The overestimated negative temperature sensitivity of the carbon sink in ecosystem models

Abstract

Understanding how the terrestrial carbon cycle responds to temperature rise is of great importance in studies on global climate change and carbon budget. This study collected eddy covariance (EC) data from 27 FLUXNET sites with over 10 years of continuous observations in mid- and high- latitude ecosystems to assess the temperature sensitivities of gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP) simulated by eight terrestrial carbon cycle models. Results showed that the temperature sensitivities of GPP, ER, and NEP were highest in spring and wet regions. The eight models could well capture seasonal patterns of the temperature sensitivities, but failed to reproduce their spatial variations, which are critical for representing regional differences in carbon–climate feedbacks. This limitation can lead to biased estimates of ecosystem carbon dynamics under future warming scenarios. Overall, model-simulated temperature sensitivities of GPP and NEP were significantly lower than those derived from EC measurements, which resulted in overestimation of carbon losses under climate warming. It indicates that current terrestrial ecosystem models overestimate the negative effects of warming on ecosystem carbon sink. These findings highlight the biases in the simulation of temperature sensitivities and an urgent need to improve the temperature response modeling in order to obtain accurate predictions of carbon cycle dynamics.

Graphical abstract

Keywords

terrestrial carbon cycle / ecosystem model / climate warming / temperature sensitivity / carbon loss

1 Introduction

The globally averaged surface temperature was 1.09°C (0.95°C to 1.20°C) larger in 2011–2020 than that during the period of 1850 to 1900 (IPCC, 2021). As temperature regulates almost all biogeochemical processes of terrestrial ecosystems, attention is paid to the temperature sensitivity of ecosystem-carbon dioxide (CO₂) exchange (Cox et al., 2000). A central question is whether climate warming induces terrestrial carbon losses that result in higher temperatures, which urgently needs to be addressed (IPCC, 2013). Although many studies have focused on the response of the terrestrial carbon cycle to temperature, there are still no firm consensus on the connection (Field et al., 2007; Mahecha et al., 2010; Messori et al., 2019; Song et al., 2019; Norton et al., 2023) and currently, coupled climate–carbon cycle models still have large uncertainties when used to predict future changes of climate and the terrestrial carbon cycle. These uncertainties arise primarily from an incomplete understanding of how to appropriately represent ecosystem processes and their responses to environmental changes, such as photosynthetic triose phosphate utilization and respiration (Lombardozzi et al., 2018; Bonan et al., 2019); the absence of physiologic acclimation and microbial dynamics in current models (Smith and Dukes, 2013); and limited ability of models to reproduce the spatial variability of carbon fluxes (Piao et al., 2013).

The fifth Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) reported that coupled climate–carbon cycle models predict positive feedback between climate warming and a decrease of land carbon sinks during the 21st century (Denman et al., 2007). Friedlingstein et al. (2006) indicated positive climate warming–terrestrial carbon cycle feedback based on 11 coupled climate-carbon cycle models. By the year 2100, a 1°C increase in the global temperature would reduce the land carbon storage by 23–177 Pg C. A recent modeling study showed that a 1°C increase in the global temperature would reduce the global terrestrial CO₂ sink by 3.5 ± 1.5 Pg C·yr^–1 based on 10 terrestrial ecosystem models (–0.5 to –6.2 Pg C·yr^–1; Piao et al., 2013).

Evidence derived from warming experiments is the major basis for the temperature response of the carbon cycle, which have been widely conducted to understand the temperature sensitivity of current models (Luo, 2007). However, the findings of these experiments contain large uncertainties because of the different methods used and the scales at which the field experiments were conducted (Luo, 2007). Experiments have shown that warming may result in positive, negative, or no effects on plant primary production, net ecosystem production, and soil respiration (Lu et al., 2013). The different measurement methods, warming methods, levels of warming, and measured variables could explain these inconsistent results (Luo, 2007). The results from field climate warming experiments showed very large variability, which makes the experimental findings difficult to use for model improvement.

