scholarly journals Process-based analysis of terrestrial carbon flux predictability

2021 ◽  
Vol 12 (4) ◽  
pp. 1413-1426
Author(s):  
István Dunkl ◽  
Aaron Spring ◽  
Pierre Friedlingstein ◽  
Victor Brovkin
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Cycle ◽  
Air Temperature ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Ecosystem Processes ◽  
Terrestrial Carbon ◽  
Ensemble Simulations

Abstract. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question of the extent to which the terrestrial carbon cycle is predictable and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max Planck Institute Earth system model. In order to assess the role of local carbon flux predictability (CFpred) in the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature, and radiation for NPP, and soil organic carbon, air temperature, and precipitation for Rh). Global NPPpred is driven to 62 % and 30 % by the predictability of soil moisture and temperature, respectively. Global Rhpred is driven to 52 % and 27 % by the predictability of soil organic carbon and temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.

scholarly journals Process-based analysis of terrestrial carbon flux predictability

2021 ◽  
Author(s):  
István Dunkl ◽  
Aaron Spring ◽  
Pierre Friedlingstein ◽  
Victor Brovkin
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Cycle ◽  
Air Temperature ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Ecosystem Processes ◽  
Terrestrial Carbon ◽  
Ensemble Simulations

Abstract. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question to what extent the terrestrial carbon cycle is predictable, and which processes explain the predictability. Here, the perfect model approach is used to assess the potential predictability of net primary production (NPPpred) and heterotrophic respiration (Rhpred) by using ensemble simulations conducted with the Max-Planck-Institute Earth System Model. In order to asses the role of local carbon flux predictability (CFpred) on the predictability of the global carbon cycle, we suggest a new predictability metric weighted by the amplitude of the flux anomalies. Regression analysis is used to determine the contribution of the predictability of different environmental drivers to NPPpred and Rhpred (soil moisture, air temperature and radiation for NPP and soil organic carbon, air temperature and precipitation for Rh). NPPpred is driven to 62 and 30 % by the predictability of soil moisture and temperature, respectively. Rhpred is driven to 52 and 27 % by the predictability of soil organic carbon temperature, respectively. The decomposition of predictability shows that the relatively high Rhpred compared to NPPpred is due to the generally high predictability of soil organic carbon. The seasonality in NPPpred and Rhpred patterns can be explained by the change in limiting factors over the wet and dry months. Consequently, CFpred is controlled by the predictability of the currently limiting environmental factor. Differences in CFpred between ensemble simulations can be attributed to the occurrence of wet and dry years, which influences the predictability of soil moisture and temperature. This variability of predictability is caused by the state dependency of ecosystem processes. Our results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.

Process-based analysis of land carbon flux predictability

2021 ◽  
Author(s):  
István Dunkl ◽  
Aaron Spring ◽  
Victor Brovkin
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Interannual Variability ◽  
Carbon Flux ◽  
Carbon Fluxes ◽  
Ecosystem Processes ◽  
Environmental Drivers ◽  
Terrestrial Carbon ◽  
State Dependency

<p>The land-atmosphere CO<sub>2</sub> exchange exhibits a very high interannual variability which dominates variability in atmospheric CO<sub>2</sub> concentration. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question to what extent the terrestrial carbon cycle is even predictable, and which processes explain the predictability. In this study, the perfect model approach is used to assess the potential predictability of net primary production (NPP) and heterotrophic respiration (Rh) by using initialized ensemble experiments simulated with the Max Planck Institute Earth System Model. In order to determine which processes are causing the derived predictability patterns, carbon flux predictability was decomposed into individual drivers. Regression analysis was used to determine the contribution of the predictability of different environmental drivers to the predictability of NPP and Rh (Soil moisture, temperature and radiation for NPP and soil organic carbon, temperature and precipitation for Rh). The main drivers of NPP predictability are soil moisture and temperature, while the predictability signal from radiation is lost after the first month of simulation. Rh predictability is predominantly driven by soil organic carbon, temperature and locally by precipitation. This decomposition of predictability shows that the relatively high Rh predictability is due to the generally high predictability of soil organic carbon. The assessed seasonality in predictability patterns can be explained by the change in limiting factors of NPP and Rh over the wet and dry months. This leads to the adjustment of carbon flux predictability to the predictability of the currently limiting environmental factor. Differences in the predictability between initializations can be attributed to the interannual variability in soil moisture and temperature predictability. This variability is caused by the state dependency of nonlinear ecosystem processes. These results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.</p>

scholarly journals Global soil organic carbon removal by water erosion under climate change and land use change during 1850–2005 AD

