scholarly journals Quantifying the role of moss in terrestrial ecosystem carbon dynamics in northern high latitudes

2021 ◽  
Vol 18 (23) ◽  
pp. 6245-6269
Author(s):  
Junrong Zha ◽  
Qianlai Zhuang
Keyword(s):  
21St Century ◽  
Land Surface ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Ecosystem Model ◽  
Carbon Dynamics ◽  
The Arctic ◽  
New Model ◽  
Terrestrial Ecosystem Model ◽  
Ecosystem Carbon

Abstract. Mosses are ubiquitous in northern terrestrial ecosystems, and play an important role in regional carbon, water and energy cycling. Current global land surface models that do not consider mosses may bias the quantification of regional carbon dynamics. Here we incorporate mosses as a new plant functional type into the process-based Terrestrial Ecosystem Model (TEM 5.0), to develop a new model (TEM_Moss). The new model explicitly quantifies the interactions between vascular plants and mosses and their competition for energy, water, and nutrients. Compared to the estimates using TEM 5.0, the new model estimates that the regional terrestrial soils currently store 132.7 Pg more C and will store 157.5 and 179.1 Pg more C under the RCP8.5 and RCP2.6 scenarios, respectively, by the end of the 21st century. Ensemble regional simulations forced with different parameters for the 21st century with TEM_Moss predict that the region will accumulate 161.1±142.1 Pg C under the RCP2.6 scenario and 186.7±166.1 Pg C under the RCP8.5 scenario over the century. Our study highlights the necessity of coupling moss into Earth system models to adequately quantify terrestrial carbon–climate feedbacks in the Arctic.

scholarly journals Quantifying the role of moss in terrestrial ecosystem carbon dynamics in northern high-latitudes

2021 ◽  
Author(s):  
Junrong Zha ◽  
Qianlai Zhuang
Keyword(s):  
21St Century ◽  
Land Surface ◽  
Higher Plants ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Carbon Dynamics ◽  
The Arctic ◽  
New Model ◽  
Terrestrial Ecosystem Model ◽  
Rcp 8.5

Abstract. In addition to woody and herbaceous plants, mosses are ubiquitous in northern terrestrial ecosystems, which play an important role in regional carbon, water and energy cycling. Current global land surface models without considering moss may bias the quantification of the regional carbon dynamics. Here we incorporate moss into a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM 5.0), as a new plant functional type to develop a new model (TEM_Moss). The new model explicitly quantifies the interactions between higher plants and mosses and their competition for energy, water, and nutrient. Compared to the estimates using TEM 5.0, the new model estimates that the regional terrestrial soils store 132.7 Pg more C at present day, and will store 157.5 Pg and 179.1 Pg more C under the RCP 8.5 and RCP 2.6 scenarios, respectively, by the end of the 21st century. Ensemble regional simulations forced with different parameters for the 21st century with TEM_Moss predict that the region will accumulate 161.1 ± 142.1 Pg C under the RCP 2.6 scenario, and 186.7 ± 166.1 Pg C under the RCP 8.5 scenario over the century. Our study highlights the necessity of coupling moss into Earth System Models to adequately quantify terrestrial carbon-climate feedbacks in the Arctic.

scholarly journals Microbial dormancy and its impacts on northern temperate and boreal terrestrial ecosystem carbon budget

2020 ◽  
Vol 17 (18) ◽  
pp. 4591-4610
Author(s):  
Junrong Zha ◽  
Qianla Zhuang
Keyword(s):  
Greenhouse Gases ◽  
Soil Carbon ◽  
Carbon Budget ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Carbon Dynamics ◽  
Earth System ◽  
New Model ◽  
Ecosystem Carbon ◽  
Earth System Models

Abstract. A large amount of soil carbon in northern temperate and boreal regions could be emitted as greenhouse gases in a warming future. However, lacking detailed microbial processes such as microbial dormancy in current biogeochemistry models might have biased the quantification of the regional carbon dynamics. Here the effect of microbial dormancy was incorporated into a biogeochemistry model to improve the quantification for the last century and this century. Compared with the previous model without considering the microbial dormancy, the new model estimated the regional soils stored 75.9 Pg more C in the terrestrial ecosystems during the last century and will store 50.4 and 125.2 Pg more C under the RCP8.5 and RCP2.6 scenarios, respectively, in this century. This study highlights the importance of the representation of microbial dormancy in earth system models to adequately quantify the carbon dynamics in the northern temperate and boreal natural terrestrial ecosystems.

