Evaluating the impacts of climate variability and disturbance regimes on the historic carbon budget of a forest landscape

Agricultural and Forest Meteorology 180 (2013) 265–280

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Evaluating the impacts of climate variability and disturbance regimes on the historic carbon budget of a forest landscape B. Chen a,b , M.A. Arain b,∗ , M. Khomik c , J.A. Trofymow d , R.F. Grant e , W.A. Kurz d , J. Yeluripati b , Z. Wang e a

International Institute for Earth System Science, Nanjing University, Nanjing, China McMaster Center for Climate Change and School of Geography and Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada c Graduate School of Geography, Clark University, Worcester, MA, USA d Natural Resources Canada, Canadian Forest Service, Pacific Forestry Center, Victoria, British Columbia, Canada e University of Alberta, Edmonton, Alberta, Canada b

a r t i c l e

i n f o

Article history: Received 3 October 2012 Received in revised form 31 May 2013 Accepted 6 June 2013 Keywords: Historic carbon dynamics Disturbance Climate sensitivity analysis Forest landscape Douglas fir Canadian Land Surface Scheme

a b s t r a c t Landscape-level understanding of forest carbon (C) dynamics is required to quantify the net contribution of forest biomes to the global C cycle and to help forest managers to understand the impacts of forest management activities on the C sequestration in forests. In this study, the effects of interannual climate variability, carbon dioxide (CO2 ) fertilization, and disturbance regimes on the C dynamics of an oldgrowth Pacific Northwest temperate conifer forest landscape (2500 ha) were studied from 1920 to 2005, using a process-based land surface model, known as the Carbon and Nitrogen coupled Canadian Land Surface Scheme (CN-CLASS). The model was parameterized with ecological, forest inventory and historical land-use data, and run using historical meteorological observations. Before performing landscape level simulations, model results were evaluated against eddy covariance flux tower observations. Simulated mean annual net ecosystem productivity (NEP) over the flux tower footprint area was 340 g C m−2 yr−1 from 1998 to 2005, while the measured value was 293 ± 20.5 g C m−2 yr−1 . When two anomalous weather ˜ (1998) and La Nina ˜ (1999) events, were excluded while performing stayears, corresponding to El Nino tistical analysis, measured and simulated fluxes were highly, but negatively, correlated to both annual mean air temperature and annual precipitation (R2 = 0.69 and 0.60, respectively). Interannual variability of simulated and measured NEP over the flux footprint area, calculated as deviations of the respective annual NEP values, was 143 g C m−2 yr−1 and 61 g C m−2 yr−1 , respectively. On the landscape-level, prior to disturbance in 1920, simulated C fluxes indicated that the forest landscape was close to C neutral, with annual net biome productivity (NBP) of 0.8 g C m−2 yr−1 . However, during the intense disturbance period from 1938 to 1944, landscape-level NBP reached about −5083 g C m−2 yr−1 . Then from 1951 to 1997, when there were no major disturbance events, NBP gradually recovered to about 365 g C m−2 yr−1 . At the end of the study period, in 2005, the landscape again became C source, due to harvesting of second growth stands that occurred from late 1990s to 2000s. The regression of age-detrended variations in the simulated annual C fluxes to mean daily maximum air temperature over the peak growing season (July–September), during an undisturbed period from 1963 to 1984, indicated that summer temperature was the dominant climatic control on landscape-level C fluxes. Higher temperatures caused a decrease in gross primary productivity at almost twice the rate of increase in ecosystem respiration (i.e. 27 g C m−2 yr−1 C−1 versus 15.7 g C m−2 yr−1 C−1 , respectively). A sensitivity analysis to evaluate the impacts of climate variability and disturbances showed that the relative effect of disturbance on carbon stocks was greater than the effect on carbon fluxes. Overall CO2 fertilization effects were minor. Disturbance type and severity, represented by the standard deviation in NBP, as described in the model, determined the magnitude of the simulated C losses to the atmosphere. This study enhances our understanding of the impacts of future climate change and forest management on landscape-level C dynamics in forests. © 2013 Elsevier B.V. All rights reserved.

1. Introduction ∗ Corresponding author. Tel.: +1 905 525 9140x27941; fax: +1 905 546 0463. E-mail address: [email protected] (M.A. Arain). 0168-1923/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agrformet.2013.06.002

Forests play an important role in the global carbon (C) cycle by sequestering large amounts of C from the atmosphere (Beer et al.,

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2010; Pan et al., 2011). Global forests store about 861 ± 66 Pg C, with 383 ± 30 Pg C (44%) in the upper 1 m soil and, 363 ± 28 Pg C (42%) in live biomass (both above- and below-ground), 73 ± 6 Pg C (8%) in deadwood and 43 ± 3 Pg C (5%) in litter (Pan et al., 2011). Thirty percent (about 1.2 billion hectares) of Earth’s forests are managed, primarily for the production of wood and non-wood products (FAO, 2010). In Canada, about 77% (240 million hectares) of forests are managed forests (Kurz et al., 2008). These forests undergo various management regimes, such as: harvesting, thinning, and reforestation. In addition, managed forests are also affected by natural disturbances, such as: fires, insect attack and wind storms. These disturbance events affect forested landscapes significantly and cause changes in their species composition, age structure and the amount of C they sequester and store. Recent measurement and modeling studies found that disturbance was the primary mechanism that changes forest ecosystems from C sinks to sources (Baldocchi, 2008; Kurz et al., 2008; Amiro et al., 2010). Therefore, disturbances, their characteristics, and forest age need to be taken into account to improve the accuracy of C flux and stock estimates in forest ecosystems (Amiro et al., 2010; Peichl et al., 2010). Significant progress has been made in studying the impacts of various disturbance and forest management regimes on landscapelevel C fluxes and stocks using forest growth and yield models or process-based models (Harmon et al., 1990; Kurz et al., 1997, 2009; Song and Woodcock, 2003; Smithwick et al., 2007). Makela et al. (2000) and Xi et al. (2009) have reviewed various forest landscape models and their applications. Most of the forest landscape models are developed as management planning and C accounting tools and often do not consider impacts of inter-annual variations in climatic conditions (e.g. temperature and precipitation) on the C fluxes and stocks. Therefore, these models have severe limitations in projecting landscape-level C fluxes and stocks under future climate changes. On the other hand, land surface schemes, used in the global and regional climate models, mechanistically simulate C, water and energy exchanges between the land surface and the atmosphere (Bonan, 1995; Foley et al., 1996; Sellers et al., 1996, 1997; Dickinson et al., 1998; Kucharik et al., 2000; Cox, 2001; Arain et al., 2002; Thornton et al., 2002; Wang et al., 2002a,b; Sitch et al., 2003; Krinner et al., 2005; Friedlingstein et al., 2006; Schaefer et al., 2008). Some of these land surface schemes also included nitrogen processes to account for nutrient cycling feedbacks on C, water and energy exchanges in vegetation ecosystems (Wang et al., 2001; Liu et al., 2005; Arain et al., 2006; Thornton et al., 2007; Yuan et al., 2007; Sokolov et al., 2008; Xu and Prentice, 2008; Thornton et al., 2009; Bonan and Levis, 2010; Huang et al., 2011) and a few have incorporated prescribed disturbances (Thornton et al., 2002; Masek and Collatz, 2006). Disturbances (i.e. forest harvest, fire, insect outbreak and wind storm) are the major mechanisms that convert forest ecosystems from C sinks to sources in the short term, spanning from a few hours to a few years (Amiro et al., 2010). Disturbance effects on forest C cycle may last many decades afterwards due to changes in stand structure, energy and water balances and decomposition of woody debris and other dead organic matter left on the ground. Therefore, inclusion of disturbance matrices describing the flow of C between pre-disturbance and post-disturbance pools in the process-based land surface models would provide capabilities of studying, both, the impact of climate variability and the effects of disturbance regimes on the C dynamics of forest landscapes. It would also allow examining the effects of long-term trends in climate and atmospheric CO2 concentration on forest C budget. In this study, we incorporated disturbance matrices from the inventory based Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3; Kurz et al., 2009) into the Carbon and Nitrogen coupled Canadian Land Surface Scheme (CN-CLASS; Arain et al., 2006). We

applied this model over a 2500 ha temperate forest landscape in British Columbia, Canada, from 1920 to 2005, to investigate its historic C budget. Trofymow et al. (2008) have described the details of the application of CBM-CFS3 to preparing a retrospective C budget for the area. The specific objectives of this study were (i) to compare model predictions of C flux with contemporary flux tower measurements at three sites within this area, (ii) to investigate the impacts of climate variability and long-term trends in atmospheric CO2 concentrations on the landscape-level historic C dynamics, and (iii) to explore how the interaction of disturbance events with climate variability and CO2 fertilization affect landscape-level C fluxes and stocks.

