Predicting climate change effects on vegetation, soil thermal dynamics, and carbon cycling in...

Ecological Modelling 175 (2004) 1–24

Predicting climate change effects on vegetation, soil thermaldynamics, and carbon cycling in ecosystems of interior Alaska

Christopher Potter∗NASA Ames Research Center, Ecosystem Science and Technology Branch, Moffett Field, CA 94035, USA

Received 8 February 2002; received in revised form 7 May 2003; accepted 28 May 2003

Abstract

A dynamic vegetation model has been used to predict patterns of recent past and potential future change in taiga forestecosystems of interior Alaska. The model, called CASA (Carnegie Ames Stanford Approach), is a process-based ecosystemdepiction of plant and soil processes, including all major cycles of water and carbon. CASA’s dynamic vegetation component isintended to facilitate coupling to general circulation models of the atmosphere, which require mechanistic fluxes and feedbacksfrom terrestrial vegetation. Simulation results for selected Alaska sites of Denali National Park suggest that the past 50-yearclimate trends of warming temperatures may shift the taiga ecosystem from dominance by evergreen needleleaf trees to a moremixed broadleaf–needleleaf tree composition. For other (higher elevation) areas of Denali, our model predicts that a difference ofonly about 3◦C in mean annual air temperatures appears to differentiate the permanent presence of tundra vegetation forms overtaiga forest. The model predicts that over the 1950–1999 climate record at Denali station, the changing taiga ecosystems were netsinks for atmospheric CO2 of about 1.3 kg C m−2. During the warm 1990s, these forests were predicted to be net carbon sinks ofmore than 15 g C m−2 per year in 8 out of 10 years. Predicted NPP for the forest continues to increase with a projected warmingtrend for the next 25 years at a mean rate of about+1.2 g C m−2 per year. On the basis of these model results, a series of crucialfield site measurements can be identified for inclusion in subsequent long-term ecological studies of the changing taiga forest.© 2003 Elsevier B.V. All rights reserved.

Keywords: Vegetation dynamics; Carbon cycle; Boreal forest; Tundra; Ecosystem modeling

1. Introduction

Atmospheric general circulation models (AGCMs)commonly predict that the greatest and earliest warm-ing caused by increasing CO2 gas concentrationswill occur at high latitudes (45–65◦N), with themost marked effects within the continental interiors(Kattenberg et al., 1996). Because the temperature ofsoil and permafrost in high latitudes is so close to themelting point, these natural ecosystems are located atwhat might be called a “thermal ecotone,” that is, the

∗ Tel.: +1-650-604-6164; fax:+1-650-604-4680.E-mail address: [email protected] (C. Potter).

region in which relatively small changes in tempera-ture could have large consequences to boreal and tun-dra ecosystems (Viereck et al., 1986). Consequently,regions like interior Alaska are important areas toinvestigate for the potential effects of climate changeon vegetation composition and soil thermal dynamics.

An atmospheric CO2 induced warming trend hasbeen hypothesized byFoster (1989)to result in anearlier snowmelt date in the high latitude regionsand, therefore, an overall increase in the length ofthe snow-free plant growing season. Warmer springair temperatures may accelerate the date of snowmelt, with uncertain long-term effects on vegetationphenology (i.e. bud-break dates and green-up rates).

0304-3800/$ – see front matter © 2003 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2003.05.004

2 C. Potter / Ecological Modelling 175 (2004) 1–24

Changes in snowmelt caused by climate warmingmay further affect surface and subsurface soil mois-ture levels through interaction with the dynamics ofpermafrost layers. Changes in surface hydrology andsoil temperature may in turn affect forest and tundravegetation production and forage quality for grazinganimals (Densmore, 1997). Associated changes inprecipitation patterns could affect the ecological situ-ation in other ways, if for example, increased snowfallresults in delayed melting of a larger snow pack,possibly inhibiting rapid rates of vegetation green-up.

There have been a number of notable studies onthe possible effects of climate change on global bo-real ecosystems in recent years (Goulden et al., 1997;Sellers, 1997; Myneni et al., 1997). Using detailed sitemeasurements,Gower et al. (1996)have documentedresponses in length of growing seasons and soil Navailability under higher air temperatures.Frolking(1997)has shown that net carbon storage is enhancedby early snowmelt and soil thaw that initiate photosyn-thetic uptake of carbon while the soil is still relativelycool and heterotrophic respiration is low.

The development of dynamic global vegetationmodels (DGVMs) is intended to explore many ofthe complex ecosystem changes that could occur inhigh latitude regions as a result of climate changeand other atmospheric conditions (Foley et al., 1996;Cramer et al., 2001). Responses of plants to past cli-matic trends can help define terrestrial componentsthat are necessary for refining feedbacks to AGCMsin prediction of future climate conditions. Hence,DGVMs can serve two major functions: (1) as under-lying global representations of land surface dynamicswithin AGCMs and other models of the Earth system,and (2) as stand-alone models for understanding thepast and future mechanisms of change in vegetationcover in response to changes in climate and potentialchemical composition of the atmosphere, particularlyin areas not well documented with field measurementsor remote (satellite) observations.

A fundamental assumption of most DGVMs is thatgroups of similar vegetation types can be character-ized through key functional attributes, mainly definedby carbon metabolism, gas exchange, and water rela-tions, into several plant functional types (PFTs) thatmay exist in near equilibrium with a relatively stableclimate (Box, 1996). As climate changes over time,DGVMs must realistically simulate the transient veg-

etation process and structure that lead to changes inmetabolism and water relations. Furthermore, factorsother than climate, including geomorphology, soil fer-tility, seed dispersal, and disturbance add the necessarycomplexity to a vegetation model that is conceived ul-timately as a tool to integrate large-scale ecologicaland geophysical processes.

Specifically for simulation of northern ecosystemdynamics,Leemans (1991)andPrentice et al. (1993)have evaluated effects of climate variability on for-est landscapes. These succession modeling approacheshave focused on patch or gap-scale forest dynamics,which are fundamentally different in scale and scopeto global vegetation dynamics modeling. Nevertheless,simulation results from these gap-scale models havepredicted a gradual elimination of conifer species suchas spruce (Picea) and an increase in abundance ofhardwood species such as oak (Quercus) with winterwarming scenarios.

For vegetation dynamic studies of interior Alaska,we describe here the application of a new DGVM thatis part of a process-oriented ecosystem model for sim-ulating plant and soil processes, water and carbon cy-cling, and biogenic fluxes of major trace gases (CO2,CH4). This DGVM is intended to be linked eventu-ally to AGCMs as a more mechanistic means to in-clude the effects of terrestrial vegetation distributionand biogeochemistry on exchanges of ecosystem tracegases with the atmosphere. An existing ecosystem pro-duction model called NASA–CASA (Carnegie AmesStanford Approach) forms the basis for our DGVMdevelopment (Potter and Klooster, 1998, 1999).

Several features set this global model apart fromother DGVMs and forest gap successional models.A seasonal vegetation phenology component for theCASA–DGVM has been calibrated as a function ofclimate controllers using a global satellite ‘greenness’index of potential photosynthetic capacity derivedfrom the advanced very high resolution (AVHRR)sensor (Potter and Brooks, 1998). These DGVM phe-nology algorithms replace the direct input of satellitegreenness data to our NASA–CASA model (Potterand Klooster, 1998) for monthly or daily estimatesof net primary production (NPP) and litter fall inputsto soil decomposition pools. Plant competition forresources (water and light) over relatively short timeperiods (months and seasons) is simulated among10 possible PFTs using a relatively small number of

C. Potter / Ecological Modelling 175 (2004) 1–24 3

climate input requirements. Soil fertility factors areincluded in the CASA–DGVM as a control on theallocation of new plant biomass to fine root tissuefor acquisition of soil nutrients. In the global appli-cation reported byPotter and Klooster (1999), theCASA–DGVM correctly predicts the presence of for-est classes in about 75–95% of known forested coverworldwide. Our model has also been validated againstdetailed field site measurements of carbon, energy,and water fluxes at a variety of northern forest andtundra sites (Potter, 1997; Amthor et al., in press). Itis important to note, however, that the current versionof the CASA–DGVM used here does not includemany effects of previous wildfires or seed dispersalon regional vegetation dynamics.

