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Article

Climate Warming Benefits Plant Growth but Not Net Carbon Uptake: Simulation of Alaska Tundra and Needle Leaf Forest Using LPJ-GUESS

by
Cui Liu
1,
Chuanhua Li
1,2,* and
Liangliang Li
1
1
College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
2
Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(5), 632; https://doi.org/10.3390/land13050632
Submission received: 21 March 2024 / Revised: 1 May 2024 / Accepted: 4 May 2024 / Published: 8 May 2024
(This article belongs to the Topic Climate Change and Environmental Sustainability, 3rd Volume)
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Abstract

:
Climate warming significantly impacts Arctic vegetation, yet its future role as a carbon sink or source is unclear. We analyzed vegetation growth and carbon exchange in Alaska’s tundra and needle leaf forests using the LPJ-GUESS model. The accuracy of the model is verified using linear regression of the measured data from 2004 to 2008, and the results are significantly correlated, which proves that the model is reliable, with R2 values of 0.51 and 0.46, respectively, for net ecosystem carbon exchange (NEE) at the tundra and needle leaf forest sites, and RMSE values of 22.85 and 23.40 gC/m2/yr for the tundra and needle forest sites, respectively. For the gross primary production (GPP), the R2 values were 0.66 and 0.85, and the RMSE values were 39.25 and 43.75 gC/m2/yr at the tundra and needle leaf forest sites, respectively. We simulated vegetation carbon exchanges for 1992–2014 and projected future exchanges for 2020–2100 using climate variables. Under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, GPP values increase with higher emissions, while the NEE showed great fluctuations without significant differences among the three pathways. Our results showed although climate warming can benefit vegetation growth, net carbon assimilation by vegetation may not increase accordingly in the future.

1. Introduction

From 2011 to 2020, the global surface temperature increased by 1.09 °C compared to the period from 1850 to 1900 [1]. This warming rate was about 50% faster in the United States and two to three times faster across the Eurasian continent, while the Arctic and Antarctic Peninsula experienced warming rates three to four times greater [2]. Additionally, significant changes in the hydrological cycle, such as precipitation and evapotranspiration, were observed in high-latitude regions [3]. Climate change has led to widespread alterations and successions in biological communities, with alpine forest–tundra ecotones shifting to higher elevations, deciduous and boreal forests moving toward polar regions, an increase in woody vegetation in subarctic tundra, and a significant reduction in global grassland areas. In temperate and polar zones, approximately half of treelines are shifting toward the poles or higher altitudes, and alpine meadows are decreasing [4]. Climate warming has advanced the spring phenological phases of species in temperate regions and has also enhanced rice and corn yields in the Kosy River Basin [5]. These changes in vegetation composition and productivity in high-latitude areas are further impacting the global carbon cycle [6,7].
Arctic ecosystems have long been exposed to low temperatures and are highly vulnerable to climate change [8,9]. According to the definition of the Arctic Monitoring and Assessment Programme (AMAP) [10], the Arctic land area accounts for 5.37% of the world’s land area, and the vegetative area in the Arctic is 71.02% [11]. Vegetation changes in this region play an important role in regulating the global carbon balance because the vast areas in this region belong to permafrost zones, where the soil organic carbon content is high [12,13,14]. The changes in vegetation dynamics and species compositions in the Arctic have attracted many concerns. However, there are still great uncertainties in the net ecosystem carbon exchanges in this region due to the lack of long-term observational data [15,16].
Vegetation is an important component of the global carbon cycle. Carbon assimilation by vegetation plays a key role in determining the roles of the ecosystem as a carbon source or sink [17]. Vegetation type shifts, particularly shrub expansion across the Arctic tundra, have been widely regarded as a result of rapid climate warming [18,19]. The changes in vegetation can also potentially alter ecosystem carbon balances by affecting interactions among the soil–plant–atmosphere [20]. Presently, process models have been widely used to study vegetation growth and the carbon cycle in terrestrial ecosystems [20,21]. For example, based on in situ observation data, ecological process-based models can assess how the carbon balance will be impacted by climate change in Arctic tundra ecosystems [22]. Among these models, the LPJ-GUESS (Lund–Potsdam–Jena general ecosystem simulator) is a coupled biogeochemical model that has a wide range of applications in vegetation productivity estimation and vegetation carbon cycle under the current and future climate scenarios [21,23,24,25,26,27].
The purpose of this study is twofold. First, to explore the trends in vegetation GPP and carbon budget changes in the tundra and boreal forest ecosystems of Alaska from 1992 to 2014. Second, to analyze whether future reliance on vegetation to absorb more carbon is feasible by simulating the trends in vegetation GPP and carbon budget changes for the two aforementioned ecosystems under three future climate scenarios. To address these scientific inquiries, this study rigorously validates the LPJ-GUESS model using field monitoring data spanning 4 to 5 years to ensure its accuracy in simulating carbon cycling during historical periods. Subsequently, detailed simulations of vegetation carbon budgets from 1992 to 2014 are conducted, extending further to predictive analyses under future climatic conditions. These research findings offer crucial insights into understanding the growth dynamics of Arctic vegetation amidst climate change and its potential contribution to the global carbon cycle. They also provide scientific grounds for assessing the possibility of future vegetation GPP as a carbon sink.