2. Materials and methods 2.1. Study area and its disturbance history The study landscape (49◦ 52 8.2 N, 49◦ 52 8.2 N) is located on Vancouver Island, British Columbia, Canada. It covers a 5 km × 5 km (2500 ha) area and is bisected by the Oyster River. The elevation of Oyster River landscape varies from about 499 m in the southwest to around 120 m in the northeast. Forests in the study area are a mix of second growth and third growth stands. However, the majority of area (1863 ha) is dominated by even-aged, second-growth Douglas-fir stands, among which there are 1426 ha of pure Douglasfir forest and around 437 ha of Douglas fir mixed with western red cedar (Thuja plicata), red alder (Alnus rubra) and western hemlock (Tsuga heterophylla). Two eddy covariance flux towers are located within the study landscape: one at the DF49 site (an intermediate aged second-growth Douglas-fir (Pseudotsuga menziesii) stand planted in 1949) and the other at the HDF00 site (a regenerating third-growth Douglas-fir clearcut planted in 2000) (Trofymow et al., 2008). A third flux tower site located about 25 km southwest of the study landscape (HDF88 site), is a third-growth Douglas-fir stand harvested in 1988 (Chen et al., 2009). These three flux towers make-up the chronosequence of coastal BC flux stations in the global Fluxnet database and their fluxes were used for comparison to model results, for similar aged stands, in the study area. Trofymow et al. (2008) reconstructed the disturbance and management history of the study area by compiling spatial data and historic aerial photos as shown in Fig. 1a. GIS coverage of current forest inventories (circa 1999) was compiled and overlaid with digitized historic disturbance maps, a 1919 timber cruise map and a series of historic orthophotographs that were prepared to produce a GIS database of forest cover polygons with unique disturbance histories dating back to 1920. Most of the forested land in the study area was purchased by private timber companies in the late 1890s (Trofymow et al., 2008). At that time the study area was a Douglasfir dominated old-growth (more than 350 years old) forest. There was no substantial logging in the study area before 1920, though there was a widespread wildfire in the region in the late 1600s (Mackie, 2000). From 1928 to 1944, intensive forest harvest activities occurred in this area. A large portion of woody debris was left on site after the logging and most of the slash was broadcast burned. In 1938, a large (75,000 ha) fire known as the Bloedel/Sayward wildfire burned part of the northeast corner of this study area (Trofymow et al., 2008). Natural regeneration was limited, therefore in much of the area seedlings were planted starting in the late 1940s. Planting activities continued in 1950s and even into the 1970s, for blocks with insufficient stocking. A productive second growth forest was eventually re-established over most of the study area and harvesting of 40–60-year-old stands started in 1989 (Trofymow et al., 2008). A digital soil map of the study area was derived from the soil map of Jungen (1985). Soil characteristics data includes information

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Fig. 1. (a) The disturbance and management events and total area covered by these events in the Oyster River landscape from 1920 to 2005 following Trofymow et al. (2008). (b) Simulated landscape-level mean annual carbon stocks. (c) Simulated landscape-level mean annual carbon fluxes: gross primary productivity (GPP), net primary productivity (NPP), net ecosystem productivity (NEP) and net biome productivity (NBP). Positive values are net additions of carbon to the ecosystem and negative values are net removal of carbon from the ecosystem.

about all 12 soil types and soil depth for each layer, soil texture, soil organic C content, soil total N content and the drainage information. The soils in the study area range from very gravelly textured duric humo-ferric podzols of fluvial origin at low elevations, to gravelly sandy loam textured duric humo-ferric and ferro-humic podzols of morainal origin at intermediate elevations and shallow stony ortho hum-ferric podzols on colluvium on higher elevation hilltops (Jungen, 1985). Topography data was built from 1:20,000 Terrain Resource Information Management (TRIM) digital elevation data (DEM) and it has 3 m horizontal spatial resolution. Mean annual precipitation in the region is 1460 mm and the mean annual temperature is 8.3 ◦ C. About 75% of precipitation falls between October and March. July and August are the warmest months, with mean monthly temperature of 16.9 ◦ C. January is the coldest month, with mean monthly temperature of 1.3 ◦ C.

2.2. Carbon and nitrogen coupled Canadian Land Surface Scheme (CN-CLASS) 2.2.1. Model overview The Carbon and Nitrogen coupled Canadian Land Surface Scheme (CN-CLASS) was developed from the Canadian Land Surface Scheme (CLASS), which is a physically based land surface model for simulating energy and water fluxes from the land surface (Verseghy, 1991, 2000; Verseghy et al., 1993). CLASS was originally developed for coupling with the Canadian Global Climate Model (CGCM) and the Canadian Regional Climate Model (CRCM). In CLASS, the land surfaces are classified into four subareas for the surface flux calculation: bare soil, vegetation covered soil, snow covered bare soil, or vegetation and snow covered soil (Verseghy, 2000). CLASS identifies up to five vegetation or land cover types in

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each grid cell: needleleaf trees, broadleaf trees, crops, grasses and urban areas. In CLASS, the soil profile is divided into three layers (0.10, 0.25 and 3.75 m thick) with total soil depth of 4.1 m, while snow is treated as an analogous of the fourth soil layer with variable depth. CN-CLASS incorporates a two-leaf (sun-lit and shaded) canopy conductance and photosynthesis module, soil and plant respiration processes and plant-soil nitrogen algorithms (Arain et al., 2006; Yuan et al., 2007). Photosynthesis (GPP) is allocated to four plant C pools: photosynthate, leaf, wood and root. Leaf maintenance respiration depends on leaf area index (LAI), base leaf respiration rate at a reference temperature (15 ◦ C) and leaf temperature as a function of a Q10 coefficient. Assuming no respiration from the heartwood, sapwood maintenance respiration is calculated as a function of sapwood volume, base sapwood respiration rate at reference temperature and leaf temperature. Root maintenance respiration is calculated as the sum of coarse root and fine root maintenance respiration, which depend on their respective biomass C pools, the base respiration at reference temperature, and the temperature of the first soil layer. Total plant maintenance respiration is the sum of leaf, wood and root maintenance respiration. Growth respiration (i.e. the carbon construction costs of producing new tissues) is estimated as a constant fraction (30%) of gross primary productivity, minus the total maintenance respiration (Amthor, 1984). The remaining carbon is then allocated as net primary productivity (NPP), comprising leaf, wood and root C pools. An allometry module is used to estimate the new heartwood and coarse root biomass after the growth of wood and roots. Stem and branch turnover rate or annual mortality rate is estimated as 1.89% per year and root turnover rate (including both fine and course roots) is estimated as 11.35% per year for this coastal Douglas-fir forest (Arain et al., 2006). Heterotrophic respiration (Rh ) is estimated as the sum of Rh from dead organic matter (DOM) (i.e. ground surface litter, dead wood, and dead roots) and soil organic matter (SOM) (i.e. shortlived and stable soil organic matter pools). Rh of these different C pools is described as Q10 -functions of organic C per unit ground area, base respiration rate at a reference temperature (10 ◦ C) and soil temperature (0–0.25 m). Rh from SOM pools was also a function of soil water content in the upper and subsurface soil layer, using one-half field capacity water content and one-half porosity (Kothavala et al., 2005; Arain et al., 2006). Temperature effects on plant or autotrophic respiration (Ra ), follow Q10 -functions for leaf, wood and root respiration. Overall, climate effects on simulated photosynthesis are estimated through a function of the minimum, optimal and maximum leaf temperature (Arain et al., 2006), while water impacts are estimated through sensitivity of canopy conductance to the available soil water content (a function of root zone water content at wilting point and at field capacity), using a modified version of Ball–Woodrow–Berry formulations (Ball et al., 1987; Arain et al., 2002). The performance of CN-CLASS model in response to seasonal weather patterns and anthropogenic disturbances is described by Wang et al. (2011) in a model inter-comparison study conducted in this Douglas fir forest landscape. 2.2.2. Disturbance matrices For this study, the C budget subroutine of the model, as described above, was modified to incorporate a module describing C transfers among different C pools for different disturbance and management types, following the CBM-CFS3 model’s disturbance matrices (Kurz et al., 2009). Twelve disturbance and management types are identified in the landscape history, including: historic harvest, historic harvest with slashburn, historic harvest with partial slashburn, historic harvest with ground fire, clearcut and broadcast burn, clearcut and pile burn, partial burn, slash burn, ground burn, partial slashburn, total burn, planting and fertilization. The disturbance

