Modeling the effects of the terrestrial carbon sink in the future depends
upon not just current-day land use and land cover (LULC) but also the
legacy of past LULC change (LULCC), which is often not considered. The age
distribution of trees in the forest depends upon the history of past
disturbances, while the nutrients in the soil depend upon past LULC. Thus,
establishing the correct initial state of the vegetation and soil is crucial
to model accurately the effect of biogeochemical cycling with environmental
change in the future. This study models the effects of LULCC from 1750 to
2014 using the land-use harmonization dataset (LUH2) of land-use transitions
with the terrestrial ecosystems model (TEM) for the conterminous US.
Modeled LULC include plant functional types (PFTs) of potential vegetation,
as well as managed cropland, pastureland, and urban areas. LULCC is treated
using a cohort approach, in which a separate cohort occurs every year there
is a land-use transition, thereby ensuring proper age structure of forests
and regrowth with the correct soil nutrients. From 2000–2014 the modeled net
ecosystem productivity (NEP) is 989 TgC yr
The hypothesis is that the initial state of the vegetation and soils
significantly affects the future state of the terrestrial carbon sink. In
this study, LULC remains constant in the future, with the NCAR CCSM4 RCP8.5
climate used to force the TEM-Hydro model. The following experiments are run
from 2015 to 2100, including (a) restarting from existing cohorts in 2014
(RESTART), (b) reinitializing in 2015 based on condensing the cohorts for
each PFT into a single cohort (CONDENSED), and (c) restarting from average
cohort conditions for each PFT (AVERAGE). The NEP is too low when using
condensed cohorts without reinitializing due to a larger increase in
heterotrophic respiration (
Globally, during the 21st century, land use and land cover change
(LULCC) has accounted for 14 % of the total anthropogenic carbon emissions
(Friedlingstein et al., 2020). LULCC has been responsible for the
largest losses of carbon from the land in the conterminous US since the
1700s, with growth enhancements from CO
Many modeling studies have been conducted to explore the role of LULCC
relative to other environmental factors like CO
Only a few models (e.g., Felzer and Jiang, 2018;
Shevliakova et al., 2009) have included forest demography to accurately
track the effects of disturbance in regrowing forests. Krause et al. (2020) showed that including land legacy effects increases future carbon
storage as ecosystems regrow and adapt to higher levels of CO
Two factors that determine the carbon sink strength of regrowing forests are
the stand age distribution of the trees in the forest and the nutrient
levels of the soil. The age distribution depends upon the timing and
magnitude of past disturbances. Soil nutrient conditions depend upon the
prior history of land use and management. Several studies show that forests
regrowing from nutrient-rich fertilized agricultural land exhibit less
resilience for climate change but higher growth rates. European beech trees
on former agricultural land had lower
This study explores the question of land legacy on the future carbon sink by comparing model simulations with full forest demography with those based on reinitializing initial conditions to the present. The analysis looks at both carbon fluxes and stocks to determine how these vary regionally and integrated over the entire conterminous US. It explores the role of forest stand age and soil nutrients in determining forest regrowth and tests the hypothesis that it is crucial to capture the effects of historical land legacy in order to accurately model the future carbon sink.
This study uses the terrestrial ecosystems model-Hydro version 2 to explore the role of historical land-use legacy (from 1750 to 2014) on future (2014–2099) carbon storage. The recent land-use harmonization (LUH2) version of land-use transitions (Hurtt et al., 2020) is used to reconstruct the full cohort of LULCC since 1750, while LULC is kept constant for the 21st century. Three sets of experiments explore the role of fully accounting for past land legacy, reinitializing initial conditions and not accounting for land legacy at all, and initial conditions based on averaging the final state of the full cohorts in 2014 to determine if corrected initial conditions are sufficient.