Many ecosystem models classify regional andglobal vegetation according to biomes. These biomesclasses are used to set vegetation characteristics suchas albedo, roughness length, rooting depth, and stom-atal physiology. Although the use of biome classesis a reasonable top-down modeling approach for sur-face energy, water, and momentum fluxes, DGVMsthat expand predictions beyond these biogeophysicaloutput parameters to include biogeochemistry andbiogenic carbon fluxes require a different classifica-tion system for vegetation. One potential solution isto recognize that biomes consist of individual speciesor PFTs that have measurable leaf physiology andcarbon allocation attributes (Bonan et al., 2002). Theuse of PFTs can reduce the complexity of speciesdiversity in ecological function to a few key planttypes. Representation of the landscape as patches ofPFTs is a common theme among DGVMs that canlink climate and ecosystems.

For this study, model simulations are reported inwhich the CASA–DGVM is driven by 50–70 yearsof climate data for a site in interior Alaska to gener-ate predictions of changes in ecosystem composition,biomass storage, seasonal leaf area index (LAI) andabsorbed radiation, inter-annual carbon exchange withthe atmosphere, as well as soil freeze–thaw dynam-ics, snowmelt patterns, and surface hydrologic fluxes.Although conclusive model validation is not possi-ble at this time against detailed tower-based measure-ments of carbon and water fluxes from forest ecosys-tem study sites in interior Alaska (Chapin et al., 2000),we are nonetheless able to demonstrate a consistencyof our model predictions with measured tower fluxes

from boreal forest locations in Canada, as part of theBOREAS project (Amthor et al., in press). This makesthe model results reported here highly relevant andtimely for potential ecosystem coupling to AGCMs inprediction of future conditions in ecosystems of in-terior Alaska. The specific location selected for thisstudy is Denali National Park (referred to henceforthas “Denali”), a federally protected area that is vulnera-ble to effects of high latitude climate change on all as-pects of vegetation community dynamics and wildlifeconservation (Roland, 1999).

2. Model background description

The NASA–CASA is a model representation ofdaily ecosystem water, carbon, and nitrogen cycling(Potter, 1997), coupled with climate-driven predictionsof vegetation change over relatively long time periods(Potter and Klooster, 1999). The model is designedto simulate daily and monthly patterns in plant func-tional types snow melt dynamics, soil freeze–thaw, netcarbon fixation, nutrient allocation, litterfall, and soilnutrient mineralization, and CO2 exchange, in addi-tion to CH4 production, consumption, and emission.Both daily and monthly simulation results are reportedin this study. A complete description of the previ-ous model design is provided byPotter and Klooster(1998, 1999)andPotter (1997).

The dynamic global vegetation model (DGVM)component of NASA–CASA is based conceptu-ally on the two main elements ofTilman’s (1985)resource-ratio hypothesis of vegetation change,namely (1) plant competition for resources (water andlight) over relatively short time periods of months andseasons, and (2) the long-term pattern in the supply ofgrowth-limiting resources such as water and nutrients,i.e. the resource-supply trajectory. Our resource-basedvegetation model is built on the assumption that, witha relatively constant resource-supply and no majordisturbance, fairly stable plant communities typesmay develop along a continuum of habitats, fromthose with resource-poor soils but high availability oflight at the soil surface, to habitats with resource-richsoils but low availability of light at the soil surface.It is assumed, by virtue of greater proportional al-location of fixed carbon by the grasses to shallowroot tissues, that grassy PFTs are able to out-compete


woody PFTs for water stored in the upper surfacelayer (0–30 cm) of the soil profile. Therefore, basedon a running 5-year average of soil water content,enough rainfall or snowmelt must percolate into thesubsurface layer of the soil after evaporation andtranspiration use by grasses to provide woody PFTswith the opportunity to grow and progressively allo-cate fixed carbon into woody tissues at set allocationratios (Potter and Klooster, 1999). Depending on howmuch carbon is available for growth, initially for fineroot biomass of woody PFTs, and secondarily forconstruction of woody structural tissues to supportcanopy FPAR (fraction of absorbed photosyntheti-cally active radiation), shrub and young tree (<5 mheight) and mature tree (=5 m height) canopies canbegin to shade out understory grasses by maintaininga competitive structural advantage to intercept anduse solar radiation.

We use running three to 5-year averages of an-nual climate data to compute index values that deter-mine the dynamic PFT (Table 1). For example, tun-dra and taiga plant types appear intolerant of a mini-mum chilling degree day sum (CDD) less than about200 days. This implies a certain seasonal low tem-perature requirement or cold-hardening advantage forhigh-latitude vegetation types. In addition, the borealforest type separates from the temperate and decid-

Table 1Climate index tolerance ranges for major plant functional types, following analysis of global cover distributions for the CASA–DGVM(Potter and Klooster, 1999)

Plant functional type GDD PPT CDD AMI

Maximum Minimum Maximum Minimum Maximum Minimum Maximum Minimum

Tundra 2,259 7 160 2 8,811 207 1.00 −0.89Mixed needleleaf deciduous forest 2,555 379 232 11 7,464 240 0.85 −0.67Evergreen needleleaf forest (taiga) 6,009 21 287 5 8,832 12 0.90 −0.84Temperate grassland 11,904 17 344 1 6,281 1 1.00 −0.99Mixed broadleaf–needleleaf forest 10,569 553 273 24 2,895 2 0.87 −0.67Temperate deciduous forest 11,397 374 364 17 3,466 1 0.88 −0.83Desert and bare ground 11,553 2 95 0 5,646 8 1.00 −1.00Semi-arid shrub land 11,315 16 276 0 5,845 1 1.00 −1.00Savanna and wooded grassland 10,589 231 576 13 1,998 0 0.91 −0.82Tropical broadleaf rain forest 11,713 1,159 563 52 0 0 0.88 −0.73Cultivated 11,854 123 747 7 3,686 0 0.88 −0.93

Growing degree days (GDD) is number of days for which mean monthly temperature is greater than 0◦C, times the mean monthlytemperature. Chilling degree days (CDD) is number of days for which mean monthly temperature is less than 0◦C, times the mean monthlytemperature. Precipitation (PPT) is in cm. Annual moisture index (AMI) is unitless (−1 to +1), with negative values for relatively dry, andpositive values for relatively wet. AMI is defined byWillmott and Feddema (1992)as: (PPT/PET) − 1 if PPT< PET and 1− (PET/PPT)if PPT ≥ PET.

uous forest types on the basis of maximum CDDtolerance, with more than 3500 relatively cold de-gree days (<0◦C) per year required for the borealforests to dominate, and on the basis of the min-imum number of growing degree day sum (GDD),with over 370 relatively warm degree days (>0◦C)per year required for the more temperate and decidu-ous forest types to pervade. Temperate deciduous andmixed broadleaf–needleleaf forest types appear to di-verge according to a minimum annual moisture index(AMI), with the deciduous forest types able to toleratea greater annual moisture deficit (Potter and Klooster,1999).

For application in this study, several other modelcomponents of NASA–CASA version described byPotter (1997)remain unchanged. For example, thedaily or monthly fraction of overstory net primaryproduction, defined as net fixation of CO2 by vegeta-tion, is computed on the basis of light-use efficiency(Monteith, 1972). New production of plant biomassis estimated as a product of intercepted photosynthet-ically active radiation (IPAR) and a light utilizationefficiency term (εmax) that is modified by temperatureand soil moisture. Freeze–thaw dynamics with soildepth operate according to the degree-day method ofJumikis (1966), as described byBonan (1989). Soilsare simulated to both thaw and freeze from the top


layer to the bottom over the course of a year. Conver-sion from mean temperature of air (Ta) to temperatureat the litter–soil surface (Ts) follows empirical rela-tionships reported byYin and Arp (1993). The com-puted fraction of water-filled pore space (WFPS; de-fined as the ratio of volumetric water content to satura-tion capacity) in moss, humus, and mineral soil layersis used to calculate scalars that represents the effectof soil moisture on organic matter turnover and CO2emission rates, following the litter decomposition al-gorithms developed byDoran et al. (1990).