matrices define the flow of C from donating (pre-disturbance) to receiving (post-disturbance) C pools, as ratios of the C stocks in each pool at the time of disturbance (Trofymow et al., 2008). Each of the live and dead C pools of the forest ecosystem was updated after the disturbance or management event. Further details are given in Trofymow et al. (2008).

2.2.3. Model initialization, parameterization and sensitivity analysis The meteorological data used to drive the time series of the model from 1920 to 1997 were derived from nearby daily climate station records (Campbell River airport and Cumberland) This long term historic weather dataset (1920–1997) had been adjusted to match the tower measurements, based on the comparison of concurrent records (1998–2005) from Campbell River airport and DF49 tower (Morgenstern et al., 2004). The historic daily weather record from 1920 to 1997 was further interpolated, to estimate hourly values for use in the model. For example, solar radiation was interpolated over calculated day length to obtain hourly values, using a sine function. Hourly air temperature (Ta ) data was also interpolated using the sine function. We used two different half-sine functions, one from dawn (=solar noon − daylength/2) to 3 h after solar noon, and another from then until the following dawn. Although the correlations between weather variables collected at the nearby climate station and the DF-49 flux tower site during 1998–2005 were high, the inconsistency of the meteorological forcing dataset may cause a systematic bias in the modeling outputs. Further details of historic climate data are given in Wang et al. (2011). Note that in our analysis, for the period from 1998 to 2005, the observed meteorology data from DF49 tower site was used. Historic annual atmospheric CO2 concentration data was obtained from a Canadian Integrated BIosphere Simulator (CanIBIS; Liu et al., 2005) model run (1765–2100), conducted under Intergovernmental Panel on Climate Change (IPCC) SRES-A2 emission scenario. In this study, initial (1920) aboveground biomass (AGB) data was provided by CBM-CFS3 model, using information derived from a digitized 1919 timber cruise map, volume to biomass conversion algorithm and regional inventory analysis unit growth curves for unmanaged stands (Trofymow et al., 2008). The ratio of belowground biomass (BGB) to aboveground biomass was assumed to be 0.222 (Li et al., 2003). Dead organic matter and soil C pools were also estimated by CBM-CFS3, following a several thousand year spin-up, and assuming a long-term historic disturbance regime of standreplacing fire every 300 years, on average, prior to 1920 (Trofymow et al., 2008). The residue of total ecosystem C stock (TEC), minus total biomass (AGB and BGB), is the necromass, which includes 50% of soil organic matter (SOM), 20% of dead wood, 19% of forest floor litter and 11% of dead root (Sun et al., 2004; Trofymow et al., 2008). Initial vegetation characteristics and C and N ratios of soil and vegetation used in the model are given in Table 1. In CN-CLASS, initial minimum and maximum LAI of old-growth Douglas-fir forest was assumed to be 8.2 and 9.3, respectively (Thomas and Winner, 2000). Annual minimum LAI was set to 0.1 for the stand-replacing disturbance year and the expected maximum LAI for that year was set empirically to 0.3, which mostly accounts for understory regeneration. Our model does not simulate understory species separately. They are implicitly included in the sunlit and shaded big leaves. In the CN-CLASS model, expected annual maximum LAI was calculated as 60% of the product of sapwood basal area (cm2 ) at diameter at breast height (DBH, 1.37 m) and plant density (Gower et al., 1997; Turner et al., 2000). Initial canopy and soil temperatures and soil moisture were derived from a 5-year model spin-up run, using observed meteorology data (2003 and 2004). The spin-up run to 1920 was

B. Chen et al. / Agricultural and Forest Meteorology 180 (2013) 265–280 Table 1 Initial C/N characteristics of soil and vegetation used in the model simulations. Characteristics

Value

Tree density (trees m−2 )a Specific leaf area (m2 kg−1 C)b C in reserved pool (kg C m−2 ) N:C ratio in plant labile reservoir N:C ratio in heartwood tissue N:C ratio in fine roots N:C ratio in coarse roots N:C ratio in surface litter N:C ratio in root litter N:C ratio in fresh SOM N:C ratio in stable SOM

0.11 15 0.01 0.1 0.001 0.02 0.01 0.0125 0.01 0.04 0.03

a b

(8) No Clim/CO2 /Dis: all three effects were excluded. For these scenario simulations, mean climate of the study period (1920–2005) was used for each year, while a linear increase in CO2 concentration was used to account for CO2 fertilization over time.

3. Results 3.1. Contemporary climate variability impacts on forest productivity

From Drewitt et al. (2002). From Warren et al. (2003a,b).

also conducted to stabilize ecosystem C stocks, at which time AGB was re-initialized with values from CBM-CFS3. Key parameters used to run the CN-CLASS model over the Oyster River forest landscape are shown in Table 2. For the model runs, the Oyster River forest landscape was resolved into 2500 grid cells of 100 m × 100 m (1 ha) each. The soils, forest inventory, vegetation cover types and disturbance data in these 2500 grid cells were then aggregated into 1146 unique combinations of soils, forest inventory, cover types and disturbances, each of which was represented by one model simulation. The results from these model runs were then allocated to corresponding individual 1 ha grid cells. Spatial variations in radiation, temperature and precipitation, as well interactions between neighboring grid cells, were not accounted for in our study. To determine the dynamics of spatial heterogeneity in C fluxes and stocks over the landscape during the study period (1920–2005), we calculated the standard deviation () of C stocks and fluxes as:

  N  1  = (xi − x¯ )2 N−1

269

(1)

i=1

where xi is simulated flux or stock at one grid cell, x¯ is the mean value of the flux or stock over the whole landscape, and N is the total number of grid cells in the landscape. To evaluate the relative influences of climatic variables on historic C dynamics in the landscape, model sensitivity tests were conducted for the whole simulation period. For climate variables, the sensitivity was assessed by increasing/decreasing hourly values of (i) Ta by ±1.0 ◦ C and ±2.0 ◦ C; (ii) precipitation by ±10% and ±25%, respectively. To isolate the compounding effects of climatic forcing on C budget, we changed one variable at a time, while all others were kept unchanged during the sensitivity runs. The reader is cautioned that this approach may not fully account for the complexities of real world climate changes and their impacts. Eight scenarios were simulated from 1920 to 2005 over the whole landscape to evaluate the impact of disturbance, environmental change (i.e. the CO2 fertilization effect) and interannual climate variability on the historic dynamics of C fluxes and stocks. These scenarios were (1) Full: base simulation (i.e. all three effects included); (2) No CO2 : anthropogenic-driven increase in atmospheric CO2 concentration with time was excluded (i.e. used CO2 concentration from 1820 to 1905 for the simulation period of 1920 to 2005); (3) No dis: disturbance effects excluded (i.e. forest harvest, planting and fire); (4) No dis/CO2 : both, CO2 fertilization and disturbance excluded (i.e. climate only effects); (5) No Clim: interannual variability in climate variables excluded; (6) No Clim/CO2 : both, climate variability and CO2 fertilization effects excluded (i.e. disturbance only effects); (7) No Clim/Dis: both, climate variability and disturbance effects excluded (i.e. CO2 -fertilization only);