The terrestrial ecosystems model version Hydro (TEM-Hydro – Felzer, 2012; Felzer et al., 2009, 2011) is a fully prognostic biogeochemical model of carbon, nitrogen, and water dynamics between vegetation and soils. A complete description of the model can be found in Felzer et al. (2009, 2011) and Felzer (2012). The model structure is illustrated in summary figures (Fig. S1a in the Supplement) along with how human disturbance is treated, which is relevant to this paper (Fig. S1b). A cohort approach is developed to convert a dataset of land-use transitions (Hurtt et al., 2011, 2020) to annual cohorts of land use and land cover change (Hayes et al., 2011; Lu et al., 2015), whose purpose is to retain the soil characteristics of the cohort from which disturbance occurred and maintain appropriate growth and stand age of newly developed cohorts (Fig. S2a). This approach involves first using the LUH2 dataset to establish the fractional land cover type at the starting year of 1750. The primary and secondary vegetation are replaced with their potential vegetation values (as described in Raich et al., 1991), while other managed lands include croplands, pasturelands, and urban, with the multiple types of crops and pastures combined into single values for each, respectively. Disturbances (including timber harvest) involve the creation of new cohorts, with the corresponding area adjusted from the original cohort. Therefore, soil nutrients and forest stand age are tracked separately for each disturbance. The outputs are then area-weighted for each of the cohorts. Since this approach tracks each cohort separately, it is possible to end up with thousands of cohorts for a single grid cell by 2014. A complete description of this approach can be found in Felzer and Jiang (2018). New to this study is that the initial vegetation is started in 1750 (consistent with Allan et al. (2021) baseline period), and subsequent transitions were determined until 2014 (Fig. S2b, c, d, e) to align with the temporal range of climate datasets. The result for a single grid cell is usually hundreds of cohorts by the year 2014, accounting for all transitions between primary and secondary vegetation, cropland, pastureland, and urban areas, as well as timber harvest.
The partitioning of disturbance products and fluxes for agriculture and
timber harvest and management practices and calibration are described in
Felzer and Jiang (2018). In this study both croplands and turf lawn
(urban) are fertilized (using the approach taken in Felzer et al. (2018),
while no additional fertilization (beyond that provided by livestock) is
applied to pasture. A few additional modifications were made for this study.
Irrigation was added to arid croplands, because inorganic nitrogen was
accumulating due to lack of leaching. The same scheme as used in Felzer
(2012) for turf lawn was applied to croplands receiving less than 200 mm
of water per month during the growing season. The other change applies to
abandoned cropland. Cropland abandoned before there was major chemical
fertilization in the 1960s was too nutrient depleted in the model, and the
forest regrowth occurred with reduced biomass, so 15 gN m
Six simulations (Table 1) were designed to determine the effect of land
legacy. The HISTORICAL run applies the full cohorts from 1750 to 2014,
allowing for the Hurtt et al. (2020) record of LULCC as described in
the Methods. The HISTCONST run is the HISTORICAL run but with LULC held
constant at 2014 value, so includes other effects related to climate,
CO
Model experiments.
The model is run monthly at a spatial resolution of
The future climate data (2015–2099) are taken from the multivariate adaptive
constructed analogs (MACA) statistically downscaled coupled model
intercomparison project 5 (CMIP5) data (Abatzoglou and Brown, 2012),
using the National Center for Atmospheric Research (NCAR) community climate
system model version 4 (CCSM4) RCP8.5 emissions scenario (r6i1p1 ensemble).
The downscaled resolution is at 4 km but has been extrapolated to the
half-degree TEM grid for this study by averaging over all the 4 km values
within the larger half-degree grid cell. Net irradiance is used instead of
clouds for the future data. The TEM cloud scheme was adjusted for the
historical cloud data to bias-correct to ensure continuity of net irradiance
between the historical and future data. The CRU4.04 data do not include
irradiance, which is why it was necessary to use clouds for the historical
period, but since net irradiance is more directly used by the model, that
was chosen for the future period. The results (Fig. S3) show a continuity
for climate during the transition between the historical CRU4.04 and future
RCP8.5 in 2014 for all the variables. Future RCP8.5 CO
The decision to base climate prior to 1900, prior to the gridded historical data, was made to capture more realistic climate variations during the period from 1750 to 1900, such as the Little Ice Age (LIA), which lasted through the 19th century (Bradley and Jonest, 1993; Mann, 2002). The temperature record from the MPI-ESM-P model does show signs of temperature climbing out of a cold peak after 1818 but remaining cool throughout the rest of the century (Fig. S3), which is consistent with Northern Hemisphere proxy records (Mann et al., 2008). Since this study is for the conterminous US, it does not show as strong an LIA signal as would be expected from records in the North Atlantic. The decision to then use historical CRU4.04 climate rather than modeled climate from 1901–2014 is to more accurately capture the true interannual variability, which would be entirely lost by using output from a climate model. All three datasets have been downscaled and bias corrected to produce a seamless record of climate from 1750–2099.