scholarly journals Microbial dormancy and its impacts on Arctic terrestrial ecosystem carbon budget

2019 ◽  
Author(s):  
Junrong Zha ◽  
Qianlai Zhuang
Keyword(s):  
Greenhouse Gases ◽  
Carbon Budget ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Carbon Dynamics ◽  
The Arctic ◽  
Earth System ◽  
Ecosystem Carbon ◽  
Rcp 8.5 ◽  
Earth System Models

Abstract. A large amount of soil carbon in the Arctic terrestrial ecosystems could be emitted as greenhouse gases in a warming future. However, lacking detailed microbial processes such as microbial dormancy in current biogeochemistry models might have biased the quantification of the regional carbon dynamics. Here the effect of microbial dormancy was incorporated into a biogeochemistry model to improve the quantification for the last and this century. Compared with the previous model without considering the microbial dormancy, the new model estimated the regional soils stored 75.9 Pg more C in the terrestrial ecosystems during the last century, and will store 50.4 Pg and 125.2 Pg more C under the RCP 8.5 and RCP 2.6 scenarios, respectively, in this century. This study highlights the importance of the representation of microbial dormancy in earth system models to adequately quantify the carbon dynamics in the Arctic.

scholarly journals Freeze/thaw processes in complex permafrost landscapes of northern Siberia simulated using the TEM ecosystem model: impact of thermokarst ponds and lakes

2014 ◽  
Vol 7 (4) ◽  
pp. 1671-1689 ◽  
Author(s):  
S. Yi ◽  
K. Wischnewski ◽  
M. Langer ◽  
S. Muster ◽  
J. Boike
Keyword(s):  
Water Depth ◽  
Land Surface ◽  
Terrestrial Ecosystem ◽  
Climate Scenario ◽  
Water Layer ◽  
Ecosystem Model ◽  
The Arctic ◽  
Thermal Processes ◽  
Freeze Thaw ◽  
Terrestrial Ecosystem Model

Abstract. Freeze/thaw (F/T) processes can be quite different under the various land surface types found in the complex tundra of the Arctic, such as polygonal tundra (wet center and dry rims), ponds, and thermokarst lakes. Proper simulation of these different processes is essential for accurate prediction of the release of greenhouse gases under a warming climate scenario. In this study we have incorporated the water layer into a dynamic organic soil version of the Terrestrial Ecosystem Model (DOS-TEM), having first verified and validated the model. Results showed that (1) the DOS-TEM was very efficient and its results compared well with analytical solutions for idealized cases, and (2) despite a number of limitations and uncertainties in the modeling, the simulations compared reasonably well with in situ measurements from polygon rims, polygon centers (with and without water), and lakes on Samoylov Island, Siberia, indicating the suitability of the DOS-TEM for simulating the various F/T processes. Sensitivity tests were performed on the effects of water depth and our results indicated that both water and snow cover are very important in the simulated thermal processes, for both polygon centers and lakes. We therefore concluded that the polygon rims and polygon centers (with various maximum water depths) should be considered separately, and that the dynamics of water depth in both polygons and lakes should be taken into account when simulating thermal processes for methane emission studies.

scholarly journals Microbial decomposition processes and vulnerable Arctic soil organic carbon in the 21st century

2018 ◽  
Author(s):  
Junrong Zha ◽  
Qianlai Zhuang
Keyword(s):  
Soil Carbon ◽  
20Th Century ◽  
21St Century ◽  
Carbon Budget ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
The Arctic ◽  
Microbial Decomposition ◽  
Terrestrial Ecosystem Model ◽  
Rcp 8.5

