Predicting ecosystem dynamics at regional scales: an ... · carbon cycle and forecasting...

doi: 10.1098/rstb.2011.0253, 222-235367 2012 Phil. Trans. R. Soc. B

David Medvigy and Paul R. Moorcroft northeastern North Americaevaluation of a terrestrial biosphere model for the forests of Predicting ecosystem dynamics at regional scales: an

Supplementary data

l http://rstb.royalsocietypublishing.org/content/suppl/2011/11/23/367.1586.222.DC1.htm

"Data Supplement"

References

http://rstb.royalsocietypublishing.org/content/367/1586/222.full.html#related-urls Article cited in:

http://rstb.royalsocietypublishing.org/content/367/1586/222.full.html#ref-list-1

This article cites 34 articles, 3 of which can be accessed free

Subject collections

(145 articles)environmental science (335 articles)ecology

Articles on similar topics can be found in the following collections

Email alerting service hereright-hand corner of the article or click Receive free email alerts when new articles cite this article - sign up in the box at the top

http://rstb.royalsocietypublishing.org/subscriptions go to: Phil. Trans. R. Soc. BTo subscribe to

This journal is 2012 The Royal Society

on December 6, 2011rstb.royalsocietypublishing.orgDownloaded from

http://rstb.royalsocietypublishing.org/content/suppl/2011/11/23/367.1586.222.DC1.html http://rstb.royalsocietypublishing.org/content/367/1586/222.full.html#ref-list-1http://rstb.royalsocietypublishing.org/content/367/1586/222.full.html#related-urlshttp://rstb.royalsocietypublishing.org/cgi/collection/ecologyhttp://rstb.royalsocietypublishing.org/cgi/collection/environmental_sciencehttp://rstb.royalsocietypublishing.org/cgi/alerts/ctalert?alertType=citedby&addAlert=cited_by&saveAlert=no&cited_by_criteria_resid=royptb;367/1586/222&return_type=article&return_url=http://rstb.royalsocietypublishing.org/content/367/1586/222.full.pdfhttp://rstb.royalsocietypublishing.org/subscriptionshttp://rstb.royalsocietypublishing.org/


Phil. Trans. R. Soc. B (2012) 367, 222235

doi:10.1098/rstb.2011.0253

Research

* Autho

Electron10.1098

One conecology:

Predicting ecosystem dynamics at regionalscales: an evaluation of a terrestrial

biosphere model for the forestsof northeastern North America

David Medvigy1,2 and Paul R. Moorcroft1,*1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA

2Department of Geosciences and Program in Atmospheric and Oceanic Sciences,Princeton University, Princeton, NJ 08544, USA

Terrestrial biosphere models are important tools for diagnosing both the current state of the terrestrialcarbon cycle and forecasting terrestrial ecosystem responses to global change. While there are anumber of ongoing assessments of the short-term predictive capabilities of terrestrial biospheremodels using flux-tower measurements, to date there have been relatively few assessments of theirability to predict longer term, decadal-scale biomass dynamics. Here, we present the results of aregional-scale evaluation of the Ecosystem Demography version 2 (ED2)-structured terrestrialbiosphere model, evaluating the models predictions against forest inventory measurements for thenortheast USA and Quebec from 1985 to 1995. Simulations were conducted using a defaultparametrization, which used parameter values from the literature, and a constrained model paramet-rization, which had been developed by constraining the models predictions against 2 years ofmeasurements from a single site, Harvard Forest (42.58 N, 72.18 W). The analysis shows that theconstrained model parametrization offered marked improvements over the default model formu-lation, capturing large-scale variation in patterns of biomass dynamics despite marked differencesin climate forcing, land-use history and species-composition across the region. These results implythat data-constrained parametrizations of structured biosphere models such as ED2 can be success-fully used for regional-scale ecosystem prediction and forecasting. We also assess the models ability tocapture sub-grid scale heterogeneity in the dynamics of biomass growth and mortality of differentsizes and types of trees, and then discuss the implications of these analyses for further reducing theremaining biases in the models predictions.

Keywords: terrestrial ecosystem dynamics; ecological prediction; terrestrial biosphere modelevaluation; northeastern USA; temperate forests

1. INTRODUCTIONTerrestrial biosphere models are critical tools for infer-ring the current state of terrestrial ecosystems, andpredicting changes in their composition, structure andfunction over the coming century. Previous modelinter-comparisons have found that terrestrial biospheremodels are able to replicate spatial patterns of potentialvegetation and seasonal patterns of changes in regionalatmospheric CO2, but that the different models divergein their predictions of ecosystem composition, struc-ture and function under novel climates [13]. Onerecent model inter-comparison [2] showed particu-larly striking discrepancies in North America, whereclimatecarbonvegetation feedbacks ranged from

r for correspondence ([email protected]).

ic supplementary material is available at http://dx.doi.org//rstb.2011.0253 or via http://rstb.royalsocietypublishing.org.

tribution of 16 to a Discussion Meeting Issue Predictivesystems approaches.

222

being minimal to causing large increases in forest cover.Because North America is still recovering from large-scale deforestation during the past century, there islarge uncertainty surrounding the magnitude of thecontinents current net carbon uptake [14], and itsability to sequester carbon in the future.

A powerful method of reducing model uncertaintyis to use observational datasets to estimate model par-ameters [5,6]. This approach is particularly relevantfor terrestrial biosphere models because a significantnumber of key parameters and aspects of model formu-lation, such as those determining patterns of carbonallocation, are difficult to measure directly. In NorthAmerica, observations spanning many spatial andtemporal scales and sensitive to many processes areavailable for constraining and evaluating the perform-ance of terrestrial biosphere models. These includeeddy-flux datasets associated with the Ameriflux net-work of eddy-flux towers [7], forest inventory datasetsfrom the USDA Forest Inventory and Analysis (FIA)and the Canadian Forest Service [8], and phenology

This journal is q 2011 The Royal Society

mailto:[email protected]://dx.doi.org/10.1098/rstb.2011.0253http://dx.doi.org/10.1098/rstb.2011.0253http://dx.doi.org/10.1098/rstb.2011.0253http://rstb.royalsocietypublishing.orghttp://rstb.royalsocietypublishing.orghttp://rstb.royalsocietypublishing.org/

Predicting regional ecosystem dynamics D. Medvigy & P. R. Moorcroft 223


data from the USA National Phenology Network [9].Canopy reflectance data [10] and measurements ofsoil carbon [11] may also be used to reduce modeluncertainty. In the near future, many other datasetswill be collected as part of the National EcologyObservatory Network (NEON) and will be broadlyavailable [12].

Early model-data synthesis studies [1315] focusedon evaluating terrestrial biosphere model predictionsagainst flux-tower measurements of net ecosystemexchange (NEE). Current model evaluation initiativessuch as the North American Carbon Programme(NACP) model-data intercomparison [16] have con-tinued to focus on NEE measurements, despite thecurrent availability of other diverse datasets. AlthoughNEE is a relevant diagnostic of current ecosystemfunction, an exclusive focus on NEE is problematicbecause NEE measurements by themselves are un-likely to provide sufficient information to adequatelyconstrain terrestrial biosphere model predictions [14].