2. Materials and Methods

2.1. Study Area

The land cover types in Alaska are dominated by forests and tundra [28]. Accordingly, we select two sites as representatives of the two land cover types in this area (Figure 1). One site is the tundra ecosystem, e.g., Atqasuk, with the site name US-ATQ (70.47° N, 157.41° W). The elevation is 15 m above sea level. From 1989 to 2014, the average annual temperature is −9.7 °C, and the average annual precipitation is 93 mm. The vegetation communities at this site are dominated by sedges, grasses, mosses, and dwarf shrubs (<40 cm). The ATQ site belongs to the continuous permafrost region with high ground ice content. The other is the needle leaf forest site, e.g., the Poker Flat Research Range Black Spruce Forest, with the site name of US-PRR (65.12° N, 147.48° W). The elevation is 210 m above the site level. At the US-PRR site, the average annual temperature is −2 °C, and the average annual precipitation is 275 mm. The land cover type belongs to the evergreen needle leaf (ENF), and this area is covered with extensive forest. The PRR site belongs to discontinuous permafrost regions with medium ground ice content.

2.2. Data

The climate data for the ATQ tundra and the PRR needle leaf forest sites (1992–2014), including the monthly precipitation, monthly air temperatures, monthly solar radiation, and annual atmospheric carbon dioxide concentrations, were downloaded from the website (https://fluxnet.org/data/download-data/, accessed on 14 April 2023). Validation data for gross primary production (GPP) and net ecosystem exchanges (NEE) were obtained from FLUXNET2015 (Supporting Information Text S1). There were two different time periods for the two sites, e.g., 2004–2008 for the ATQ site and 2011–2014 for the PRR site were downloaded from the website (https://fluxnet.org/data/fluxnet2015-dataset/, accessed on 12 April 2023). We selected the NEE and NPP data to assess the model accuracy.
We selected three scenario datasets from the CMIP6 SSP1-2.6, SSP2-4.5, and SSP5-8.5: the SSP1-2.6 “2 °C scenario” of the “sustainability” SSP1 socio-economic family, whose nameplate 2100 radiative forcing level is 2.6 W m−2; the SSP2-4.5 of the “middle of the road” socio-economic family SSP2 with a nominal 4.5 W m−2 radiative forcing level by 2100; and the SSP5-8.5 marks the upper edge of the SSP scenario spectrum with a high reference scenario in a high fossil-fuel development world throughout the 21st century. The precipitation, temperature, solar radiation, carbon dioxide, gross primary production, and leaf area index in the three pathways were downloaded (https://esgf-node.llnl.gov/search/cmip6/, accessed on 14 April 2023)(Supporting Information Text S2). The detailed description of the data is shown in Table 1 and Table 2. We converted units of air temperature from Kelvin to Celsius, units of precipitation are mm year−1, and units of GPP from kgC m−2 s−1 to gC m−2 yr−1. The changing trends of precipitation (Figure 2), temperature (Figure 3), solar radiation (Figure 4), and carbon dioxide concentration at the ATQ tundra and PRR needle leaf forest sites from 2020 to 2100 are shown in Figure 2, Figure 3 and Figure 4.