Abstract. Inadequate representation of biogeochemical processes in current biogeochemistry models results in a large uncertainty in carbon budget quantification. Here, detailed microbial mechanisms were incorporated into a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM). Ensemble regional simulations with the model estimated the Arctic ecosystem carbon budget is 76.0 ± 114.8 Pg C during the 20th century, −3.1 ± 61.7 Pg C under the RCP 2.6 scenario and a sink of 94.7 ± 46 Pg C under the RCP 8.5 scenario during the 21st century. Compared to the estimates using a simpler soil decomposition algorithm in TEM, the new model estimated that the Arctic terrestrial ecosystems stored 12 Pg less carbon over the 20th century, 19 Pg C and 30 Pg C less under the RCP 8.5 and RCP 2.6 scenarios, respectively, during the 21st century. When soil carbon within depths 30 cm, 100 cm and 300 cm was considered as initial carbon in the 21st century simulations, the region was estimated to accumulate 65.4, 88.6, and 109.8 Pg C, respectively, under the RCP 8.5 scenario. In contrast, under the RCP 2.6 scenario, the region lost 0.7, 2.2, and 3 Pg C, respectively, to the atmosphere. We conclude that the future regional carbon budget evaluation largely depends on whether or not the adequate microbial activities are represented in earth system models and the sizes of soil carbon considered in model simulations.

scholarly journals Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1)

2017 ◽  
Author(s):  
Joe R. Melton ◽  
Reinel Sospedra-Alfonso ◽  
Kelly E. McCusker
Keyword(s):  
Soil Texture ◽  
Land Surface ◽  
Clustering Analysis ◽  
Clustering Algorithms ◽  
Terrestrial Ecosystem ◽  
Grid Cell ◽  
Moisture Stress ◽  
Ecosystem Model ◽  
Global Scale ◽  
Terrestrial Ecosystem Model

Abstract. We investigate the application of clustering algorithms to represent sub-grid scale variability in soil texture for use in a global-scale terrestrial ecosystem model. Our model, the coupled Canadian Land Surface Scheme – Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented at a coarse spatial resolution (ca. 2.8° × 2.8°) due to its use as the land surface component of the Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as a means to represent sub-grid heterogeneity. We first determined that the model was sensitive to tiling of the soil textures via an idealized test case before attempting to cluster soil textures globally. To cluster a high-resolution soil texture dataset onto our coarse model grid, we use two linked algorithms (OPTICS (Ankerst et al., 1999; Daszykowski et al., 2002) and Sander et al. (2003)) to provide tiles of representative soil textures for use as CLASS-CTEM inputs. The clustering process results in, on average, about three tiles per CLASS-CTEM grid cell with most cells having four or less tiles. Results from CLASS-CTEM simulations conducted with the tiled inputs (Cluster) versus those using a simple grid-mean soil texture (Gridmean) show CLASS-CTEM, at least on a global scale, is relatively insensitive to the tiled soil textures, however differences can be large in arid or peatland regions. The Cluster simulation has generally lower soil moisture and lower overall vegetation productivity than the Gridmean simulation except in arid regions where plant productivity increases. In these dry regions, the influence of the tiling is stronger due to the general state of vegetation moisture stress which allows a single tile, whose soil texture retains more plant available water, to yield much higher productivity. Although the use of clustering analysis appears promising as a means to represent sub-grid heterogeneity, soil textures appear to be reasonably represented for global scale simulations using a simple grid-mean value.

The Effects of Different Climate Input Datasets on Simulated Carbon Dynamics in the Western Arctic

2007 ◽  
Vol 11 (12) ◽  
pp. 1-24 ◽  
Author(s):  
Joy Clein ◽  
A. David McGuire ◽  
Eugenie S. Euskirchen ◽  
Monika Calef
Keyword(s):  
Climate Model ◽  
Net Primary Production ◽  
Temporal Trends ◽  
Terrestrial Ecosystem ◽  
Ecosystem Model ◽  
Carbon Dynamics ◽  
Total Carbon ◽  
Terrestrial Ecosystem Model ◽  
Western Arctic ◽  
Climate Model Simulations

Abstract As part of the Western Arctic Linkage Experiment (WALE), simulations of carbon dynamics in the western Arctic (WALE region) were conducted during two recent decades by driving the Terrestrial Ecosystem Model (TEM) with three alternative climate datasets. Among the three TEM simulations, we compared the mean monthly and interannual variability of three carbon fluxes: 1) net primary production (NPP), 2) heterotrophic respiration (Rh), and 3) net ecosystem production (NEP). Cumulative changes in vegetation, soil, and total carbon storage among the simulations were also compared. This study supports the conclusion that the terrestrial carbon cycle is accelerating in the WALE region, with more rapid turnover of carbon for simulations driven by two of the three climates. The temperature differences among the climate datasets resulted in annual estimates of NPP and Rh that varied by a factor of 2.5 among the simulations. There is much spatial variability in the temporal trends of NPP and Rh across the region in the simulations driven by different climates, and the spatial pattern of trends is quite different among simulations. Thus, this study indicates that the overall response of NEP in simulations with TEM across the WALE region depends substantially on the temporal trends in the climate dataset used to drive the model. Similar to the recommendations of other studies in the WALE project, this study indicates that coupling methodologies should use anomalies of future climate model simulations to alter the climate of more trusted datasets for purposes of driving ecosystem models of carbon dynamics.

