scholarly journals Quantifying the model structural error in carbon cycle data assimilation systems

2012 ◽  
Vol 5 (3) ◽  
pp. 2259-2288 ◽  
Author(s):  
S. Kuppel ◽  
F. Chevallier ◽  
P. Peylin
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Structural Model ◽  
Co2 Flux ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Simple Method ◽  
Total Uncertainty ◽  
Structural Error ◽  
The Impact

Abstract. This study explores the impact of the structural error of biosphere models when assimilating net ecosystem exchange (NEE) measurements or CO2 concentration measurements to optimize uncertain model parameters within carbon cycle data assimilation systems (CCDASs). This error has been proven difficult to identify and is often neglected in the total uncertainty budget. We propose a simple method which derives it from the model-minus-observation mismatch statistics. This diagnosis is applied to a state-of-the-art biogeochemical model using measurements of the net surface CO2 flux at twelve sites located in temperate deciduous broadleaf forests. We find that the structural model error in the NEE space has a standard deviation of 1.7 g C m−2 d−1, without a significant correlation structure beyond lags of a few days, and a large spatial structure that can be approximated with an exponential decay of e-folding length 500 km. In the space of concentrations, its characteristics are commensurate with the transport errors, both for surface air sample measurements and total column measurements. The inferred characteristics are confirmed by complementary optimality diagnostics performed after site-scale parameter optimizations.

scholarly journals Quantifying the model structural error in carbon cycle data assimilation systems

2013 ◽  
Vol 6 (1) ◽  
pp. 45-55 ◽  
Author(s):  
S. Kuppel ◽  
F. Chevallier ◽  
P. Peylin
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Structural Model ◽  
Co2 Flux ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Simple Method ◽  
Total Uncertainty ◽  
Structural Error ◽  
The Impact

Abstract. This study explores the impact of the structural error of biosphere models when assimilating net ecosystem exchange (NEE) measurements or CO2 concentration measurements to optimise uncertain model parameters within carbon cycle data assimilation systems (CCDASs). This error has been proven difficult to identify and is often neglected in the total uncertainty budget. We propose a simple method which is derived from the model-minus-observation mismatch statistics. This diagnosis is applied to a state-of-the-art biogeochemical model using measurements of the net surface CO2 flux at twelve sites located in temperate, deciduous, broadleaf forests. We find that the structural model error in the NEE space has a standard deviation of 1.5 to 1.7 gC m−2 d−1, without a significant correlation structure beyond the lag of a few days, and a large spatial structure that can be approximated with an exponential decay of e-folding length of 500 km. In the space of concentrations, its characteristics are commensurate with the transport errors, both for surface air sample measurements and total column measurements. The inferred characteristics are confirmed by complementary optimality diagnostics performed after site-scale parameter optimisations.

scholarly journals A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment

2018 ◽  
Vol 12 (7) ◽  
pp. 2287-2306 ◽  
Author(s):  
Gaia Piazzi ◽  
Guillaume Thirel ◽  
Lorenzo Campo ◽  
Simone Gabellani
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Multivariate Data ◽  
Model Parameters ◽  
Parameter Perturbation ◽  
Point Scale ◽  
Meteorological Forcing ◽  
System Sensitivity ◽  
Filter Performance ◽  
The Impact

Abstract. The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling – particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size.The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application.

scholarly journals Using satellite-derived backscattering coefficients in addition to chlorophyll data to constrain a simple marine biogeochemical model

2009 ◽  
Vol 6 (2) ◽  
pp. 4201-4231
Author(s):  
H. Kettle
Keyword(s):  
Carbon Cycle ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Ocean Carbon Cycle ◽  
Empirical Relationships ◽  
Biogeochemical Models ◽  
Ocean Carbon ◽  
Model Tuning ◽  
Different Characteristics ◽  
Satellite Chlorophyll

Abstract. Biogeochemical models of the ocean carbon cycle are frequently validated by, or tuned to, satellite chlorophyll data. However, ocean carbon cycle models are required to accurately model the movement of carbon, not chlorophyll and due to the high variability of the carbon to chlorophyll ratio in phytoplankton, chlorophyll is not a robust proxy for carbon. Using inherent optical property (IOP) inversion algorithms it is now possible to also derive the amount of light backscattered by the upper ocean (bb) which is related to the amount of particulate organic carbon (POC) present. Using empirical relationships between POC and bb, a 1-d biogeochemical model is used to simulate bb at 490 nm thus allowing the model to be compared with either remotely-sensed chlorophyll or bb data. Here I test the hypothesis that using bb in conjunction with chlorophyll data can help to constrain more model parameters than using chlorophyll alone. This is done by tuning the parameters of the biogeochemical model with a genetic algorithm, so that the model is fitted to either chlorophyll or to both chlorophyll and bb data at three sites in the Atlantic with very different characteristics. There are several IOP algorithms available for estimating bb. Four of these are investigated and three of them used for model tuning. The effect of the different bb datasets on the behaviour of the tuned model is examined to ascertain whether the uncertainty in bb is significant. The results show that the addition of bb data can have a large effect on the modelled detritus and that differences in the IOP algorithms are not particularly significant.