The NASA–CASA carbon–methane flux modulehas been designed for seasonally inundated ecosys-tems with three main sub-components (Fig. 1): (1) soiltemperature and water table depth (WTD) predictedas a function of moisture inputs and field capacityof poorly drained organic soils; (2) CH4 productionwithin the anoxic soil layer predicted as a function ofWTD and CO2 production (from litter decomposition)

Fig. 1. Schematic representation of components in the NASA–ACASA model. The soil profile (a) is layered with depth into a surfaceponded layer (M0), a surface organic layer (M1), a surface organic-mineral layer (M2), and a subsurface mineral layer (M3), showingtypical levels of soil water content (%). The production and decomposition component (b) shows separate pools for carbon cycling amongpools of leaf litter, root litter, woody detritus, microbes, and soil organic matter. Microbial respiration rate is controlled by temperature(T) and litter quality (q), whereas coupled production of methane within the anoxic soil layers is a function of water table depth (WTD).Pathways in the gas emission component (c) for carbon dioxide, methane and other biogenic trace gases.

under poorly drained conditions; and (3) CH4 gaseoustransport pathways (molecular diffusion, ebullition,and plant vascular transport) for stored soil methanepredicted as a function of WTD, seasonal plant cover,and ecosystem type. Consumption of CH4 in relativelywell-drained soil profiles is simulated independentlyin NASA–CASA using a modified version of Fick’sfirst law based on computations for diffusivity in ag-gregated media (Potter et al., 1996), together with thedaily soil water balance model. These algorithms areapplicable for estimation of oxidative methane con-sumption in better drained topographic sites in highlatitude study areas.

For the soil carbon cycling component (Fig. 2), ourdesign remains comparable to a somewhat simplifiedversion of the CENTURY ecosystem model (Partonet al., 1992), which simulates soil C cycling with aset of compartmental difference equations. First-orderequations simulate heterotrophic respiration fluxes


Fig. 2. Litter and soil C and N transformations in the NASA–CASA model which lead to substrates for trace gas production. Structurefollows the CENTURY model ofParton et al. (1992). Carbon pools are outlined in black and labeled with C-to-N ratios, C fluxes insolid arrows, CO2 production in stippled arrows; nitrogen pools in gray, N fluxes in gray arrows. Levels of litter, microbe (MIC) and soilorganic (SLOW and OLD) pools are shown. Structural (S) and metabolic (M) pools are shown for leaf and root litter.

of CO2 and exchange of decomposing plant residue(metabolic and structural fractions) at the soil sur-face, together with surface soil organic matter (SOM)fractions that presumably vary in age and chemicalcomposition. Active (microbial biomass and labilesubstrates), slow (chemically protected), and passive(physically protected) fractions of the SOM are rep-resented. In the CASA model, as with many otherecosystem models, C and N cycling are consideredto be tightly coupled, with NPP considered a usefulmodel driver for nutrient transformation rates. Theeffect of temperature on microbially controlled litterand soil C and N mineralization fluxes was definedas an exponential response using a Q10 (the mul-tiplicative increase in soil biological activity for a10◦C rise in temperature), with a value of 1.5 forsurface litter decomposition and a value of 2.0 forsoil decomposition (Raich and Potter, 1995; Potter,1997). Following measurements reported byHogget al. (1992), a soil temperature level of 12◦C wasused to set the near-optimum Q10-based litter decom-

position scalar for organic matter in cold ecosystems.To estimate total CO2 fluxes from soil, including rootrespiration sources, the algorithm derived byRaichand Potter (1995)was applied for prediction of dailysoil respiration.

3. Refinements for high-latitude ecosystemsimulations

In order to more accurately represent ecosystemcontrols and soil processes for boreal forest car-bon cycles, several modifications are introduced inthis study for the NASA–CASA soil hydrology andnutrient cycling model described byPotter (1997).These changes implemented for high-latitude zonesinclude refinement of ecosystem water balance equa-tions, carbon and water relations of the living groundcover and humus, and soil temperature controls onorganic matter decomposition rates (Potter et al.,2001).


3.1. Water balance equations

To estimate plant and soil water balance in themonthly version of the model, we use a formulationof the empiricalPriestly and Taylor (1972)evapotran-spiration equation developed byCampbell (1977)andBonan (1989). However, plant physiological measure-ments have shown that atmospheric desiccation can re-sult from the boreal forest’s strong biological controllimiting surface evaporation (Hall et al., 1996). Hence,a more physiologically based daily potential evapo-transpiration (PET) flux for the canopy is estimatedusing a Penman–Monteith algorithm (Eq. (1)), derivedaccording to the methods described byWoodward(1987)andMonteith and Unsworth (1990).

PET= Rnets + (ρcp(es(Ta) − e)/ra)

s + γ(ra + rs)/ra(1)

whereRnet is the daily net shortwave radiation flux tothe canopy (W m−2), s is the rate of change of satura-tion vapor pressure with temperature (mbar◦C−1), ρ

is the density of air (kg m−3), cp is the specific heatof air (J g−1 ◦C−1), (es(Ta) − e) is the difference inwater vapor pressure (mbar) between ambient air (e)and air at saturation (es(Ta)), γ is the psychrometricconstant (mbar◦C−1), r−1

a is the boundary layer re-sistance (s m−1), andr−1

s is the stomatal resistance towater vapor (s m−1) in the canopy. Model settings formaximum conductance in the canopy conform to ob-served values for boreal ecosystem sites, as reportedby Saugier et al. (1997). Stomatal conductance is com-puted for up to five canopy layers, depending on leafarea density.

In addition, daily evaporation flux from the groundcover (Egc) surface, typically a living bryophyte(moss) layer with near continuous surface cover-age, is estimated from a simplified Penman version(Monteith and Unsworth, 1990) of Eq. (1), drivenby radiation flux through the overstory canopy. Themain difference betweenEq. (1) and Egc is that theterm for stomatal conductance to water vapor in thecanopy is absent from thisEgc evaporation flux calcu-lation. This is based on the assumption that mosses,as non-vascular plants, lose water principally byevaporation.

Estimated evapotranspiration flux (ET) for the standis calculated by comparing daily canopy PET plusEgcto the multi-layer model estimate for daily soil mois-

ture content. It is assumed that canopy PET demandcan be satisfied after theEgc daily evaporation fluxfrom ground cover layers is allowed to adequately drydown the surface moisture supply. We assume that thetop soil layer must be completely thawed before ETand NPP fluxes can begin in the spring.

The soil profile is treated as a series of three lay-ers: M1 is living moss (gc) surface, M2 is humifiedorganic matter under the moss surface layers, and M3is the mineral subsoil (Fig. 1). These layers can differby ecosystem type in terms of bulk density, moistureholding capacity, texture, and carbon–nitrogen stor-age. Where drainage is impeded, water can accumu-late upwardly in a ponded layer (M0) above the livingmoss surface.

Water balance in each of the organic and mineralsoil layers is modeled as the difference between netinputs of precipitation (PPT) and run-on of water fromneighboring micro-topographic (generally<10 m2)units (plus, in the case of lower soil layers, addi-tion of volumetric percolation inputs), and outputs ofET, followed by drainage for each profile layer. Theamount of water run-on to the profile from neighbor-ing micro-topographic units of equivalent area is cali-brated using a multiplier variable (β) on the daily PPTand snow melt inputs, which is adjusted to reproducesite measurements for seasonal dynamics of the watertable (Potter, 1997), namely timing and magnitude ofmaximum daily WTD for the growing season.

In the absence of a rising water table, all moisture in-puts and outputs are assumed to progress from the sur-face layer downward. Inputs from rainfall can rechargethe organic and mineral soil layers to estimated fieldcapacity (FC). For organic layers, FC is estimated bythe product of bulk density and measured water hold-ing capacity of moss and humus at high-latitude sites(Price et al., 1995). Where drainage is unimpeded, ex-cess water percolates through to lower layers and mayeventually leave the system as run-off.

3.2. Snow melt

Snow dynamics algorithms from the Regional Hy-droecological Simulation System (RHESSys) devel-oped by Coughlan and Running (1997)have beenadded to the NASA–CASA model to improve predic-tions of snow accumulation rate, and the timing andflow rates of spring snow melt at high-latitude sites.