2018 ◽  
Author(s):  
Victoria Naipal ◽  
Philippe Ciais ◽  
Yilong Wang ◽  
Ronny Lauerwald ◽  
Bertrand Guenet ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Erosion ◽  
Organic Carbon ◽  
Land Use Change ◽  
Soil Organic Carbon ◽  
Climate Variability ◽  
Carbon Cycle ◽  
Atmospheric Co2 ◽  
Terrestrial Carbon

Abstract. The onset and expansion of agriculture has accelerated soil erosion by rainfall and runoff substantially, mobilizing vast quantities of soil organic carbon (SOC) globally. Studies show that at timescales of decennia to millennia this mobilized SOC can significantly alter previously estimated carbon emissions from land use change (LUC). However, a full understanding of the impact of erosion on land-atmosphere carbon exchange is still missing. The aim of our study is to better constrain the terrestrial carbon fluxes by developing methods compatible with Earth System Models (ESMs) in order to explicitly represent the links between soil erosion by rainfall and runoff and carbon dynamics. For this we use an emulator that represents the carbon cycle of a land surface model, in combination with the Revised Universal Soil Loss Equation model. We applied this modeling framework at the global scale to evaluate the effects of potential soil erosion (soil removal only) in the presence of other perturbations of the carbon cycle: elevated atmospheric CO2, climate variability, and LUC. We found that over the period 1850–2005 AD acceleration of soil erosion leads to a total potential SOC removal flux of 100 Pg C of which 80 % occurs on agricultural, pasture and natural grass lands. Including soil erosion in the SOC-dynamics scheme results in a doubling of the cumulative loss of SOC over 1850–2005 due to the combined effects of climate variability, increasing atmospheric CO2 and LUC. This additional erosional loss decreases the cumulative global carbon sink on land by 5 Pg for this specific period, with the largest effects found for the tropics, where deforestation and agricultural expansion increased soil erosion rates significantly. We also show that the potential effects of soil erosion on the global SOC stocks cannot be ignored when compared to the effects of climate change or land use change on the carbon cycle. We conclude that it is necessary to include soil erosion in assessments of LUC and evaluations of the terrestrial carbon cycle.

scholarly journals Soil moisture influence on the interannual variation in temperature sensitivity of soil organic carbon mineralization in the Loess Plateau

2015 ◽  
Vol 12 (11) ◽  
pp. 3655-3664 ◽  
Author(s):  
Y. J Zhang ◽  
S. L Guo ◽  
M. Zhao ◽  
L. L. Du ◽  
R. J. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Moisture Content ◽  
Carbon Cycle ◽  
Temperature Sensitivity ◽  
Interannual Variation ◽  
Soil Moisture Content ◽  
The Loess Plateau ◽  
Soc Mineralization

Abstract. Temperature sensitivity of soil organic carbon (SOC) mineralization (i.e., Q10) determines how strong the feedback from global warming may be on the atmospheric CO2 concentration; thus, understanding the factors influencing the interannual variation in Q10 is important for accurately estimating local soil carbon cycle. In situ SOC mineralization rate was measured using an automated CO2 flux system (Li-8100) in long-term bare fallow soil in the Loess Plateau (35°12' N, 107°40' E) in Changwu, Shaanxi, China from 2008 to 2013. The results showed that the annual cumulative SOC mineralization ranged from 226 to 298 g C m−2 yr−1, with a mean of 253 g C m−2 yr−1 and a coefficient of variation (CV) of 13%, annual Q10 ranged from 1.48 to 1.94, with a mean of 1.70 and a CV of 10%, and annual soil moisture content ranged from 38.6 to 50.7% soil water-filled pore space (WFPS), with a mean of 43.8% WFPS and a CV of 11%, which were mainly affected by the frequency and distribution of precipitation. Annual Q10 showed a quadratic correlation with annual mean soil moisture content. In conclusion, understanding of the relationships between interannual variation in Q10, soil moisture, and precipitation are important to accurately estimate the local carbon cycle, especially under the changing climate.

