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 Effect of increasing CO2 on the terrestrial carbon cycle

2014 ◽  
Vol 112 (2) ◽  
pp. 436-441 ◽  
Author(s):  
David Schimel ◽  
Britton B. Stephens ◽  
Joshua B. Fisher
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Terrestrial Ecosystems ◽  
Future Climate Change ◽  
Carbon Uptake ◽  
Model Diagnostics ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
New Space ◽  
Improved Model

Feedbacks from the terrestrial carbon cycle significantly affect future climate change. The CO2 concentration dependence of global terrestrial carbon storage is one of the largest and most uncertain feedbacks. Theory predicts the CO2 effect should have a tropical maximum, but a large terrestrial sink has been contradicted by analyses of atmospheric CO2 that do not show large tropical uptake. Our results, however, show significant tropical uptake and, combining tropical and extratropical fluxes, suggest that up to 60% of the present-day terrestrial sink is caused by increasing atmospheric CO2. This conclusion is consistent with a validated subset of atmospheric analyses, but uncertainty remains. Improved model diagnostics and new space-based observations can reduce the uncertainty of tropical and temperate zone carbon flux estimates. This analysis supports a significant feedback to future atmospheric CO2 concentrations from carbon uptake in terrestrial ecosystems caused by rising atmospheric CO2 concentrations. This feedback will have substantial tropical contributions, but the magnitude of future carbon uptake by tropical forests also depends on how they respond to climate change and requires their protection from deforestation.

Constraining predictions of the carbon cycle using data

2011 ◽  
Vol 369 (1943) ◽  
pp. 1955-1966 ◽  
Author(s):  
P. J. Rayner ◽  
E. Koffi ◽  
M. Scholze ◽  
T. Kaminski ◽  
J.-L. Dufresne
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Carbon Cycle ◽  
Model Parameters ◽  
Atmospheric Measurements ◽  
Using Data ◽  
Linearized Model ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

We use a carbon-cycle data assimilation system to estimate the terrestrial biospheric CO 2 flux until 2090. The terrestrial sink increases rapidly and the increase is stronger in the presence of climate change. Using a linearized model, we calculate the uncertainty in the flux owing to uncertainty in model parameters. The uncertainty is large and is dominated by the impact of soil moisture on heterotrophic respiration. We show that this uncertainty can be greatly reduced by constraining the model parameters with two decades of atmospheric measurements.

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.

scholarly journals Model study of the impacts of future climate change on the hydrology of Ganges–Brahmaputra–Meghna basin

2015 ◽  
Vol 19 (2) ◽  
pp. 747-770 ◽  
Author(s):  
M. Masood ◽  
P. J.-F. Yeh ◽  
N. Hanasaki ◽  
K. Takeuchi
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Hydrologic Model ◽  
Future Climate Change ◽  
Model Parameters ◽  
Net Radiation ◽  
Fine Grid ◽  
Daily Streamflow ◽  
Basin Scale ◽  
Gbm Basin

Abstract. The intensity, duration, and geographic extent of floods in Bangladesh mostly depend on the combined influences of three river systems, the Ganges, Brahmaputra and Meghna (GBM). In addition, climate change is likely to have significant effects on the hydrology and water resources of the GBM basin and may ultimately lead to more serious floods in Bangladesh. However, the assessment of climate change impacts on the basin-scale hydrology by using well-calibrated hydrologic modeling has seldom been conducted in the GBM basin due to the lack of observed data for calibration and validation. In this study, a macroscale hydrologic model H08 has been applied over the basin at a relatively fine grid resolution (10 km) by integrating the fine-resolution DEM (digital elevation model) data for accurate river networks delineation. The model has been calibrated via the analysis of model parameter sensitivity and validated based on long-term observed daily streamflow data. The impacts of climate change (considering a high-emissions path) on runoff, evapotranspiration, and soil moisture are assessed by using five CMIP5 (Coupled Model Intercomparison Project Phase 5) GCMs (global circulation models) through three time-slice experiments; the present-day (1979–2003), the near-future (2015–2039), and the far-future (2075–2099) periods. Results show that, by the end of 21st century, (a) the entire GBM basin is projected to be warmed by ~4.3 °C; (b) the changes of mean precipitation (runoff) are projected to be +16.3% (+16.2%), +19.8% (+33.1%), and +29.6% (+39.7%) in the Brahmaputra, Ganges, and Meghna, respectively; and (c) evapotranspiration is projected to increase for the entire GBM (Brahmaputra: +16.4%, Ganges: +13.6%, Meghna: +12.9%) due to increased net radiation as well as warmer temperature. Future changes of hydrologic variables are larger in the dry season (November–April) than in the wet season (May–October). Amongst the three basins, the Meghna shows the highest increase in runoff, indicating higher possibility of flood occurrence. The uncertainty due to the specification of key model parameters in model predictions is found to be low for estimated runoff, evapotranspiration and net radiation. However, the uncertainty in estimated soil moisture is rather large with the coefficient of variation ranging from 14.4 to 31% among the three basins.