These snow algorithms were developed to improveestimates of annual forest snow hydrology for pointand regional calculations of annual forest productivity.Model algorithms depend upon surface air tempera-ture, solar insolation, precipitation inputs, and canopyleaf area to compute snowpack water equivalent, snowthermal content, albedo, and albation from snow meltand sublimation fluxes. Snow accumulation rates aredependent on estimated night time air temperatures.A heat summation function is used for estimation ofsnow thermal content to determine when the snowpackis isothermal. The RHESSys snow model has beensuccessfully tested at 10 snow telemetry (SNOTEL)stations in the western United States (Coughlan andRunning, 1997). Comparisons of simulation results topublished snow depletion dates have shown that thesnow model accurately predicts the relative rankingand magnitude of depletion for different combinationsof forest cover, elevation, and aspect.

3.3. Ground cover productivity

The moss gc layer growing on the taiga forest floorhas been identified as an important component of theboreal carbon cycle (Oechel and van Cleve, 1986;Johnson and Damman, 1993). In black spruce stands,non-vascular plants may account for at least 15–25%of the annual C-uptake (Hall et al., 1996). Therefore,to simulate seasonal patterns in NPP for the moss gclayer, we adapted the NASA–CASA model algorithmdescribed byPotter and Klooster (1998)for groundcover production estimates.

Daily NPPgc is estimated as a product of cloud-corrected surface solar irradiance (S), fractional inter-cepted photosynthetically active radiation (FPAR) bythe overstory canopy, and a maximum light use effi-ciency term (εgc ) for moss, modified by temperature(T) and moisture (Wgc) stress scalars, as expressed inthe following equation:

NPPgc = S(1 − FPAR)εgcTWgc (2)

An estimated maximumεgc value of 0.06 g C MJ−1

PAR for moss cover is derived from measurements ofCO2 exchange in black spruce stands (Goulden andCrill, 1997). TheT stress term is computed with ref-erence to a derivation of optimal air surface tempera-tures (Topt) for plant growth. Over the globe,Topt set-

tings range from near 0◦C in the Arctic to the mid30◦C in low latitude deserts.

Our estimation of the moisture control function forWgc is derived from water balance in the M1 organicsoil layer following a parabolic physiological response(Eq. (3)), similar to that proposed byFrolking et al.(1996):

Wgc = [−0.0006(FC− 50)2] + 1 (3)

where the optimal moss water content for primary pro-duction, expressed as a percent FC, occurs at around50 vol.% water.

Moss litter contribution to decaying organic matterpools was set equal to the annual production estimatefor the moss gc layer. Lacking better information, par-titioning of gc litter biomass between decomposingleaf and root pools was set at a constant ratio of 60:40.

4. Model input data and initialization

4.1. Site description

The Denali National Park and Preserve (DNPP) ininterior Alaska was selected for this study as the loca-tion for evaluating our ecosystem model performanceat high latitudes. Because of its protected federal sta-tus, vast size, and diversity of ecosystems, Denali is alogical choice for study of potential long-term climatechange effects on high-latitude vegetation types andsoil thermal dynamics. Ecosystems called “taiga,” theRussian term for northern coniferous forests, occupynumerous valley locations throughout Denali (Fig. 3),commonly below about 800 m elevation (Dean andHeebner, 1982). These forests are dominated by whitespruce (Picea glauca) or black spruce (Picea mari-ana) trees, interspersed with a variety of deciduoustree species (Betula, Salix, Populus spp.), and a nearcontinuous ground cover of feathermoss (e.g.Hylo-comium spp.) and lichens. Moist tundra ecosystems,found below about 2000 m elevation, are dominatedby shrub species of willow, alder, and birch (Salix,Alnus, andBetula spp.) with an herb layer ofDryasspp., and mixed moss ground cover (Viereck, 1966;Roland, 1999).

The climate at Denali is characterized by the num-ber of days each year with freezing temperatures,which generally exceeds 200. Sub-zero (◦C) temper-


Fig. 3. Land cover of Denali National Park, Alaska (Source: University of Alaska, developed from Landsat 80-m resolution satelliteimagery for the National Park Service;Dean and Heebner, 1982). Location of the Denali Headquarters weather station is shown on theland cover map.

atures have been reported in every month except July.Temperature differences due to elevation are oftenextreme. Like much of the Alaskan taiga, Denali issubject to steep and persistent winter temperatureinversions, as great as 21◦C per 100 m elevationchange during periods of extreme cold (Slaughterand Viereck, 1986). Annual precipitation averages39 cm for the area. Most precipitation falls as rainin the summer months, a result of short-durationthunder storms and moist air masses from the Bering

Sea. Approximately 35% of the annual precipitationfalls as snow from mid-October through April andremains as a permanent cover for 6–7 months eachyear (Slaughter and Viereck, 1986). Maximum snowdepths are commonly reached in February and March.

The best studied forest ecosystem in close prox-imity to the Denali Headquarters weather station islocated on the Rock Creek watershed, which rangesin elevation from 525 to 1735 m (Thorsteinson andTaylor, 1997). The taiga forest on Rock Creek water-


shed includes numerous spruce trees, most of whichoriginated on the site between 100 and 150 years ago(Juday and Solomon, 2000). This taiga community istypically found on well-drained, south-facing slopesand consists mainly of white spruce, paper birch (Be-tula papyrifera), balsam poplar (Populus balsamifera),and quaking aspen (Populus tremuloides). Elsewherein Denali, a wide variety of vegetation-soil communi-ties can be identified, including more poorly drainedblack spruce on north-facing slopes. Understory plantsare typically mosses and grasses in dry sites and brushspecies in lower, wetter locations on shallow peat soils.

4.2. Model driver data

Initialized site parameter and climate driver vari-ables used in the model were designed to repro-duce, as closely as possible, ecosystem conditionsbeginning during the early 1950s at the approxi-mate location of the Rock Creek watershed adjacentto Denali Headquarters weather station (63◦43′N,148◦58′W) near Mt. Healy, AK, otherwise knownas ‘McKinley Park station’ (National Weather Ser-vice Coop. Station #50-5778-8). Nearly 50 years ofmonthly climate records are available through theWestern Regional Climate Center (WRCC) for theDenali Headquarters station. The WRCC is admin-istered by the National Oceanic and AtmosphericAdministration (NOAA), with oversight provided bythe National Climatic Data Center (NCDC), to dis-seminate quality-checked climate data pertaining tothe western States and Alaska. The WRCC record forDenali includes monthly records 1950–1999 for totalprecipitation, average, minimum, and maximum sur-face temperature. A quality assurance check for dataanomalies was performed. Missing data values, whichoccurred almost exclusively in 1988–1990, were filledin with observations from the same monthly periodof the previous year. Prior to model input, WRCCtemperature records were adjusted according to therecommendations fromJuday and Solomon (2000).

Surface solar radiation flux (Srad) at the Denali lo-cation was not reported in the WRCC record. There-fore, we estimatedSrad in monthly average units ofW m−2, based on diurnal temperature range (DTR)data reported in the WRCC minimum and maximumtemperature record. Our estimation technique forSradis founded on the atmospheric transmittance theory

from Bristow and Campbell (1984), who reported thatthe difference between maximum and minimum dailytemperatures is closely correlated with the amount ofsolar radiation received. At times when the net fluxof solar radiation at the Earth’s surface is low (e.g.during overcast sky conditions), the difference in sur-face temperature extremes is also generally low. Theopposite is true for clear sky conditions. Thus, mea-surements of daily temperature extremes should berelated to the atmospheric transmittance for solar ra-diation flux using relatively simple least squares re-gression functions. This method to estimate regionalpatterns inSrad has been demonstrated for the conti-nental United States as part of the VEMAP ecosystemsimulation experiment (Kittel et al., 1995).