scholarly journals Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS

2018 ◽  
Vol 11 (1) ◽  
pp. 27 ◽  
Author(s):  
Mousong Wu ◽  
Marko Scholze ◽  
Michael Voßbeck ◽  
Thomas Kaminski ◽  
Georg Hoffmann
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Carbon Cycle ◽  
Model Performance ◽  
Carbon Fluxes ◽  
Research Centre ◽  
Future Climate Change ◽  
Model Parameters ◽  
Terrestrial Carbon ◽  
Terrestrial Biosphere

The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO2 fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre—Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO2. This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands.

scholarly journals Recent Climate-Driven Increases in Vegetation Productivity for the Western Arctic: Evidence of an Acceleration of the Northern Terrestrial Carbon Cycle

2007 ◽  
Vol 11 (4) ◽  
pp. 1-30 ◽  
Author(s):  
J. S. Kimball ◽  
M. Zhao ◽  
A. D. McGuire ◽  
F. A. Heinsch ◽  
J. Clein ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Cycle ◽  
Carbon Storage ◽  
Total Carbon ◽  
Positive Trend ◽  
Terrestrial Carbon ◽  
Vegetation Productivity ◽  
Terrestrial Carbon Cycle

Abstract Northern ecosystems contain much of the global reservoir of terrestrial carbon that is potentially reactive in the context of near-term climate change. Annual variability and recent trends in vegetation productivity across Alaska and northwest Canada were assessed using a satellite remote sensing–based production efficiency model and prognostic simulations of the terrestrial carbon cycle from the Terrestrial Ecosystem Model (TEM) and BIOME–BGC (BioGeoChemical Cycles) model. Evidence of a small, but widespread, positive trend in vegetation gross and net primary production (GPP and NPP) is found for the region from 1982 to 2000, coinciding with summer warming of more than 1.8°C and subsequent relaxation of cold temperature constraints to plant growth. Prognostic model simulation results were generally consistent with the remote sensing record and also indicated that an increase in soil decomposition and plant-available nitrogen with regional warming was partially responsible for the positive productivity response. Despite a positive trend in litter inputs to the soil organic carbon pool, the model results showed evidence of a decline in less labile soil organic carbon, which represents approximately 75% of total carbon storage for the region. These results indicate that the regional carbon cycle may accelerate under a warming climate by increasing the fraction of total carbon storage in vegetation biomass and more rapid turnover of the terrestrial carbon reservoir.

scholarly journals Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity

2013 ◽  
Vol 9 (3) ◽  
pp. 1111-1140 ◽  
Author(s):  
M. Eby ◽  
A. J. Weaver ◽  
K. Alexander ◽  
K. Zickfeld ◽  
A. Abe-Ouchi ◽  
...  
Keyword(s):  
Land Use ◽  
Carbon Cycle ◽  
Air Temperature ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Carbon Fluxes ◽  
Carbon Uptake ◽  
Earth System ◽  
Model Experiments ◽  
Earth System Models

Abstract. Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate–carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2 × and 4 × CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate–carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from palaeoclimate reconstructions. This in turn could be a result of unforced variability within the climate system, uncertainty in the reconstructions of temperature and CO2, errors in the reconstructions of forcing used to drive the models, or the incomplete representation of certain processes within the models. Given the forcing datasets used in this study, the models calculate significant land-use emissions over the pre-industrial period. This implies that land-use emissions might need to be taken into account, when making estimates of climate–carbon feedbacks from palaeoclimate reconstructions.

scholarly journals Soil greenhouse gas fluxes from different tree species on Taihang Mountain, North China

2014 ◽  
Vol 11 (6) ◽  
pp. 1649-1666 ◽  
Author(s):  
X. P. Liu ◽  
W. J. Zhang ◽  
C. S. Hu ◽  
X. G. Tang
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Temperature ◽  
Soil Ph ◽  
Tree Species ◽  
Soil Bulk Density ◽  
Total N ◽  
Gas Fluxes ◽  
Greenhouse Gas Fluxes