As far as we are aware, the Ecosystem Demographyversion 2 (ED2) biosphere model was the first to besimultaneously constrained using eddy-flux mea-surements and forest inventory measurements [17].Specifically, these measurements included eddy-fluxtower measurements of CO2 and H2O and forestinventory measurements of tree growth and mortalityspanning a 2 year period at Harvard Forest (42.58 N,72.18 W). Subsequent comparison with independentdatasets showed that the constrained model formula-tion produced realistic estimates of eddy-fluxes, treegrowth and mortality dynamics on timescales rangingfrom hours to a decade. The generality of the modelwas illustrated by comparing model predictions againstecosystem measurements at Howland Forest (45.18 N,68.88 W), where the model realistically predictedthe observed patterns of carbon fluxes and treegrowth without further parameter adjustment, despitethe vegetation composition being markedly differentfrom that of Harvard Forest.

One reason that this approach to model par-ameterization and evaluation has not been adoptedpreviously is that conventional terrestrial biospheremodels use a canopy-as-big-leaf approximation thatlimits their ability to connect to tree-level measurementsof growth and mortality. In contrast, the structured bio-sphere model used by Penner et al. [18] comprises asystem of partial differential equations that approximatethe behaviour of a spatially distributed ensemble ofindividual plants [19], enabling it to predict thegrowth and mortality rates of individual plants, andthus directly connect to forest inventory measurementsof tree demography.

The goal of this paper is to evaluate ED2s ability topredict the regional decadal-scale biomass dynamics ofthe forests of northeastern North America. In 2, wedescribe the USA and Quebec (QC) forest inventorymeasurements that we used to evaluate the modelsperformance, summarize the ED2 model, and givedetails on our numerical simulation experiments. In3, we compare the predicted regional patterns ofgrowth and mortality to the observations. In 4, weexplore how the optimization improved the predic-tions and investigate the reasons for the remaining

Phil. Trans. R. Soc. B (2012)

discrepancies between the model and the observations.In 5, we present our conclusions.

2. METHODS(a) DatasetsDecadal scale forest inventory measurements coveringthe past two decades are available for the northeasternUSA and Quebec. In the USA, these forest censuseshave been undertaken under the auspices of the nationalFIA programme [8,20]. While the sampling designsvary, the methodology used in the northeastern USA isrelatively uniform: a grid of photo points overlaid onaerial photographs is used to generate a series of plotlocations stratified by land use and timber volume;then, individual measurements of tree diameter growthand tree status (alive or dead) are made over a censusinterval of approximately 10 years for trees having diam-eters greater than 5 inches (12.7 cm). Plots are assignedweighting factors specifying their proportion of the totallandscape sample. For further details see http://www.fs.fed.us/ne/fia/.

In a similar way, Canadian forest inventories havebeen undertaken on the provincial level and have beenused to obtain a detailed picture of Canadian forests[18]. In each on-the-ground inventory plot, all treeswith a diameter at breast height (DBH) greater than10 cm in a one-twentieth hectare area were measuredand identified to species. Unlike the FIA inventorydata, the Quebec inventory contains information on asubstantial fraction of the understory. Within eachplot, trees between 1 and 10 cm DBH were censusedin a 1/200th hectare sub-plot. The species and the sizeof each stem sub-plot were recorded; however, thetrees were not tagged, and thus were not re-censused.

The regional distribution of the forest inventoryplots is illustrated in figure 1a. Over 16 000 plotswere sampled in the US northeast of 408 N, 818 W,and over 11 000 plots were sampled in Quebec southof 528 N. Since most plots were inventoried twice,once in the mid-1980s and once in mid-1990s, itwas possible to compute rates of growth, naturalmortality and harvesting across the region.

(b) Model descriptionED2 is a terrestrial biosphere model providing a phys-ically and biologically consistent framework for bothshort-term and long-term studies of terrestrial ecosys-tem dynamics. The model uses a system of size- andage-structured partial differential equations to closelyapproximate the first moment behaviour of a cor-responding individual-based stochastic gap model torealistically represent fine-scale heterogeneity incanopy structure within each grid cell (figure 2). Thebiomass density of trees of type i, of size vector zexisting in a patch of age a at time t is denotedCi(z,a,t) and evolves according to:

@Ciz; a; t@t|fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl}

change in plant density

@Ciz; a; t@a|fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl}

ageing

giz; a; t @Ciz; a; t

@z|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}growth

miz; a; tCiz; a; t|fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}mortality

: 2:1

http://www.fs.fed.us/ne/fia/http://www.fs.fed.us/ne/fia/http://www.fs.fed.us/ne/fia/http://rstb.royalsocietypublishing.org/

50

(a)

(c) (d)

(b)

48

46

44

42

40

50

QC

ME

R1

NY

CT

VTNH

MAPA

48

46

44

42

50

48

46

44

42

AGB(tC ha1)40

78 76 74

10 20 30 40 50 60 70 80

harvest(tC ha1 y1) 0.1 0.3 0.4 0.5 0.7 0.8 0.9 1.1

72 70 68 78 76 74 72 70 68

50

48

46

44

42

40

78 76 74 72 70 6880 78 76 74 72 70 68 66

Figure 1. Forest inventory plots for the USA and Quebec. (a) Locations of USA (green) and Quebec (blue) inventory plots.There are over 16 000 plots northeast of 408 N, 818 W in the USA and over 11 000 plots south of 528 N in Quebec.(b) Locations of states and provinces. Abbreviations are expanded in the text. (c) AGB from forest inventories aggregated at0.258 resolution. (d) Decadal-mean harvesting rates of AGB for the forested plots. Data are missing for all Canadian gridcells outside of Quebec as well as for a few grid cells in northern Quebec; these grid cells are coloured black.

224 D. Medvigy & P. R. Moorcroft Predicting regional ecosystem dynamics


Note that z has three components correspondingto the sizes of the plants active, storage and dead bio-mass pools. Plant DBH and height are computed fromthese biomass pools via allometric relationships. Thegrowth rate of each compartment of z is given bygi(z,a,t), while the mortality rate is mi(z,a,t).

The changing landscape age-structure and associ-ated sub-grid scale heterogeneity arising from priordisturbance history is tracked in the model by:

@pja; t@t|fflfflfflffl{zfflfflfflffl}

change in landscape age structure

@pja; t@a|fflfflfflffl{zfflfflfflffl}

ageing

X

k

L jka; tpja; t|fflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

disturbance

;

2:2

where pj(a,t) is a vector whose elements describe thedistributions of times since disturbance for each ofthe m land-use states represented in the model, andLjk is an m m matrix whose elements describe thedisturbance history forcingthe rate at which landtransitions between the different land-use states as afunction of time and age (time since last disturbance).

