scholarly journals The potential of Orbiting Carbon Observatory-2 data to reduce the uncertainties in CO<sub>2</sub> surface fluxes over Australia using a variational assimilation scheme

2020 ◽  
Vol 20 (14) ◽  
pp. 8473-8500
Author(s):  
Yohanna Villalobos ◽  
Peter Rayner ◽  
Steven Thomas ◽  
Jeremy Silver
Keyword(s):  
Boundary Conditions ◽  
Dispersion Model ◽  
Grid Point ◽  
Surface Fluxes ◽  
Co2 Fluxes ◽  
Error Statistics ◽  
Error Covariance ◽  
Horizontal Grid ◽  
Assimilation Scheme ◽  
Total Column

Abstract. This paper addresses the question of how much uncertainties in CO2 fluxes over Australia can be reduced by assimilation of total-column carbon dioxide retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite instrument. We apply a four-dimensional variational data assimilation system, based around the Community Multiscale Air Quality (CMAQ) transport-dispersion model. We ran a series of observing system simulation experiments to estimate posterior error statistics of optimized monthly-mean CO2 fluxes in Australia. Our assimilations were run with a horizontal grid resolution of 81 km using OCO-2 data for 2015. Based on four representative months, we find that the integrated flux uncertainty for Australia is reduced from 0.52 to 0.13 Pg C yr−1. Uncertainty reductions of up to 90 % were found at grid-point resolution over productive ecosystems. Our sensitivity experiments show that the choice of the correlation structure in the prior error covariance plays a large role in distributing information from the observations. We also found that biases in the observations would significantly impact the inverted fluxes and could contaminate the final results of the inversion. Biases in prior fluxes are generally removed by the inversion system. Biases in the boundary conditions have a significant impact on retrieved fluxes, but this can be mitigated by including boundary conditions in our retrieved parameters. In general, results from our idealized experiments suggest that flux inversions at this unusually fine scale will yield useful information on the carbon cycle at continental and finer scales.

scholarly journals The potential of OCO-2 data to reduce the uncertainties in CO<sub>2</sub> surface fluxes over Australia using a variational assimilation scheme

2019 ◽  
Author(s):  
Yohanna Villalobos ◽  
Peter Rayner ◽  
Steven Thomas ◽  
Jeremy Silver
Keyword(s):  
Dispersion Model ◽  
Surface Fluxes ◽  
Co2 Fluxes ◽  
Error Statistics ◽  
Satellite Measurements ◽  
Error Covariance ◽  
Horizontal Grid ◽  
Assimilation Scheme ◽  
Data Assimilation System ◽  
Total Column

Abstract. This paper addresses the question of how much uncertainties in CO2 fluxes over Australia can be reduced by assimilation of total-column carbon dioxide retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite instrument. We apply a four-dimensional variational data assimilation system, based around the Community Multiscale Air Quality (CMAQ) transport-dispersion model. We ran a series of observing system simulation experiments to estimate posterior error statistics of optimized monthly mean CO2 fluxes in Australia. Our assimilations were run with a horizontal grid resolution of 81 km using OCO-2 data for 2015. We found that on average, the total Australia flux uncertainty was reduced by up to 40 % using only OCO-2 nadir measurements. Using both nadir and glint satellite measurements produces uncertainty reductions up to 80 %, which represents 0.55 PgC y−1 for the whole continent. Uncertainty reductions were found to be greatest in the more productive regions of Australia. The choice of the correlation structure in the prior error covariance was found to play a large role in distributing information from the observations. Overall the results suggest that flux inversions at this unusually fine scale will yield useful information on the Australian carbon cycle.

scholarly journals Towards better error statistics for atmospheric inversions of methane surface fluxes

2013 ◽  
Vol 13 (14) ◽  
pp. 7115-7132 ◽  
Author(s):  
A. Berchet ◽  
I. Pison ◽  
F. Chevallier ◽  
P. Bousquet ◽  
S. Conil ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Transport Model ◽  
Vertical Mixing ◽  
Real Data ◽  
Surface Fluxes ◽  
Error Statistics ◽  
The North ◽  
Error Covariance ◽  
Methane Fluxes ◽  
General Statistical

Abstract. We adapt general statistical methods to estimate the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. Using a minimal set of physical hypotheses on the patterns of errors, we compute a guess of the error statistics that is optimal in regard to objective statistical criteria for the specific inversion system. With this very general approach applied to a real-data case, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge while inferred from objective criteria and with affordable computation costs. By not assuming any specific error patterns, our results depict the variability and the inter-dependency of errors induced by complex factors such as the misrepresentation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of concentrations. Situations with probable significant biases (e.g., during the night when vertical mixing is ill-represented by the transport model) can also be diagnosed by our methods in order to point at necessary improvement in a model. By additionally analysing the sensitivity of the inversion to each observation, guidelines to enhance data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea and found methane fluxes of the same magnitude than what was officially declared.