2.3. The LPJ-GUESS Model

The Lund–Potsdam–Jena general ecosystem simulator (LPJ-GUESS model) is developed based on the dynamic global vegetation model (LPJ-DGVM model), which combines the generalized physiological and biological processes in the LPJ-DGVM with representations of the tree population, demography, and canopy structures [29]. The LPJ-GUESS model combines terrestrial vegetation dynamics and land–atmosphere–carbon–water exchanges in a modular framework [30]. The model can simulate vegetation dynamics and carbon and water fluxes at species, communities, ecosystems, and even global scales. This model can predict vegetation changes under future climate scenarios [24], and it has been successfully used for modeling vegetation structure and biomass around the world [31,32]. The model simulates soil freeze–thaw processes and is suitable for the studies of processes at northern high latitudes. Therefore, we used it to study the future vegetation change in the Arctic ecosystem.
The LPJ-GUESS 4.1 version considers the soil freeze–thaw process and therefore performs well over the high latitudes in the north [31]. The data required to drive the model include monthly meteorological data (monthly temperature, monthly precipitation, and monthly solar radiation), annual atmospheric CO2 concentration, and soil texture. The soil texture data applied in the LPJ-GUESS model were the soil dataset from the Food and Agriculture Organization of the United Nations (FAO) [33]. The soil type code is extracted from the file that comes with the model based on the geographic coordinates of the simulated points. In this model, vegetation is defined as a competing set of plant functional types (PFTs), grouped by similar attributes such as phenology, growth, life history, and bioclimatic constraints. The vegetation functional types used in this study consisted of three tree PFTs, a boreal needle-leaved evergreen tolerant tree (BNE), a boreal needle-leaved evergreen intolerant tree (BINE), and a broad-leaved evergreen intolerant tree (IBS), and a generic herbaceous PFT, grass (C3G) (Table 3). For the vegetation growth and carbon exchange simulation [34], the main output variables used in this study included the leaf area index (LAI), carbon biomass (C biomass), net primary production of ecosystems (NPP), net ecosystem exchange (NEE), and gross primary production of ecosystems (GPP).
In this study, climate data from 1989 to 2014 were used as input data for spin-up to repeatedly simulate the 200-year spin cycle and establish the equilibrium state of vegetation (Table 4). The input data of the model are SSP scenario data, such as temperature, precipitation, solar radiation, and carbon dioxide concentration, with a spatial resolution of 250 km. The vegetation conditions of the Arctic sites, such as the ATQ site and PRR site under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios in the future are simulated correspondingly.

3. Results

3.1. Model Validation

The linear regressions showed that there were significant correlations between the simulated and observed values for NEE and GPP (Figure 5), indicating that the accuracy of the model was good. Therefore, it is possible to simulate the changes in NEE and GPP in this area under future climate conditions.
The R2 values of NEE at the tundra and needle leaf forest sites in Alaska were approximately 0.5, and the RMSE values were approximately 20 gC/m2/yr. The R2 values of GPP at the tundra and needle leaf forest sites in Alaska were 0.66 and 0.85, respectively, and the RMSE values were 39.25 and 43.75 gC/m2/yr, respectively (Figure 5). This simulation accuracy was largely comparable with previous reports to the LPJ-GUESS. For example, the R2 value of GPP in the Hindu Kush Himalayan forest simulated using LPJ-GUESS was 0.54 [26]. The R2 value was 0.76 for the NEE simulation using a model of net CO2 exchange in the Arctic tundra ecosystem from 2003 to 2009 [35]. Our results showed that the LPJ-GUESS model is a useful tool to simulate NEE and GPP in the Arctic tundra and needle leaf forest.

3.2. NEE and GPP Simulation during 1992–2014

There are clear seasonal changes in NEE and GPP (Figure 6). During December-March, vegetation was dormant, and the NEE values showed that the ecosystems in the tundra and needle leaf forest sites were weak carbon sources. From May to August, the GPP at the tundra site was lower than that at the needle leaf forest site. The needle leaf forest was also a stronger carbon sink than the tundra.
The simulation results from 1992 to 2014 showed that the NEE values of the tundra site ranged from −123 to 8 gC/m2/yr, with an average value of −43.65 gC/m2/yr (Figure 7). The GPP values in the tundra forest ranged from 56 to 255 gC/m2/yr, with an average value of 129 gC/m2/yr. For the needle leaf forest, the average values of NEE and GPP were −65 gC/m2/yr and 299 gC/m2/yr, respectively.