Before conducting landscape-level analysis of the impacts of interannual climate variability in air temperature and precipitation on C fluxes and stocks, we first evaluated model performance by comparing simulated and measured annual NEP values at the flux tower footprint scale for the DF49 site. We also evaluated how these NEP values responded to key climatic factors during the flux observation period (i.e. 1998–2005). Simulated annual NEP was calculated as an average of 173 grid (1 ha) cells that were located within the 85% of the flux footprint at the DF49 tower site (Chen et al., 2009). Simulated mean annual NEP over the flux footprint area was 340 g C m−2 yr−1 from 1998 to 2005, while the measured value was 293 ± 20.5 g C m−2 yr−1 (mean ± uncertainty) (Table 3). Uncertainty in observed NEP, using the eddy covariance technique, was calculated as the mean of uncertainty values reported in Schwalm et al. (2007). Interannual variability of simulated and measured annual NEP values, calculated as deviations, was 143 g C m−2 yr−1 and 61 g C m−2 yr−1 , respectively. During this 8-year period, mean annual Ta varied from 7.62 to 9.07 ◦ C, with ˜ year and the highest value recorded in 1998, which was an El Nino ˜ year (Table 3). lowest values recorded in 1999, which was a La Nina Annual precipitation ranged from 935 mm yr−1 to 1777 mm yr−1 . Highest annual precipitation values of 1432 mm and 1777 mm were again observed in 1998 and 1999, respectively. There was a strong correlation between simulated annual NEP and annual Ta (R2 = 0.83) over 1998–2005 period, while correlation between observed annual NEP and annual Ta was weak (R2 = 0.27). There was no correlation between observed and simulated annual NEP and annual precipitation over the same period (R2 = 0.05 and 0.06, respectively). However, when the two anomalous years (1998 and 1999) were excluded from statistical analysis, both simulated and measured annual NEP showed strong but negative correlation with annual Ta and precipitation values (Fig. 2). After evaluating model performance and climatic controls at the flux footprint scale, climate variability impacts on simulated C fluxes (i.e. GPP, Re , NEP) over the whole landscape were examined. For this purpose we selected an undisturbed period from 1963 to 1984. To reduce stand age effects on simulated C fluxes, annual deviations of C fluxes using 7-year moving-averages were derived for all grid cells, which were occupied by young to intermediate aged forest stands with closed canopies. The 7-year moving averages were considered short enough to include most short-term ˜ Durweather variability, caused by weather events such as El Nino. ing this undisturbed period, mean daily maximum air temperature (Tamax ), recorded over the peak growing season (July to September), was 18.6 ◦ C. Peak growing season temperatures (20–21 ◦ C range) in 1967, 1974, 1978 and 1979 were warmer when compared to the mean Tamax of 18.6 ◦ C and they were colder (17–18 ◦ C range) in 1964, 1969, 1970, 1976, 1983 and 1984 (Fig. 3a). Deviations in simulated landscape-level annual NEP were more negative (increasing C loss) during years with warmer summers and more positive (increasing C gain) during years with cooler summers (Fig. 3b). GPP values also followed similar trends. The regression of annual GPP and NEP deviations with Tamax during peak growing season indicated a rate of decrease of 27 g C m−2 yr−1 ◦ C−1 (R2 = 0.22) and 43 g C m−2 yr−1 ◦ C−1 (R2 = 0.35), respectively (Fig. 4a

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Table 2 Photosynthesis and respiration parameters used in the model simulations. Parameters

Value

Maximum carboxylation rate, Vcmax (␮mol m−2 s−1 ) Empirical constant related to intercellular CO2 concentration, a1 Empirical parameter for stomatal sensitivity to VPD, D0 (Pa) Extinction coefficient for canopy nitrogen Maximum transfer rate of assimilate to leaf structure, kp (s−1 ) Quantum efficiency of RuBP regeneration (mol e mol−1 quanta) Leaf respiration rate at reference condition, Rref (␮mol m−2 s−1 ) Living wood respiration rate at reference condition (␮mol m−3 s−1 ) Fine root respiration rate at reference condition (␮mol kg−1 C s−1 ) Coarse root respiration rate at reference condition (␮mol kg−1 C s−1 ) Base litter respiration rate (␮mol kg−1 C s−1 ) Base dead wood respiration rate (␮mol kg−1 C s−1 ) Base dead root respiration rate (␮mol kg−1 C s−1 ) Base short-lived SOM respiration rate (␮mol kg−1 C s−1 ) Base stable SOM respiration rate (␮mol kg−1 C s−1 ) Relative change in stem/coarse root respiration for 10 ◦ C (Q10 ) Relative change in leaf/fine root respiration for 10 ◦ C (Q10 ) Q10 in soil/litter respiration when temperature in 10–30 ◦ C Q10 in soil/litter respiration when temperature is under 10 ◦ C Q10 in soil/litter respiration when temperature is over 30 ◦ C One-half field capacity water content One-half saturated water content (porosity) Wood turnover rate (␮mol kg−1 C s−1 )a Root turnover rate (␮mol kg−1 C s−1 )

39.0 3.9 1000 0.14 5.0 × 10−6 0.2 0.3 (15 ◦ C) 2.5 (15 ◦ C) 0.5 (15 ◦ C) 0.3 (15 ◦ C) 0.20 (10 ◦ C) 0.10 (10 ◦ C) 0.15 (10 ◦ C) 0.50 (10 ◦ C) 0.20 (10 ◦ C) 2.0 2.0 2.0 6.0 2.0 0.20 0.23 0.05 (1.89% yr−1 ) 0.30 (11.35% yr−1 )

a

Wood includes both stem and branch wood.

Table 3 Measured and footprint-weighted simulated annual net ecosystem productivity (NEP) and measured mean annual temperature and precipitation in the flux footprint area ˜ year and 1999 was a La Nina ˜ year. of tower at the DF49 site. The standard errors (SE) of annual NEP are given in parenthesis. 1998 was an El Nino NEP (g C m−2 yr−1 ) 1998 1999 2000 2001 2002 2003 2004 2005 Mean S.D. a b c

Measureda 296 (20) 328 (20) 337 (20) 376 (20) 219 (20) 318 (20) 194 (22) 277 (22) 293 61

Simulated c

197 (69) 608 (146) 456 (93) 412 (192) 315 (149) 235 (195) 208 (155) 288 (193) 340 143

Air temperatureb (◦ C)

Precipitationb (mm)

9.07 7.62 8.20 8.06 8.45 8.44 8.75 8.28 8.36 0.44

1432 1777 1145 935 1249 1277 1349 1352 1315 242

From Schwalm et al. (2007). From Chen et al. (2009). SE based on the spatial variations in model results for the 173 grid cells in the tower footprint.

and b). An opposite trend was observed for landscape-level annual Re with positive deviations during warmer years and negative deviations during colder years (Fig. 3). Deviations in annual Re were less pronounced compared to GEP and NEP. The regression of Re deviation with summer Tamax indicated that the rate of increase of Re was 15.7 g C m−2 yr−1 ◦ C−1 (Fig. 4c). Therefore the decline in annual NEP with increasing temperature during this undisturbed period was attributed to both the decline in GPP and the rise in Re (Fig. 4b and c).