The model is initially calibrated for specific PFTs without disturbance,
though with agricultural and urban management where necessary, to determine
coefficients for the flux equations before extrapolation to the entire US.
Note that each experiment is not calibrated individually. The HISTORICAL run
is first equilibrated based on repeated use of the 1750–1779 climate in
order to establish initial conditions of carbon and nitrogen stocks (which
are required to numerically solve the fundamental model equations), and then
the transient runs are started from 1750 to 2014. The CONDENSED run is first
equilibrated based on repeated use of the 1986–2015 climate from the
HISTORICAL run, and the transient runs are from 2015 to 2099. Results of NEP
or net carbon exchange (NCE) fluxes are reported as TgC yr
Net carbon exchange (NCE) and net ecosystem productivity (NEP) for the HISTORICAL run. NCE includes fluxes from agricultural conversion and abandonment and decomposition of agricultural products.
The historical (1750–2014) NEP (from HISTORICAL) starts to increase in the
1870s (Fig. 1), consistent with the time period when CO
Historical experiments
The effect of including LULCC is evident in the difference between
HISTORICAL and HISTCONST (Fig. 2). While the final cumulative NEP is close
by the year 2014, the use of actual land-use transitions lowers the NEP,
especially during the early years, consistent with the results of Felzer and
Jiang (2018) that the effect of deforestation reduces the NEP, while the
larger area of mature forest do not contribute much to positive NEP. The
vegetation and soil carbon start out substantially higher in HISTORICAL,
while without LULCC they remain relatively constant in HISTCONST, which
shows the effects of the other environmental changes like climate, CO
Comparison of NEP and NEC between the RESTART, CONDENSED, and
TEMRESTART runs;
In the future runs, the RESTART run is considered the “actual” to validate
the others against, as it is the run that includes effects of all the
individual cohorts. The CONDENSED run is the effect of condensing all the
cohorts to single PFTs and the TEMRESTART is the result of averaging the
initial conditions for each of the cohorts in 2014. The NEP and NCE of the
CONDENSED is lower than the RESTART and TEMRESTART, especially at the start
of the runs (Fig. 3), because reinitializing each grid is based on the
assumption of NEP as close to zero as possible. The cumulative result in
2099 is NEP of 76 PgC in the RESTART run, 80 PgC in the TEMRESTART, and 63 PgC in the CONDENSED. The cumulative NCE of the RESTART and TEMRESTART is
close beyond the starting years, resulting in 20 and 18 PgC respectively,
while it is lower (9.6 PgC) for the CONDENSED run. NCE still differs from
NEP without LULCC because of crop decomposition, animal respiration, and
crop residue fluxes. NCE of the RESTART and TEMRESTART runs are much lower
than NEP of those runs because of product decomposition left over from the
HISTORICAL run. By the end of the century there are no significant
differences in the annual carbon fluxes, but the condensed run has
significantly lower cumulative NEP and NCE than the other runs (Fig. 3e, f).
These results show that averaging the initial conditions is a good way to
reduce cohort complexity. The mapped patterns (Fig. 4) show that large
positive NEP differences between the CONDENSED and RESTART runs occur in the
upper Midwest and central California, which are dominated by cropland (Fig. S2b). This results from the reinitialization process in which the net primary productivity (NPP) of
cropland starts out larger than after, accounting for transient conditions.
Forested areas in the southeast are lower NEP in the CONDENSED, which would
be expected of more mature forests. Differences in the rest of the country
are minor. The largest differences in NCE are the negative differences in
the southeast corresponding to the NEP differences there. The lower NEP in
the CONDENSED run is the result of larger heterotrophic respiration
(
Mapped differences in NEP and NCE, illustrating effect of land
legacy as difference between the CONDENSED and RESTART runs,
While the more mature forests in CONDENSED would be expected to have lower NEP (Besnard et al., 2018; He et al., 2012), they would also have more biomass. By the end of the century regrowing forests in the RESTART run will still be younger than those in CONDENSED run, and 85 years is not enough time to reach full equilibration in the model. The CONDENSED vegetation carbon is 14 % higher than the RESTART value by the year 2099, while the TEMRESTART is only 5 % higher (Fig. 6). The larger values in the CONDENSED run are due to the fact that the larger percentage of mature trees (since all trees are considered mature in the CONDENSED run) result in much more biomass. Starting with averaged initial conditions lowers the vegetation carbon so that it is close to that of using the full cohorts. The soil carbon is 31 % higher in the CONDENSED run, while differences are minimal with the TEMRESTART run (Fig. 6). Note that the absolute differences are larger with vegetation carbon, while the percent differences are more similar since the soil carbon has lower absolute values. The mapped pattern of vegetation carbon differences between the CONDENSED and RESTART runs (Fig. 7a) shows that the large positive bias results almost entirely from the eastern half of the US, especially in the forested eastern portion, while the west exhibits smaller negative biases. The soil carbon differences (Fig. 7b) are more scattered, with largest positive biases along the East Coast and negative biases largest in the southwest US or Great Plains.