For our modeling purposes, a set global regressionfunctions was developed for prediction ofSrad fromDTR measurements. Calibration data sets for DTRwere obtained as monthly mean values from the Cli-matic Research Unit (CRU), University of East Anglia,gridded originally to 0.5◦ resolution (New et al., 2000).Multi-year DTR data (1983–1991) from the CRU dataset were used to develop third-order polynomial equa-tions, which predict monthly meanSrad within fourglobal latitude zones: >50◦N, 50–10◦N, 10◦N–20◦Sand<20◦S. The measuredSrad data for these regres-sions were obtained from the SeaWiFS (Sea-viewingWide Field-of-view Sensor) radiation flux estimatesfrom Bishop and Rossow (1991), which are derived asa product of the International Satellite Cloud Climatol-ogy Project (ISCCP) and gridded originally at a spa-tial resolution of 2.5◦ for the period July 1983 to June1991. Starting from weather station locations listed inthe Global Historical Climate Network (GHCN;Voseet al., 1992), we sub-sampled from the CRU globallydistributed DTR coverage across each latitude zone onthe basis of those locations producing the highestR2

regression coefficients withSrad as the dependent vari-able. This subset of regression curves was selected torepresent the four latitude zones (Table 2). In cases ofextreme DTR values beyond the applicable range ofthe regression curves (either lower or higher than theindependent variable bounds), we set minimum andmaximumSrad values predicted at the extremes of thecalibration DTR values. The resultingSrad regressionequation for Denali and other high latitude locations(>50◦N) has anR2 of 0.58, with broad applicabilityover a DTR of 3–16◦C. Despite the differences in spa-


Table 2Regression model results for prediction ofSrad (W m−2) from dirunal temperature range (DTR)

Latituderegion

R2 Standard error(W m−2)

Regression model coefficientsa Range of temperature (◦C)

a b c d Minimum Maximum

>50◦N 0.58 60 −16.892079 −0.22111317 3.0699711 −0.1159774 3 1650–10◦Nb 0.54 66 0.4272665 −9.4002161 5.1565 −0.22262 3 1610◦N–20◦S 0.69 33 −213.03716 88.128125 −4.6894525 0.064486 3 22<20◦Sc 0.61 54 200.33242 −65.113682 7.8314716 −0.2128573 7 20

a Srad = a + bDTR + cDTR2 + dDTR3.b IncreaseSrad values by 50 if Lat> 10◦N for months of May, June or July, otherwise decreaseSrad values by 50 if Lat> 10◦N for

months of November, December or January.c DecreaseSrad values by 50 if Lat< 20◦S for months of May, June or July, otherwise increaseSrad values by 50 if Lat< 20◦S for

months of November, December or January.

tial resolution of the various data used to derive thisSrad parameter, we are confident that this regressionapproach provides the most reliable input time-seriesavailable for the plant production component of ourmodel.

5. Modeling results and interpretation

A series of CASA–DGVM results are presentedhere for past and future climate conditions. In all ofour coupled vegetation–carbon cycling simulations,NPP and ET, including moss cover and below-groundcomponents of the ecosystem, were estimated usingthe NASA–CASA algorithms described byPotter andKlooster (1999)andPotter et al. (2001). Algorithmsfor seasonal patterns of litterfall, plus initial statesfor forest floor litter pools and soil C pools at thesites were also adopted from methods described byPotter (1997). The model was initialized for litter andsoil C and N pool sizes using mean monthly climatedrivers. No further “tuning” of internal model vari-ables was necessary to initiate the simulations. Forexample, timing of seasonal frost conditions reportedto exist in the organic and mineral soils are not pre-scribed, but instead develop according to the model’sseasonal freeze–thaw algorithms. This version of theCASA–DGVM does not, however, include many ef-fects of previous ecosystem disturbances (e.g. wild-fires) nor of seed dispersal patterns on vegetation dy-namics and carbon cycling at the Rock Creek water-shed site. These simulations did not include potentialeffects of rising atmospheric CO2 concentrations onpredicted plant physiology.

5.1. Initializing the DGVM

Default model settings for taiga and tundra ecosys-tem parameters were derived from published fieldstudies conducted at nearby sites of interior Alaska(Table 3). To establish baseline conditions for50+ year simulations of vegetation dynamics at asingle point representative of the Rock Creek water-shed location, our CASA–DGVM soil carbon andnitrogen pools were initialized at 1950s conditionsusing a run time of 100 years (1200 monthly timesteps) for WRCC-reported climate conditions fromthe early 1950s, which are the earliest complete dataavailable from the Denali weather station records.A non-equilibrium initialization period of 100 yearsfor these Denali simulations is justified for two mainreasons: (a) the taiga forest on Rock Creek watershedoriginated on the site around 150 years ago (Judayand Solomon, 2000), which would be 100 yearsprevious to 1950, and (b) typical fire return time inboreal forests of interior Alaska is not greater than150 years (Van Cleve et al., 1991; Kasischke et al.,1995). This initialization procedure cannot accountfor soil carbon pools remaining after any presumeddisturbance (fire) events prior to our 100 year initial-ization, mainly because of the lack of informationon long-term decomposition rates of charred organicmaterial in northern forest soils (Harden et al., 2000).

Based on these long-term climate controls, the equi-librium vegetation type predicted by the DGVM forthis low elevation (500 m) location within the De-nali area is dominated by evergreen needleleaf for-est (i.e. taiga). Annual forest NPP is predicted to be77 g C m−2 per year for trees plus 55 g C m−2 per year


Table 3Estimated NASA–CASA parameter settings and sources for interior Alaska ecosystem simulations

Model parameter Units Tundra Taiga

GeographicElevation m 1000–2000 0–800

Salix, Alnus, Equisetum spp. Picea glauca or Picea mariana

Overstory vegetationLeaf nitrogen content % 1.7a 1.0b

Leaf lignin content % 7.1b 13.0b

Maximum stomatal conductance mm s−1 5.0 1.0e

Maximum C fixation efficiency g C MJ−1 0.4f

Litter C allocation from NPP % Leaf:root:stem 10:10:80g 15:25:60h

Mixed mossa Feather mossa

Ground coverCover typeNitrogen content % 1.0g 2.1c

Ground cover thickness m 0.3 0.6c

Bulk density g cm−3 0.03i

Water holding capacity g g−1 dry 2.0j

Thickness of humus (including peat) m 0.2 0.3i

Humus bulk density g cm−3 0.5a

Water holding capacity of humus g g−1 dry 3.5j

Mineral soilsBulk density g cm−3 1.3d 1.1d

Texture % Sand:silt:clay 30:66:4d 15:80:5d

Minimum water content % Bed volume 13 22j

Field capacity % Bed volume 40 36j

Total porosity % Bed volume 45 45j

Permafrost Absenta Seasonala

Plant rooting depth m 0.5a 0.8–1.2a

Sources: (a)Yarie et al. (1998), (b) Yarie and Van Cleve (1996), (c) Viereck et al. (1983), (d) Van Cleve et al. (1993), (e) Saugier et al.(1997), (f) Goetz and Prince (1998), (g) Shaver and Chapin (1991), (h) Hunt and Running (1992), (i) Harden et al. (1997), (j) Frolkinget al. (1996). Best estimates are shown initalics, based on the compilation of related literature sources cited under this parameter category.Blanks are shown for parameters with no readily available values from published sources, in which case the default model value for borealecosystems was used.

for moss ground cover. The relatively young taigaforest is estimated to be a net carbon sink of about26 g C m−2 per year, presumably because soil carbonpools have not yet reached the equilibrium size re-quired to fully balance carbon inputs from NPP by de-composition respiration losses. Aboveground biomass(leaves and wood) in living trees is predicted to benearly 2.1 kg C m−2 with canopy LAI of 2.0, whereassurface soil (20–30 cm depth) is predicted to storeabout 2.6 kg C m−2 by the end of this century-longsimulation. As mentioned above, additional informa-tion on long-term decomposition rates of charred or-ganic material in Alaskan taiga soils is needed to com-plete this carbon sink budget.

Tundra ecosystems are generally located at higher,colder elevations in Denali than are taiga forests. Todetermine the rate and duration of temperature coolingrequired for the CASA–DGVM to correctly predict thepresence of a tundra vegetation type based long-termclimate controls at this location, we reinitialized themodel for a potential run time of 100 years, begin-ning from WRCC-reported climate conditions fromthe early 1950s, and adjusted monthly surface temper-ature inputs by at an initial rate of−0.2◦C per year.