Terrestrial ecosystem models serve as a complementary approach to field experiments for understanding the temperature sensitivity of the terrestrial carbon cycle. However, direct assessments of the temperature sensitivity using process-based ecosystem models remain scarce (Randerson et al., 2009; Mahecha et al., 2010). Several studies have estimated and analyzed the temperature sensitivities of the carbon cycle simulated by process-based ecosystem models, but the simulated temperature sensitivities have not been evaluated comprehensively with eddy covariance (EC) measurements, and the uncertainty of the simulated temperature sensitivities in the carbon cycle is still unclear (Wolf et al., 2008; Pappas et al., 2013; Piao et al., 2013). For example, Piao et al. (2013) analyzed the temperature sensitivities simulated by ten terrestrial carbon cycle models and found discrepancies between the model simulations and field experiments, yet the specific biases and sources in the model-simulated temperature sensitivities were not explicitly identified (Piao et al., 2013). Therefore, systematically evaluating the temperature sensitivity of terrestrial carbon cycles and elucidating the discrepancies between model simulations and field observations remain critical research priorities.

Currently, hundreds of EC flux towers had been built around the world to measure the CO₂ flux exchange between the land and atmosphere. These long-term continuous carbon flux observations can be used for site-scale carbon balance studies. These EC flux towers simultaneously observe different climate variables, which are necessary for studying the temperature response of the carbon cycle (Pastorello et al., 2020; Matthews and Schume, 2022). Currently, many EC sites have more than 10 years of CO₂ flux observations. This kind of data set provides a unique chance to study the response processes of the terrestrial carbon cycle in relation to climate warming among different regions and ecosystems. Therefore, the main objective of this current study is to investigate the temperature sensitivities of gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP) over multiple sites and ecosystems, and evaluate the terrestrial model performance in reproducing the temperature sensitivities in the carbon cycle.

2 Materials and methods

2.1 Eddy covariance measurements

The EC flux measurements used in our study were downloaded from the FLUXNET2015 webpage. The FLUXNET2015 data set provides ecosystem-scale measurements of water, CO₂, and energy fluxes between the biosphere and atmosphere, along with ancillary meteorological and biological variables (Pastorello et al., 2020). We used data at 27 EC sites with more than 10 years of continuous measurements in this study (Table 1). The daily averaged solar radiation, air temperature, precipitation, and vapor pressure deficit were used together with the EC-based GPP, ER, and NEP in this study. Missing data in the carbon flux observations were filled using the marginal distribution sampling method (Reichstein et al., 2005). FLUXNET2015 also provides daily meteorological data sets. The ERA-interim reanalysis data sets were used to create time-continuous meteorological variables for the EC sites according to the method of Vuichard and Papale (2015).

2.2 Terrestrial carbon cycle models

The TRENDY (Trends in Net Land-Atmosphere Carbon Exchange) project is an international model intercomparison initiative aimed at assessing the global carbon cycle and its response to environmental changes and human activities. It provides a coordinated framework for evaluating terrestrial carbon cycle models using consistent protocols and input data sets. In this study, we used eight terrestrial ecosystem models in the TRENDY project and evaluated and compared each’s performance in reproducing the temperature sensitivity of the carbon cycle (Sitch et al., 2015): Community Atmosphere Biosphere Land Exchange (CABLE, Wang et al., 2010), Canadian Land Surface Scheme (CLASS, Arain et al., 2006), Community Land Model (CLM, Lawrence et al., 2011), Jena Scheme for Biosphere-Atmosphere Coupling in Hamburg (JSBACH, Raddatz et al., 2007; Schneck et al., 2022), Joint UK Land Environment Simulator (JULES, Best et al., 2011), Land surface Processes and eXchanges (LPX, Keller et al., 2017), Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE, Krinner et al., 2005), and Vegetation Integrated Simulator for Trace gases (VISIT, Kato et al., 2013). For all eight models, we used the same simulation protocol and forcing data sets. The CRU-NCEP v4 climate data sets used in the TRENDY project can be downloaded from CEA website. The global mean CO₂ concentration data set was obtained from Keeling and Whorf (2005).