How uncertainties in future climate change predictions translate into future terrestrial carbon fluxes

2005 ◽  
Vol 11 (6) ◽  
pp. 959-970 ◽  
Author(s):  
Marie Berthelot ◽  
Pierre Friedlingstein ◽  
Philippe Ciais ◽  
Jean-Louis Dufresne ◽  
Patrick Monfray
Keyword(s):  
Climate Change ◽  
Carbon Fluxes ◽  
Future Climate ◽  
Future Climate Change ◽  
Terrestrial Carbon

scholarly journals Model study of the impacts of future climate change on the hydrology of Ganges–Brahmaputra–Meghna (GBM) basin

2014 ◽  
Vol 11 (6) ◽  
pp. 5747-5791 ◽  
Author(s):  
M. Masood ◽  
P. J.-F. Yeh ◽  
N. Hanasaki ◽  
K. Takeuchi
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Hydrologic Model ◽  
Future Climate Change ◽  
Model Parameters ◽  
Net Radiation ◽  
Basin Scale ◽  
Macro Scale ◽  
The Impact ◽  
Gbm Basin

Abstract. The intensity, duration, and geographic extent of floods in Bangladesh mostly depend on the combined influences of three river systems, Ganges, Brahmaputra and Meghna (GBM). In addition, climate change is likely to have significant effects on the hydrology and water resources of the GBM basins and might ultimately lead to more serious floods in Bangladesh. However, the assessment of climate change impacts on basin-scale hydrology by using well-constrained hydrologic modelling has rarely been conducted for GBM basins due to the lack of data for model calibration and validation. In this study, a macro-scale hydrologic model H08 has been applied regionally over the basin at a relatively fine grid resolution (10 km) by integrating the fine-resolution (~0.5 km) DEM data for accurate river networks delineation. The model has been calibrated via analyzing model parameter sensitivity and validated based on a long-term observed daily streamflow data. The impact of climate change on not only the runoff, but also the basin-scale hydrology including evapotranspiration, soil moisture and net radiation have been assessed in this study through three time-slice experiments; present-day (1979–2003), near-future (2015–2039) and far-future (2075–2099) periods. Results shows that, by the end of 21st century (a) the entire GBM basin is projected to be warmed by ~3°C (b) the changes of mean precipitation are projected to be +14.0, +10.4, and +15.2%, and the changes of mean runoff to be +14, +15, and +18% in the Brahmaputra, Ganges and Meghna basin respectively (c) evapotranspiration is predicted to increase significantly for the entire GBM basins (Brahmaputra: +14.4%, Ganges: +9.4%, Meghna: +8.8%) due to increased net radiation (Brahmaputra: +6%, Ganges: +5.9%, Meghna: +3.3%) as well as warmer air temperature. Changes of hydrologic variables will be larger in dry season (November–April) than that in wet season (May–October). Amongst three basins, Meghna shows the largest hydrological response which indicates higher possibility of flood occurrence in this basin. The uncertainty due to the specification of key model parameters in predicting hydrologic quantities, has also been analysed explicitly in this study and found that the uncertainty in estimation of runoff, evapotranspiration and net radiation is relatively less. However, the uncertainty in estimation of soil moisture is quite large (coefficient of variation ranges from 11 to 33% for three basins). It is significant in land use management, agriculture in particular and highlights the necessity of physical observation of soil moisture.