The CASA–DGVM predicts that after 14 years ofclimate cooling at a annual rate of−0.2◦C per year,which is equivalent to an overall drop of 2.8◦C inmean annual air temperatures (to an average of about


10◦C in July at this Denali location), the vegetationtype shifts to favor the permanent presence of tun-dra plants in dominance over taiga forest. Assum-ing that this rate of cooling continues for another10 years, the model predicts annual tundra NPP of72 g C m−2 per year for shrubs plus 53 g C m−2 peryear for ground cover. The ecosystem is estimated tobe a net carbon sink of about1 g C m−2 per year. Com-pared to the lower elevation taiga conditions, above-ground biomass (leaves and wood) in living plantsdecreased to 1.2 kg C m−2 with canopy LAI of 0.5,whereas surface soil (20–30 cm depth) was estimatedto increase to about 2.1 kg C m−2.

We note that these CASA–DGVM results are sim-ilar to those of an independent modeling study ofvegetation-climate change on the Seward Peninsula innorthwestern Alaska byRupp et al. (2000). Using amodel that considers climate, disturbance, and plantseed dispersal,Rupp et al. (2000)similarly found thattemperature changes of about 2◦C could lead to transi-tion between tundra and mixed deciduous-spruce for-est.

5.2. Predicted vegetation change

Starting from these 1950 baseline conditions forthe predicted lower elevation taiga forest stand, ourDGVM was run forward using actual monthly cli-mate values from the WRCC record for the Denalistation, to generate vegetation change predictions cov-ering the period 1953–1999. To extend model predic-tions beyond 1999, the 50-year linear trends in precip-itation, temperature, and surface solar radiation fromthe WRCC record were determined using simple lin-ear regression. Annual rates of change in these trendswere doubled and then used to extend forward 25years for future climate-driven simulations of vege-tation dynamics and ecosystem changes at this De-nali location. The potential doubling of current annualrates of change in the climate forcing is justified basedon observations that in Alaska, a warming trend of0.75◦C per decade has been identified for the last threedecades over land bordering the Bering Sea (Chapmanand Walsh, 1993; Serreze et al., 2000), which togetherwith climate modeling results, suggest a further riseof 1–2◦C over 20 years and 4–5◦C over 100 yearscan be expected (BESIS, 1997). Monthly values from1998 were used as the baseline year to extend the dou-

bled annual rates of change forward in time to 2025.On top of these doubled annual rates of change, weimposed an adjustment for interannual variability inthe future climate patterns that was consistent withthe past range (1950–1999) of interannual variabilityobserved for each of the individual climate variables.The level of interannual variability was assigned ran-domly to each year of the future climate drivers withinthe observed range limits of 1950–1999.

In response to the progressive warming trend ob-served in the Denali climate station record over theperiod 1950–1999, our DGVM predicts the increasingimportance of broadleaf deciduous tree species in thisforest ecosystem, particularly during periods of declin-ing annual CDD sums (Fig. 4a). For several years dur-ing the early 1960s, and regularly from 1978 onward,an annual accumulation of fewer than 2500 CDD isreported, which signals a warming wintertime climateat this location and possibly an earlier onset of springtemperature conditions (T > 0 ◦C) as suggested by theupward trends in annual GDD sums. During severalyears (e.g. 1960, 1970, and 1977), particularly warmtemperatures were reported in association with lowprecipitation amounts (Fig. 4b), with which the modelpredicts the potential for increased establishment ofbroadleaf deciduous trees in the taiga ecosystem.

As broad confirmation of the CASA–DGVM pre-dictions for Denali, studies of tree-ring records fromstands of white spruce throughout the interior ofAlaska indicate that, over the past 90 years, radialgrowth in these conifers has decreased with increasingair surface temperature (Barber et al., 2000). Thesewhite spruce growth records further imply that fre-quent summer drought could be an important factorlimiting carbon uptake and increasing tree mortalityover a large portion of the North American spruceforest. This raises a pressing ecological question ofwhat plant species are likely to replace spruce inthe Alaskan forest landscape of the state’s Interiorregion?

In our post-1999 projections, doubled annual ratesof change from the 50-year climate trends sustain theDGVM’s predicted shift from dominance by evergreenneedleleaf trees (taiga) to mixed broadleaf–needleleaftree composition. This model prediction of an in-creasing importance of deciduous tree species in taigaforests is consistent with observations on the RockCreek watershed byRoland (1999)on the likelihood


Fig. 4. Seventy-year trends (1952–2023) expressed as 12-month running averages for (a) estimated chilling degree day sum (CDD) andgrowing degree day sum (GDD) at Denali, shown in relation to predicted changes in northern forest types from CASA–DGVM simulationsfor the Rock Creek site; (b) estimated precipitation sum (PPT) and potential evapotranspiration (PET) predicted from CASA–DGVM.


of increased colonization by paper birch (Betula pa-pyrifera) seedlings with climate warming in forestplots under study in Denali.

In a detailed study of forest history on Rock Creekwatershed,Juday and Solomon (2000)draw severalconclusions that are also relevant to evaluation of theDGVM results presented here. These investigators re-port the tree ring growth increment of white sprucetrees appears to be slowed notably by dry summer con-ditions (such as those recorded in 1957–1958), and thatsignificant declines in white spruce growth rates haveoccurred starting after 1976 when summer air temper-atures increased about 1.3◦C. Spruce growth appearsweakly but positively correlated with annual precipi-tation patterns, suggesting that drought stress could bea controller of production and survival in these trees.While forest cover may have advanced in elevation onthe watershed during the 20th century at the highest(coolest) elevations,Juday and Solomon (2000)con-clude that favorable climate for white spruce growthat low and mid elevations has declined steadily over

Fig. 5. Seventy-year trends (1952–2023) in FPAR and LAI predicted from CASA–DGVM for the Denali Rock Creek forest site.

the past 80 years. Species of deciduous poplar and as-pen (Populus spp.) trees appear to be better adapted towarmer and drier summer conditions on Rock Creekwatershed. Each of these findings from recent analy-sis of forest history on Rock Creek watershed serve asevidence that the CASA–DGVM is generally provid-ing accurate predictions of vegetation dynamics andcarbon cycling for the Denali sites.

The highest predicted values from the CASA–DGVM for FPAR and LAI for forests on Rock Creekwatershed were during the periods of 1967–1969 and1991–1993 (Fig. 5), which were among of the warmestand wettest in the 50-year time series. Years imme-diately before 1973 and 1984, when mid-summerFPAR and LAI predicted to be among the lowest ofthe time series, were notably cooler and drier thanthe 50-year climate averages. From our post-1999projections, the model predicts sustained increases ofmid-summer FPAR and LAI in response to doubledannual rates of change from the 50-year climate trends.Despite internannual variations, the peak predicted


summer FPAR after 25 years of projected climatewarming is 40–60% higher than predicted for 1999conditions.

5.3. Predicted changes in water andcarbon cycling

Predicted water fluxes in the simulated Denali for-est ecosystems are represented by monthly ET fromthe tree canopy, evaporation flux (Egc) from the mossground cover and stored moisture content in the (M2)soil profile layer (Fig. 6a). Estimated trends basedon the Denali climate station record from 1950 to1999 show, for example, years of highest ET fluxesas 1962, 1976, and 1990. Rainfall recorded during thesummer months of June–August commonly exceeded10 cm per month in each of these years. Combinedwith relatively high predictedSrad fluxes, mid-summerET fluxes from the forest canopy were simulated asa transfer of about 10 cm per month from the storedsoil pools to the atmosphere during these periods. Theyears of notably low predicted ET flux (e.g. 1958 and1972) were among the driest of all in the 50-year De-nali record, with annual precipitation totals of<30 cm.Low predicted ET fluxes in other years of ample rain-fall (e.g. 1963 and 1992) were caused mainly by lowpredictedSrad fluxes.

We note that the simulated moss ground cover tendsto lose moisture rapidly during the warming springperiods of May–June. However, predicted canopy ETfluxes could continue to drain the soil moisture poolfor 1 or 2 months afterEgc fluxes had completelydried the ground cover layer. Soil moisture was pre-dicted to increase toward near-saturation levels duringthe wet periods of 1970 and 1990–1994. In additionalruns made with the CASA–DGVM model under set-tings for impeded soil water flow (i.e. more poorlydrained sites than are generally found on the RockCreek watershed) these are same climate years whenthe DGVM predicts increased probability for recov-ery of the evergreen needleleaf ecosystem and slowerestablishment and regrowth by broadleaf deciduoustrees.