2.3 Temperature sensitivity of the carbon cycle

We calculate the temperature sensitivities of GPP, ER, and NEP on the air temperature using a regression approach:

(1)C_y=S_y\times T_x+S_{\mathrm{p}\mathrm{y}}\times P_x+S_{\mathrm{r}\mathrm{y}}\times R_x+\epsilon ,

where C_y is either GPP, ER, or NEP simulated by the models or observed at EC sites. The variables T_x, P_x,, and R_x are the air temperature (°C), precipitation (mm), and shortwave solar radiation (W·m^–2), respectively. The fitted regression coefficients of S_y (S_GPP, S_ER, or S_NEP) represent the temperature sensitivities of GPP, ER, or NEP, respectively. S_py and S_ry are the response coefficients of GPP, ER, and NEP to precipitation and radiation, respectively, and ɛ is the residual error term.

In this study, we calculated S_GPP, S_ER, and S_NEP at all 27 EC sites on the monthly, seasonal, and annual scales (Table 1, Fig. 1). The simulated S_GPP, S_ER, and S_NEP from the eight models were calculated using model estimates and meteorological data sets during the same time period as the EC measurements. According to previous studies (Niu et al., 2008; Lin et al., 2015), precipitation may significantly affect the temperature sensitivities of ecosystem carbon fluxes. Therefore, we further investigated the spatial change patterns of model-simulated S_GPP, S_ER, and S_NEP with variations in the monthly average precipitation at the 27 flux observation sites using ordinary least squares regression. In addition, we also compared the simulated and measured S_GPP, S_ER, and S_NEP across all the study sites using indicators of R² (i.e., indication of the goodness-of-fit) and PE (i.e., prediction error indicated by the difference between model predictions and EC measurements) to evaluate the model performance in simulating spatial variations in the temperature sensitivities.

3 Results

The temperature sensitivities of GPP (S_GPP), ER (S_ER), and NEP (S_NEP) show substantial seasonal differences (Fig. 2). During spring (March to May), the interannual variability of GPP strongly correlates with temperature, where 76% of the sites show a significant positive average S_GPP (Fig. 3). For all sites, the average temperature sensitivity (S_GPP) during spring is the highest compared to those of summer (June to August) and autumn (September to November). In addition, during spring, S_GPP is larger than S_ER, which results in the positive temperature sensitivity of NEP (Fig. 2). In contrast, during summer and autumn, S_GPP and S_ER do not show significant differences and NEP shows either a negative or neutral response to climate warming (Fig. 2).

We examined the performance of the eight global ecosystem models in terms of their ability to reproduce the temperature sensitivity of the carbon cycle. Most of the investigated models are able to reproduce the seasonal patterns of S_GPP, S_ER, and S_NEP derived from the EC observations (Fig. 4). For example, all models except CLASS indicate the largest S_GPP in spring, and the lowest in summer (Fig. 4(a)). The results also show that the eight models significantly underestimate S_GPP during spring, summer, and autumn (Fig. 4(a)). Most of the models estimate the negative temperature sensitivity of NEP (S_NEP) for terrestrial ecosystem (Fig. 4(c)), which means ecosystem releases CO₂ in response to rising temperature. However, the S_NEP derived from the EC observations shows that NEP has a neutral response to rising temperature (Fig. 4(c)).

The spatial patterns of the simulated temperature sensitivities of GPP and NEP during spring and autumn show a good correlation with precipitation (Fig. 5). S_GPP and S_NEP during spring and autumn significantly increase with the amount of precipitation (p < 0.05). S_ER does not show any significant changes at regional scales during spring and autumn (p > 0.1). However, the simulated annual S_GPP and S_NEP in all eight ecosystem models do not show any significant trends with precipitation (data not shown).

Compared to the observed temperature sensitivities, all eight models poorly reproduce annual S_GPP, S_ER, and S_NEP over all sites (Table 2). Only a few models show significant correlations with observations for S_GPP, S_ER, and S_NEP, and the R² values remain low, with the highest R² being only 0.24. Additionally, the majority of models show negative PE values for S_GPP, S_ER, and S_NEP. Notably, almost all models exhibit negative PE values for S_GPP and S_NEP, highlighting common biases in ecosystem models in simulating carbon sink. These results show that the correlations between these simulated and observed temperature sensitivities are relatively weak in space.

