scholarly journals Apparent ecosystem carbon turnover time: uncertainties and robust features

2020 ◽  
Vol 12 (4) ◽  
pp. 2517-2536 ◽  
Author(s):  
Naixin Fan ◽  
Sujan Koirala ◽  
Markus Reichstein ◽  
Martin Thurner ◽  
Valerio Avitabile ◽  
...  

Abstract. The turnover time of terrestrial ecosystem carbon is an emergent ecosystem property that quantifies the strength of land surface on the global carbon cycle–climate feedback. However, observation- and modeling-based estimates of carbon turnover and its response to climate are still characterized by large uncertainties. In this study, by assessing the apparent whole ecosystem carbon turnover times (τ) as the ratio between carbon stocks and fluxes, we provide an update of this ecosystem level diagnostic and its associated uncertainties in high spatial resolution (0.083∘) using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble of data, we estimated the global median τ to be 43-7+7 yr (median-difference to percentile 25+difference to percentile 75) when the full soil is considered, in contrast to limiting it to 1 m depth. Only considering the top 1 m of soil carbon in circumpolar regions (assuming maximum active layer depth is up to 1 m) yields a global median τ of 37-6+3 yr, which is longer than the previous estimates of 23-4+7 yr (Carvalhais et al., 2014). We show that the difference is mostly attributed to changes in global Csoil estimates. Csoil accounts for approximately 84 % of the total uncertainty in global τ estimates; GPP also contributes significantly (15 %), whereas Cveg contributes only marginally (less than 1 %) to the total uncertainty. The high uncertainty in Csoil is reflected in the large range across state-of-the-art data products, in which full-depth Csoil spans between 3362 and 4792 PgC. The uncertainty is especially high in circumpolar regions with an uncertainty of 50 % and a low spatial correlation between the different datasets (0.2<r<0.5) when compared to other regions (0.6<r<0.8). These uncertainties cast a shadow on current global estimates of τ in circumpolar regions, for which further geographical representativeness and clarification on variations in Csoil with soil depth are needed. Different GPP estimates contribute significantly to the uncertainties of τ mainly in semiarid and arid regions, whereas Cveg causes the uncertainties of τ in the subtropics and tropics. In spite of the large uncertainties, our findings reveal that the latitudinal gradients of τ are consistent across different datasets and soil depths. The current results show a strong ensemble agreement on the negative correlation between τ and temperature along latitude that is stronger in temperate zones (30–60∘ N) than in the subtropical and tropical zones (30∘ S–30∘ N). Additionally, while the strength of the τ–precipitation correlation was dependent on the Csoil data source, the latitudinal gradients also agree among different ensemble members. Overall, and despite the large variation in τ, we identified robust features in the spatial patterns of τ that emerge beyond the differences stemming from the data-driven estimates of Csoil, Cveg and GPP. These robust patterns, and associated uncertainties, can be used to infer τ–climate relationships and for constraining contemporaneous behavior of Earth system models (ESMs), which could contribute to uncertainty reductions in future projections of the carbon cycle–climate feedback. The dataset of τ is openly available at https://doi.org/10.17871/bgitau.201911 (Fan et al., 2019).

2020 ◽  
Author(s):  
Naixin Fan ◽  
Sujan Koirala ◽  
Markus Reichstein ◽  
Martin Thurner ◽  
Valerio Avitabile ◽  
...  