scholarly journals Importance of El Niño reproducibility for reconstructing historical CO<sub>2</sub> flux variations in the equatorial Pacific

2020 ◽  
Author(s):  
Michio Watanabe ◽  
Hiroaki Tatebe ◽  
Hiroshi Koyama ◽  
Tomohiro Hajima ◽  
Masahiro Watanabe ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
El Niño ◽  
Dissolved Inorganic Carbon ◽  
El Nino ◽  
Southern Oscillation ◽  
Subsurface Layer ◽  
Co2 Flux ◽  
Equatorial Pacific ◽  
Ocean Temperature

Abstract. In the equatorial Pacific, air–sea CO2 flux is known to fluctuate in response to inherent climate variability, predominantly the El Niño–Southern Oscillation (ENSO). For both investigation of the response of the carbon cycle to human-induced radiative perturbations and prediction of future global CO2 concentrations, representation of the interannual fluctuation of CO2 fluxes in Earth system models (ESMs) is essential. This study attempted to reproduce observed air–sea CO2 flux fluctuations in the equatorial Pacific using two ESMs, to which observed ocean temperature and salinity data were assimilated. When observations were assimilated into an ESM whose inherent ENSO variability was weaker than observations, nonnegligible correction terms on the governing equation of the equatorial ocean temperature caused anomalously false equatorial upwelling during El Niño periods that brought water rich in dissolved inorganic carbon from the subsurface layer to the surface layer. Contrary to observation, this resulted in an unusual upward air–sea CO2 flux anomaly that should not occur during El Niño periods. The absence of such unrealistic upwelling anomalies in the other ESM with the data assimilation reflects better representation of ENSO and the mean thermocline in this ESM without data assimilation. Our results demonstrate that adequate simulation of ENSO in an ESM is crucial for accurate reproduction of the variability in air–sea CO2 flux and hence, in the carbon cycle.

scholarly journals Consistent assimilation of multiple data streams in a carbon cycle data assimilation system

2016 ◽  
Author(s):  
Natasha MacBean ◽  
Philippe Peylin ◽  
Frédéric Chevallier ◽  
Marko Scholze ◽  
Gregor Schürmann
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Data Streams ◽  
Land Surface ◽  
Inversion Algorithm ◽  
Spatial And Temporal Scales ◽  
Multiple Data ◽  
Combining Data ◽  
Multiple Data Streams ◽  
The Impact

Abstract. Data assimilation methods provide a rigorous statistical framework for constraining the parametric uncertainty of land surface models (LSMs), with the aim of improving our predictive capability as well as identifying areas in which the models need improvement. The increase in the number of available datasets in recent years allows us to address different aspects of the model at a variety of spatial and temporal scales. However, combining data streams in a DA system is not a trivial task. In this study we highlight some of the challenges surrounding multiple data stream assimilation, with a particular focus on the carbon cycle component of LSMs. We examine the impact of biases and inconsistencies between the observations and the model (resulting in non Gaussian error distributions) and the impact of non-linearity in model dynamics. In addition we explore the differences between performing a simultaneous assimilation (in which all data streams are included in one optimisation) and a step-wise approach (in which each data steam is assimilated sequentially), given the assumptions inherent to the inversion algorithm chosen for this study. We demonstrate some of these issues by assimilating synthetic observations into two simple models: the first a simplified version of the carbon cycle processes represented in many LSMs, and the second a non-linear toy model. We further discuss these experimental results in the context of recent studies in the carbon cycle data assimilation literature, and finally we provide some perspectives and advice to other land surface modellers wishing to use multiple data streams to constrain their models.

scholarly journals Constraining a global ecosystem model with multi-site eddy-covariance data

2012 ◽  
Vol 9 (10) ◽  
pp. 3757-3776 ◽  
Author(s):  
S. Kuppel ◽  
P. Peylin ◽  
F. Chevallier ◽  
C. Bacour ◽  
F. Maignan ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Vegetation Index ◽  
Ecosystem Respiration ◽  
Terrestrial Ecosystem ◽  
Co2 Flux ◽  
Parameter Analysis ◽  
Single Site ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Area Index