Abstract. The objectives of this study were to investigate seasonal variation of greenhouse gas fluxes from soils on sites dominated by plantation (Robinia pseudoacacia, Punica granatum, and Ziziphus jujube) and natural regenerated forests (Vitex negundo var. heterophylla, Leptodermis oblonga, and Bothriochloa ischcemum), and to identify how tree species, litter exclusion, and soil properties (soil temperature, soil moisture, soil organic carbon, total N, soil bulk density, and soil pH) explained the temporal and spatial variation in soil greenhouse gas fluxes. Fluxes of greenhouse gases were measured using static chamber and gas chromatography techniques. Six static chambers were randomly installed in each tree species. Three chambers were randomly designated to measure the impacts of surface litter exclusion, and the remaining three were used as a control. Field measurements were conducted biweekly from May 2010 to April 2012. Soil CO2 emissions from all tree species were significantly affected by soil temperature, soil moisture, and their interaction. Driven by the seasonality of temperature and precipitation, soil CO2 emissions demonstrated a clear seasonal pattern, with fluxes significantly higher during the rainy season than during the dry season. Soil CH4 and N2O fluxes were not significantly correlated with soil temperature, soil moisture, or their interaction, and no significant seasonal differences were detected. Soil organic carbon and total N were significantly positively correlated with CO2 and N2O fluxes. Soil bulk density was significantly negatively correlated with CO2 and N2O fluxes. Soil pH was not correlated with CO2 and N2O emissions. Soil CH4 fluxes did not display pronounced dependency on soil organic carbon, total N, soil bulk density, and soil pH. Removal of surface litter significantly decreased in CO2 emissions and CH4 uptakes. Soils in six tree species acted as sinks for atmospheric CH4. With the exception of Ziziphus jujube, soils in all tree species acted as sinks for atmospheric N2O. Tree species had a significant effect on CO2 and N2O releases but not on CH4 uptake. The lower net global warming potential in natural regenerated vegetation suggested that natural regenerated vegetation were more desirable plant species in reducing global warming.

scholarly journals A global CO<sub>2</sub> flux dataset (2015–2019) inferred from OCO-2 retrievals using the Tan-Tracker inversion system

2021 ◽  
Author(s):  
Zhe Jin ◽  
Xiangjun Tian ◽  
Rui Han ◽  
Yu Fu ◽  
Xin Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Carbon Cycle ◽  
Carbon Flux ◽  
Carbon Budget ◽  
Terrestrial Ecosystem ◽  
Carbon Fluxes ◽  
Global Carbon Cycle ◽  
Ocean Carbon ◽  
Global Carbon ◽  
Regional Fluxes

Abstract. Accurate assessment of the various sources and sinks of carbon dioxide (CO2), especially terrestrial ecosystem and ocean fluxes with high uncertainties, is important for understanding of the global carbon cycle, supporting the formulation of climate policies, and projecting future climate change. Satellite retrievals of the column-averaged dry air mole fractions of CO2 (XCO2) are being widely used to improve carbon flux estimation due to their broad spatial coverage. However, there is no consensus on the robust estimates of regional fluxes. In this study, we present a global and regional resolved terrestrial ecosystem carbon flux (NEE) and ocean carbon flux dataset for 2015–2019. The dataset was generated using the Tan-Tracker inversion system by assimilating Observing Carbon Observatory 2 (OCO-2) column CO2 retrievals. The posterior NEE and ocean carbon fluxes were comprehensively validated by comparing posterior simulated CO2 concentrations with OCO-2 independent retrievals and Total Carbon Column Observing Network (TCCON) measurements. The validation showed that posterior carbon fluxes significantly improved the modelling of atmospheric CO2 concentrations, with global mean biases of 0.33 ppm against OCO-2 retrievals and 0.12 ppm against TCCON measurements. We described the characteristics of the dataset at global, regional, and Tibetan Plateau scales in terms of the carbon budget, annual and seasonal variations, and spatial distribution. The posterior 5-year annual mean global atmospheric CO2 growth rate was 5.35 PgC yr−1, which was within the uncertainty of the Global Carbon Budget 2020 estimate (5.49 PgC yr−1). The posterior annual mean NEE and ocean carbon fluxes were −4.07 and −3.33 PgC yr−1, respectively. Regional fluxes were analysed based on TransCom partitioning. All 11 land regions acted as carbon sinks, except for Tropical South America, which was almost neutral. The strongest carbon sinks were located in Boreal Asia, followed by Temperate Asia and North Africa. The entire Tibetan Plateau ecosystem was estimated as a carbon sink, taking up −49.52 TgC yr−1 on average, with the strongest sink occurring in eastern alpine meadows. These results indicate that our dataset captures surface carbon fluxes well and provides insight into the global carbon cycle. The dataset can be accessed at https://doi.org/10.11888/Meteoro.tpdc.271317 (Jin et al., 2021).

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