3.3. Future Simulation of GPP and NEE

The simulation results of ATQ and PRR sites in the Arctic ecosystem have different results under the three future scenarios. Under future climate conditions, both the GPP at the tundra and needle leaf forest sites showed increasing trends (Figure 8). The average GPP values from 2020 to 2100 at the tundra site under SSP1-2.6, SSP2-4.5, and SSP5-8.5 were 22, 31, and 40 gC/m2/yr, respectively. The average GPP values from 2020 to 2100 at the needle leaf forest site under the three pathways were 651, 574, and (831 gC/m2/yr. For the NEE values, the tundra sites from 2020 to 2100 under SSP1-2.6, SSP2-4.5, and SSP5-8.5 were −36, −43, and −48 gC/m2/yr, respectively. The average NEE values at the needle leaf forest site from 2020 to 2100 were −229, −226, and −276 gC/m2/yr, respectively (Figure 8). The rate of decline of NEE at the ATQ tundra site was the fastest under the SSP5-8.5 pathway from 2020 to 2100. For the PRR needle leaf forest site, the carbon uptake rate was the fastest under the SSP1-2.6 pathway, followed by SSP5-8.5. For the GPP values, both the ATQ tundra site and PRR needle leaf forest site had the greatest increasing value under the SSP5-8.5 pathway.
The carbon exchange of the Arctic ecosystem in a future climate is simulated (Table 5). In the future scenario, the carbon exchange of the PRR site is higher than that of the ATQ site, in which the SSP5-8.5 pathway is higher than that of the SSP1-2.6 pathway, but the transformation rate of the SSP1-2.6 pathway is faster than that of SSP5-8.5 pathway. The net vegetation carbon exchange rate was higher than the net soil carbon exchange rate.

3.4. Comparison of GPP and LAI Simulation Results

We compared the simulated GPP and LAI values from LPJ-GUESS model, and the GPP and LAI values of the ESM output (Figure 9 and Figure 10). We found that in the GPP simulation, the PRR site fits the ESM output value better than the ATQ site, and the simulated value of the ATQ site is lower than the ESM output value, while the simulated value of the PRR site is higher than the ESM output value. For LAI simulation, the ATQ site fitting degree was higher than that of the PRR site, the model fitting value of the PRR site was generally lower than that of the ATQ site, and the model simulation value had periodic fluctuations.

4. Discussion

4.1. Model Simulation Values

The LPJ-GUESS model has been widely used to estimate the impacts of climate on terrestrial ecosystems [36,37,38,39]. In light of this, our study simulated NEE and GPP using the LPJ-GUESS model, achieving a level of accuracy comparable to previous research reports, thereby ensuring the reliability of our results [26]. The NEE and GPP in our study area showed obvious seasonal changes (Figure 6). It is reasonable that the NEE values are negative during the growing season because vegetation growth can assimilate carbon [40]. The maximum carbon assimilation rates largely occurred in July or August because during this period, the air temperature was high, and the solar radiation was strong in the Arctic region. From April to July, the GPP values also increase rapidly, and this pattern clearly demonstrates the seasonal changes in vegetation growth. In the tundra ecosystem, GPP began to increase from early to mid-June and peaked from late July to early August. After the peak values in summer, GPP began to decline. In the tundra ecosystem, the seasonality of ecosystem respiration was less obvious [41]. Thus, the seasonality of NEE was determined by GPP rather than ecosystem respiration in tundra ecosystems. Compared with the tundra, GPP and ecosystem respiration in boreal forest ecosystems show similar seasonality, peaking between late June and mid-July. Although the specific peak times of GPP and NEE vary from site to site, they show similar seasonal variation [42].
Our simulation results showed that the NEE at the needle leaf forest site was lower than that of the tundra, indicating that the needle leaf forest is a stronger carbon sink during the growing season. Similarly, the GPP of the needle leaf forest site was higher than that of the tundra site (Figure 5). Compared with tundra ecosystems, forest ecosystems have richer biodiversity, more complex structural levels, and higher productivity, and their annual carbon sequestration capacity is higher [43]. Fisher et al. showed a comparison of NEE and GPP simulations using 10 NACP site synthesis models in 2002 and 2003 in Alaska tundra sites. NEE reached its lowest value, and GPP reached its highest value around July and gradually approached zero in winter. The terrestrial biosphere model was used for the simulation; GPP was 220 ± 500 gC/m2/yr, and NEE was 10 ± 190 gC/m2/yr, which were consistent with the results of this study [44].