From our post-1999 climate warming projections,the model does not predict sustained increases ofmid-summer ET cycling of precipitation through themixed broadleaf–needleleaf forest system. Annual ETfluxes (tree plus moss) are controlled strongly by in-

terannual variability of solar irradiance over 25 yearsof projected climate warming. Throughout this simu-lation of future water fluxes and increasing summerrainfall, relatively stable soil moisture levels weremaintained at near saturation levels.

Carbon fluxes in the simulated Denali forest ecosys-tems are represented by predicted monthly NPP oftrees and moss ground cover, and by the overall car-bon balance of the stand, NPP minus soil heterotrophicrespiration fluxes of CO2, a quantity also called netecosystem production (NEP). Using the Denali cli-mate station record from 1950 to 1999 as input, annualNPP (Fig. 6b) is predicted to be lowest (<150 g C m−2

per year) in years with relatively cool and periodi-cally dry summer growing seasons (e.g. 1956–1958,1972–1977, and 1985–1986). During the 1990s, NPPconsistently exceeded 150 g C m−2 per year, with theexception of 1993 when air temperatures were thecoolest measured during the decade (<1400 GDD peryear, Fig. 4a). The range of predicted NPP by themodel is highly consistent with a measured annualaboveground production range (160–270 g C m−2 peryear) for white spruce stands of interior Alaska sitesfrom the late 1980s (Van Cleve et al., 1991).

The DGVM predicts that over the 1950–1999 cli-mate record at Denali station, the changing taigaecosystem could be a net sink of carbon on the or-der of 1.3 kg C m−2. Estimated annual NEP duringthis 50-year time series ranges from−78 g C m−2 peryear (net C source) to+106 g C m−2 per year (net Csink), with most years being net sinks for atmosphericCO2 in the growing forest. Years predicted to be netsources of CO2 are characterized mainly by lowerthan normal growing season temperatures. Duringthe warm years of the 1990s, 8 out of 10 years werepredicted with NEP >15 g C m−2 per year.

By the end of our post-1999 climate warmingprojections, the model predicts NEP fluctuating be-tween−18 and+77 g C m−2 per year in the mixedbroadleaf–needleleaf forest, depending on warm andrelatively sunny growing seasons to increase net car-bon storage. Predicted NPP continues to increasenevertheless, at a mean rate of around+1.2 g C m−2

per year. Over the 25 years of projected climatewarming, the model predicts these overall increasesin stand production to exceed 30 g C m−2 per yearin the mixed broadleaf–needleleaf forest, except inunusually cool years.


Fig. 6. Seventy-year trends (1952–2023) expressed as 12-month running averages for (a) estimated evapotranspiration (ET) and soil watercontent; (b) NPP and NEP predicted from CASA–DGVM for the Denali Rock Creek forest site.


5.4. Predicted daily snow, soil, and carbon dynamics

To examine the performance of the NASA–CASAmodel over shorter time periods (i.e. outside theDGVM mode of changing vegetation composition),we ran simulations for the Denali Rock Creek taigasite at a daily model time step over the period Jan-uary 1, 1997 to December 31, 1999. In this mode,it is possible to evaluate the model’s predicted sea-sonal dynamics, including the date and rates of initialsnowmelt, freeze–thaw patterns of the soil profile,soil temperature at 20 cm depth, and fluxes of waterand carbon from plants and soil layers. Daily modelpredictions lend themselves more easily to eventualvalidation using field measurements, although this iscurrently not feasible, because there is a lack of suchmeasurement data sets from locations within Denalior from nearby forest research sites of interior Alaska.

Comparison of these three years in terms of the dailymeteorological record from Denali Headquarters sta-tion shows that the period of January through May in1998 was somewhat warmer on average than in either1997 or 1999, and then was slightly cooler during thesummer and early fall months, compared to the other2 years. Because of relatively dry periods during thewinter months, the yearly precipitation total for 1998was the lowest of the 3 years at 36.5 cm, followed by1997 with 37.8 cm and 1999 with 41.3 cm.

In terms of seasonal soil moisture and water ta-ble dynamics, timing of snow melt and the patternof spring thaw are documented as an important fac-tors controlling early season physiology at taiga sites.Moss ground cover can become active as early snowmelt occurs (Potter et al., 2001), weeks before lowersoil layers thaw to tree rooting depth. The freeze–thawregime could be an important controller of stand car-bon fluxes for the early growing season, and hencerepresents a key model prediction to validate. OurNASA–CASA model predicts the timing and seasonalrates of soil freeze–thaw as fairly similar for all 3 years(Fig. 7), with spring thaw of the top soil layers begin-ning during the first week of May. In 1998, warmerspring temperatures lead to prediction of a 2-day ad-vance in the initiation of surface thaw. According tothe model, full thaw of the soil profile was not reacheduntil about the beginning of August; the simulated pro-file was entirely frozen again by about the first day ofDecember each year.

These model predictions are plausibly validatedby measurements during 1998 and 1999 of the soilfreeze–thaw regime in interior Alaska made by L.Viereck (unpublished data). This set of soil measure-ments using ‘frost tubes’ shows that the surface of thesoil began to thaw rapidly in late April or early Mayand reached an “active layer” depth of nearly 20 cm bythe first week in June. The active layer can be definedas the depth of ground above the permafrost whichthaws and freezes annually. Full measured thaw ofthe soil profile was reached by the middle or late Au-gust of both years. The measured soil profile becamecompletely frozen again by the middle of December.

The CASA–DGVM predicts snowmelt beginningin 1997 around April 11 and lasting for the follow-ing 5 days with 1.7 cm (liquid) water melted, afterwhich time a 9-day period of cold (less than−5◦C)air temperatures were recorded, delaying the year’smajor predicted period of snowmelt until April 26(when 12.6 cm water melted over 26 subsequent days).The effect of this cold period in April 1997 can beseen also as a delay in the predicted soil thaw pattern(Fig. 7). For 1998, the model predicts snowmelt begin-ning on March 24 (Julian day 83), weeks earlier thanin 1997, during which time 9.5 cm water were melted.For 1999, the model predicts snowmelt beginning onApril 21 (Julian day 111) and lasting until May 17during which time 9.0 cm water were melted. Overall,the model predicted that 20–40% of the annual pre-cipitation amount at these taiga sites entered the soilas snowmelt beginning in mid to late April each year.

Predicted daily temperatures at 20 cm depth in thesoil profile were damped, as expected, compared tosurface air temperatures, and delayed in terms of peakyearly values by at least 30 days (Fig. 7). Soil temper-atures at 20 cm depth are predicted by our model topeak at about 7◦C near the beginning of August eachyear. Taiga soils could be fairly well insulated due tomoss ground cover, which should be taken into consid-eration with more detail in future model applications.

Once the thaw period in the surface soil layers hasended, summer rainfall is predicted to penetrate read-ily through the drying moss ground cover and mineralsoil layers on a well-drained taiga site like the RockCreek watershed. In more poorly drained sites else-where in Denali, our model would predict that rainfallpenetrates the surface layers until it reaches an under-lying semi-impermeable layer of permafrost, where it


Fig. 7. Three-year patterns (1997–1999) in daily predicted water table depth (WTD), freeze–thaw depth (both left axis values), soiltemperature (T) at 20 cm depth and reported air surfaceT (both right axis values) for the Denali Rock Creek forest site. Soils are actuallysimulated to both thaw and freeze from the top layer to the bottom over the course of a year.

subsequently either accumulates upward with a ris-ing water table, or runs off, depending on the localdrainage and topography around the forest stand.

Predicted carbon fluxes for this taiga stand show thedifferent patterns of daily NPP for both trees and mossground cover during the three year period (Fig. 8). Inall three years, predicted NPP of the tree canopy wasinitiated in the last week of May and increased rapidlyduring this spring period to peak flux levels in earlyJune. NPP carbon gains in the trees cease by the be-ginning of October in all years. Moss NPP is predictedto start early in the spring as snowmelt begins, butthen lags tree NPP (in terms of peak seasonal fluxes)markedly in all years, as does soil respiration fluxes ofCO2 from microbial decomposition of organic matter.Predicted NEP for the stand shows a strong seasonalsignal with peak sink fluxes of CO2 in June and peaksource fluxes to the atmosphere in November.