Under the assumption that a fraction ri of seedsis randomly dispersed between gaps within a grid


cell and that all other seeds remain within theirgap of origin, the recruitment of new seedlingsfi(z,a,t) corresponds to a flux of individuals intothe system at (z0,a), yielding the following boundarycondition:

Ciz0; a; t 10

1z0

Ciz; a0; tfiz; a0; tpa0; tridz da0Pj gi;jz0; t|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

random dispersal

1z0

Ciz; a; tfiz; a; t1 ridzPj gi;jz0; t|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

local dispersal

:

2:3

Equation (2.1) has a second boundary conditionthat describes the state of the ecosystem after thedisturbance event:

Ciz; 0; t X

k

10

si;kz; a; tpka; tda|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}plant community following disturbance event

;

2:4

where the function si,k(z,a,t) describes the survivorshipof individuals of size z and type i following a disturbance

http://rstb.royalsocietypublishing.org/

atmospheric grid cell

patch 1 patch 2

(a)

(b)

(c)

(b) direct and diffuse NIR; (c) diffuse TIRradiation: (a) direct and diffuse PAR;

patch 3

fluxes: W, H, C

fluxes: W, H, C

dispersal and recruitment: f

gap y

C(y,t) N(y,t)

disturbance: lF

mortality: m

foliar growth: ga

stem growth: gs

fluxes: W, H, C

fluxes: W, H, C

patch 4(harvested)

patch 5

W3W2W1

W3W2W1

H3H2H1

Figure 2. ED2 model structure and processes. (a) Each grid cell is subdivided into a series of tiles. The relative area of eachtile is determined by the proportion of canopy-gap-sized areas within the grid cell having a similar canopy structure as a resultof a common disturbance history. (b) ED2 computes the multi-layer canopy fluxes of water, internal energy and carbon withineach sub-grid scale tile. (c) Summary of the long-term vegetation dynamics within each tile arising from the integrationof short-term fluxes shown in (b).



of type k. The rates of growth, mortality and recruit-ment within canopy are computed from acorresponding vertically and horizontally stratified setof equations that determine the landatmosphereexchange of carbon, water and energy between the eco-system and the atmosphere (figure 2). For a completedescription of the model, see [17].

Following Albani et al. [21], five empirically definedplant functional types (PFTs) differing in their physi-ology and life-history characteristics were used torepresent the range of vegetation found within thenortheastern USA: three deciduous broad-leaved treetypes (early-, mid- and late-successional), and twoconiferous tree types (northern pines (NP) and late-successional conifers (LWC)). These PFTs exhibitcorrelated differences in their leaf physiological traits,such as specific leaf area, leaf longevity, foliar C : Nratio, photosynthetic rate per unit leaf area (Vm0),and in their growth, mortality and recruitment of life-history characteristics (see electronic supplementarymaterial, table S1).


(c) Numerical experimentsWe evaluated the ED2 models regional-scale predic-tions of above-ground biomass (AGB) dynamics byconducting two simulations for the northeastern USAand southern Quebec at 0.258 0.258 resolution for19831995, a time interval that corresponds to theapproximate period of the measurements. The firstsimulation, which we refer to as ORIG, was a litera-ture-based parametrization used previously to conductsimulations for eastern North America [21]. Thesecond simulation, which we refer to as HF-OPT,used the constrained model parametrization that wasdeveloped by fitting the models predictions to 2 yearsof eddy-flux and tree census datasets from HarvardForest [17]. The first 2 years of simulation (19831984) were discarded to avoid the potential impact oftransient dynamics arising from the soil moisture initialcondition. The model was forced with solar radiation,long wave radiation, temperature, humidity, precipi-tation, wind speed and pressure data from theEuropean Centre for Medium-Range Weather Forecasts




(ECMWF) ERA-40 re-analysis dataset [22], while soiltextural class for each grid cell was specified from the18 18 resolution USDA global soil database, sincehigher resolution data were unavailable for Quebec. Asin Medvigy et al. [17], we drove the optimized modelwith average phenology obtained from moderate reso-lution imaging spectroradiometer (MODIS) between2001 and 2004 because region-wide phenology datawere not available for this period. The forest inventorymeasurements were also used to calculate the spatialpattern of forest harvesting (figure 1d), which werethen applied as a disturbance forcing to the model.

Initial conditions for the vegetation structure andcomposition at the beginning of the simulation wereprescribed using forest inventory measurements fromapproximately 1985. Each inventory plot was represen-ted as a sub-grid-scale tile (figure 2a), and individualtrees were assigned to the closest PFT [17]. However,our analysis excluded the relatively small number ofplots that occurred on highly atypical soils and plotsfor which growth and mortality information was notavailable. The resulting initial condition for total AGBis shown in figure 1c. There is a general latitudinal gra-dient, with values of about 50 tC ha21 typical in thesouth decreasing to about 20 tC ha21 in the north.Lower AGB levels near the Great Lakes and in Maine(ME) were exceptions to the general trend. Hardwoodshad more AGB than conifers in all US states, but hard-woods and conifers have roughly equal amounts ofbiomass in Quebec (figure 3a; see also figure 1b for amap of state and province locations). Early-successionalhardwoods (ESHWs; figure 3b) comprise 1020% ofthe AGB in all states and provinces (figure 3a),and are the dominant PFT in central Quebec. Mid-successional hardwoods (MSHWs; figure 3c) aredominant in Pennsylvania (PA), southern New York(NY), Connecticut (CT), Massachusetts (MA) andparts of southern Quebec. Late-successional hardwoods(LSHWs; figure 3d) are particularly common in north-ern NY, Vermont (VT), New Hampshire (NH) andME. NP are generally found in eastern MA, southernNH and southern ME (figure 3e). They do not domin-ate any of the grid cells. LWC are found mainly in thenorthern half of the domain and are the dominantspecies north of about 488 N (figure 3f ).

3. ANALYSIS AND RESULTS(a) Forest growth dynamicsThe observed pattern of forest growth within the regioncalculated from the forest inventory measurements isshown in figure 4a. Rates of biomass growth are highestin PA and western NY, and generally decrease as onemoves north and east across the region, with the lowestrates of accumulation being found in northern Quebecand ME. The corresponding predictions of the origin-al (ORIG) and constrained ED2 (HF-OPT) modelformulations are shown in figure 4b,c, respectively.ORIG systematically over-predicts biomass growthrates across the entire region (figure 4b), although itqualitatively reproduces the observed spatial gradientof higher biomass growth in NY and PA and lowerbiomass growth in ME and Quebec. Its area-averagedbias and root mean square error (r.m.s.e.) were 0.68


and 0.84 tC ha21 y21, respectively. The systematicover-prediction of growth rates was largely corrected inHF-OPT. (figure 4c; bias 20.06 tC ha21 y21;r.m.s.e. 0.41 tC ha21 y21). In particular, HF-OPTreproduces the observed variation in patterns of biomassgrowth across the New England states and Quebec (CT,RI, MA, VT, NH, ME and Quebec) both qualitativelyand quantitatively, although it under-predicts biomassgrowth in NYand PA. Figure 4d summarizes the overallspatial pattern, showing the observed and predictedaverage growth rates for each state/province.

(b) Forest mortality dynamicsThe observed pattern of forest biomass mortality acrossthe region is shown in figure 5a. The observed rates ofbiomass mortality are higher in southern Quebec thanin northern Quebec, the New England states, NY, orPA. The corresponding predictions of the original andconstrained ED2 model formulations are shown infigure 5b,c. The area-averaged biomass mortality pre-dicted by ORIG had a bias of 0.07 tC ha21 y21 and anr.m.s.e. of 0.35 tC ha21 y21. While the ORIG simu-lation captures the high-biomass mortality rates insouthern Quebec, it under-predicts biomass mortalityrates in northern Quebec, and its predictions of biomassmortality rates to the south (in the New England states,PA and NY) are unrealistically high, with discrepan-cies as high as 0.51 tC ha21 y21 in the southwest ofthe domain. The biomass mortality rates predictedby HF-OPT (bias 20.11 tC ha21 y21; r.m.s.e. 0.28 tC ha21 y21) are generally lower than those ofORIG. These lower biomass mortality rates are acloser match to the observations throughout the south,but the mismatch is increased in northern areas.Figure 5d shows the observed and predicted averagebiomass mortality rates for each state/province. As thefigure indicates, the ORIG formulation has reasonablemortality rates in Quebec but systematically over-predicts biomass mortality in all the US states exceptME. The predictions in the southernmost states (CT,NY and PA) are two to four times higher than theobserved rates. In contrast, the HF-OPT model formu-lation has substantially improved predictions for all ofthe US states, but under-predicts biomass mortalityrates in ME and Quebec.