The response of GPP to temperature changes dominates over the temperature sensitivities of ER and NEP for both EC observations and model simulations. Over almost all sites and models, the estimated S_GPP shows a significant positive correlation (p < 0.05) with both S_ER and S_NEP (Fig. 6). However, we note that there are large differences in the slope of S_GPP with S_ER and S_NEP among the different ecosystem models. For example, the slope of S_GPP and S_ER in CLASS is less than 0.4, while in JULES it approaches 0.8. These results indicate significant differences in temperature response and temperature sensitivity simulation among different models.

4 Discussion

Predicting future changes of climate and ecosystem is an important goal of global change studies. In recent years, several studies have evaluated climate change–global carbon cycle feedback (Friedlingstein et al., 2006; Piao et al., 2013). Various terrestrial ecosystem models generally predict carbon loss from terrestrial ecosystems due to climate warming (Cramer et al., 2001; Berthelot et al., 2005; Ito et al., 2005). However, this study indicates the overestimated negative temperature sensitivities of GPP, ER, and NEP in current ecosystem models compared to the carbon flux observations made with EC towers (Fig. 4). All models estimate that NEP has a negative temperature sensitivity over the whole year, yet the EC tower observations appear to indicate that NEP has a neutral temperature sensitivity, i.e., rising temperatures do not result in a loss of terrestrial ecosystem carbon. Liu et al. (2017) have also reported the biases in model-simulated temperature sensitivity and reached a similar conclusion. They indicated that the carbon cycle components (e.g., NPP and NBP) in Earth system models (ESMs) generally exhibit neutral or even positive temperature sensitivity in high-latitude ecosystems of the Northern Hemisphere, which is generally consistent with satellite-based estimates. On the contrary, the ESMs show a more negative temperature sensitivity of the carbon cycle in subtropical and tropical ecosystems compared with those satellite-based estimates. These all highlight that the temperature sensitivity of the carbon cycle needs to be reexamined carefully before these models are used to predict changes in the carbon cycle under the influence of climate warming.

This study revealed that most terrestrial ecosystem models significantly overestimated the negative temperature sensitivities of GPP and NEP in summer, leading to underestimated carbon sink compared to EC measurements. There may be two main reasons for the overestimation of the negative temperature sensitivities. One is that current terrestrial ecosystem models commonly use fixed parameters (e.g., optimum photosynthetic temperature T_opt and respiratory temperature sensitivity coefficient Q₁₀) and functions to simulate stomatal conductance, photosynthesis, and respiration, resulting in an inability to accurately reflect plant adaptation to temperature changes or thermal acclimation over time and space (Atkin and Tjoelker, 2003; Yuan et al., 2012; Famiglietti et al., 2021). The second is that models have not comprehensively considered or accurately simulated the interactions between temperature and other environmental factors, and that other factors (e.g., solar radiation, precipitation, soil moisture, and atmospheric CO₂ concentration) can affect the sensitivity of vegetation photosynthetic production and ecosystem carbon sink to temperature changes (Huang et al., 2012; Wu et al., 2017). In addition, it should be noted that the overestimated negative temperature sensitivities (i.e., underestimated carbon sinks) by the ecosystem models may also be due to an overestimation of carbon sinks observed through the eddy covariance method at the flux sites. In fact, there are also uncertainties in the carbon fluxes observed by flux towers, though they are usually used for model validation and evaluation. The eddy covariance method is difficult to monitor carbon transport and loss from anthropogenic disturbances and other non-meteorological processes, and the complex terrain and changes in underlying surface can also introduce uncertainty into the flux observations, which may lead to overestimation of NEP and GPP (Wang et al., 2010; Xu et al., 2014).