6. Summary and future research directions

Ecosystem simulation modeling is a necessarycomponent of any integrated ecological study of re-sponses to climate change, particularly in the case ofa region as vast and spatially heterogeneous as thehigh-latitude taiga and tundra. Use of models likethe one examined in this paper, which represent asynthesis of process-level understanding about ma-jor controls on ecosystem carbon and water cycles,can suggest research hypotheses and improve under-standing of potential effects of global environmentalchange, principally altered temperature and precipita-tion patterns, on the taiga–tundra region.

Field-based studies, on the other hand, can providecrucial evidence of certain physiological and ecologi-cal controls over vegetation dynamics, carbon fluxes,and soil processes. However, comprehensive site mea-


Fig. 8. Three-year patterns (1997–1999) in daily predicted carbon fluxes for the Denali Rock Creek forest site.

surements of this type can be prohibitively expensiveto make over vast areas of the land surface for manyyears. Moreover, areas of relatively remote or moun-tainous terrain do not lend themselves to operation ofmicro-meteorological tower flux techniques necessaryto assess whole ecosystem carbon and water exchangewith the lower atmosphere. In the Ameriflux carbonflux network, there are four tower sites in Alaska, allare tundra site, none are in forests. Consequently, be-cause few data sets have been collected to date thatpermit model validation of carbon fluxes or on-goingchanges in vegetation composition for interior Alaskaforest sites, we are presented with inherent difficul-ties in trying to compare model predictions to obser-vations.

There is an urgent need to refine ecosystem mod-els and to further evaluate their predictions againstkey field measurements at ecological studies areasthat are potentially important in terms of global car-bon cycle assessments and patterns of vegetation

change with climate. For instance, in a comparisonof several DGVMs,Cramer et al. (2001)suggestedthat climate-induced decline in NEP resulting fromincreased heterotrophic respiration, although thisstudy also revealed major uncertainties about theresponse of NEP to climate change resulting, primar-ily, from differences in the way that modeled globalNPP responds to a changing climate. Models thatcan incorporate uses of satellite remote sensing, likeNASA–CASA, can add new dimensions in terms ofscaling up predictions over large areas at sufficientlevels of ecological and topographic detail for re-gional assessment. However, these models requirerepeated validation and refinements for applicationsto taiga and tundra environments, as steps towards thepoint from which a model can be extrapolated withconfidence to entire regions.

It should be noted that predictions from theCASA–DGVM model carry several major uncertain-ties, namely with respect to the vegetation dynamics


of taiga and tundra ecosystems. Our DGVM doesnot predict which tree or shrub species will replaceanother plant species in these ecosystems. Rather, themodel predicts PFTs with important differences inlight use capacities and nutrient cycling potentials.Furthermore, any errors in predicting an observedPFT will affect the prediction of biomass and NPPvalues at that location because, in the CASA model,FPAR and the potential for conversion of solar irradi-ance to plant carbon is dependent to a degree on PFT(Potter and Klooster, 1999).

In the summary sections that follow, severalfield-based studies are described which appear to beof generally high priority in terms of evaluating un-certainties new ecosystem model predictions againstfuture field measurements. While it is well noted thatnot all measurements can be made at many field sitesin places like Denali and environs, this review of re-search priorities for model evaluation purposes drawsupon numerous previous field studies in areas of in-terior Alaska and other high-latitude locations aroundthe world. The intent is to use results and hypothe-ses derived from our modeling activities to suggestpriorities for a set of field measurement techniquesand approaches to better understand major controlson vegetation processes and ecosystem carbon cyclesof interior Alaska.

6.1. Vegetation dynamics and climate change

To aid in understanding potential changes andsurvival of taiga vegetation, removal and transplantstudies of PFTs (e.g. evergreen perennials, decidu-ous perennials, graminoids) among various habitatlocations have proven useful (Chapin et al., 1997).For example, transplant results may help determinewhether a plant type that is abundant in a relativelycold habitat location (higher elevation) can surviveand grow when introduced into a relatively warmerhabitat (lower elevation), and vise-versa. In a similarfashion, seed-sowing experiments can be conductedin both recently burned and unburned areas in whichseed germination and early seedling survivorship arecompared in plots with intact vegetation and plots inwhich aboveground vegetation has been artificiallyremoved. Because the climate in certain high-latitudeenvironments appears to be changing, a long-termmonitoring program of reciprocal transplants and

seed-sowing may show unexpected results over aperiod of as short as 5–10 years.

Manipulations in levels of surface warming, shad-ing, and fertilizing in taiga ecosystems are also im-portant field trials (Stenström et al., 1997; Jones et al.,1997). As an example, small open-topped plexiglasscones can be placed on the soil surface to cause a2–4◦C warming similar to that predicted for atmo-spheric temperatures in response to a doubling of car-bon dioxide in the atmosphere (Marion et al., 1997).In coordination with these types of field experiments,validation studies for DGVMs must be designed inwhich effects of growth of different plant functionaltypes on ecosystem-level resource supply and cyclingare determined through field measurements and com-pared to model predictions. Harvest-based measure-ments of primary productivity and plant–soil nutrientcycling patterns in the ecosystem can serve as primarydiagnostic variables for this type of model validationactivity.

6.2. Ecosystem carbon storage

A combination of field sampling and paleo-ecolo-gical reconstruction techniques can be used to studychanges in the ecosystem carbon cycle as a resultof climate change and vegetation dynamics (McKaneet al., 1997). As is the case for most terrestrial ecosys-tems, there is an urgent need for fundamental mea-surements of plant carbon allocation to below groundproduction, fine root turnover, rates of carbon transferfrom plants to soil and atmospheric pools, and ratesof soil carbon turnover on time scales of days to years(Nadelhoffer et al., 1997). Tree-ring based approachescan aid in reconstruction of changes in the distribu-tion, density, and growth form of trees to identify thepace and pattern of taiga response to past warming.Fire scarred trees and charcoal layers in soil can beused to reconstruct the historical frequency of fire.

6.3. Soil active layer and permafrost dynamics

Soil and air temperature profiles, together with soilmoisture content measurements, can become highlyuseful data sets to help determine thaw depth of the ac-tive layer (Hinkel et al., 1996; Hinzman et al., 1998),which is a soil property that remains unvalidated inmost ecosystem models. While the importance of moss


ground cover as an insulating layer for soil and per-mafrost development is well known (Viereck, 1966),there is little data available from taiga ecosystem fieldmeasurements to verify and quantify any growth en-hancing effect of the ground cover in different foreststand types of Alaska. As a reference source, standard-ized protocols have been developed for these measure-ments through the International Tundra Experiment(ITEX; http://www.itex-science.net) and CALM, theCircumpolar Active Layer Monitoring program.

7. Concluding remarks

The ecosystem model results presented in thisstudy, although only partially verified with extensivefield measurements, imply that taiga forests of inte-rior Alaska have changed markedly over the past 50years of climate warming, both in terms of vegetationcomposition and net ecosystem carbon fluxes. Theseforest are likely to continue changing in these re-spects, potentially with major implications for expla-nation of biosphere feedbacks to global atmosphericchemistry (e.g. CO2 levels) and coupled climate pro-cesses. While the need is clear and urgent for newfield studies to provide sources of model validation,elaboration of results from today’s models of dynamicvegetation and ecosystem carbon flux can providevaluable insights into historical and possible futurestates of terrestrial ecosystems that are not achievablethrough any other study methods.

Acknowledgements

This work was supported by grants from theNASA Terrestrial Ecology Program and from theNASA Land Surface Hydrology Program. Thanks toJoseph Coughlan for guidance in the implementa-tion of snow melt algorithms. The author thanks LesViereck, Carl Roland, Dot Helm, and Karen Oakleyfor field data, comments, and improvements in anearlier version of this paper. Glenn Juday and PamelaSousanes provided climate data sets for Denali Head-quarters station. We thank the investigators in theDenali National Park Long-Term Ecological Moni-toring (LTEM) program for their time and interest incollaboration.

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