4.2. Changes in NEE and GPP in Alaska from 1992 to 2014

From 1992 to 2014, NEE and GPP of ATQ sites showed a trend of drastic fluctuation, which was due to fire disturbance. The ecosystem was disturbed by fires during 1982–2002 and 2003–2011 at the ATQ site, which led to drastic changes in the carbon flux of the ecosystem [42]. At the beginning of the fire period (1982–1995), the disturbance of the ecosystem was remarkable. During this period, the fire led to a decrease in the flux and GPP showing a downward trend, with less GPP in burn sites than in uninterrupted areas [42].
With the decrease of GPP related to recent disturbances, NEE showed an overall trend of increasing. In the late period of the fire (1995–2002), the vegetation in the burned ecosystem began recovering slowly, and the GPP showed an upward trend while the NEE showed a downward trend [42]. The change trend of NEE and GPP of the PRR site was consistent during the same period, because they were not disturbed.
From 1992 to 2014, the decreasing NEE values indicated that the carbon uptake rates increased, and the GPP values also increased. Compared with the tundra site, the needle leaf forest site had a faster-increasing rate of carbon uptake and GPP values. Similar findings have also been reported by in situ observation data, i.e., the carbon uptake rates of forest regions have been increasing during the past decades, while the tundra areas showed little change in carbon uptake rates [45]. This can be attributed to the fact that the tundra area is dominated by herbaceous species, which have limited biomass and carbon uptake capacity. It has also been found that Arctic tundra may already be a carbon source because of limited vegetation growth but high soil organic carbon loss [46,47]. In addition, the NEE and GPP at the tundra sites showed greater fluctuations than those at the needle leaf forest site. This pattern confirmed that the growth of perennial herbs is more sensitive to climate conditions, while woody forests have a stronger adaptative capacity to climate conditions. Consequently, the GPP values for the forest usually showed smaller changes than those in grassland [48].