The keys to these differences are the distribution of stand age in the
forests and nutrients in the soil during regrowth. Forest stand age in 2014 at the start of the future
runs (when there is no further disturbance) shows that while the largest bin
of tree area is mature trees (
Mapped patterns in
The inorganic nitrogen in the soil is crucial for regrowth following disturbance. The dependence of available inorganic nitrogen following a disturbance on the final vegetation carbon by the year 2100 is generally a positive slope, but there is a lot of variability due to so many other factors affecting forest regrowth. Larger amounts of initial inorganic nitrogen generally lead to greater forest growth, as long as values are low enough to be limiting. There are also many cohorts that have low growth regardless of initial nitrogen levels, so they are limited by other climate or environmental factors. This is only true for the more mesic forests of the eastern US, where moisture is less limiting. The final amount of available inorganic nitrogen in 2100 will be compensated by the fact that mature forests provide more nutrients because of the greater litter but also use more nutrients due the higher biomass.
The soil moisture is based on a bucket model and accounts for the excess of precipitation over evapotranspiration, with runoff resulting if the bucket (whose capacity equals the difference between field capacity and wilting point) is overflowed. The soil moisture of the CONDENSED run over the last 30 years is not statistically different from RESTART, while the TEMRESTART, though higher, is not significantly different during that time period (Fig. 11a, d). The evapotranspiration flux of the CONDENSED run is too low compared to RESTART while it is too high in the TEMRESTART run, but the runoff fluxes are nearly identical between the three runs (Fig. 11b, c, e).
The measured stand age frequency in the US is given in Pan et al. (2011) for different regions of the US. The eastern regions are dominated by younger trees, the Rocky Mountains by more mature trees as well as a peak in very young trees, and the West Coast by more younger and mid-age trees. Lu et al. (2015), using a similar LULCC dataset as used here based on Hurtt et al. (2011) land-use transitions, specifically corrected that dataset to better represent the data from Pan et al. (2011). The resulting correction was younger forest stand ages in the eastern US after 1850, with overall younger stand ages in the conterminous US as a whole. In fact, the stand age distribution for the NE US before the correction (Fig. S2 in Lu et al., 2015) shows most forests as older than 70 years, whereas the Pan et al. (2011) data show that most forests are younger. The more recent land-use dataset developed from Hurtt et al. (2020) actually shows a majority of forests in the eastern US as less than 70 years old (Fig. 8), but for the conterminous US the frequency of mature forests is larger because of forests in the western US.
Vegetation carbon in the year 2014 for
Total biomass increases with age, such that more mature trees have higher amounts of vegetation carbon (Chapin Iii et al., 2011; Pan et al., 2002), consistent with the results presented here (Fig. 9a, b). The slight decrease in biomass for some of the more mature stand age classes can represent the differences between geographic areas in which different classes dominate, as biomass for similar trees will be larger under more favorable climate conditions. For example, more mature trees in intermountain forests in the western US may be expected to have less biomass than less mature trees in the more mesic eastern US. In the eastern US 101–500 year class, for example, the reduction in biomass is due to trees in the northeast (Fig. S5). Note that there is no explicit mortality modeled in TEM-Hydro, so biomass in mature forests is not decreasing because of increased mortality, which is another cause for reduced biomass in old stands (Xu et al., 2012). The mapped differences at the end of the 21st century (Figs. 4, 7) represent the aging of all forests in the experiments, so the age distribution in the RESTART run would be shifted upward by 70 years, so all the forests will be in the upper age categories in both RESTART and CONDENSED runs. Positive biomass differences in the eastern US (Fig 7a) may represent the even more mature status of the forests in the CONDENSED runs in that region. Forests in the CONDENSED run would be expected to have lower NEP since they are more mature, which is generally true of forests, especially in the southeast US (Fig. 4a), but by the end of the century all the forests have matured more in the RESTART run as well, so differences are more muted with time.