scholarly journals The African contribution to the global climate-carbon cycle feedback of the 21st century

2008 ◽  
Vol 5 (6) ◽  
pp. 4847-4866 ◽  
Author(s):  
P. Friedlingstein ◽  
P. Cadule ◽  
S. L. Piao ◽  
P. Ciais ◽  
S. Sitch
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
21St Century ◽  
Climate Model ◽  
Global Climate ◽  
Future Climate ◽  
Future Climate Change ◽  
Amazon Forest ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model

Abstract. Future climate change will have impact on global and regional terrestrial carbon balances. The fate of African tropical forests over the 21st century has been investigated through global coupled climate carbon cycle model simulations. Under the SRES-A2 socio-economic CO2 emission scenario of the IPCC, and using the Institut Pierre Simon Laplace coupled ocean-terrestrial carbon cycle and climate model, IPSL-CM4-LOOP, we found that the warming over African ecosystems induces a reduction of net ecosystem productivity, making a 20% contribution to the global climate-carbon cycle positive feedback. However, the African rainforest ecosystem alone makes only a negligible contribution to the overall feedback, much smaller than the one arising from the Amazon forest. This is first because of the two times smaller area of forest in Africa, but also because of the relatively lower local land carbon cycle sensitivity to climate change. This beneficial role of African forests in mitigating future climate change should be taken into account when designing forest conservation policy.

scholarly journals Multi-model analysis of terrestrial carbon cycles in Japan: limitations and implications of model calibration using eddy flux observations

2010 ◽  
Vol 7 (7) ◽  
pp. 2061-2080 ◽  
Author(s):  
K. Ichii ◽  
T. Suzuki ◽  
T. Kato ◽  
A. Ito ◽  
T. Hajima ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Model Calibration ◽  
Future Climate Change ◽  
Support Vector ◽  
Model Parameters ◽  
Eddy Flux ◽  
Terrestrial Biosphere ◽  
Flux Data ◽  
Default Model ◽  
Model Based

Abstract. Terrestrial biosphere models show large differences when simulating carbon and water cycles, and reducing these differences is a priority for developing more accurate estimates of the condition of terrestrial ecosystems and future climate change. To reduce uncertainties and improve the understanding of their carbon budgets, we investigated the utility of the eddy flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine – based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four eddy flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and a modified model (based on model parameter tuning using eddy flux data). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using eddy flux data (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs. This study demonstrated that careful validation and calibration of models with available eddy flux data reduced model-by-model differences. Yet, site history, analysis of model structure changes, and more objective procedure of model calibration should be included in the further analysis.

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.

Terrestrial Carbon Cycle Dynamics under Recent and Future Climate Change

2005 ◽  
Vol 18 (10) ◽  
pp. 1609-1628 ◽  
Author(s):  
H. Damon Matthews ◽  
Andrew J. Weaver ◽  
Katrin J. Meissner
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Future Climate ◽  
First Century ◽  
Future Climate Change ◽  
Cycle Model ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle ◽  
Twenty First Century

Abstract The behavior of the terrestrial carbon cycle under historical and future climate change is examined using the University of Victoria Earth System Climate Model, now coupled to a dynamic terrestrial vegetation and global carbon cycle model. When forced by historical emissions of CO2 from fossil fuels and land-use change, the coupled climate–carbon cycle model accurately reproduces historical atmospheric CO2 trends, as well as terrestrial and oceanic uptake for the past two decades. Under six twenty-first-century CO2 emissions scenarios, both terrestrial and oceanic carbon sinks continue to increase, though terrestrial uptake slows in the latter half of the century. Climate–carbon cycle feedbacks are isolated by comparing a coupled model run with a run where climate and the carbon cycle are uncoupled. The modeled positive feedback between the carbon cycle and climate is found to be relatively small, resulting in an increase in simulated CO2 of 60 ppmv at the year 2100. Including non-CO2 greenhouse gas forcing and increasing the model’s climate sensitivity increase the effect of this feedback to 140 ppmv. The UVic model does not, however, simulate a switch from a terrestrial carbon sink to a source during the twenty-first century, as earlier studies have suggested. This can be explained by a lack of substantial reductions in simulated vegetation productivity due to climate changes.

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