(c) Sub-grid scale variability in growthand mortality dynamics

One of the distinguishing features of ED2s structuredbiosphere model equations (equations (2.1)(2.4)) isthat they predict AGB dynamics not only at the reso-lution of climatological grid cells, but also at thefiner sub-grid scales that characterize biotic heterogen-eity in canopy structure and dynamics. In this section,we evaluate the models predictions of size- and PFT-related sub-grid scale variability in patterns of biomassgrowth and mortality dynamics. Our objective in doingso is to identify patterns of sub-grid scale variability inmodel bias that can assist in identifying mechanismsand processes responsible for mis-matches in pre-dictions, For each grid cell, the relative bias in AGBgrowth (calculated as predicted growth/observedgrowth 2 1) for each tree size class was computed,


1.0

(a) (b)

(c) (d)

(e) ( f )

0.8

0.6

prop

ortio

n

0.4

0.2

PA NY CT RI MA VT NH ME QC0

5 10 15 20 25 30 35 40 5 10 15 20 25 30 35 40

5 10 15 20 25 30 35 405 10 15 20 25 30 35 40

5

50

48

46

44

42

50

48

46

44

42

50

48

46

44

42

50

48

46

44

42

50

48

46

44

42

76 74 72 70 68 66

76 74 72 70 68 66

76 74 72 70 68 6676 74 72 70 68 66

76 74 72 70 68 66

10 15 20 25 30 35 40

LSC LSHW ESHW

MSHWNP

Figure 3. AGB for different PFTs derived from forest inventories. (a) Shows the proportion of AGB contributed by each PFT,broken down by state and province. The remaining panels show the spatial distributions of PFT AGB: (b) early-successionalhardwoods (ESHWs); (c) mid-successional hardwoods (MSHWs); (d) late-successional hardwoods (LSHWs); (e) northernpines (NP); ( f ) late-successional conifers (LWC).



and then the bias values for each grid cell were strati-fied according to the observed mean growth rate.This grouping by size and by growth rate enablesassessment of the accuracy of the models predictionsfor plants with different levels of resource availabilitybecause the observed growth rate incorporates boththe direct impacts of a plants physical environmentand its biological environment arising from localcompetition for light, water and nutrients.

The growth rate bias of ORIG is shown in figure 6a.As would be expected from figure 4, the bias is


generally large and uniformly positive, with an averagebias across all growth rate and size classes of 150 percent. However, as the plot shows, the bias is largerfor small trees, and is generally larger for trees growingin unfavourable environments (i.e. in sites with lowgrowth rates). The corresponding growth rate biasfor HF-OPT model formulation (figure 6b) showsthat the bias is generally much smaller in magnitudethan in ORIG: the average bias in HF-OPT is only4.7 per cent. The bias of the trees in the largest sizeclass has shifted from a positive bias to a negative


50

(a) (b)

(c)

(d)

48

46

44

42

growth(tC ha1y1)

growth(tC ha1y1)40

50

48

46

44

42

40

50

48

46

44

42

40

4

3

2

1

0

grow

th r

ate

(tC

ha

1 y

1 )

PA NY CT RI MA VT NH ME QC

78 76 74

0.2 0.4 0.6 0.9 1.1 1.3 1.5 1.7

0.2 0.4 0.6 0.9 1.1 1.3 1.5 1.7

72 70 68

78 76 74 72 70 68

78 76 74 72 70 68

growth(tC ha1 y1) 0.2 0.4 0.6 0.9 1.1 1.3 1.5 1.7

Figure 4. Annual-average growth rates. (a) Forest inventory data (missing data are coloured black). (b) Results from ORIGsimulation. (c) Results from HF-OPT simulation. (d) State- and province-averaged growth rates: black bars, ORIG (initialmodel); dark grey bars, observed; pale grey bars, HF-OPT (optimized model).

(a) (b)

(c) (d)

0.90.80.70.60.50.40.20.1

mort(tC ha1 y1)

40

42

44

46

48

50

78 76 74 72 70 68

0.90.80.70.60.50.40.20.1

7840

42

44

46

48

50

76 74 72 70 68

mort(tC ha1 y1)

PA0

0.5

mor

talit

y ra

te (

tC h

a1

y1 )

1.0

1.5

NY CT RI MA VT NH ME QC

40

42

44

46

48

50

78 76 74 72 70 68

0.90.80.70.60.50.40.20.1

mort(tC ha1 y1)

Figure 5. Annual-average mortality rates. (a) Forest Inventory data. (b) Results from ORIG simulation. (c) Results fromHF-OPT simulation. (d) State- and province-averaged mortality rates: black bars, ORIG; dark grey bars, observed; palegrey bars, HF-OPT.





0.125

1.375

100

0

100

200

300

400(a)

(b)

15 25 35 45 55 65 75 85 growth rateclass (tC ha1 y1)

grow

th r

ate

bias

(%

)

DBH size class (cm)

0.125

1.375

100

0

100

200

300

400

15 25 35 45 55 65 75 85

grow

th r

ate

bias

(%

)

DBH size class (cm)

growth rateclass (tC ha1 y1)

Figure 6. Growth rate biases, 100% (simulated/observed 21), for (a) ORIG and (b) HF-OPT. For each grid cell, thebias was computed for each diameter size class. Then, gridcells were grouped according the grid cell-level growth rate.The mean bias from ORIG is 150% and the mean bias

from HF-OPT is 4.7%.

0.125

1.875

200

100

0

100

200

300(a)

(b)

15 25 35 45 55 65 75 85


mor

talit

y ra

te b

ias

(%)

DBH size class (cm)

0.125

1.875

200

100

0

100

200

300

15 25 35 45 55 65 75 85mor

talit

y ra

te b

ias

(%)

DBH size class (cm)


Figure 7. Panels (a) and (b) show the mortality rate bias,100% (simulated/observed 2 1), for ORIG and HF-OPT,respectively. For each site, the bias was computed for eachdiameter size class; then sites were grouped according thesite-level growth rate.



bias of similar magnitude, but in all other size classesthe bias is shifted towards zero. In terms of variationwith respect to grid-cell productivity, HF-OPToutper-formed ORIG in all productivity classes except those withthe highest growth rates. As the figure indicates, the re-parametrization improved region-wide growth rates byreducing the growth for trees of all size classes in allenvironments, but particularly for trees in moderatelyproductive sites. This remaining bias impacts northernQuebec, where growth is over-predicted, and also PAand NY, where growth is under-predicted. In the north,where observed growth rates are generally low, the pre-dicted growth of large trees is reasonably accurate butthe predicted growth rates of smaller trees remain toohigh. Conversely, in areas with high growth rates suchas PA and NY, the predicted growth rate of small treesis reasonable, but the large trees are predicted to growtoo slowly.