3.2. Disturbance impacts on landscape-level C stocks and fluxes The disturbance history of the Oyster River landscape indicates four distinct periods as shown in Fig. 1a and b. They include (a) the period before large-scale forest clearing and fire events (1920–1927); (b) the period of intensive forest logging, slash burn, fire and planting (1928–1949); (c) the regeneration period in which there were very few disturbance events, except some small-scale plantings (1950–1989); and (d) the period of the onset of second-growth forest harvest (1990–2005). Associated changes in C stocks and fluxes, representing the severity and magnitude of disturbance events, are shown in Fig. 1b and c, respectively.

Over the intensive disturbance period from 1938 to 1949, annual net biome productivity (NBP) declined to −5083 g C m−2 yr−1 (Fig. 1c). Then from 1951 to 1997, when there were no major disturbance events, NBP gradually recovered to about 365 g C m−2 yr−1 . During the most recent disturbance period from 1990 to 2005, when second-growth forests were logged, maximum NBP reached only −1093 g C m−2 yr−1 , which was much smaller than past disturbance events. The standard deviation of landscape-level NBP, which reflects spatial variability of disturbances, was about 290 g C m−2 from 1920 to 1927 (Fig. 5), when there were no large-scale disturbance. In contrast, the largest values (10,666 and 4391 g C m−2 ) were observed during the periods of intensive disturbances, from 1928 to 1943, and 1990 to 2005, respectively (Figs. Fig. 11c and Fig. 55).

3.2.1. Disturbance impacts on C stocks In 1920–1927, initial landscape-level above-ground tree biomass (AGB) (leaf, wood and bark biomass) and below-ground biomass (BGB) (live fine and coarse root biomass) were estimated as 28.5 kg C m−2 and 5.8 kg C m−2 , respectively, derived from the CBMCFS3 model (Fig. 1b). In the following intensive disturbance period (1928–1949), simulated AGB and BGB experienced large declines, and reached the lowest values of 1.7 kg C m−2 and 0.7 kg C m−2

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Fig. 2. Correlations between measured (open symbols, solid line) and footprint-weighted simulated (filled-symbols, dashed line) annual net ecosystem productivity (NEP) and measured annual air temperature (from Schwalm et al., 2007) and precipitation (from Chen et al., 2009) in the flux footprint area of the tower at the DF49 site, showing ˜ ˜ years were excluded from the regressions shown above. Nina years 1998–2005. Abnormal climate years (triangles), representing El Nino/La

in 1943, respectively (Fig. 1b). After that, the simulated AGB and BGB gradually increased to 16.2 kg C m−2 and 5.5 kg C m−2 in 1997, respectively, until the second-growth forests were cut in the late 1990s. In 2005, simulated landscape-level AGB and BGB decreased to 12.3 kg C m−2 and 3.9 kg C m−2 , respectively, due to harvest and burning events in the second growth forest stands (Fig. 1b). For comparison, the AGB in 2005 derived from the inventory based model (CBM-CFS3) was 13.3 kg C m−2 . Standard deviation of AGB indicates spatial heterogeneity of the forest landscape. Our analysis showed that from 1920 to 1927, standard deviations of AGB ranged from 8.1 to 8.9 kg C m−2 (Fig. 5). In this period, no large-scale disturbances took place and the spatial heterogeneity of the forest landscape was predominantly determined by other factors, such as stand age-class structure and site fertility (Trofymow et al., 2008). Standard deviation of AGB increased to 10.3 kg C m−2 in 1928 (Fig. 5), when the first forest stands (131 ha) were harvested (Fig. 1). Standard deviation of AGB reached the highest value (16.0 kg C m−2 ) in 1934 (Fig. 5), when extensive logging and burning of the forest occurred (Fig. 1). The lowest standard deviation of AGB (1.7 kg C m−2 ) was in 1943 and

onwards (Fig. 5), when the conversion process of old-growth forest to second-growth forests was completed and there were very few disturbance events (Fig. 1). The standard deviation of AGB started to increase in 1990s and reached 8.2 kg C m−2 in 2005 (Fig. 5), due to the harvest within the second growth forest over this period (Fig. 1). Simulated landscape-level dead organic matter (DOM) (forest floor litter, dead wood and belowground dead roots) and soil organic matter (SOM) (short-lived soil organic matter and stable soil organic matter) in 1920 were 20.6 kg C m−2 and 21.0 kg C m−2 , respectively (Fig. 1b). DOM increased over the intensive disturbance period from 1928 to 1949, when logging and fires left tree residues on site. DOM reached its lowest value of 5.3 kg C m−2 in 1971, during relatively undisturbed period that lasted from 1950 to 1989 (Fig. 1b). After that, DOM gradually recovered and reached 9.9 kg C m−2 in 2005, as young forests aged and litter fall and the wood/root turnover exceeded the DOM decomposition. Simulated landscape-level SOM was only slightly reduced from 21.0 kg C m−2 in 1920 to 20.7 kg C m−2 in 2005 over the study period.

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Fig. 3. (a) Mean maximum daily air temperature (Ta ) in summer (July through September) from 1963 to 1984; (b) Deviations of simulated annual gross primary productivity (GPP), ecosystem respiration (Re ) and net ecosystem productivity (NEP) from 7-year moving averages.

CN-CLASS simulated a significant reduction in total ecosystem C stocks, following conversion of old-growth stands to second growth managed forests. A mass balance calculation indicated that the conversion of this 2500 ha of old-growth forest to managed forest from 1920 through 2005 induced 29.0 kg C m−2 loss of total ecosystem C (TEC), as simulated by the model. The change in TEC stock matched with the cumulative NBP over the study period (1920–2005). 3.2.2. Disturbance impacts on C fluxes In 1920, simulated landscape-level GPP, NPP and NEP values were 2032, 880 and 0.8 g C m−2 yr−1 , respectively (Fig. 1c). They reached low levels of 857, 589 and −524 g C m−2 yr−1 , respectively, from 1938 to 1944, during intensive disturbance and then gradually recovered from 1951 to 1997, when there were no major disturbance events (Fig. 1a and c). After that, the harvesting of second-growth forests (Fig. 1a) again reduced simulated landscapelevel GPP, NPP and NEP to 1630 g C m−2 yr−1 , 758 g C m−2 yr−1 and 165 g C m−2 yr−1 , respectively (Fig. 1c). The CN-CLASS simulated stand-level and landscape-level C fluxes (i.e. GPP, Re and NEP), following the years after standreplacing disturbances, are shown in Fig. 6. Estimates based on eddy covariance measurements for DF49 site by Jassal et al. (2007) and for the HDF00-HDF88-DF49 tower sites by Schwalm et al. (2007) and Humphreys et al. (2006) are also shown for comparison. Simulated landscape- and DF49 stand-level GPP values agreed well with measured values (Fig. 6a), though simulated GPP values were high for the first few years after stand generation – likely an artifact of prescribed LAI in the model. While both landscape- and DF49 stand-level simulated Re values were close to observations during the first few years of regeneration and later intermediate ages (around 60 years after the disturbance), they largely differed during juvenile ages (i.e. around 15–20 years) (Fig. 6b). The simulated landscape- and DF49 stand-level NEP trajectories indicated that the forest became a C sink about 8 years after the disturbance. NEP

peaked around 20–25 years post disturbance (Fig. 6c), after which there was a steady decline in NEP due to changes in C stocks as the forest matured. 3.3. Climate sensitivity and scenario analysis The sensitivity of simulated landscape-level C fluxes and stocks to the changes of Ta is shown in Fig. 7. The impact of Ta on simulated landscape-level GPP was not obvious, except when Ta was reduced by 2 ◦ C, causing simulated GPP to become much lower than the control value. On the other hand, Ta had a large impact on Ra . Simulated Rh was less sensitive to the changes in Ta compared to simulated Ra . It may be because changes in soil temperature lagged those of Ta . Simulated AGB, BGB, DOM and SOM were sensitive to the change in Ta , because of temperature impacts on foliage, wood and root turnovers rates and C loss through decomposition (Fig. 7e–h). In contrast, simulated landscape-level C fluxes and stocks were not sensitive to the change of precipitation (Fig. 8). Our scenario simulations revealed long-term impacts of all three effects: disturbance, rising atmospheric CO2 concentrations (i.e. the CO2 fertilization effect), and interannual climate variability, on C stock accumulation, but the degree of their impact was variable. When comparing the impact of the three different effects individually, with respect to the null model simulation (i.e. none of the three effects were included when comparing the absolute percentage difference in the variable size), we observed that the disturbance events as compared climate variability and CO2 fertilization had the most significant impacts on C stocks (Table 4). Including disturbance in the model caused an overall reduction in all stocks, with AGB and DOM being most affected (i.e. they were lower by 58% and 56%, respectively, in the model simulation that included disturbance compared to the null simulation, Table 4). CO2 fertilization had the least effect on stock changes, but a positive one compared to disturbances (Table 4). The effect of interannual climate variability was

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Fig. 4. Deviations of simulated annual gross primary productivity (GPP), ecosystem respiration (Re ) and net ecosystem productivity (NEP) from 7-year moving averages plotted against mean maximum daily air temperature in summer (July through September) from 1963 to 1984.