Our study also shows that most current ecosystem models are able to reproduce the seasonal change patterns in the temperature sensitivities of GPP, ER, and NEP, which demonstrate a larger positive temperature sensitivity in spring than in autumn (Fig. 4). Previous research has also illustrated the larger temperature sensitivity of GPP in spring than in autumn. For example, Richardson et al. (2010) found that the temperature sensitivity of vegetation productivity in autumn is weaker than that in spring because of light and/or drought limitations. In spring, higher temperatures cause plants to unfold their leaves earlier, and at the same time, due to an increased amount of solar radiation, plants have a stronger photosynthetic capacity (Tanja et al., 2003; Richardson et al., 2009), which eventually leads to a higher GPP. In autumn, lower air temperature and shorter sunshine duration promote leaf chlorophyll decomposition and decrease the enzyme activity, thereby reducing the photosynthetic response rate and carbon sequestration capacity of plants (Lang et al., 2019; Sun et al., 2024). Moreover, autumn warming stimulates ecosystem respiration (ER) more than photosynthesis (GPP) (Piao et al., 2008). As a result, the mean temperature sensitivity of NEP in autumn is close to neutral instead of obviously positive.

The influence of temperature on the terrestrial carbon cycle is highly variable, depending on various factors such as water conditions (Chen et al., 2003; D’Arrigo et al., 2008). In water-limited temperate ecosystems of the Northern Hemisphere, several researchers have shown that warming can lead to increased drought stress, which weakens the response of plant growth to interannual variations of the growing season temperature (D’Arrigo et al., 2008; Wu et al., 2013 and 2017; Buermann et al., 2014; Piao et al., 2014). In arid and semi-arid regions, there are high atmosphere evaporative demand and water deficit (Huang et al., 2012). However, in these areas, the temperature has larger interannual variability that can result in notable changes in water availability, including droughts (Wu et al., 2017). However, as plants generally tend to have adaptive growth strategies, plant growth shows a lower interannual sensitivity to changes in temperature.

The temperature sensitivity of GPP dominates the changes of S_ER and S_NEP (Fig. 6). Previous study arrived at the same conclusion that vegetation primary productivity controls ecosystem carbon cycle and climate feedback (Matthews et al., 2005). This means that vegetation acclimation as a response to climate warming is still an open question in terrestrial carbon cycle models (Booth et al., 2012). Therefore, photosynthetic response processes of plants to warming need to be better understood. This is because these processes, as demonstrated in this study, are major contributors to the magnitude of changes in the carbon cycle. Our results indicate that current models overestimate the temperature sensitivity of GPP; therefore, developing dynamic response functions for GPP to climate change, optimizing key model parameters involved in photosynthesis and carbon cycle, incorporating the interactions of multiple environmental factors, and conducting more comprehensive model assessments are important areas and directions for future research on carbon cycle modeling and carbon sink estimation (Yuan et al., 2012). The finding of this study improves our understanding of the response of carbon cycle processes to warming and helps to reduce the uncertainties in predictions of terrestrial carbon sinks and sequestration potential.

5 Conclusions

This study investigated the temperature sensitivities of GPP, ER, and NEP at the ecosystem scale based on EC observations at 27 FLUXNET sites over more than 10 years. We find that the observed temperature sensitivities of GPP, ER, and NEP are larger in spring than in autumn. In addition, GPP and ER are more sensitive to temperature changes in wet regions. Almost all of the ecosystem models (except CLASS) used in this study can well reproduce the seasonal patterns of the temperature sensitivities of GPP, ER, and NEP; however, there are substantial differences between the observed and simulated temperature sensitivities across sites. In conclusion, our results indicate the tendency of current models to overestimate the negative temperature sensitivity of the carbon cycle, challenging the simulated feedback of the carbon cycle to climate warming that showed substantial carbon losses due to rising temperature. Future model improvements should focus on refining the temperature response functions of key processes such as photosynthesis and ecosystem respiration, optimizing parameters related to leaf nitrogen allocation, root distribution, and temperature sensitivity coefficient (Q₁₀), incorporating the interactions among multiple environmental factors, and conducting more comprehensive model evaluations.

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