NEP generally peaks between 20 and 30 years stand age, yet remains positive
for hundreds of years (Luyssaert et al., 2008). The TEM-Hydro
results from the HISTORICAL run show maximum NEP occurring between 11–30 years for temperate forests across the US or up to 40 years in the eastern
US (Fig. 9c, d), with NEP generally remaining positive except for very old
trees when including the western US. In fact for the conterminous US as a
whole, Lu et al. (2015) found that the Pan et al. (2011) corrected data, with much younger stand age distribution,
had a cumulative NCE of 323 TgC yr
The interannual variability of fluxes, like NEP and NCE, is very large
(493–579 TgC yr
The effect of nutrient loading on abandoned land, such as fertilization on
abandoned cropland, can increase the final growth of the forest, but final
growth rates are dependent upon many other environmental factors as well,
which is why the relationship does not hold true everywhere, and above a
certain level of nutrient availability the system is not nitrogen limited,
so it does not matter at all. Other studies have confirmed that increased
nutrient availability, in the form of lower
Most other terrestrial ecosystem models do not include the effect of forest demography. The dynamic global vegetation models (DGVMs) included in trends in net land–atmosphere exchange (TRENDY-v2) (Li et al., 2017) mostly include annual changes in PFTs to represent LULCC. They include the conversion and product fluxes resulting from these changes, and often include the effects of mortality and regrowth within existing grids, but do not incorporate the effects of forest regrowth due to LULCC. Two of the models (VISIT and JSBACH) (Kato et al., 2013; Reick et al., 2013) include elaborate methods of applying the LULCC transition matrices to ensure the correct redistribution of PFTs and correct carbon fluxes. Shevliokova et al. (2009) does use a tiling approach to consider forest stand age and reduce the large number of cohorts used here. The HISTCOND run was designed specifically to explore the effects of forest demography by trying to emulate the effect of just redistributing annual land-use fractions, without including the effect of forest demography or keeping track of soil nutrients. As seen in the results, it does substantially overestimate the carbon stocks and underestimate the NEP compared to the run that includes the full effects of forest demography.
Restarting from averaged initial conditions more closely approximates the full cohort approach with a large computation advantage by avoiding the need for reinitializing and enabling the use of condensed cohorts, but with the corrected initial conditions. In the fluxes (Fig. 3), cumulative NEP of TEMRESTART is higher than the RESTART run, but cumulative NCE of the TEMRESTART is nearly the same as the RESTART run in the latter half of the century. The vegetation carbon of TEMRESTART diverges slightly from RESTART, while the soil carbon barely diverges at all (Fig. 6).
To address the issue of discontinuity between using clouds as input for the historical period (1750–2014) and net irradiance for the future (2015–2099), an additional FUTURE run was implemented to use clouds for the future period as well. The reason for using clouds historically is because net irradiance is not available from the CRU4.04 dataset. The model, and actual ecosystems, are affected more directly by net irradiance than clouds. The model code is designed to convert clouds to net irradiance if net irradiance is unavailable (Raich et al., 1991), which means there can be considerable error in the net irradiance values calculated from cloud data. Therefore it is most accurate to correct the historical cloud data to the bias-corrected MACA net irradiance, which is what was done in this study. The additional run involved using total cloud fraction output directly from the same r6i1p1 NCAR CCSM4 RCP8.5 simulation. Note that since these data are not available from MACA, they were bias corrected and downscaled to the corrected cloud data using the period 2006–2014 and a similar method as used to bias correct and downscale the MPI model output to CRU. The results are all statistically insignificant differences in NEP, NCE, cumulative NEP, cumulative NCP, vegetation carbon, and soil carbon.