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4. Discussion 4.1. Climate variability

Fig. 5. Standard deviations of simulated landscape-level aboveground biomass (AGB) and net biome productivity (NBP) from 1920 to 2005.

variable, decreasing DOM stocks (−44%), but increasing AGB (11%) and BGB (4%), compared to the null model simulation. SOM was affected only by disturbances (Table 4). When the three effects were considered in combination, the direction and magnitude of the combined effects were largely driven by disturbance effects. Whenever disturbance effects were included, all stocks were reduced compared to the null model simulation (Table 4). Including CO2 fertilization ameliorated the negative impact of disturbances (i.e. compare the % changes in the simulation that included all three effects versus one that had only disturbance and climate variability, Table 4). While stocks were most sensitive to disturbance events, short term variability in fluxes (1998–2005) was sensitive to both climate variability and disturbance (Table 4). Climate variability had greatest effect on GPP and NPP, while disturbance had greatest effect on NEP and Re (Table 4). Interannual climate variability alone caused a reduction of 20–47% in the modeled C fluxes (Table 4). Overall, NPP and NEP were most affected among the four fluxes. Disturbance alone caused a reduction of 13–83% in the modeled C fluxes, having the greatest affect on NEP, among the four fluxes (Table 4). CO2 fertilization had a slight positive impact on all fluxes. In the combined-effect simulations, changes in fluxes were reflective of the impacts of climate variability, with CO2 fertilization ameliorating somewhat the negative effect of climate variability (Table 4).

Mean total C stocks in mature to old forests in the Pacific Northwestern region of North America vary from 75 kg C m−2 to 113 kg C m−2 , with 30–50% of this C being stored in the soil (Smithwick et al., 2002). These forest C stocks are being affected by climate change and natural and human disturbances. Environmental variability (e.g. CO2 fertilization) and climate change directly affect forest growth, structure, rotation age, species composition and C storage (Black et al., 2008; Canadell and Raupach, 2008). In general, there is a positive relationship between temperature and annual C sequestration rates, with warmer temperatures causing longer growing season lengths and an increase in annual photosynthetic C uptakes (Black et al., 2005; Baldocchi, 2008). The results of our study are consistent with these previous findings, suggesting that in the absence of disturbances, temperature was the dominant control on the landscape-level forest productivity. There is a large body of literature suggesting that rising temperatures increase C losses in forest ecosystems due to increase in respiratory fluxes (Valentini et al., 2000; Law et al., 2002; Arain and Restrepo-Coupe, 2005; Baldocchi, 2008). Our simulated landscapelevel annual Re showed positive deviations during warmer years and negative deviations during colder years, over the undisturbed period from 1963 to 1984. Toward the end of the study period, when eddy covariance flux measurements were available at the DF49 tower site, analysis of observed fluxes indicated that annual Re increased during warmer years. Similar findings were reported by Morgenstern et al. (2004) at the DF49 site, where high Ta during ˜ year resulted in the greatest annual Re measured the 1998 El Nino between 1998 to 2001, resulting in the lowest annual NEP of their reported four year study. Similarly in a three year study at HDF88, Jassal et al. (2008) reported the greatest annual soil respiration in 2004, which was the warmest year of the three years. Krishnan et al. (2009) reported that interannual variability in annual NEP at the DF49 site was mainly due to interannual variability in annual Re , while at the two younger stands (HDF88 and HDF00) it was due to GPP. Their analysis suggested that during the spring – temperature – and during summer – soil water availability – were the main factors determining the interannual variability in GPP and Re of the three sites (Krishnan et al., 2009). However, we have not observed these trends in our landscape-level simulations during the undisturbed period. Instead, our results suggest that temperature was the dominant control on simulated landscape-level C fluxes. The rate of decrease of landscape-level

Table 4 Simulated landscape-level total carbon stocks and fluxes calculated for eight different model scenarios, where model sensitivity to the three effects (i.e. climate variability, CO2 fertilization, and disturbances) was tested, separately and in combination. For the stocks, model scenarios were run from 1920 to 2005 and the final values for 2005 are shown, while for the fluxes means of annual values from 1998 to 2005 are shown. In all model runs, mean values (1920–2005) of the climate variables were used. The numbers in the parenthesis show the percentage change in the stock or flux value, as a result of including the given effect(s) in the model, with respect to the null model simulation (i.e. none of the three effects were included). The sign in the brackets indicate an increase (+) or a decrease (−) with respect to the null model simulation. The negative sign in front of NEP indicates C losses to the atmosphere. Effect(s) included

None Clim only CO2 only Dis only Clim and CO2 only Clim and dis only Dis and CO2 only All three effects

Stocks (kg C m−2 )

Fluxes (g C m−2 yr−1 )

AGB

BGB

DOM

SOM

GPP

Re

NEP

NPP

29.1 (0) 32.2 (+11) 31.0 (+7) 12.2 (−58) 34.7 (+19) 11.0 (−62) 13.2 (−55) 12.3 (−58)

4.9 (0) 5.1 (+4) 5.2 (+6) 3.4 (−31) 5.9 (+20) 3.4 (−31) 3.7 (−24) 3.9 (−20)

35.3 (0) 19.6 (−44) 38.1 (+8) 15.6 (−56) 20.9 (−41) 8.6 (−76) 17.4 (−51) 9.9 (−72)

21.0 (0) 21.0 (0) 21.0 (0) 19.3 (−8) 21.0 (0) 20.6 (−2) 19.6 (−7) 20.7 (−1)

2242 (0) 1800 (−20) 2367 (+6) 1901 (−15) 1983 (−12) 1581 (−29) 2015 (−10) 1767 (−21)

2676 (0) 2053 (−23) 2832 (+6) 1829 (−32) 2172 (−19) 1384 (−48) 1967 (−26) 1516 (−43)

−434 (0) −253 (−42) −464 (+7) 73 (−83) −189 (−56) 197 (−55) 48 (−89) 251 (−42)

1183 (0) 626 (−47) 1255 (+6) 1033 (−13) 728 (−38) 686 (−42) 1099 (−7) 782 (−34)

Legend: clim, interannual variability in climate; dis, disturbance; CO2 , CO2 fertilization; AGB, aboveground tree biomass; BGB, belowground tree biomass; DOM, dead organic matter, including dead wood, dead root and forest floor litter; SOM, soil organic matter; GPP, gross primary productivity; Re, ecosystem respiration; NEP, net ecosystem productivity; NPP, net primary productivity.

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Fig. 6. Stand-level (a) gross primary productivity, GPP, (b) ecosystem respiration, Re and (c) net ecosystem productivity, NEP plotted against years after stand-replacing disturbance. CN-CLASS estimates for all stands in the study area (Oyster River mean ± 1 standard deviation) are shown as dashed lines with error bars, while CN-CLASS simulated value for the 1 ha grid cell containing the flux tower at the DF49 site is shown as solid line. Estimates based on flux measurements for DF49 by Jassal et al. (2007) and for the HDF00-HDF88-DF49 tower sites by Schwalm et al. (2007) are also shown for comparison.

