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 The Impact of Rainfall Error Characterization on the Estimation of Soil Moisture Fields in a Land Data Assimilation System

2012 ◽  
Vol 13 (3) ◽  
pp. 1107-1118 ◽  
Author(s):  
Viviana Maggioni ◽  
Rolf H. Reichle ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Root Zone ◽  
Error Model ◽  
Near Surface ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the “uncertain” and “reference” model rainfall forcing, respectively. Soil moisture simulations using the Catchment land surface model (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ~0.79 and ~0.90 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower root-mean-square errors (e.g., ~0.034 m3 m−3 and ~0.024 m3 m−3 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall error model in the assimilation system leads to slightly improved assimilation estimates. In particular, the relative enhancement due to SREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.

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 From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States

2020 ◽  
Vol 12 (12) ◽  
pp. 2020 ◽  
Author(s):  
Anthony Mucia ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Clément Albergel ◽  
Jean-Christophe Calvet
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Model ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.

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 A new step-wise Carbon Cycle Data Assimilation System using multiple data streams to constrain the simulated land surface carbon cycle

2016 ◽  
Author(s):  
P. Peylin ◽  
C. Bacour ◽  
N. MacBean ◽  
S. Leonard ◽  
P. J. Rayner ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Data Streams ◽  
Land Surface ◽  
Atmospheric Co2 ◽  
Model Parameters ◽  
Modis Ndvi ◽  
Optimized Model ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Large uncertainties in Land surface models (LSMs) simulations still arise from inaccurate forcing, incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model-data integration techniques, in order to reduce the simulated carbon fluxes and stocks uncertainties. In this study we present a Carbon Cycle Data Assimilation System (CCDAS) that assimilates three major data streams, namely MODIS-NDVI observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), and atmospheric CO2 concentrations at 53 surface stations, in order to optimize the main parameters of the ORCHIDEE LSM (around 180 parameters in total). The system relies on a step-wise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO2, using the atmospheric transport model LMDz (step 3), provides an overall constraint (i.e., constraint on large scale net CO2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO2 growth rate. Thus the optimized model predicts a land C sink of around 2.2 PgC.yr−1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the step-wise approach is evaluated with backcompatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.

scholarly journals LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

2018 ◽  
Vol 10 (10) ◽  
pp. 1627 ◽  
Author(s):  
Clement Albergel ◽  
Simon Munier ◽  
Aymeric Bocher ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
The United States ◽  
Agricultural Drought ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Land data assimilation system (LDAS)-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived surface soil moisture (SSM) and leaf area index (LAI) estimates to constrain the interactions between soil, biosphere, and atmosphere (ISBA) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the total runoff integrating pathways (CTRIP) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center for Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a seven-year, quarter degree spatial resolution offline reanalysis of land surface variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Taking advantage of the relatively dense data networks over CONUS, we have also evaluated the impact of the assimilation against in situ measurements of soil moisture from the USCRN (US Climate Reference Network), together with river discharges from the United States Geological Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared with an open-loop simulation (i.e., no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions, which impacts persist through time. LDAS-Monde reanalysis also has the potential to be used to monitor extreme events like agricultural drought. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

scholarly journals Quantifying the constraint of biospheric process parameters by CO<sub>2</sub> concentration and flux measurement networks through a carbon cycle data assimilation system

2012 ◽  
Vol 12 (9) ◽  
pp. 24131-24172 ◽  
Author(s):  
E. Koffi ◽  
P. Rayner ◽  
M. Scholze ◽  
F. Chevallier ◽  
T. Kaminski
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Process Parameters ◽  
Primary Productivity ◽  
High Frequency ◽  
Flux Measurement ◽  
Co2 Concentration ◽  
Model Parameters ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The sensitivity of the process parameters of the biosphere model BETHY (Biosphere Energy Transfer HYdrology) to choices of atmospheric concentration network, high frequency terrestrial fluxes, and the choice of flux measurement network is investigated by using a carbon cycle data assimilation system. Results show that monthly mean or low-frequency observations of CO2 concentration provide strong constraints on parameters relevant for net flux (NEP) but only weak constraints for parameters controlling gross fluxes. The use of high-frequency CO2 concentration observations, which has allowed a great refinement of spatial scales in direct inversions, adds little to the observing system in this case. This unexpected result is explained by the fact that the stations of the CO2 concentration network we are using are not well placed to measure such high frequency signals. Indeed, CO2 concentration sensitivities relevant for such high frequency fluxes are found to be largely confined in the vicinity of the corresponding fluxes, and are therefore not well observed by background monitoring stations. In contrast, our results clearly show the potential of flux measurements to better constrain the model parameters relevant for gross primary productivity (GPP) and net primary productivity (NPP). Given uncertainties in the spatial description of ecosystem functions we recommend a combined observing strategy.

Impacts of soil hydrologial modeling on long-term terrestrial carbon cycle inferred from CCDAS (Carbon Cycle Data Assimilation System)

2020 ◽  
Author(s):  
Mousong Wu ◽  
Marko Scholze ◽  
Fei Jiang ◽  
Hengmao Wang ◽  
Wenxin Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Carbon Cycle ◽  
Carbon Budget ◽  
Terrestrial Carbon ◽  
Global Carbon Budget ◽  
Terrestrial Carbon Cycle ◽  
Global Carbon ◽  
Data Assimilation System ◽  
Assimilation System

&lt;p&gt;The terrestrial carbon cycle is an important part of the global carbon budget due to its large gross exchange fluxes with the atmosphere and their sensitivity to climate change. Terrestrial biosphere models show large uncertainties in estimating carbon fluxes, which impacts global carbon budget assessments. The land surface carbon cycle is tightly controlled by soil moisture through plant physiological processes. In this context, accurate soil moisture data will improve the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36 years (1980-2015) of surface soil moisture data as provided by the ESA CCI in combination with atmospheric CO&lt;sub&gt;2&lt;/sub&gt; concentration observations at global scale. We will present the methods used for assimilating long-term remotely sensed soil moisture into the terrestrial biosphere model, and demonstrate the importance of soil moisture in modeling ecosystem carbon cycle processes. We will also investigate the impacts of soil moisture on the terrestrial carbon cycle during climate extremes at various scales.&lt;/p&gt;

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