2, but not by CENTURY. The general indeed partially offset increased temperature-induced soil

respiration through enhanced net primary productivity (Penginference from these results is that a climate change similar to

that projected by the GISS GCM for a stable 2×CO2 scenario & Apps, 1998). With FORSKA2, further research is needed to

examine the implications of increased water use efficiency withwould generally reduce ecosystem C storage in the region of

the BFTCS, typically by 16–19%, with the greatest losses increased CO2. Hence the predicted minor changes in biomass

productivity could well be maintained under the 2×CO2occurring in the northern portion of the transect area.

scenario, even if growing season soil water deficits proved more

severe than the models allow. The use of the Priestley–TaylorImplications

equation in the FORSKA2 simulations to estimate

evapotranspiration also requires further consideration.When driven by the variable climate record, the modified

version of FORSKA2 was able to improve significantly the McKenney & Rosenberg (1993) have shown that the sensitivity

of potential evapotranspiration to a warmer climate variesrealism with which it predicted latitudinal changes in species



Tab

le4.

Est

imati

on

of

spati

all

y-a

ver

aged

tota

lec

osy

stem

Cd

ensi

tyfr

om

data

sim

ula

ted

for

ind

ivid

ual

soil

an

db

iom

ass

Cp

oo

lsass

um

ing

vari

ab

lecu

rren

tcl

imate

,co

mp

are

dw

ith

avail

ab

lefi

eld

data

ob

serv

edat

sam

ple

plo

tses

tab

lish

edin

the

vic

init

yo

fea

chcl

imate

stati

on

.A

llq

uan

titi

esare

inu

nit

so

fM

gC

ha−

1.

No

teth

at

CE

NT

UR

Y4.0

esti

mate

sare

on

lyti

me-

aver

aged

,w

her

eas

the

esti

mate

sfr

om

FO

RSK

A2

are

als

osp

ati

all

yaver

aged

.F

OR

SK

A2

bel

ow

gro

un

d(B

/G)

bio

mass

Ces

tim

ate

sw

ere

ob

tain

edu

sin

gth

era

tio

of

ab

oveg

rou

nd

(A/G

)to

bel

ow

gro

un

db

iom

ass

Csi

mu

late

db

yC

EN

TU

RY

giv

enin

Tab

le2.

Fie

ldd

ata

fro

m:

Hall

iwel

l&

Ap

ps

(1997a,

1997b

),Sil

tan

enet

al.

(1997)

an

dP

rice

etal.

(1993).

Sim

ula

ted

data

Fie

ldd

ata

CE

NT

UR

Y4.0

FO

RSK

A2

Co

mb

ined

So

ilB

iom

ass

Eco

syst

emSo

ilB

iom

ass

Bio

mass

Eco

syst

em

Cli

mate

stati

on

(to

20

cm)

Lit

ter

A/G

tota

l(t

o20

cm)

Lit

ter

A/G

B/G

tota

l

Ch

urc

hil

l20∗

30

6.0

56∗

81.3

54.1

1.6

0.4

137

Gil

lam

45.0

31.2

31.1

116

79.2

43.6

17.5

4.9

145

Th

om

pso

n30.7

36.6

27.9

103

76.8

40.9

20.4

5.8

144

Wab

ow

den

47.5

32.5

49.3

141

69.0

37.7

22.8

6.4

136

Fli

nF

lon

42.2

30.5

37.6

121

48.4

34.6

18.7

5.3

107

Wask

esiu

Lak

e25.1

52.2

61.2

154

50.2

36.0

25.5

7.2

119

Nip

aw

in13.0

41.8

44.4

110

54.8

32.1

22.4

6.3

116

Pri

nce

Alb

ert

16.7

38.5

42.3

108

48.8

32.0

12.5

3.5

97

Ro

sth

ern

23.0

23.5

10.3

59

57.3

36.1

10.9

3.1

107

Sask

ato

on

15∗

15∗

5.0

25∗

44.5

1.4

2.9

0.3

49

Med

icin

eH

at

5∗

15∗

5.0

20∗

29.8

1.3

60.1

0.0

31

∗T

hes

evalu

esfo

rn

on

-fo

rest

edla

nd

esti

mate

db

yin

terp

ola

tio

nfr

om

the

map

of

Tarn

oca

i&

Lace

lle

(1996).