GPP (27 g C m−2 yr−1 ◦ C−1 ), in response to warmer mean daily maximum temperatures (Tamax ) during the peak growing season, was almost twice the rate of increase in Re (15.7 g C m−2 yr−1 ◦ C−1 ) during an undisturbed period from 1963 to 1984. Consequently, variation in annual NEP of the landscape was also negatively

correlated with mean daily Tamax during the growing season. However, we note that CN-CLASS uses modified Ball–Woodrow–Berry formulation to simulate photosynthesis-canopy conductance relationship, which includes the effects of vapor pressure deficit and root zone soil moisture (Leuning et al., 1995; Arain et al., 2002).

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Fig. 7. Sensitivity of simulated landscape-level carbon fluxes and stocks to air temperature. (a) Gross primary productivity, GPP; (b) autotrophic respiration, Ra ; (c) heterotrophic respiration, Rh ; (d) net ecosystem productivity, NEP; (e) aboveground biomass, AGB; (f) belowground biomass, BGB; (g) dead organic matter, DOM; and (h) soil organic matter, SOM.

Fig. 8. Sensitivity of simulated landscape-level carbon fluxes and stocks to precipitation. (a) Gross primary productivity, GPP; (b) autotrophic respiration, Ra ; (c) heterotrophic respiration, Rh ; (d) net ecosystem productivity, NEP; (e) aboveground biomass, AGB; (f) belowground biomass, BGB; (g) dead organic matter, DOM; and (h) soil organic matter, SOM.

These indirect effects can also reduce GPP, when higher temperature results in higher vapor pressure deficit causing canopy conductance to decline, in turn, forcing sharp declines in CO2 uptake (Arain et al., 2006; Wang et al., 2011). Finally, we also showed that the effects of variability in precipitation on the C sink and source strengths of this landscape was evident only when ˜ and La Nina ˜ anomalies were the years corresponding to El Nino excluded from analysis.

Furthermore, our climate sensitivity analysis indicated that temperature impacts on C fluxes (i.e. GPP, Ra , Rh and NEP) and C stocks (i.e. AGB, BGB, DOM and SOM) were highly non-linear. Within the prescribed Ta range (±1.0 ◦ C and +2.0 ◦ C), GPP appeared less sensitive to Ta , because the direct effects of Ta on photosynthetic biochemical processes were offset by the indirect effects of Ta on photosynthetic activity through plant stomatal closure caused by increasing vapor pressure deficit. However, when Ta was

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decreased by 2 ◦ C, the indirect stomatal closure effect of Ta was small, and the resulting GPP was lower compared to the control simulation. Our results suggest that the higher sensitivity of Ra to Ta was largely due to the use of the Q10 function in our model to calculate plant maintenance respiration (Arain et al., 2002, 2006). In our model, temperature-mediated variations in Ra are modeled as a simple exponential function of temperature, with a constant Q10 of 2.0, which can overestimate respiration at higher temperatures. Furthermore, it has been long recognized that the Q10 derived from in situ field observation is not constant throughout the year due to the confining effects of concurrent physical and physiological processes. For example, as the growing season progresses, Ta increases, but also fine root biomass tends to grow, so the response of fluxes to Ta over time, can be confined by the increase of respiring biomass with time (Luo and Zhou, 2006). Over longer time periods, acclimation of respiration could also take place (Larigauderie and Korner, 1995; Collier, 1996; Fitter et al., 1998; Tjoelker et al., 1999a,b; Atkin et al., 2000a,b). Thermal acclimation causes the base respiration rate at a reference temperature (R0 ) to increase in colder temperatures and to decline in warmer temperatures (Atkin and Tjoelker, 2003). Therefore, using a temperature-corrected Q10 and R0 may improve the accuracy of modeled respiratory fluxes in response to changes in temperature. In our study, the impact of Ta on Rh before the disturbance period was largely due to the accumulation of non-living C stocks. However, after major disturbances Rh was a dominant component of Re , due to large C emissions due to decomposition of woody debris and other litter left on the site. In addition, higher Ta meant higher evapotranspiration in forested areas and drier surface soil layers in open areas. Lower surface soil moisture caused declines in soil organism activity and hence Rh (Jassal et al., 2008). Therefore, Ta had a dominant effect on the landscape-level NEP because of its changing impacts on GPP and Re components over the course of this historic study. In a scenario modeling study of an intensively managed forested landscape in the upper mid-west United States, Desai et al. (2007) reported a doubling of biomass in response to an almost 40% increase in atmospheric CO2 over 150 years. The effects of elevated atmospheric CO2 on NEP in forest ecosystems are complex (Euskirchen et al., 2002). CO2 -fertilization phenomenon may increase NPP, and hence AGB and BGB in the short term, however, over longer periods of time gains in NPP may be offset by nutrient availability, soil water stress, acclimation or changes in ambient ozone (Amthor, 1991; Mickler et al., 2000). In our simulation runs, CO2 -fertilization alone produced an increase of only 0–8% in fluxes and stocks. The effect was more variable when both climate and CO2 -fertilization were changed simultaneously, but without disturbance effects in the simulation. In that second case, all fluxes and DOM-stocks were reduced between 12% and 56%, while aboveground and belowground stocks were increased by about 20%. 4.2. Disturbance impacts Results of our study suggest that disturbances had a more profound impact on landscape-level C stocks compared to interannual climate variability and CO2 fertilization effects. Our scenario simulations, indicated that the disturbance alone, especially standreplacing events (e.g. harvesting), reduced landscape-level C stocks by about 8–58%. It also took a long period of time (>150 years) for the recovery of the C stocks. Our results are supported by the findings of (Kurz et al., 1997), who used CBM-CFS3 to examine the impacts of changes in the disturbance regime on landscapelevel C content and fluxes for six representative forest landscapes. Similarly, Smithwick et al. (2007) used a process-based model to investigate the impact of two contrasting disturbance types (fire

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and forest harvest) on landscape-level stocks and NBP. They found that C stocks were higher for random than for regular disturbance intervals, and they increased as the mean disturbance interval increased and disturbance intensity decreased (Smithwick et al., 2007). Timber harvest considerably reduces on-site C storage and with shortened rotations preventing the site from approaching old-growth storage capacity (Harmon et al., 1990; Trofymow and Blackwell, 1998; Trofymow et al., 2008), as compared to stand succession following a fire disturbance, where woody debris remains on site (Harmon et al., 1990). In addition, storage of harvested forest products and consumption of fuels, used during forest management processes, is not accounted for in our model and could have resulted in differences with a complete C accounting. Sun et al. (2004), in a chronosequence study for the range of forest types in the US Pacific Northwest, have shown that soil C storage reaches an asymptote between 150 and 200 years following a stand-replacing disturbance. When forests become older than 200 years, they become C neutral (Song and Woodcock, 2003) or a small C sink (Harmon et al., 2004; Luyssaert et al., 2008). Song and Woodcock (2003), who used a regional forest ecosystem C budget model for forests in the Pacific Northwest, USA, found that a forest stand can be a C sink for up to 200 years with a peak C uptake at 30–40 years old (Song and Woodcock, 2003). Initial values of DOM and SOM for CN-CLASS were obtained from TEC values generated by a several thousand years’ spinup using CBM-CFS3 (Trofymow et al., 2008). The frequency of stand-destroying wild fire, before the settlement of Europeans in this region, was estimated to have ranged from 150 to 350 years (Hamilton and Nicholson, 1990). CBM-CFS3 results for the area indicated that the variation in long-term pre-logging natural fire frequency (200, 300 or 400 yrs) had minor effects on the DOM and SOM pool sizes of the old-growth forest in 1920 (Trofymow et al., 2008). The timber cruise conducted in 1919 was a survey of estimated net milled wood volume, considered to be merchantable by foresters at the time and the merchantability standards have changed considerably since 1919. Therefore, adjustments were made to the cruise volumes to develop estimates of total standing merchantable volumes according to contemporary standards (Trofymow et al., 2008). These issues contributed to the uncertainty in the estimated initial biomass C stocks in 1920. CN-CLASS simulated pre-logging landscape-level mean annual NPP was 880 g C m−2 yr−1 , which was much higher compared to published estimates for old-growth forests in the Pacific Northwest (Grier and Logan, 1977; Harmon et al., 2004). For example Harmon et al. (2004) reported NPP of 597 g C m−2 yr−1 at the Wind River Experimental Forest (∼500 years old), Washington, USA, and Grier and Logan (1977) reported NPP value of 544 g C m−2 yr−1 in an old-growth Douglas fir stands (∼450 years old) in Western Oregon, USA. Our values of above and below ground NPP components (573 and 307 g C m−2 yr−1 , respectively) were also overestimated compared to the two studies mentioned above. The above and below ground NPP were 455 g C m−2 yr−1 and 142 g C m−2 yr−1 , respectively, at the Wind River Experimental Forest (Harmon et al., 2004) while they were 399 g C m−2 yr−1 and 145 g C m−2 yr−1 , respectively, at the old-growth Douglas fir stands in Western Oregon, USA (Grier and Logan, 1977). However, CN-CLASS simulated pre-logging annual NPP was close to the estimate (770 g C m−2 yr−1 ) suggested by Hudiburg et al. (2009) for a similar aged Douglas-fir stands in Oregon, USA. Schwalm et al. (2007) observed that the forests we studied acted as net C sources for at least 17 years following clearcut harvesting (Schwalm et al., 2007). However, our NEP simulations indicated that the DF49 site first became a net C sink around 8 years after the disturbance. Overall, the general pattern of our simulated NEP, following disturbance, was similar to the patterns reported by