4.3. Simulations under Future Climate Scenarios

The simulation results show that from 2020 to 2100, the GPP values at both sites increased. The carbon uptake rates in tundra vary greatly under different climate scenarios. Climate warming at high latitudes can alleviate the effects of cold stress on vegetation growth [49] and thus promote vegetation growth [50]. With the increase in temperature, precipitation, and atmospheric CO2 content, the Arctic will show a “greening” trend [51]. A previous study based on the CoupModel simulation in the Greater Khingan Mountains permafrost region also suggested that the GPP and carbon uptake tended to increase in a warming climate. We found that the maximum GPP values appeared under the SSP5-8.5 scenario, and the minimum values appeared under the SSP1-2.6 scenario. However, the carbon uptake rates showed much lower increasing rates. Under the SSP5-8.5 scenario, the GPP values are the highest, due to this prediction of higher greenhouse gas emissions and temperature rise, which typically enhances the growth rate and biomass of vegetation in certain regions, particularly in temperate and high-latitude areas [52]. In contrast, the SSP1-2.6 scenario forecasts more moderate greenhouse gas emissions and climate change, resulting in relatively lower GPP values. However, despite the increase in GPP under certain scenarios, the rate of carbon sequestration is relatively slow. This is partly the elevated concentrations of greenhouse gases and rising temperatures accelerate the thawing of permafrost. As permafrost thaws, the previously frozen organic is released and decomposed through microbial activity, promoting soil respiration [53,54]. Soil respiration, the process by which soil microorganisms decompose organic matter and release carbon dioxide, is enhanced by these conditions, leading to the release of carbon from the soil into the atmosphere, thereby offsetting some of the carbon fixed by the increased photosynthesis due to vegetation growth [55]. In summary, while climate warming may promote vegetation growth and primary productivity in the short term, the accelerated thawing of permafrost and the increase in soil respiration could lead to a future where carbon sequestration in the Arctic region may not significantly increase.
In the future scenario, the NEE value simulated using the model shows a decreasing trend, which means that the carbon sequestration capacity of the ATQ tundra site and PRR needle leaf forest site in the Arctic region will increase, and the carbon sequestration capacity of PRR site will be stronger and the vegetation growth condition will be better. Under the future warming scenario, the net vegetation carbon exchange of tundra sites is larger than that of forest sites, while the net soil carbon exchange is smaller than that of forest sites. The decomposition of soil organic matter is positively correlated with soil temperature, and the net soil carbon exchange rate will accelerate with future temperature increases. In the Arctic ecosystem, the net soil carbon exchange rate of forest sites is greater than that of tundra sites. In the SSP1-2.6 scenario, the rate of carbon sequestration in coniferous forest sites is the fastest, likely reflecting the relatively mild climate conditions of this scenario that favor carbon fixation. Overall, the carbon exchange at PRR coniferous forest sites is higher than that at ATQ permafrost sites, possibly due to the greater biomass and carbon storage capacity of coniferous forests [56]. Although carbon exchange is higher under the SSP5-8.5 scenario than under SSP1-2.6, the transformation rate is quicker in the SSP1-2.6 scenario, indicating that ecosystems under scenarios with lower greenhouse gas emissions adapt and respond more rapidly to climate change.
We re-drive the model based on the SSP1-2.6 scenario, changing the driving factor one at a time, and changing the carbon dioxide concentration, air temperature, precipitation, and solar radiation to the corresponding factors of the SSP2-4.5 scenario and SSP5-8.5 scenario, respectively. The results show that in the GPP and NEE simulation results, carbon dioxide concentration has the largest effect, followed by air temperature and solar radiation, and precipitation has the least effect. The increase in air temperature, solar radiation, and precipitation has a positive effect on GPP in Alaska. This suggests that future improvements in hydrothermal conditions induced by climate change will alleviate temperature stress in Alaska and have a positive effect on improving ecosystem productivity (Supporting Information Text S3).

5. Conclusions

We discuss the impact of future climate on Arctic ecosystems by simulating vegetation growth at a tundra site and a forest site. In this study, we simulated the changing trends of NEE and GPP of the tundra and needle leaf forest in Alaska from 1992 to 2014 and their future changes by 2100 using the LPJ-GUESS model. Our results showed that the LPJ-GUESS model is a useful tool to simulate the NEE and GPP for these two land cover types. During the past decades, the GPP showed a faster-increasing rate in the needle leaf forest site than in the tundra site, and the carbon uptake rates for the two areas fluctuated considerably, with nonsignificant increasing trends. Under the future emission pathways, the GPP values of the tundra and needle leaf forest were also higher, while the carbon uptake showed no significant increasing trends in the future. Under the future emission pathways, the GPP of the high emission SSP5-8.5 scenario increases faster and NEE decreases faster. In this study, we utilized the LPJ-GUESS model to simulate vegetation growth and carbon exchange processes in Alaska. The model validation period was relatively short, spanning only from 2004 to 2008, which may not fully capture the impacts of long-term climate change on the vegetation ecosystems. Additionally, while LPJ-GUESS is a well-recognized ecological model, its structure and parameter settings may not entirely reflect the complexities of the world.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/land13050632/s1, Text S1. The supporting information contains detailed information of FLUXNET data; Text S2. The supporting information contains detailed information of CMIP6 data; Text S3, The supporting information contains detailed information of Supplementary data; Figure S1. Relationships among GPP, NEE and CO2 concentration, Precipitation, Solar radiation, and air temperature for ATQ tundra site under SSP2-4.5 (a,c) and SSP 5-8.5 (b,d); Figure S2. Relationships among GPP, NEE and CO2 concentration, Precipitation, Solar radiation, and air temperature for PRR needle leaf forest site under SSP2-4.5 (a,c) and SSP 5-8.5 (b,d).

Author Contributions

C.L. (Chuanhua Li) is responsible for the design of the project, revision of the paper, and acquisition of funding; C.L. (Cui Liu) is responsible for the implementation of the project, analysis of data, and drafting of the manuscript; L.L. is responsible for the collection and analysis of data. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China grant 42161058.