The dependence of biomass mortality on tree size andgrid cell-averaged growth rate is shown in figure 7a,bfor ORIG and HF-OPT. For small trees in sites withmoderate to high rates of growth, ORIG over-predictsmortality. However, the magnitude of the over-predic-tion is reduced in larger size classes and in grid cellswith lower growth rates. For trees with a DBH greaterthan 50 cm, ORIG under-predicts mortality regardlessof grid-cell productivity class. HF-OPT significantlyimproves the biomass mortality rates of small trees inmoderate- to high-productivity grid cells; however,there is very little difference between ORIG and


HF-OPT for trees in grid cells with the highest pro-ductivity, where both model formulations over-predictbiomass mortality. Mortality rates for trees with DBHgreater than about 40 cm are consistently under-pre-dicted. These results indicate that, in the generallyhigh-productivity sites found in the southern part ofthe domain (figure 5), the improved biomass mortalityrates in the constrained parametrization reflect a cancel-ling of errors: small trees experience excessive mortality,while larger trees experience too little. In contrast, in thegenerally low-productivity north, total mortality in theconstrained model remained too low, primarily becauseof insufficient large-tree mortality.

(d) Biomass dynamics of different plantfunctional types

Growth: The ESHW PFT was parametrized usingbirch and aspen as canonical species [21]. The pat-terns of biomass growth largely reflected the patternof occurrence (cf. figures 3b and 8a). The ORIGmodel formulation fared very poorly in predictingearly hardwood growth, over-predicting by a factor of23 (figure 8b and table 1). In contrast, the HF-OPT formulation had far more realistic biomassgrowth rates (figure 8c and table 1) throughout theregion, though in a few areas such as northern andwestern NY, the HF-OPT growth rates were slightlylower than observed.

MSHWs were parametrized on the basis of red oakand red maple. Observed growth rates decrease with




increasing latitude (figure 8d). As with the ESHWs, theORIG formulation substantially over-predicts MSHWbiomass growth (figure 8e and table 1). HF-OPTs pre-dictions are dramatically improved (figure 8f and table1), especially in New England and Quebec. However,in NY and PA, the growth rates predicted by HF-OPTare lower than observed, with the magnitude of under-prediction comparable with the under-prediction ofESHWs. It is the under-prediction of these two PFTsthat is responsible for the under-prediction of the bio-mass growth in the southwest of the domain seen infigure 4. LSHWs were parametrized on the basis ofsugar maple and beech [21]. As with all other PFTs,ORIG over-predicted the biomass growth of the late-successional PFT (compare figure 8g,h and table 1),and this over-prediction was corrected in HF-OPT(figure 8i and table 1), which closely matched theobserved pattern of late-successional growth (comparefigure 8g,i).

Evergreen needleleaf PFTs were classed either asNP or as LWC (principally hemlock, spruce and fir).The observed biomass growth rates of the northernpine PFT is highest in areas of southern New Englandand Quebec, where it is relatively abundant (figure 8j).The ORIG formulation systematically over-predictsthe rates of northern pine biomass growth (figure 8kand table 1), while HF-OPT successfully corrects theover-prediction of northern pine growth, yielding aspatial pattern that is quantitatively and qualitativelyaccurate (figure 8l and table 1). The observedgrowth rate of LWC increases with latitude, reachinga maximum in the northeast corner of the domain(figure 8m). The ORIG formulation systematicallyover-predicts the magnitude of late-successional coni-fer growth (figure 8n and table 1). The growth ratespredicted by the HF-OPT formulation are more realis-tic (figure 8o and table 1), but are still over-predicted,especially in the north of the domain. It is this discrep-ancy in the biomass growth of the late-successionalconifer PFT that is primarily responsible for HF-OPTs over-prediction of total biomass growth innorthern regions (figure 4).

(e) MortalityThe observed biomass mortality of the early hardwoodPFT was generally higher in the north (Quebec) thanin the south (figure 9a). In contrast, the observedbiomass mortality of MSHWs was highest in the south-ernmost part of the domain (figure 9d), and the LSHWshad their greatest biomass mortality rates in southernQuebec (figure 9g). In both ORIG (figure 9b) and HF-OPT (figure 9c), the ESHW biomass mortality ratesare too low in Quebec, but their mortality rates in theUS states match the observations more closely. In theORIG model formulation, the mid- and LSHW biomassmortality rates are over-predicted across the US states(figure 9e,h, respectively). HF-OPT reduces these ratesfor both the MSHWs and LSHWs (figure 9f,i, respect-ively) to the level of the observations throughout muchof the domain. The main exception is in western NY,where HF-OPT continues to over-predict mid- andlate-successional biomass mortality; however, this is


relatively small compared to the under-prediction ofESHW mortality in Quebec.

The observed northern pine mortality rates (figure 9j)are largest in north central Quebec but are generallya small component of the total mortality. The late-successional conifer biomass mortality rates are highestin the northeast of the domain (figure 9m), where theyare a significant component of the total AGB (figure3e). The ORIG model formulation over-predicts north-ern pine and late-successional conifer biomass mortalityin most of the USA, but under-predicts their biomassmortality in Quebec (figure 9k,n, respectively). In con-trast, HF-OPT predicts more realistic mortality ratesacross the US states; however, the under-prediction ofnorthern pine and late-successional conifer biomassmortality in Quebec increases (figure 9l,o, respectively).These results are summarized in table 2, which presentsthe area-averaged biomass mortality biases and r.m.s.e.sfrom ORIG and HF-OPT.

4. DISCUSSIONWe found that the HF-OPT formulation of the ED2biosphere model offered marked improvements overthe ORIG model parametrization in predicting regionaldecadal-scale biomass dynamics in the northeasternNorth America. HF-OPT predicted AGB growthmuch more realistically than ORIG (figure 4), decreas-ing the region-wide bias from 150 per cent to less than 5per cent, and also resulted in significant improvementsin rates of biomass mortality across the region (figure5). As discussed in more detail in Medvigy et al. [17],there were a number of important differences betweenthe ORIG and HF-OPT model formulations that con-tribute to this improved predictive ability. Inparticular, the improvements in AGB growth in HF-OPT relative to ORIG reflected higher rates of fineroot turnover, a shorter growing season for hardwoodsand a lower maximum photosynthetic rate for conifers,which all reduce rates of AGB growth.

The predictions of sub-grid scale heterogeneityhighlight how further improvements in the modelspredictions of AGB growth will require simultaneouslydecreasing the growth rates of small trees, particularlyin low-productivity sites, while increasing growthof high-productivity sites, especially for large trees(figure 6b). Several candidate mechanisms may explainthis sub-grid scale signature of the remaining inaccur-acies in HF-OPT AGB growth predictions. One sourceof model error may be the current model formulationsrelatively simple representation of plant diversity[2325]. ED2 differs from other biosphere modelsin having several temperate deciduous and coniferousPFTs represented; however, the particular speciesthat comprise each PFT class varies by location. Forexample, the predominant late-successional coniferspecies at Harvard Forest is eastern hemlock (Tsugacanadensis), while the dominant late-successional treein northern Quebec is black spruce (Picea mariana), aspecies that is not found at Harvest Forest. Thus, theover-prediction of late-successional conifer growth andunder-prediction of its biomass mortality, seen in figures8 and 9, respectively, implies that ED2 may benefitfrom the introduction of a separate boreal conifer


Table 1. Area-averaged biases and r.m.s.e.s for biomass

growth. Results are listed for all trees, and then forindividual PFTs. Grid cells not containing a particular PFTwere excluded from the calculations corresponding to thatPFT. Units are tC ha21 y21.

bias r.m.s.e.

PFT ORIG HF-OPT ORIG HF-OPT

all 0.68 20.06 0.84 0.41ESHW 0.34 20.04 0.44 0.16MSHW 0.20 20.10 0.30 0.19LSHW 0.08 0.01 0.16 0.10NP 0.06 0.03 0.15 0.11LSC 0.08 0.05 0.20 0.15



PFT. Similarly, in NY and PA, where growth rates areunder-predicted, Prunus spp. comprise over 90 percent of the ESHW PFT, while in the New Englandstates and Quebec, the ESHWs are primarily birches(Betula spp.) and poplar (Populus spp.).