Abstract. Assimilation of in situ and satellite data in mechanistic terrestrial ecosystem models helps to constrain critical model parameters and reduce uncertainties in the simulated energy, water and carbon fluxes. So far the assimilation of eddy covariance measurements from flux-tower sites has been conducted mostly for individual sites ("single-site" optimization). Here we develop a variational data assimilation system to optimize 21 parameters of the ORCHIDEE biogeochemical model, using net CO2 flux (NEE) and latent heat flux (LE) measurements from 12 temperate deciduous broadleaf forest sites. We assess the potential of the model to simulate, with a single set of inverted parameters, the carbon and water fluxes at these 12 sites. We compare the fluxes obtained from this "multi-site" (MS) optimization to those of the prior model, and of the "single-site" (SS) optimizations. The model-data fit analysis shows that the MS approach decreases the daily root-mean-square difference (RMS) to observed data by 22%, which is close to the SS optimizations (25% on average). We also show that the MS approach distinctively improves the simulation of the ecosystem respiration (Reco), and to a lesser extent the gross primary productivity (GPP), although we only assimilated net CO2 flux. A process-oriented parameter analysis indicates that the MS inversion system finds a unique combination of parameters which is not the simple average of the different SS sets of parameters. Finally, in an attempt to validate the optimized model against independent data, we observe that global-scale simulations with MS optimized parameters show an enhanced phase agreement between modeled leaf area index (LAI) and satellite-based observations of normalized difference vegetation index (NDVI).

The Impact of Stairs on the Aseismic Properties of the Structure

2013 ◽  
Vol 663 ◽  
pp. 210-214
Author(s):  
Yang Sun ◽  
Feng Xiong ◽  
Rong Jie Zhu
Keyword(s):  
Seismic Behavior ◽  
Structural Model ◽  
Current Method ◽  
Base Shear ◽  
Simple Method ◽  
Vibration Period ◽  
Current Design ◽  
Internal Forces ◽  
The Impact ◽  
Structural Base

The stair is a important structural component. In current design modeling, it is cut from the entire structure as a simple beam. But stairs show serious damages in some earthquake events. It implies that the seismic capacities of stair structures could be not enough when designed as current method. To investigate the seismic behavior and the effect on the structures for the stairs, a 5-story school building is selected as a case. Two structure models are established, including and without stair. First the reinforcement inside the stairs are compared. It shows the current simple method underestimate the loadings. Second the changes in the structure are investigated when adding the stair to the structural model. The stair's participation decreases the vibration period and storey drift, and increases the structural base shear and the internal forces of the members surrounding the stair. So, it is necessary to consider the stair in analyzing models.

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.

Data assimilation and ensemble method applied to Upernavik Isstrom

2021 ◽  
Author(s):  
Eliot Jager ◽  
Fabien Gillet-Chaulet ◽  
Jérémie Mouginot
Keyword(s):  
Data Assimilation ◽  
Mass Balance ◽  
Surface Elevation ◽  
Model Parameters ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Front Position ◽  
Transient Data ◽  
The Impact ◽  
Different Sources

&lt;p&gt;Lack of observation is one of the main limitations for improving model prediction in glaciology. However, over the past few years, the amount of observations from satellites has increased at a phenomenal rate. Hopefully, this amount of data will allow to validate the models and their parameterizations. In addition, data assimilation seems to be an optimal method to combine the model and these frequent observations, allowing to reduce the uncertainties of the model and thus potentially improve the projections. While inverse methods are now common in glaciology to infer uncertain parameters from observed surface velocities acquired at a given date, transient data assimilation algorithms are still under development. Recently, the performance of an Ensemble Kalman Filter has been studied on a synthetic case. Here, the goal of this study is to investigate the feasibility of applying this assimilation scheme on a real case : evolution of Upernavik Isstr&amp;#248;m since 1985 using the open source finite element software Elmer/Ice. To do so, we first need to generate an ensemble of simulations that sample the model uncertainties and to evaluate this ensemble against available observations.&lt;/p&gt;&lt;p&gt;We first assemble a set of observations that will serve for model setup and validation. In this sense, we have collected ice velocity measurements, from optical and radar source, surface elevation and bed topography, ice front position and surface mass balance that give us a fairly good a priori knowledge of the evolution of Upernavik Isstr&amp;#248;m between 1985 and 2020. These datasets are divided into two parts : one is used to better characterize and set up the initial state of the system, and the other is used to evaluate model outputs.&lt;/p&gt;&lt;p&gt;Uncertainties in the model comes from different sources: (i) the model parameters, (ii) the initial topography as the surface elevation in 1985 is only partially known, and (iii) the forcings (i.e. the surface mass balance, the ice front position).&lt;br&gt;For the model parameters we take into account uncertainties in the ice rheology by perturbing the Glen&amp;#8217;s enhancement factor and by generating an ensemble of friction coefficients for different friction laws using a set of inversions that has been performed for the whole Greenland using present day observations. Using these perturbed parameters and a set of surface mass balance representative of the period we generate and evaluate an ensemble of initial topographies for 1985.&lt;/p&gt;&lt;p&gt;&lt;br&gt;With this ensemble of initial states, we perform transient simulations where the position of glacier terminus and a set of perturbed SMB are prescribed each year. Each simulation is scored with specifically designed metrics in terms of dynamics and geometry using the observations described previously. This analysis allows to evaluate the impact of different sources of uncertainty on the transient simulation. Using the results of this study, we will discuss the capacity of Elmer/Ice to reconstruct the trend of the evolution of Upernavik Isstr&amp;#248;m and the possibility to perform transient data assimilation.&lt;/p&gt;

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