Inhomogeneous Background Error Modeling and Estimation over Antarctica

2010 ◽  
Vol 138 (6) ◽  
pp. 2229-2252 ◽  
Author(s):  
Yann Michel ◽  
Thomas Auligné
Keyword(s):  
Wrf Model ◽  
Error Modeling ◽  
Limited Area ◽  
Error Statistics ◽  
Background Error ◽  
Recursive Filters ◽  
Error Covariance ◽  
Function Decomposition ◽  
The Antarctic ◽  
Assimilation Scheme

Abstract The structure of the analysis increments in a variational data assimilation scheme is strongly driven by the formulation of the background error covariance matrix, especially in data-sparse areas such as the Antarctic region. The gridpoint background error modeling in this study makes use of regression-based balance operators between variables, empirical orthogonal function decomposition to define the vertical correlations, gridpoint variances, and high-order efficient recursive filters to impose horizontal correlations. A particularity is that the regression operators and the recursive filters have been made spatially inhomogeneous. The computation of the background error statistics is performed with the Weather Research and Forecast (WRF) model from a set of forecast differences. The mesoscale limited-area domains of interest cover Antarctica. Inhomogeneities of background errors are shown to be related to the particular orography and physics of the area. Differences seem particularly pronounced between ocean and land boundary layers.

scholarly journals Towards better error statistics for atmospheric inversions of methane surface fluxes

2013 ◽  
Vol 13 (2) ◽  
pp. 3735-3782
Author(s):  
A. Berchet ◽  
I. Pison ◽  
F. Chevallier ◽  
P. Bousquet ◽  
S. Conil ◽  
...  
Keyword(s):  
Expert Knowledge ◽  
Transport Model ◽  
Real Data ◽  
Surface Fluxes ◽  
Mass Composition ◽  
Error Statistics ◽  
The North ◽  
Error Covariance ◽  
Data Framework ◽  
General Statistical

Abstract. In this study, we adapt general statistical methods to compute the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. We optimally estimate the error statistics with a minimal set of physical hypotheses on the patterns of errors. With this very general approach applied within a real-data framework, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge. By not assuming any specific error patterns, our results show the variability and the inter-dependency of errors induced by complex factors such as the mis-representation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of air mass composition in the atmosphere. By analyzing the sensitivity of the inversion to each observation, ways to improve data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea.

scholarly journals Structure of the transport uncertainty in mesoscale inversions of CO<sub>2</sub> sources and sinks using ensemble model simulations

2009 ◽  
Vol 6 (6) ◽  
pp. 1089-1102 ◽  
Author(s):  
T. Lauvaux ◽  
O. Pannekoucke ◽  
C. Sarrat ◽  
F. Chevallier ◽  
P. Ciais ◽  
...  
Keyword(s):  
Diffusion Equation ◽  
Boundary Conditions ◽  
Model Error ◽  
Scale Model ◽  
Surface Model ◽  
Observation Error ◽  
Co2 Fluxes ◽  
Horizontal Structure ◽  
Temporal Correlations ◽  
Error Covariance

Abstract. We study the characteristics of a statistical ensemble of mesoscale simulations in order to estimate the model error in the simulation of CO2 concentrations. The ensemble consists of ten members and the reference simulation using the operationnal short range forecast PEARP, perturbed using the Singular Vector technique. We then used this ensemble of simulations as the initial and boundary conditions for the meso scale model (Méso-NH) simulations, which uses CO2 fluxes from the ISBA-A-gs land surface model. The final ensemble represents the model dependence to the boundary conditions, conserving the physical properties of the dynamical schemes, but excluding the intrinsic error of the model. First, the variance of our ensemble is estimated over the domain, with associated spatial and temporal correlations. Second, we extract the signal from noisy horizontal correlations, due to the limited size ensemble, using diffusion equation modelling. The computational cost of such ensemble limits the number of members (simulations) especially when running online the carbon flux and the atmospheric models. In the theory, 50 to 100 members would be required to explore the overall sensitivity of the ensemble. The present diffusion model allows us to extract a significant part of the noisy error, and makes this study feasable with a limited number of simulations. Finally, we compute the diagonal and non-diagonal terms of the observation error covariance matrix and introduced it into our CO2 flux matrix inversion for 18 days of the 2005 intensive campaign CERES over the South West of France. Variances are based on model-data mismatch to ensure we treat model bias as well as ensemble dispersion, whereas spatial and temporal covariances are estimated with our method. The horizontal structure of the ensemble variance manifests the discontinuities of the mesoscale structures during the day, but remains locally driven during the night. On the vertical, surface layer variance shows large correlations with the upper levels in the boundary layer (> 0.6), dropping to 0.4 with the lower levels of the free troposphere. Large temporal correlations were found during the afternoon (> 0.5 for several hours), reduced during the night. The diffusion equation model extracted relevant error covariance signals horizontally, with reduced correlations over mountain areas and during the night over the continent. The posterior error reduction on the inverted CO2 fluxes accounting for the model error correlations illustrates the predominance of the temporal over the spatial correlations when using tower-based CO2 concentration observations.