An alternative explanation for the above discrepanciesis the absence of physiological, morphological or bio-chemical acclimation of PFT traits in different regions[2629]. Distinguishing between these two potentialexplanations is important because the time scales of eco-system responses arising from acclimation responseswithin individuals to changes in climate forcing arelikely to be considerably shorter than ecosystem responsesarising from climate-induced shifts in the species-compo-sition. Furthermore, the simulations did not account forthe effects of nitrogen deposition and increasing CO2concentrations. In particular, the absence of nitrogendeposition could potentially explain the under-predictionof growth rates in PA and western NY [30], where nitro-gen deposition rates are high. We also expect that animproved radiative transfer scheme that accounted forthe structure of neighbouring patches would likelyreduce the amount of light captured by small trees, redu-cing their growth rates, and thus reducing the bias withthe observations (figure 6b). The under-estimation ofAGB growth of large trees may be corrected by allow-ing leaf nitrogen to vary as a function of an individualsposition in the canopy [31,32], resulting in increasedphotosynthetic rates for the largest trees.

HF-OPToffered better mortality rate predictions thanthe default model, especially for small trees in thesouthern part of the region (figures 5 and 7). The totalmortality rate in ED2 (equation 2.1) is the sum of a den-sity-dependent rate and a density-independent rate. Thedensity-dependent rate becomes large as trees fall intonegative carbon balance. Because ORIG and HF-OPThave identical density-independent rates, the differencesbetween their mortality predictions must be due to differ-ences in the density-dependent mortality rates. The sitesin NY and PA have relatively large AGB (figure 1c), andmay also be expected to have large leaf area indices.The small trees in such sites are relatively shaded and,in the model, would have relatively large density-depen-dent mortality rates. Re-parametrization in HF-OPTdecreased the impact of shading on mortality, thoughfigure 7b suggests that it has not yet been reducedenough in the highest productivity sites. One way to


address this problem would be to reduce leaf nitrogenfor understorey trees, which would reduce leaf respirationand thus improve the carbon balance of these trees [33].More realistic representations of crown shape andaccounting for phototropism would also be likely toreduce mortality of shaded individuals [34].

Trees in larger size classes are not shaded, and soexperience mainly density-independent mortality. Inboth the ORIG and HF-OPT, density-independentmortality was treated as a constant rate for each PFT,independent of size and was calibrated on the basis ofthe mortality of trees solely from the FIA. The Quebecinventory was not used to calibrate these mortalityrates. In the highly productive sites of the northeasternUSA, over-prediction of small tree mortality balancedthe under-prediction of large tree mortality, leadingto a realistic average. However, in Canada, biomassdensities are lower (figure 1c), and so smaller trees areless shaded and do not experience excessive density-dependent mortality. Since the large trees continue toexperience insufficient density-independent mortality,the overall result is an under-prediction of mortality.Several avenues will likely have to be explored in orderto address this issue. First, model error may be reflectingthe absence of climatic variation in density-independentmortality [35,36]. Allowing mortality rates to increasein areas experiencing cold winter, or springtime temp-eratures would increase mortality rates in Quebecrelative to states like NY or PA. Second, ED2 did notexplicitly include the impact of other sources of den-sity-dependent mortality, such as insect outbreaks. Inparticular, omitting the impacts of the spruce budworm(Choristoneura fumiferana Clem.), for example, mayhave caused the model to under-predict mortality ofLWC in Quebec [37].

5. CONCLUSIONSWe found that the HF-OPT model parametrizationof the ED2 biosphere model developed by Medvigyet al. [17] offers a marked improvement over the ORIGliterature-based model parametrization of Albani et al.[21] in predicting regional decadal-scale biomassdynamics in the northeastern USA and in Quebec.The biomass growth rates of all trees were reduced,and the mean model bias was nearly eliminated. Mor-tality improved mainly for small trees in moderate tohigh-productivity grid cells. These results of the analysisare encouraging as they demonstrate how short-termflux tower measurements, when combined with forestinventory measurements of forest demography can besuccessfully used to develop terrestrial biosphere modelparametrizations that are general, not site-specific,and that these parametrizations can yield dramaticincreases in the accuracy of a terrestrial biospheremodels predictions of long-term, large-scale, terrestrialecosystem dynamics.

An important next milestone in assessing thepredictive capabilities of terrestrial biosphere modelsis assessing their ability to capture inter-annualvariability in biomass dynamics. The infrequent,approximately once per decade, re-measurementperiod in historical forest inventory data precludes anexamination of inter-annual variability in AGB


0.1

(a) (b) (c)

(d) (e) ( f )

(g) (h) (i)

( j) (k) (l)

(m) (n) (o)

0.1 0.1

50

48

46

44

42

50

48

46

44

42

50

48

46

44

42

50

48

46

44

42

50

48

46

44

42

78 76 74 72 70 68 78 76 74 72 70 68 78 76 74 72 70 68

0.2 0.3 0.4 0.6 0.7 0.8 0.9

0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9

0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9

0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9

0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9

0.2 0.3 0.4 0.6 0.7 0.8 0.9 0.2 0.3 0.4 0.6 0.7 0.8 0.9

Figure 8. (ac) Growth rates (tC ha21 y21) for ESHWs; (d f ) MSHWs; (g i) LSHWs; ( j l ) NP and (mo) LSC. The leftcolumn shows the forest inventory observations, the centre column shows the results from the ORIG simulation, and the rightcolumn shows results from the HF-OPT simulation.





50

(a) (b) (c)

(d) (e) ( f )

(g) (h) ( i)

( j) (k) ( l)

(m) (n) ( o)

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

48

46

44

42

50

48

46

44

42

50

48

46

44

42

50

48

46

44

42

50

48

46

44

42

78 76 74 72 70 68 78 76 74 72 70 68 78 76 74 72 70 68

Figure 9. Mortality rates (tC ha21 y21) for (ac) ESHWs; (d f ) MSHWs; (g i) LSHWs; ( j l ) NP and (mo) LWC. The leftcolumn shows the forest inventory observations, the centre column shows the results from the ORIG simulation and the rightcolumn shows results from the HF-OPT simulation.





Table 2. Area-averaged biases and r.m.s.e.s for biomass

mortality. Results are listed for all trees, and then forindividual PFTs. Grid cells not containing a particular PFTwere excluded from the calculations corresponding to thatPFT. Units are tC ha21 y21.

bias r.m.s.e.

PFT ORIG HF-OPT ORIG HF-OPT

all 0.07 20.11 0.35 0.28ESHW 0.01 20.05 0.12 0.11MSHW 0.07 0 0.14 0.10LSHW 0.02 0 0.09 0.07NP 0 20.01 0.04 0.03LSC 20.02 20.05 0.11 0.11



dynamics. However, the FIA has also recently beenchanged, and now the re-censusing of plots is staggeredin time. This will help forest inventories to be sensitive tointerannual variability, and, as a result, the regionalimpacts of climate and disturbances such as ice stormsand pathogen attacks on vegetation growth and mor-tality rates should be accessible to observation.Evaluations of terrestrial biosphere model predictionsof interannual variability in AGB dynamics againstthese datasets offer the promise of providing a criticalassessment of whether current terrestrial biospheremodel formulations have the accurate levels of climatesensitivity that are essential for accurate prediction ofterrestrial ecosystem responses to future climate change.