Table 5. Estimation of spatially-averaged total ecosystem C density from data simulated for individual soil and biomass C pools using

CENTURY and FORSKA2, under the GISS 2×CO2 climate scenario. FORSKA2 belowground (B/G) biomass C estimates were obtained using

the ratios of aboveground (A/G) to belowground biomass C simulated by CENTURY given in Table 3. All other modelling assumptions as

indicated in Table 4. All quantities expressed in units of Mg C ha−1, except for the net change in total ecosystem C, which is expressed as a

percentage of the comparable value reported in Table 4.

CENTURY FORSKA2 Combined

Soil Biomass Biomass Net change due

Climate station (to 20 cm) Litter A/G B/G Ecosystem total to GISS 2×CO2 (%)

Churchill 47.9 25.4 1.6 0.4 75 −45.2

Gillam 64.4 32.7 19.0 5.4 122 −16.2

Thompson 61.0 30.5 23.1 6.5 121 −15.8

Wabowden 55.1 28.8 22.8 6.4 113 −16.8

Flin Flon 36.3 25.1 20.5 5.8 88 −18.2

Waskesiu Lake 37.5 25.8 25.6 7.2 96 −19.2

Nipawin 41.8 23.0 24.8 7.0 97 −16.5

Prince Albert 36.7 22.8 14.6 4.1 78 −19.2

Rosthern 42.4 23.8 18.9 5.3 90 −15.7

Saskatoon 43.3 1.7 7.8 0.8 54 +9.1

Medicine Hat 28.6 1.5 0.1 0.0 30 −3.5

greatly, depending on the method of estimation (and on the reduction in total ecosystem C storage. This conclusion should

not be treated as a firm prediction for several reasons (discussedGCM scenario assumed). Interestingly, when the more

physically correct Penman–Monteith equation (Monteith, 1964) above), not least because direct effects of a changed climate

on the natural disturbance regime have not been considered.is parameterized from GCM-derived projections with plausible

values for aerodynamic and stomatal conductance (including Although further refinements are needed, this preliminary study

indicates that the combined modelling approach is potentiallyresponses to increased ambient CO2), it can predict quite

significant reductions in potential evapotranspiration under the useful for estimating likely responses of boreal forest ecosystem

C dynamics to anticipated climate change.GISS 2×CO2 scenario (McKenney & Rosenberg, 1993).

CONCLUSIONS ACKNOWLEDGMENTSA comparison of two dissimilar dynamic ecosystem models,

This work was funded, in part, by the Climate Change NetworkFORSKA2 and CENTURY 4.0, produced good agreement in

of the Canadian Forest Service. Discussions with I. A. Naldertheir predictions of aboveground biomass carbon (C) density

and R. Kelly contributed significantly to our understanding ofwhen driven by the same detrended long-term climate records

CENTURY 4.0. C. H. Peng acknowledges receipt of a Visitingfor several locations distributed along a transect across the

Fellowship from the Natural Sciences and Engineering Researchboreal zone of central Canada. FORSKA2 appeared to simulate

Council of Canada. The International Institute for Appliedobserved distributions of aboveground biomass productivity

Systems Analysis, Austria, also provided the opportunity andacceptably while CENTURY was able to generate realistic

funding to disuss aspects of this study at an informal workshop.distributions of the litter and soil C pools. Both models also

The content of this paper benefited greatly from careful reviewsreported generally small increases in aboveground C density

by E. H. Hogg, A. Fischlin and I. D. Campbell. We also wishwhen subjected to the effects of simulated climate change

to acknowledge the very careful and perceptive reviews fromderived from a 2×CO2 scenario generated by the GISS general

the two anonymous reviewers.circulation model (GCM). Having established that the model

estimates were consistent, it was possible to use them in

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BIOSKETCHES

David Price has worked with the Canadian Forest Service (CFS) as a research scientist since 1992. He is investigating potential

effects of global change on Canada’s forest ecosystems, using process-based productivity models and global vegetation models.

Changhui Peng is a research scientist at the Ontario Forest Research Institute. He is using forest growth, ecosystem and global

carbon cycle models to assess the impacts of climate change and forest management regimes on Canada’s boreal forest ecosystems.

Mike Apps is a research scientist with CFS and adjunct professor at the University of Alberta. His research focuses on the

contribution of northern forest ecosystems to the global carbon budget and their responses to climate change.

David Halliwell is a climatologist whose work has included arctic microclimatology (soil temperatures, evaporation, and radiation),

and forest carbon dynamics. Recent publications include three CFS reports on BOREAS site measurements (with Mike Apps),

published in 1997.


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