scholarly journals Carbon balance of South Asia constrained by passenger aircraft CO<sub>2</sub> measurements

2011 ◽  
Vol 11 (2) ◽  
pp. 5379-5405 ◽  
Author(s):  
P. K. Patra ◽  
Y. Niwa ◽  
T. J. Schuck ◽  
C. A. M. Brenninkmeijer ◽  
T. Machida ◽  
...  
Keyword(s):  
South Asia ◽  
Transport Model ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Ecosystem Model ◽  
Terrestrial Ecosystem Model ◽  
Terra Incognita ◽  
Annual Carbon ◽  
Carbon Dioxide Co2 ◽  
Passenger Aircraft

Abstract. Quantifying the fluxes of carbon dioxide (CO2) between the atmosphere and terrestrial ecosystems in all their diversity, across the continents, is important and urgent for implementing effective mitigating policies. Whereas much is known for Europe and North America for instance, in comparison, South Asia, with 1.6 billion inhabitants and considerable CO2 fluxes, remained terra incognita in this respect. We use regional measurements of atmospheric CO2 aboard a Lufthansa passenger aircraft between Frankfurt (Germany) and Chennai (India) at cruise altitude, in addition to the existing network sites for 2008, to estimate monthly fluxes for 64-regions using Bayesian inversion and transport model simulations. The applicability of the model's transport parameterization is confirmed using SF6, CH4 and N2O simulations for the CARIBIC datasets. The annual carbon flux obtained by including the aircraft data is twice as large as the fluxes simulated by a terrestrial ecosystem model that was applied to prescribe the fluxes used in the inversions. It is shown that South Asia sequestered carbon at a rate of 0.37±0.20 Pg C yr−1 (1Pg C = 1015 g of carbon in CO2) for the years 2007 and 2008. The seasonality and the strength of the calculated monthly fluxes are successfully validated using independent measurements of vertical CO2 profiles over Delhi and spatial variations at cruising altitude over Asia aboard Japan Airlines passenger aircraft.

scholarly journals An improved parameterization of leaf area index (LAI) seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) modelling framework

2018 ◽  
Author(s):  
Ali Asaadi ◽  
Vivek K. Arora ◽  
Joe R. Melton ◽  
Paul Bartlett
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Terrestrial Ecosystem ◽  
Ecosystem Model ◽  
Area Index ◽  
Terrestrial Ecosystem Model ◽  
Modelling Framework ◽  
Canadian Land Surface Scheme ◽  
Surface Scheme

Abstract. Leaf area index (LAI) and its seasonal dynamics are key determinants of vegetation productivity in nature and as represented in terrestrial biosphere models seeking to understand land-surface atmosphere flux dynamics and its response to climate change. Non-structural carbohydrates (NSCs) and their seasonal variability are known to play a crucial role in seasonal variation of leaf phenology and growth and functioning of plants. The carbon stored in NSC pools provides a buffer during times when supply and demand of carbon are asynchronous. An example of this role is illustrated when NSCs from previous years are used to initiate leaf onset at the arrival of favourable weather conditions. In this study, we incorporate NSC pools and associated parameterizations of new processes in the modelling framework of the Canadian Land Surface Scheme-Canadian Terrestrial Ecosystem Model (CLASS-CTEM) with an aim to improve the seasonality of simulated LAI. The performance of these new parameterizations is evaluated by comparing simulated LAI and atmosphere-land CO2 fluxes, to their observation-based estimates, at three sites characterized by broadleaf cold deciduous trees selected from the Fluxnet database. Results show an improvement in leaf onset and offset times with about 2 weeks shift towards earlier times during the year in better agreement with observations. These improvements in simulated LAI help to improve the simulated seasonal cycle of gross primary productivity (GPP) and as a result simulated net ecosystem productivity (NEP) as well.

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