We thank the USDA FIA programme and the Quebec ForestInventory for providing the forest inventory measurements thatwere used in this analysis. These datasets were an invaluablesource of information for assessing the regional-scalepredictions of the ED2 model.

REFERENCES1 Cramer, W. et al. 2001 Global response of terrestrial

ecosystem structure and function to CO2 and climate

change: results from six dynamic global vegetationmodels. Glob. Change Biol. 7, 357373. (doi:10.1046/j.1365-2486.2001.00383.x)

2 Sitch, S. et al. 2008 Evaluation of the terrestrial carboncycle, future plant geography and climate-carbon cyclefeedbacks using five dynamic global vegetation models(DGVMs). Glob. Change Biol. 14, 20152039. (doi:10.1111/j.1365-2486.2008.01626.x)

3 Friedlingstein, P. et al. 2006 Climate-carbon cyclefeedback analysis: results from the C4MIP model inter-comparison. J. Climate 19, 33373353. (doi:10.1175/JCLI3800.1)

4 Peters, W. et al. 2007 An atmospheric perspective onNorth American carbon dioxide exchange: carbonTracker.

Proc. Natl Acad. Sci. USA 104, 18 92518 930. (doi:10.1073/pnas.0708986104)

5 Raupach, M. R., Rayner, P. J., Barrett, D. J., DeFries,R. S., Heimann, M., Ojima, D. S., Quegan, S. &Schmullius, C. C. 2005 Model-data synthesis in terres-

trial carbon observation: methods, data requirementsand data uncertainty specifications. Glob. Change Biol.11, 378397. (doi:10.1111/j.1365-2486.2005.00917.x)

6 Williams, M. et al. 2009 Improving land surface modelswith FLUXNET data. Biogeosciences 6, 13411359.(doi:10.5194/bg-6-1341-2009)


7 Baldocchi, D. D. 2003 Assessing the eddy covariancetechnique for evaluating carbon dioxide exchange ratesof ecosystems: past, present and future. Glob. ChangeBiol. 9, 114. (doi:10.1046/j.1365-2486.2003.00629.x)

8 Smith, W. B., Miles, P. D., Vissage, J. S. & Pugh, S. A.2004. Forest resources of the United States, 2002. Gen-eral Technical Report NC-241. USDA Forest Service,North Central Research Station, St. Paul, USA.

9 Betancourt, J. L., Schwartz, M. D., Breshears, D. D.,Brewer, C. A., Frazer, G., Gross, J. E., Mazer, S. J.,Reed, B. C. & Wilson, B. E. 2007 Evolving plans forthe USA National Phenology Network. EOS Trans.AGU 88, 211. (doi:10.1029/2007EO190007)

10 Quaife, T., Lewis, P., Dekauwe, M., Williams, M., Law, B.,Disney, M. & Bowyer, P. 2008 Assimilating canopy reflect-ance data into an ecosystem model with an EnsembleKalman Filter. Remote Sens. Environ. 112, 13471364.(doi:10.1016/j.rse.2007.05.020)

11 Xu, T., White, L., Hui, D. F. & Luo, Y. Q. 2006 Prob-abilistic inversion of a terrestrial ecosystem model:analysis of uncertainty in parameter estimation andmodel prediction. Glob. Biogeochem. Cycle 20, GB2007.(doi:2010.1029/2005GB002468)

12 Keller, M., Schimel, D. S., Hargrove, W. W. & Hoffman,F. M. 2008 A continental strategy for the National Eco-logical Observatory Network. Front. Ecol. Environ. 6,282284. (doi:10.1890/1540-9295(2008)6[282:ACSF

TN]2.0.CO;2)13 Reichstein, M. et al. 2003 Inverse modeling of seasonal

drought effects on canopy CO2/H2O exchange in threeMediterranean ecosystems. J. Geophys. Res. 108, 4726.(doi:10.1029/2003JD003430)

14 Braswell, B. H., Sacks, W. J., Linder, E. & Schimel, D. S.2005 Estimating diurnal to annual ecosystem par-ameters by synthesis of a carbon flux model with eddycovariance net ecosystem exchange observations. Glob.Change Biol. 11, 335355. (doi:10.1111/j.1365-2486.2005.00897.x)

15 Knorr, W. & Kattge, J. 2005 Inversion of terrestrial eco-system model parameter values against eddy covariancemeasurements by Monte Carlo sampling. Glob. ChangeBiol. 11, 13331351. (doi:10.1111/j.1365-2486.2005.00977.x)

16 Post, M., Huntzinger, D. N., Michalak, A. M., Wei, Y.,Jacobson, A. R., Cook, R. B. & Regional-Interim SynthesisParticipants, Regional/Continental Interim-Synthesis

Team Author(s) 2010 North American Carbon Program(NACP) Interim Synthesis Project: regional forwardmodel intercomparision. Abstract B31D-0337 presented at2010 Fall Meeting, AGU, San Francisco, CA, USA, 1317December.

17 Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger,D. Y. & Moorcroft, P. R. 2009 Mechanistic scaling ofecosystem function and dynamics in space and time:Ecosystem Demography model version 2. J. Geophys.Res. 114, G01002. (doi:10.1029/2008JG000812)

18 Penner, M., Power, K., Muhairwe, C., Tellier, R. &Wang, Y. 1997 Canadas forest biomass resources: deriv-ing estimates from Canadas Forest Inventory.Information Report No BC-X-370. Canadian Forest

Service, Victoria, BC.19 Moorcroft, P. R., Hurtt, G. C. & Pacala, S. W. 2001

A method for scaling vegetation dynamics: the Eco-system Demography model (ED). Ecol. Monogr. 71,557586. (doi:10.1890/0012-9615(2001)071[0557:

AMFSVD]2.0.CO;2)20 Frayer, W. E. & Furnival, G. M. 1999 Forest survey

sampling designs: a history. J Forestry 97, 410.21 Albani, M. A., Medvigy, D., Hurtt, G. C. & Moorcroft,

P. R. 2006 The contributions of land-use change, CO2

http://dx.doi.org/10.1046/j.1365-2486.2001.00383.xhttp://dx.doi.org/10.1046/j.1365-2486.2001.00383.xhttp://dx.doi.org/10.1111/j.1365-2486.2008.01626.xhttp://dx.doi.org/10.1111/j.1365-2486.2008.01626.xhttp://dx.doi.org/10.1175/JCLI3800.1http://dx.doi.org/10.1175/JCLI3800.1http://dx.doi.org/10.1073/pnas.0708986104http://dx.doi.org/10.1073/pnas.0708986104http://dx.doi.org/10.1111/j.1365-2486.2005.00917.xhttp://dx.doi.org/10.5194/bg-6-1341-2009http://dx.doi.org/10.1046/j.1365-2486.2003.00629.xhttp://dx.doi.org/10.1029/2007EO190007http://dx.doi.org/10.1016/j.rse.2007.05.020http://dx.doi.org/2010.1029/2005GB002468http://dx.doi.org/10.1890/1540-9295(2008)6[282:ACSFTN]2.0.CO;2http://dx.doi.org/10.1890/1540-9295(2008)6[282:ACSFTN]2.0.CO;2http://dx.doi.org/10.1029/2003JD003430http://dx.doi.org/10.1111/j.1365-2486.2005.00897.xhttp://dx.doi.org/10.1111/j.1365-2486.2005.00897.xhttp://dx.doi.org/10.1111/j.1365-2486.2005.00977.xhttp://dx.doi.org/10.1111/j.1365-2486.2005.00977.xhttp://dx.doi.org/10.1029/2008JG000812http://dx.doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2http://dx.doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2http://rstb.royalsocietypublishing.org/



fertilization, and climate variability to the eastern UScarbon sink. Glob. Change Biol. 12, 23702390.(doi:10.1111/j.1365-2486.2006.01254.x)