Data Availability Statement

Land cover data were downloaded from the website Liu, Gong, Wang, Clinton, Bai and Liang [27] (https://doi.pangaea.de/10.1594/PANGAEA.913496, accessed on 12 April 2023). The climate data and validation data were downloaded from the website (https://fluxnet.org/data/download-data/, accessed on 12 April 2023). Precipitation changes in the future [dataset] (Figure 2), temperature changes in the future [dataset] (Figure 3), solar radiation changes in the future [dataset] (Figure 4), simulated and observed NEE and GPP [dataset] (Figure 5), monthly NEE and GPP [dataset] (Figure 6), changes in NEE and GPP from 1992 to 2014 [dataset] (Figure 7), Changes in NEE and GPP from 2020 to 2100 [dataset] (Figure 8) are available at Dryad Digital Repository via https://doi.org/10.5061/dryad.m63xsj451.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the sites and the land cover types in Alaska. Alaska boundary data were downloaded from the website (https://gadm.org/download_country_v3.html, accessed on 12 April 2023). Land cover data were downloaded from the website https://doi.pangaea.de/10.1594/PANGAEA.913496, accessed on 12 April 2023.
Figure 1. Location of the sites and the land cover types in Alaska. Alaska boundary data were downloaded from the website (https://gadm.org/download_country_v3.html, accessed on 12 April 2023). Land cover data were downloaded from the website https://doi.pangaea.de/10.1594/PANGAEA.913496, accessed on 12 April 2023.
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Figure 2. Precipitation changes in the future at the ATQ tundra site (a) and PRR needle leaf forest site (b).
Figure 2. Precipitation changes in the future at the ATQ tundra site (a) and PRR needle leaf forest site (b).
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Figure 3. Temperature changes in the future at the ATQ tundra site (a) and PRR needle leaf forest site (b).
Figure 3. Temperature changes in the future at the ATQ tundra site (a) and PRR needle leaf forest site (b).
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Figure 4. Solar radiation changes in the future at the ATQ tundra site (a) and PRR needle leaf forest site (b).
Figure 4. Solar radiation changes in the future at the ATQ tundra site (a) and PRR needle leaf forest site (b).
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Figure 5. Simulated and observed NEE and GPP at the ATQ tundra site (a,b) and PRR needle leaf forest site (c,d).
Figure 5. Simulated and observed NEE and GPP at the ATQ tundra site (a,b) and PRR needle leaf forest site (c,d).
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Figure 6. Monthly values of NEE and GPP values at the ATQ tundra site (a,b) and PRR needle leaf forest site (c,d).
Figure 6. Monthly values of NEE and GPP values at the ATQ tundra site (a,b) and PRR needle leaf forest site (c,d).
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Figure 7. Changes in NEE and GPP from 1992 to 2014 at the ATQ tundra (a,b) and PRR needle leaf forest (c,d) sites.
Figure 7. Changes in NEE and GPP from 1992 to 2014 at the ATQ tundra (a,b) and PRR needle leaf forest (c,d) sites.
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Figure 8. Changes in GPP and NEE from 2020 to 2100 under future climate conditions at the ATQ tundra site (a,b) and PRR needle leaf forest site (c,d).
Figure 8. Changes in GPP and NEE from 2020 to 2100 under future climate conditions at the ATQ tundra site (a,b) and PRR needle leaf forest site (c,d).
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Figure 9. Comparison of GPP values between model simulation and ESM output at the ATQ tundra site ((a) (SSP1-2.6), (c) (SSP2-4.5), (e) (SSP5-8.5)) and PRR needle leaf forest site ((b) (SSP1-2.6), (d) (SSP2-4.5), (f) (SSP5-8.5)) in SSP scenario.