Water variables depend upon precipitation (which is similar between the runs but can be rain or snowmelt) and evapotranspiration, which ultimately depends upon environmental conditions (i.e., solar radiation, vapor pressure deficit), stomatal conductance, and soil texture (Felzer et al., 2011; Shuttleworth and Wallace, 1985). The CONDENSED run exhibits a lower evapotranspiration than RESTART, which is primarily due to low values in pasture grids (Fig. 11). Pasture in the CONDENSED run has higher leaf area index (LAI) then in the RESTART run, due to reinitializing from equilibrium conditions, and that reduced the net irradiance, which limits the amount of soil evaporation. The effect of LAI on soil evaporation in the Shuttleworth–Wallace or Penman–Monteith approaches takes the form of an exponential decay, resulting in a much sharper drop-off in evaporation with smaller changes in low LAI than large LAI, which is why the effect is predominant in low-height vegetation like pastures. The soil moisture is slightly too large in the TEMRESTART run even though it starts off at the correct value, which also results in a larger evapotranspiration rate, though neither is significantly different from RESTART by the end of the century. The larger biases in the evapotranspiration flux do not lead to larger biases in the soil moisture stock. While evapotranspiration depends upon vapor pressure deficit, net irradiance, stomatal conductance and surface roughness, and its value affects the soil moisture, the amount of soil moisture also affects the amount of water available for evapotranspiration. Increasing vegetation cover has competing effects of reducing soil moisture by shading the ground and increasing evapotranspiration, yet the relative effect of the two depends upon the range of the LAI change.
This study explores the role of past land use and land cover legacy on the future carbon and water dynamics of terrestrial ecosystems in the conterminous US. While most models simulating the future start with current LULCC by reinitializing initial conditions, the actual value of the initial conditions should be different because ecosystems are not in a state of equilibrium but are changing due to past disturbances and climate change. This study determines whether it is nevertheless possible to use a single realization for each PFT if the initial conditions are set correctly based on a past run that includes land use and land cover legacy effects.
NEP, a measure of carbon sequestration, is too low compared to using all the cohorts when reinitializing initial conditions because the assumption of mature forests rebalances the NEP to become more neutral through enhanced heterotrophic decomposition. There are some offsetting geographic differences across the US when accounting for all ecosystems. The NCE differences are somewhat reduced, however, due to continued product decomposition in runs that account for transient changes to LULC in the past. Cumulatively, condensed cohorts have a negative bias in both NEP and NCE, which becomes a positive bias in the case of NEP and is eliminated in the case of NCE by the end of the century when initializing correctly (TEMRESTART). This is evident in the larger values in the biomasses (vegetation and soil carbon) relative to RESTART, which are too large for the CONDENSED cohorts but greatly improved with TEMRESTART. When PFTs are condensed into single cohorts, the forests are all assumed to be mature forests, which leads to an overestimation of the biomass. The NEP of mature forests is generally less than that of younger forests, though the actual biases between the CONDENSED and RESTART runs by the end of the century are more muted as the forests have had a chance to mature more in both. Correcting for initial conditions reduces the bias in vegetation carbon and eliminates the bias in soil carbon. Starting with the correct initial conditions do not have a large impact on the water variables, as they are more dependent on environmental factors, though the vegetation cover does have some minor effects.
In addition to forest stand age, the initial nutrient loading of the soil is also an important factor for future forest regrowth. With low levels of nitrogen, higher starting values often lead to a larger overall biomass as the forest develops, though there are other environmental factors (e.g., climate) that are important. Past agricultural use could deplete the soil of nutrients if cropland was abandoned at a time period before chemical fertilization was frequently used (i.e., before the 1950s), or could enhance the soil nutrients if abandoned from heavily fertilized soil. These effects will be accounted for if the correct initial soil conditions are determined.
This study illustrates the importance of accounting for the correct forest stand age and initial soil nutrient conditions in order to model the future carbon sink. Although starting model runs in the 1700s or earlier is computationally expensive, it is possible to average values from such a run for each PFT to allow a run to start in the present with correct initial condition and achieve a result more consistent with a detailed representation of land-use cohorts. While this research assumed constant LULC for the future, the next step is to use the corrected initial conditions as a basis for future LULCC. A similar approach can be used to start land-use transitions at any particular year based on the complete history of land-use transitions from 850 CE to serve as starting conditions for one of the shared socioeconomic pathway (SSP) scenarios. Modeling groups need to consider this effect of past LULC legacy to accurately estimate future carbon biomass and fluxes.
The C++ code for TEM-Hydro version 2 used in this study, the batch files for each of the model experiments, and Make files for compilation are available at
All model input data (land use, climate, etc.) and output summary statistic files, mapping files, and final data used to prepare figures are available at
The supplement related to this article is available online at:
The author declares that there is no conflict of interest.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
I would like to thank Yuning Shi and Dork Sahagian for reviewing this document, as well as my graduate students (Christopher Andrade, Dannielle Waugh, and Jared Kodero) for patiently discussing this research at our weekly research meetings.
This paper was edited by David McLagan and reviewed by two anonymous referees.