Abstract. The turnover time of terrestrial carbon (τ) controls the global carbon cycle – climate feedback and, yet, is poorly simulated by the current Earth System Models (ESMs). In this study, by assessing apparent carbon turnover time as the ratio between carbon stocks and fluxes, we provide a new, updated ensemble of diagnostic terrestrial carbon turnover times and associated uncertainties on a global scale using multiple, state-of-the-art, observation-based datasets of soil organic carbon stock (Csoil), vegetation biomass (Cveg) and gross primary productivity (GPP). Using this new ensemble, we estimated the global average τ to be 42−5+9 years when the full soil depth is considered, longer than the previous estimates of 23−4+7 years. Only considering the top 1 m (assuming maximum active layer depth is up to 1 meter) of soil carbon in circumpolar regions yields a global τ of 35−4+9 years. Csoil in circumpolar regions account for two thirds of the total uncertainty in global τ estimates, whereas Csoil in non-circumpolar contributes merely 9.38 %. GPP (2.25 %) and Cveg (0.05 %) contribute even less to the total uncertainty. Therefore, the high uncertainty in Csoil is the main factor behind the uncertainty in global τ, as reflected in the larger range of full-depth Csoil (3152–4372 PgC). The uncertainty is especially high in circumpolar regions with a uncertainty of 50 % and the spatial correlations among different datasets are also low compared to other regions. Overall, we argue that current global datasets do not support robust estimates of τ globally, for which we need clarification on variations of Csoil with soil depth and stronger estimates of Csoil in circumpolar regions. Despite the large variation in both magnitude and spatial patterns of τ, we identified robust features in the spatial patterns of τ that emerge regardless of soil depth and differences in data sources of Csoil, Cveg and GPP. Our findings show that the latitudinal gradients of τ are consistent across different datasets and soil depth. Furthermore, there is a strong consensus on the negative correlation between τ and temperature along latitude that is stronger in temperate zones (30º N–60º N) than in subtropical and tropical zones (30º S–30º N). The identified robust patterns can be used to infer the response of τ to climate and for constraining contemporaneous behaviour of ESMs which could contribute to uncertainty reductions in future projections of the carbon cycle – climate feedback. The dataset of the terrestrial turnover time ensemble (DOI: 10.17871/bgitau.201911) is openly available from the data portal: https://doi.org/10.17871/bgitau.201911 (Fan et al., 2019).


2019 ◽  
Vol 5 (8) ◽  
pp. eaav1131 ◽  
Author(s):  
Quan Quan ◽  
Dashuan Tian ◽  
Yiqi Luo ◽  
Fangyue Zhang ◽  
Tom W. Crowther ◽  
...  

It has been well established by field experiments that warming stimulates either net ecosystem carbon uptake or release, leading to negative or positive carbon cycle–climate change feedback, respectively. This variation in carbon-climate feedback has been partially attributed to water availability. However, it remains unclear under what conditions water availability enhances or weakens carbon-climate feedback or even changes its direction. Combining a field experiment with a global synthesis, we show that warming stimulates net carbon uptake (negative feedback) under wet conditions, but depresses it (positive feedback) under very dry conditions. This switch in carbon-climate feedback direction arises mainly from scaling effects of warming-induced decreases in soil water content on net ecosystem productivity. This water scaling of warming effects offers generalizable mechanisms not only to help explain varying magnitudes and directions of observed carbon-climate feedback but also to improve model prediction of ecosystem carbon dynamics in response to climate change.


2017 ◽  
Author(s):  
Yaner Yan ◽  
Xuhui Zhou ◽  
Lifeng Jiang ◽  
Yiqi Luo

Abstract. Carbon (C) turnover time is a key factor in determining C storage capacity in various plant and soil pools and the magnitude of terrestrial C sink in a changing climate. However, the effects of C turnover time on C storage have not been well quantified for previous researches. Here, we first analyzed the difference among different definition of mean turnover time (MTT) including ecosystem MTT(MTTEC) and soil MTT (MTTsoil) and its variability in MTT to climate changes, and then evaluated the changes of ecosystem C storage driven by MTT changes. Our results showed that total GPP-based ecosystem MTT (MTTEC_GPP : 25.0 ± 2.7 years) was shorter than soil MTT (35.5 ± 1.2 years) and NPP-based ecosystem MTT (MTTEC_NPP:50.8 ± 3 years) MTTEC_GPP = Cpool/GPP &amp; MTTsoil = Csoil/NPP &amp; MTTEC_NPP = Cpool/NPP, Cpool and Csoil referring as the ecosystem or soil carbon storage, respectively). At the biome scale, temperature is still the predictor for MTTEC (R2 = 0.77, p 


Author(s):  
Naixin Fan ◽  
Sujan Koirala ◽  
Markus Reichstein ◽  
Martin Thurner ◽  
Valerio Avitabile ◽  
...  

2016 ◽  
Author(s):  
G. J. Schürmann ◽  
T. Kaminski ◽  
C. Köstler ◽  
N. Carvalhais ◽  
M. Voßbeck ◽  
...  

Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the land surface scheme of the MPI-Earth System Model v1 (JSBACH). The simulated terrestrial biosphere processes (phenology and carbon balance) were constrained by observations of the fraction of photosynthetically active radiation (TIP-FAPAR product) and by observations of atmospheric CO2 at a global set of monitoring stations for the years 2005–2009. The system successfully, and computationally efficiently, improved average foliar area and northern extra-tropical seasonality of foliar area when constrained by TIP-FAPAR. Global net and gross carbon fluxes were improved when constrained by atmospheric CO2, although the system tended to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, therefore overall increasing the appropriateness of the resultant parameter values and biosphere dynamics. Our study thus highlights the role of the TIP-FAPAR product in stabilising the underdetermined atmospheric inversion problem and demonstrates the value of multiple-data stream assimilation for the simulation of terrestrial biosphere dynamics. The constraint on regional gross and net CO2 flux patterns is limited through the parametrisation of the biosphere model. We expect improvement on that aspect through a refined initialisation strategy and inclusion of further biosphere observations as constraints.


2018 ◽  
Vol 15 (21) ◽  
pp. 6559-6572 ◽  
Author(s):  
Xingjie Lu ◽  
Ying-Ping Wang ◽  
Yiqi Luo ◽  
Lifen Jiang

Abstract. Ecosystem carbon (C) transit time is a critical diagnostic parameter to characterize land C sequestration. This parameter has different variants in the literature, including a commonly used turnover time. However, we know little about how different transit time and turnover time are in representing carbon cycling through multiple compartments under a non-steady state. In this study, we estimate both C turnover time as defined by the conventional stock over flux and mean C transit time as defined by the mean age of C mass leaving the system. We incorporate them into the Community Atmosphere Biosphere Land Exchange (CABLE) model to estimate C turnover time and transit time in response to climate warming and rising atmospheric [CO2]. Modelling analysis shows that both C turnover time and transit time increase with climate warming but decrease with rising atmospheric [CO2]. Warming increases C turnover time by 2.4 years and transit time by 11.8 years in 2100 relative to that at steady state in 1901. During the same period, rising atmospheric [CO2] decreases C turnover time by 3.8 years and transit time by 5.5 years. Our analysis shows that 65 % of the increase in global mean C transit time with climate warming results from the depletion of fast-turnover C pool. The remaining 35 % increase results from accompanied changes in compartment C age structures. Similarly, the decrease in mean C transit time with rising atmospheric [CO2] results approximately equally from replenishment of C into fast-turnover C pool and subsequent decrease in compartment C age structure. Greatly different from the transit time, the turnover time, which does not account for changes in either C age structure or composition of respired C, underestimated impacts of warming and rising atmospheric [CO2] on C diagnostic time and potentially led to deviations in estimating land C sequestration in multi-compartmental ecosystems.


2016 ◽  
Vol 10 (4) ◽  
pp. 1721-1737 ◽  
Author(s):  
Wenli Wang ◽  
Annette Rinke ◽  
John C. Moore ◽  
Duoying Ji ◽  
Xuefeng Cui ◽  
...  

Abstract. A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C−1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.


1997 ◽  
Vol 25 ◽  
pp. 46-50 ◽  
Author(s):  
Jeffrey S. Tilley ◽  
William L. Chapman ◽  
Wanli Wu

We have conducted tests of the Canadian Land Surface Scheme (CLASS V2.5) for Arctic tundra applications. Our tests emphasize sensitivities to initial conditions, external forcings and internal parameters, and focus on the Alaskan North Slope during the summer of 1992. Observational data from the National Science foundation (NSF), Arctic Systems Science (ARCSS), Land/Atmosphere/Ice Interactions (LAII) Flux Study is available to serve as forcing and validation for our simulations.Comparisons of the runs show strong sensitivities to the composition and depth of the soil layers, and we find that a minimum total soil depth of 5.0 m is needed to maintain permafrost. The response of the soil to diurnal variations in forcing is strong, while sensitivities to other internal parameters, as well as to precipitation, were relatively small. Some sensitivity to air temperatures and radiative fluxes, particularly the incoming shortwave flux, was also present. Significant sensitivity to the specification of the initial water and ice contents of the soil was found, while the sensitivity to initial soil temperature was somewhat less.


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