scholarly journals Mesoscale inversion: first results from the CERES campaign with synthetic data

2008 ◽  
Vol 8 (13) ◽  
pp. 3459-3471 ◽  
Author(s):  
T. Lauvaux ◽  
M. Uliasz ◽  
C. Sarrat ◽  
F. Chevallier ◽  
P. Bousquet ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Dispersion Model ◽  
Regional Scale ◽  
Error Reduction ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Co2 Fluxes ◽  
Aircraft Measurements ◽  
Imperfect Knowledge ◽  
The Impact

Abstract. We investigate the ability of a mesoscale model to reconstruct CO2 fluxes at regional scale. Formally, we estimate the reduction of error for a CO2 flux inversion at 8 km resolution in the South West of France, during four days of the CarboEurope Regional Experiment Strategy (CERES) in spring 2005. Measurements from two towers and two airplanes are available for this campaign. The lagrangian particle dispersion model LPDM was coupled to the non-hydrostatic model Meso-NH and integrated in a matrix inversion framework. Impacts of aircraft and tower measurements are quantified separately and together. We find that the configuration with both towers and aircraft is able to significantly reduce uncertainties on the 4-day averaged CO2 fluxes over about half of the 300×300 km2 domain. Most of this reduction comes from the tower measurements, even though the impact of aircraft measurements remains noticeable. Imperfect knowledge of boundary conditions does not significantly impact the error reduction for surface fluxes. We test alternative strategies to improve the impact of aircraft measurements and find that most information comes from measurements inside the boundary layer. We find that there would be a large improvement in error reduction if we could improve our ability to model nocturnal concentrations at tower sites.

scholarly journals Structure of the transport uncertainty in mesoscale inversions of CO<sub>2</sub> sources and sinks using ensemble model simulations

2008 ◽  
Vol 5 (6) ◽  
pp. 4813-4846 ◽  
Author(s):  
T. Lauvaux ◽  
O. Pannekoucke ◽  
C. Sarrat ◽  
F. Chevallier ◽  
P. Ciais ◽  
...  
Keyword(s):  
Diffusion Equation ◽  
Boundary Conditions ◽  
Model Error ◽  
Scale Model ◽  
Surface Model ◽  
Observation Error ◽  
Co2 Fluxes ◽  
Model Simulations ◽  
Temporal Correlations ◽  
Error Covariance

Abstract. We study the characteristics of a statistical ensemble of mesoscale simulations in order to estimate the model error in the simulation of CO2 concentrations. The ensemble consists of ten members and the reference simulation using the operationnal short range forecast PEARP, perturbed by Singular Vector (SV) technic. We then used this ensemble of simulations as the initial and boundary conditions for the meso scale model simulations, here the atmospheric transport model Méso-NH, transporting CO2 fluxes from the ISBA-A-gs land surface model. The final ensemble represents the model dependence to the boundary conditions, conserving the physical properties of the dynamical schemes. First, the variance of our ensemble is estimated over the domain, with associated spatial and temporal correlations. Second, we extract the signal from noisy horizontal correlations, due to the limited size ensemble, using diffusion equation modelling. Finally, we compute the diagonal and non-diagonal terms of the observation error covariance matrix and introduced it into our CO2 flux matrix inversion over 18 days of the 2005 intensive campaign CERES over the South West of France. On the horizontal plane, variance of the ensemble follows the discontinuities of the mesoscale structures during the day, but remain locally driven during the night. On the vertical, surface layer variance shows large correlations with the upper levels in the boundary layer (>0.6), down to 0.4 with the low free troposphere. Large temporal correlations were found during the afternoon (>0.5 for several hours), reduced during the night. Diffusion equation model extracted relevant error covariance signals on the horizontal space, and shows reduced correlations over mountain area and during the night over the continent. The posterior error reduction on the inverted CO2 fluxes accounting for the model error correlations illustrates finally the predominance of the temporal over the spatial correlations when using tower-based CO2 concentration observations.

scholarly journals On the use of satellite-derived CH<sub>4</sub> : CO<sub>2</sub> columns in a joint inversion of CH<sub>4</sub> and CO<sub>2</sub> fluxes