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the inventory-based model, CBM-CFS3, for the same Oyster River landscape. CBM-CFS3 showed that forests first became a C sink at age of 10 and NEP peaked at about the age of 20 years (Trofymow et al., 2008). Our simulated NEP pattern was also similar to the pattern described by Pregitzer and Eugenie (2004), where NEP reached its maximum while the forest stand was still quite young (11–30 years old). We found a good agreement between simulated and observed trajectories of GPP following the years after the disturbance, except during the first few years due to the prescribed LAI, but our simulated Re was underestimated, compared to observations made at HDF88 site (Fig. 6), because of the large amount of coarse woody debris (CWD) left on-site after disturbance at HDF88 site (Grant et al., 2010). Earlier, Trofymow et al. (2008) have argued that age is not a perfect proxy for time-since-disturbance for the DF49 forest site because it was not immediately regenerated following the first harvest. The DF49 site was harvested on the Eastern half in 1937–1938, slash burned in 1939 (burned partly into the Western half) and then it was harvested and slash burned in 1943, and the entire site was planted in 1949 (Ferster et al., 2011). The DF49 stand was not planted until up to 12 years after the site was harvested in 1937–1943. At the DF49 site, a limited amount of CWD was likely left after the first harvest, since cut blocks were broadcast burned once or twice and DOM left to decayed for up to 15 years before planting. However, in the second harvest broadcast burns were not as severe, as spring burning was used, with more CWD was left on site and the site was planted the same or following year. Therefore, these same aged stands (∼15 year old) actually had quite different disturbance histories. This difference may be the reason why the simulated NEP and Re for the regeneration and juvenile stages of DF49 stand were so different from the estimates derived from the EC flux measurements of HDF00 and HDF88 sites. More CWD, and other dead organic matter, was left on HDF00 and HDF88 sites after the disturbance and the sites were planted sooner than the DF49 site, leading these sites to have a higher Re and lower NEP, reflecting the legacy effects of disturbance history on the site’s recent C budget (Trofymow et al., 2008). The number of years, during which the DF49 site was a net C source after disturbance, estimated by CNCLASS, were actually very similar to the period (about 20 years) inferred from the estimates reported by Schwalm et al. (2007). Furthermore, Grant et al. (2007), using simulations of ecosystem carbon dynamics, suggested that this forest was a large source of C for 5 years after clear cutting, a declining source until 14 years after clearcutting, and finally became a C sink after 20 years. Desai et al. (2007) has suggested that natural disturbance processes, land use history, atmospheric CO2 concentration, and inter-annual climate variability cannot be neglected for ecosystem–atmosphere CO2 flux modeling. They further argued that because the managed landscapes are representative of a large portion of the terrestrial biosphere, accurate prediction of future biosphere response to climate change and increasing CO2 will require specification of land management, land use change and disturbance in land surface schemes and ecosystem models. Our study results are a step forward in this direction and show that once adequately parameterized, land surface schemes such as CN-CLASS may become a valuable tool to study land management, land use and disturbance impacts on C cycle in forest landscapes. Although land surface schemes may have their own biases, a more process based explanation of weather effects on the interannual variation in forest productivity would help inventory models, and would make projections of changes in productivity with changes in climate more reliable. Recent predictions of future climatic changes (Solomon et al., 2007) point toward an increase in extreme weather events (including heat waves, droughts, fires and wind storms) that may induce additional stresses on a forest landscape. In the future, as C takes on economic value, the integration and use of multiple approaches (such as biometric,

eddy covariance flux and remote sensing measurements, as well as, inventory-based, process-based and inverse atmospheric modeling) would help to accurately quantify C exchanges at all relevant spatial and temporal scales in forest ecosystems.

5. Conclusions In this study, improvements made in the process-based forest C dynamics model CN-CLASS, by incorporating the disturbance matrices from an inventory-based model, helped to analyze and further understand the impacts of climate variability and disturbance regimes on the C dynamics of a forest landscape (2500 ha), from 1920 to 2005, where an old-growth forest was replaced by a second-growth forest. The study results suggested that: • The studied landscape was a C source of about 303 g C m−2 yr−1 in 2005, while it was C neutral prior to disturbances in 1920. • Simulated mean net ecosystem productivity, over the eddy covariance tower flux footprint area, in a 50–60-yr-old stand was 340 ± 143 g C m−2 yr−1 for the 1998–2005 period, while the corresponding measured value was 293 ± 61 g C m−2 yr−1 . Both simulated and measured net ecosystem productivities were negatively correlated with mean annual air temperature during this flux observation period. • Analysis of landscape-level, age-detrended, simulated C fluxes during the undisturbed period (1963 to 1984) indicated that temperature was the dominant control on C fluxes, with higher temperatures causing a decrease in GPP at almost twice the rate of rate of increase in Re (i.e. 27.0 versus 15.7 g C m−2 yr−1 ◦ C−1 , respectively). Hence, the decline in landscape-level annual NEP with temperature was attributed to both the decline in GPP and the rise in Re . • Sensitivity analysis showed that landscape level fluxes were most sensitive to interannual variability in climate as well as disturbances, while landscape level stocks were most sensitive to disturbance regimes. The disturbance type and intensity determined the magnitude of the C losses to atmosphere. Our study highlights the importance of historic climate and landscape-scale inventory and disturbance data for process-based land surface models, when applied at forested landscapes.

Acknowledgements This study was conducted as a part of the historic carbon modeling project of the Canadian Carbon Program (CCP). CCP was supported through funding from Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) grants. Funding from National Science and Engineering Research Council (NSERC) is also acknowledged. We also acknowledge support from Natural Resources Canada (NRC) – Canadian Forest Service (CFS) and provincial forest agencies in British Columbia (BC) for providing historic climate records and forest companies for providing inventory and historic data records. We would like to thank Biometeorology group at University of British Columbia (UBC) for providing flux and ancillary data and CBM-CFS3 group for providing simulated data and disturbance matrices parameter values. In particular, we would like to thank Dr. David Price, Canadian Forest Service for CO2 concentration data from Can-IBIS model and Dave Spittlehouse, BC Ministry of Forest and Range for providing the historic daily weather data and two anonymous reviewers for their very constructive comments. We also acknowledge two anonymous reviewers for their constructive comments.

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