scholarly journals Evaluation of terrestrial carbon cycle models with atmospheric CO2measurements: Results from transient simulations considering increasing CO2, climate, and land-use effects

2002 ◽  
Vol 16 (4) ◽  
pp. 39-1-39-15 ◽  
Author(s):  
R. J. Dargaville ◽  
M. Heimann ◽  
A. D. McGuire ◽  
I. C. Prentice ◽  
D. W. Kicklighter ◽  
...  
Keyword(s):  
Land Use ◽  
Carbon Cycle ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Transient Simulations ◽  
Land Use Effects

scholarly journals Potential strong contribution of future anthropogenic land-use and land-cover change to the terrestrial carbon cycle

2018 ◽  
Vol 13 (6) ◽  
pp. 064023 ◽  
Author(s):  
Benjamin Quesada ◽  
Almut Arneth ◽  
Eddy Robertson ◽  
Nathalie de Noblet-Ducoudré
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Cycle ◽  
Land Cover Change ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Anthropogenic Land Use

The importance of three centuries of land-use change for the global and regional terrestrial carbon cycle

2009 ◽  
Vol 97 (1-2) ◽  
pp. 123-144 ◽  
Author(s):  
Jelle G. Van Minnen ◽  
Kees Klein Goldewijk ◽  
Elke Stehfest ◽  
Bas Eickhout ◽  
Gerard van Drecht ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Cycle ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle

scholarly journals Quantifying the Impacts of Land-Use and Climate on Carbon Fluxes Using Satellite Data across Texas, U.S.

2019 ◽  
Vol 11 (14) ◽  
pp. 1733 ◽  
Author(s):  
Ram L. Ray ◽  
Ademola Ibironke ◽  
Raghava Kommalapati ◽  
Ali Fares
Keyword(s):  
Land Use ◽  
Carbon Cycle ◽  
Co2 Emissions ◽  
Carbon Emissions ◽  
Climatic Conditions ◽  
Terrestrial Carbon ◽  
Climate Zones ◽  
South Central ◽  
Sequestration Potential ◽  
Terrestrial Carbon Cycle

Climate change and variability, soil types and soil characteristics, animal and microbial communities, and photosynthetic plants are the major components of the ecosystem that affect carbon sequestration potential of any location. This study used NASA’s Soil Moisture Active Passive (SMAP) Level 4 carbon products, gross primary productivity (GPP), and net ecosystem exchange (NEE) to quantify their spatial and temporal variabilities for selected terrestrial ecosystems across Texas during the 2015–2018 study period. These SMAP carbon products are available at 9 km spatial resolution on a daily basis. The ten selected SMAP grids are located in seven climate zones and dominated by five major land uses (developed, crop, forest, pasture, and shrub). Results showed CO2 emissions and uptake were affected by land-use and climatic conditions across Texas. It was also observed that climatic conditions had more impact on CO2 emissions and uptake than land-use in this state. On average, South Central Plains and East Central Texas Plains ecoregions of East Texas and Western Gulf Coastal Plain ecoregion of Upper Coast climate zones showed higher GPP flux and potential carbon emissions and uptake than other climate zones across the state, whereas shrubland on the Trans Pecos climate zone showed lower GPP flux and carbon emissions/uptake. Comparison of GPP and NEE distribution maps between 2015 and 2018 confirmed substantial changes in carbon emissions and uptake across Texas. These results suggest that SMAP carbon products can be used to study the terrestrial carbon cycle at regional to global scales. Overall, this study helps to understand the impacts of climate, land-use, and ecosystem dynamics on the terrestrial carbon cycle.

scholarly journals Terrestrial Carbon Cycle Variability

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2371 ◽  
Author(s):  
Dennis Baldocchi ◽  
Youngryel Ryu ◽  
Trevor Keenan
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Carbon Sink ◽  
Global Scale ◽  
Gross Primary Productivity ◽  
Arid Ecosystems ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle

A growing literature is reporting on how the terrestrial carbon cycle is experiencing year-to-year variability because of climate anomalies and trends caused by global change. As CO2 concentration records in the atmosphere exceed 50 years and as satellite records reach over 30 years in length, we are becoming better able to address carbon cycle variability and trends. Here we review how variable the carbon cycle is, how large the trends in its gross and net fluxes are, and how well the signal can be separated from noise. We explore mechanisms that explain year-to-year variability and trends by deconstructing the global carbon budget. The CO2 concentration record is detecting a significant increase in the seasonal amplitude between 1958 and now. Inferential methods provide a variety of explanations for this result, but a conclusive attribution remains elusive. Scientists have reported that this trend is a consequence of the greening of the biosphere, stronger northern latitude photosynthesis, more photosynthesis by semi-arid ecosystems, agriculture and the green revolution, tropical temperature anomalies, or increased winter respiration. At the global scale, variability in the terrestrial carbon cycle can be due to changes in constituent fluxes, gross primary productivity, plant respiration and heterotrophic (microbial) respiration, and losses due to fire, land use change, soil erosion, or harvesting. It remains controversial whether or not there is a significant trend in global primary productivity (due to rising CO2, temperature, nitrogen deposition, changing land use, and preponderance of wet and dry regions). The degree to which year-to-year variability in temperature and precipitation anomalies affect global primary productivity also remains uncertain. For perspective, interannual variability in global gross primary productivity is relatively small (on the order of 2 Pg-C y-1) with respect to a large and uncertain background (123 +/- 4 Pg-C y-1), and detected trends in global primary productivity are even smaller (33 Tg-C y-2). Yet residual carbon balance methods infer that the terrestrial biosphere is experiencing a significant and growing carbon sink. Possible explanations for this large and growing net land sink include roles of land use change and greening of the land, regional enhancement of photosynthesis, and down regulation of plant and soil respiration with warming temperatures. Longer time series of variables needed to provide top-down and bottom-up assessments of the carbon cycle are needed to resolve these pressing and unresolved issues regarding how, why, and at what rates gross and net carbon fluxes are changing.

scholarly journals Spatiotemporal patterns of terrestrial carbon cycle during the 20th century

2009 ◽  
Vol 23 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Shilong Piao ◽  
Philippe Ciais ◽  
Pierre Friedlingstein ◽  
Nathalie de Noblet-Ducoudré ◽  
Patricia Cadule ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
20Th Century ◽  
Spatiotemporal Patterns ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle

scholarly journals Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

2017 ◽  
Author(s):  
Marko Scholze ◽  
Michael Buchwitz ◽  
Wouter Dorigo ◽  
Luis Guanter ◽  
Shaun Quegan
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Full Description ◽  
Model Data ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle ◽  
Uncertainty Estimates ◽  
Global Carbon

Abstract. The global carbon cycle is an important component of the Earth system and it interacts with the hydrological, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification 5 of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by model-data fusion or data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation, and systematic and 10 well error-characterized observations relevant to the carbon cycle. Relevant observations for assimilation include various in-situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model 15 benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current 20 observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al. (2005) emphasising the rapid advance in relevant space-based observations.

scholarly journals Examining the sensitivity of the terrestrial carbon cycle to the expression of El Niño

2020 ◽  
Author(s):  
Lina Teckentrup ◽  
Martin G. De Kauwe ◽  
Andrew J. Pitman ◽  
Benjamin Smith
Keyword(s):  
Carbon Cycle ◽  
El Niño ◽  
El Nino ◽  
Terrestrial Carbon ◽  
Near Surface ◽  
Terrestrial Carbon Cycle ◽  
El Niño Events ◽  
Ep El Niño ◽  
The Impact ◽  
El Nino Events

Abstract. The El Niño‐Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) Central–Pacific (CP) and (ii) Eastern–Pacific (EP). Both types of El Nino are characterised by above average sea surface temperature anomalies in the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the amplitude of the concentration of interannual atmospheric carbon dioxide (CO2) variability, as well as different lags in terrestrial CO2 release to the atmosphere following increased tropical near surface air temperature. We employ the dynamic global vegetation model LPJ–GUESS within a synthetic experimental framework to examine the sensitivity and potential long term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the later half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was negligible for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle.

scholarly journals The climate dependence of the terrestrial carbon cycle; including parameter and structural uncertainties

2012 ◽  
Vol 9 (10) ◽  
pp. 13439-13496 ◽  
Author(s):  
M. J. Smith ◽  
M. C. Vanderwel ◽  
V. Lyutsarev ◽  
S. Emmott ◽  
D. W. Purves
Keyword(s):  
Carbon Cycle ◽  
Empirical Data ◽  
Current Knowledge ◽  
Empirical Support ◽  
Data Sets ◽  
Earth System ◽  
Model Formulation ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Component Processes

Abstract. The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System over the coming decades and centuries. However Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Essential to achieving this goal will be assessing the empirical support for alternative models of component processes, identifying key uncertainties and inconsistencies, and ultimately identifying the models that are most consistent with empirical evidence. To begin meeting these requirements we data-constrained all parameters of all component processes within a global terrestrial carbon model. Our goals were to assess the climate dependencies obtained for different component processes when all parameters have been inferred from empirical data, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model, and to identify a methodology by which alternative component models could be compared within the same framework in future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies although it was not possible to data-constrain all parameters indicating some potentially redundant details. Similar climate dependencies were obtained for most processes whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models make better constrained projections.

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