Figure 9. Comparison of GPP values between model simulation and ESM output at the ATQ tundra site ((a) (SSP1-2.6), (c) (SSP2-4.5), (e) (SSP5-8.5)) and PRR needle leaf forest site ((b) (SSP1-2.6), (d) (SSP2-4.5), (f) (SSP5-8.5)) in SSP scenario.
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Figure 10. Comparison of LAI value between model simulation and ESM output at the ATQ tundra site ((a) (SSP1-2.6), (c) (SSP2-4.5), (e) (SSP5-8.5)) and PRR needle leaf forest site ((b) (SSP1-2.6), (d) (SSP2-4.5), (f) (SSP5-8.5)) in SSP scenario.
Figure 10. Comparison of LAI value between model simulation and ESM output at the ATQ tundra site ((a) (SSP1-2.6), (c) (SSP2-4.5), (e) (SSP5-8.5)) and PRR needle leaf forest site ((b) (SSP1-2.6), (d) (SSP2-4.5), (f) (SSP5-8.5)) in SSP scenario.
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Table 1. SSP scenario data required by the model.
Table 1. SSP scenario data required by the model.
VariableName (Unit)PathwayModel
CO2Mole Fraction of CO2
(mol mol−1)		SSP126NorESM2-LM
SSP245NorESM2-MM
SSP585NorESM2-MM
PrPrecipitation
(mm year−1)		SSP126UKESM1-0-LL
SSP245UKESM1-0-LL
SSP585UKESM1-0-LL
TaAir Temperature
(K)		SSP126UKESM1-0-LL
SSP245UKESM1-0-LL
SSP585UKESM1-0-LL
RssNet Shortwave Surface Radiation
(W m−2)		SSP126ACCESS-ESM1-5
SSP245ACCESS-ESM1-5
SSP585ACCESS-ESM1-5
Table 2. SSP scenario data required for the model comparison.
Table 2. SSP scenario data required for the model comparison.
VariableName (Unit)PathwayModel
GPPCarbon Mass Flux out of Atmosphere Due to Gross Primary Production on Land (kgC m−2 s−1)SSP126ACCESS-ESM1-5
SSP245ACCESS-ESM1-5
SSP585ACCESS-ESM1-5
LAILeaf Area Index (1)SSP126ACCESS-ESM1-5
SSP245ACCESS-ESM1-5
SSP585ACCESS-ESM1-5
Table 3. Plant functional types used in this study.
Table 3. Plant functional types used in this study.
PFTDistributionGrowth FormLeaf PhenologyShade-TolerancePhotosynthetic Pathway
BNEBorealNeedle-leaved treeEvergreenTolerantC3
BINEBorealNeedle-leaved treeEvergreenIntolerantC3
IBSBoreal/temperateBroad-leaved treeSummer-greenIntolerantC3
C3GBoreal/temperateGrassSummer-green/rain-greenIntolerantC3
Table 4. Parameter values in LPJ-GUESS Model.
Table 4. Parameter values in LPJ-GUESS Model.
ParameterParameter Value
vegmodecohort
nyear_spinup 200
npatch25
patcharea1000
Table 5. Future simulation of carbon exchange.
Table 5. Future simulation of carbon exchange.
SiteClimate ScenariosCarbon Exchange (gC/m2/yr)Rate of Change (gC/m2/yr2)
NEEVeg.SoilNEEVeg.Soil
ATQSSP1-2.6−36.00−45.0016.03−0.73−3.590.41
SSP2-4.5−43.00−115.3340.98−0.43−3.000.45
SSP5-8.5−48.00−113.1153.90−0.73−0.270.91
PRRSSP1-2.6−229.00−348.71282.93−2.99−2.892.27
SSP2-4.5−226.00−298.55231.98−2.19−2.121.29
SSP5-8.5−276.00−454.78361.36−1.71−1.132.61
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Liu, C.; Li, C.; Li, L. Climate Warming Benefits Plant Growth but Not Net Carbon Uptake: Simulation of Alaska Tundra and Needle Leaf Forest Using LPJ-GUESS. Land 2024, 13, 632. https://doi.org/10.3390/land13050632

AMA Style

Liu C, Li C, Li L. Climate Warming Benefits Plant Growth but Not Net Carbon Uptake: Simulation of Alaska Tundra and Needle Leaf Forest Using LPJ-GUESS. Land. 2024; 13(5):632. https://doi.org/10.3390/land13050632

Chicago/Turabian Style

Liu, Cui, Chuanhua Li, and Liangliang Li. 2024. "Climate Warming Benefits Plant Growth but Not Net Carbon Uptake: Simulation of Alaska Tundra and Needle Leaf Forest Using LPJ-GUESS" Land 13, no. 5: 632. https://doi.org/10.3390/land13050632

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