22 Uppala, S. M. et al. 2005 The ERA-40 re-analysis.Q. J. R. Meteorol. Soc. 131, 29613012. (doi:10.1256/qj.04.176)

23 Neilson, R. P., Pitelka, L. F., Solomon, A. M., Nathan, R.,Midgley, G. F., Fragoso, J. M. V., Lischke, H. &

Thompson, K. 2005 Forecasting regional to global plantmigration in response to climate change. Bioscience 55,749759. (doi:10.1641/0006-3568(2005)055[0749:FRTGPM]2.0.CO;2)

24 Hector, A. & Bagchi, R. 2007 Biodiversity and ecosystemmultifunctionality. Nature 448, 188191. (doi:10.1038/nature05947)

25 Purves, D. & Pacala, S. 2008 Predictive models of forestdynamics. Science 320, 14521453. (doi:10.1126/science.1155359)

26 Atkin, O. K., Atkinson, L. J., Fisher, R. A., Campbell,C. D., Zaragoza-Castells, J., Pitchford, J. W., Woodward,F. I. & Hurry, V. 2008 Using temperature-dependentchanges in leaf scaling relationships to quantitatively

account for thermal acclimation of respiration in a coupledglobal climate-vegetation model. Glob. Change Biol. 14,27092726. (doi:10.1111/j.1365-2486.2008.01664.x)

27 Rodrguez-Calcerrada, J., Atkin, O. K., Robson, T. M.,Zaragoza-Castells, J., Gil, L. & Aranda, I. 2010 Thermal

acclimation of leaf dark respiration of beech seedlingsexperiencing summer drought in high and low lightenvironments. Tree Physiol. 30, 214224. (doi:10.1093/treephys/tpp104)

28 Nicotra, A. B. et al. 2010 Plant phenotypic plasticity in achanging climate. Trends Plant Sci. 15, 684692. (doi:10.1016/j.tplants.2010.09.008)

29 Falster, D. S., Brannstrom, A., Dieckmann, U. &Westoby, M. 2011 Influence of four major plant traits

on average height, leaf-area cover, net primary prod-uctivity, and biomass density in single-species forests: a


theoretical investigation. J. Ecol. 99, 148164. (doi:10.1111/j.1365-2745.2010.01735.x)

30 Goodale, C. L. et al. 2002 Forest carbon sinks inthe northern hemisphere. Ecol. Appl. 12, 891899.(doi:10.1890/1051-0761(2002)012[0891:FCSITN]2.0.CO;2)

31 Reich, P. B., Walters, M. B., Ellsworth, D. S., Vose,J. M., Volin, J. C., Gresham, C. & Bowman, W. D.

1998 Relationships of leaf dark respiration to leaf nitro-gen, specific leaf area and leaf life-span: a test acrossbiomes and functional groups. Oecologia 114, 471482.(doi:10.1007/s004420050471)

32 Wright, I. J. et al. 2004 The worldwide leaf economicsspectrum. Nature 428, 821827. (doi:10.1038/nature02403)

33 Toda, M. et al. 2011 Simulating seasonal and inter-annual variations in energy and carbon exchanges and

forest dynamics using a process-based atmosphere-vegetation dynamics model. Ecol. Res. 26, 105121.(doi:10.1007/s11284-010-0763-6)

34 Strigul, N., Pristinski, D., Purves, D., Dushoff, J. &Pacala, S. 2008 Scaling from trees to forests: tractable

macroscopic equations for forest dynamics. Ecol.Monogr 78, 523545. (doi:10.1890/08-0082.1)

35 Keane, R. E., Austin, M., Field, C., Huth, A., Lexer,M. J., Peters, D., Solomon, A. & Wyckoff, P. 2001Tree mortality in gap models: application to climate

change. Clim. Change 51, 509540. (doi:10.1023/A:1012539409854)

36 Allen, C. D. et al. 2010 A global overview of drought andheat-induced tree mortality reveals emerging climate

change risks for forests. Forest Ecol. Manag. 259,660684. (doi:10.1016/j.foreco.2009.09.001)

37 Bouchard, M., Kneeshaw, D. & Bergeron, Y. 2005Mortality and stand renewal patterns following the lastspruce budworm outbreak in mixed forests of western

Quebec. Forest Ecol. Manag. 204, 297313. (doi:10.1016/j.foreco.2004.09.017)

http://dx.doi.org/10.1111/j.1365-2486.2006.01254.xhttp://dx.doi.org/10.1256/qj.04.176http://dx.doi.org/10.1256/qj.04.176http://dx.doi.org/10.1641/0006-3568(2005)055[0749:FRTGPM]2.0.CO;2http://dx.doi.org/10.1641/0006-3568(2005)055[0749:FRTGPM]2.0.CO;2http://dx.doi.org/10.1038/nature05947http://dx.doi.org/10.1038/nature05947http://dx.doi.org/10.1126/science.1155359http://dx.doi.org/10.1126/science.1155359http://dx.doi.org/10.1111/j.1365-2486.2008.01664.xhttp://dx.doi.org/10.1093/treephys/tpp104http://dx.doi.org/10.1093/treephys/tpp104http://dx.doi.org/10.1016/j.tplants.2010.09.008http://dx.doi.org/10.1016/j.tplants.2010.09.008http://dx.doi.org/10.1111/j.1365-2745.2010.01735.xhttp://dx.doi.org/10.1111/j.1365-2745.2010.01735.xhttp://dx.doi.org/10.1890/1051-0761(2002)012[0891:FCSITN]2.0.CO;2http://dx.doi.org/10.1890/1051-0761(2002)012[0891:FCSITN]2.0.CO;2http://dx.doi.org/10.1007/s004420050471http://dx.doi.org/10.1038/nature02403http://dx.doi.org/10.1038/nature02403http://dx.doi.org/10.1007/s11284-010-0763-6http://dx.doi.org/10.1890/08-0082.1http://dx.doi.org/10.1023/A:1012539409854http://dx.doi.org/10.1023/A:1012539409854http://dx.doi.org/10.1016/j.foreco.2009.09.001http://dx.doi.org/10.1016/j.foreco.2004.09.017http://dx.doi.org/10.1016/j.foreco.2004.09.017http://rstb.royalsocietypublishing.org/

Predicting ecosystem dynamics at regional scales: an evaluation of a terrestrial biosphere model for the forests of northeastern North AmericaIntroductionMethodsDatasetsModel descriptionNumerical experiments

Analysis and resultsForest growth dynamicsForest mortality dynamicsSub-grid scale variability in growth and mortality dynamicsBiomass dynamics of different plant functional typesMortality

DiscussionConclusionsWe thank the USDA FIA programme and the Quebec Forest Inventory for providing the forest inventory measurements that were used in this analysis. These datasets were an invaluable source of information for assessing the regional-scale predictions of the ED2 model.REFERENCES

Date post:	06-Apr-2019
Category:	Documents
Upload:	lamhuong
View:	215 times
Download:	0 times

Predicting ecosystem dynamics at regional scales: an ... · carbon cycle and forecasting...

Documents