2015 ◽  
Vol 15 (15) ◽  
pp. 8615-8629 ◽  
Author(s):  
S. Pandey ◽  
S. Houweling ◽  
M. Krol ◽  
I. Aben ◽  
T. Röckmann
Keyword(s):  
Joint Inversion ◽  
Surface Fluxes ◽  
Surface Measurement ◽  
Ratio Method ◽  
Co2 Fluxes ◽  
Hard Constraints ◽  
Surface Measurements ◽  
Total Column ◽  
Dual Tracer ◽  
Better Than

Abstract. We present a method for assimilating total column CH4 : CO2 ratio measurements from satellites for inverse modeling of CH4 and CO2 fluxes using the variational approach. Unlike conventional approaches, in which retrieved CH4 : CO2 are multiplied by model-derived total column CO2 and only the resulting CH4 is assimilated, our method assimilates the ratio of CH4 and CO2 directly and is therefore called the ratio method. It is a dual tracer inversion, in which surface fluxes of CH4 and CO2 are optimized simultaneously. The optimization of CO2 fluxes turns the hard constraint of prescribing model-derived CO2 fields into a weak constraint on CO2, which allows us to account for uncertainties in CO2. The method has been successfully tested in a synthetic inversion setup. We show that the ratio method is able to reproduce assumed true CH4 and CO2 fluxes starting from a prior, which is derived by perturbing the true fluxes randomly. We compare the performance of the ratio method with that of the traditional proxy approach and the use of only surface measurements for estimating CH4 fluxes. Our results confirm that the optimized CH4 fluxes are sensitive to the treatment of CO2, and that hard constraints on CO2 may significantly compromise results that are obtained for CH4. We find that the relative performance of ratio and proxy methods have a regional dependence. The ratio method performs better than the proxy method in regions where the CO2 fluxes are most uncertain. However, both ratio and proxy methods perform better than the surface-measurement-only inversion, confirming the potential of spaceborne measurements for accurately determining fluxes of CH4 and other greenhouse gases (GHGs).

scholarly journals ENSO’s Impact on the Gap Wind Regions of the Eastern Tropical Pacific Ocean*

2012 ◽  
Vol 25 (10) ◽  
pp. 3549-3565 ◽  
Author(s):  
Michael A. Alexander ◽  
Hyodae Seo ◽  
Shang Ping Xie ◽  
James D. Scott
Keyword(s):  
Pacific Ocean ◽  
Boundary Conditions ◽  
El Niño ◽  
El Nino ◽  
Tropical Pacific ◽  
Surface Fluxes ◽  
Ocean Dynamics ◽  
Tropical Pacific Ocean ◽  
Variable Surface ◽  
Sst Anomalies

Abstract The recently released NCEP Climate Forecast System Reanalysis (CFSR) is used to examine the response to ENSO in the northeast tropical Pacific Ocean (NETP) during 1979–2009. The normally cool Pacific sea surface temperatures (SSTs) associated with wind jets through the gaps in the Central American mountains at Tehuantepec, Papagayo, and Panama are substantially warmer (colder) than the surrounding ocean during El Niño (La Niña) events. Ocean dynamics generate the ENSO-related SST anomalies in the gap wind regions as the surface fluxes damp the SSTs anomalies, while the Ekman heat transport is generally in quadrature with the anomalies. The ENSO-driven warming is associated with large-scale deepening of the thermocline; with the cold thermocline water at greater depths during El Niño in the NETP, it is less likely to be vertically mixed to the surface, particularly in the gap wind regions where the thermocline is normally very close to the surface. The thermocline deepening is enhanced to the south of the Costa Rica Dome in the Papagayo region, which contributes to the local ENSO-driven SST anomalies. The NETP thermocline changes are due to coastal Kelvin waves that initiate westward-propagating Rossby waves, and possibly ocean eddies, rather than by local Ekman pumping. These findings were confirmed with regional ocean model experiments: only integrations that included interannually varying ocean boundary conditions were able to simulate the thermocline deepening and localized warming in the NETP during El Niño events; the simulation with variable surface fluxes, but boundary conditions that repeated the seasonal cycle, did not.

On the benefit of the Canadian Small Lake Model to better represent the impact of small natural lakes on GEM-Hydro streamflow simulations.

2021 ◽  
Author(s):  
Etienne Gaborit ◽  
Murray MacKay ◽  
Camille Garnaud ◽  
Vincent Fortin
Keyword(s):  
Water Balance ◽  
Grid Point ◽  
Hydrologic Model ◽  
Surface Fluxes ◽  
Dew Point ◽  
Multiple Model ◽  
Small Lake ◽  
Surface Component ◽  
Lake Model ◽  
The Impact

&lt;p&gt;This study aims at assessing the impact of a new lake model on streamflow simulations performed with the GEM-Hydro hydrologic model developed at ECCC. GEM-Hydro is at the heart of the National Surface and River Prediction System (NSRPS) which ECCC uses to forecast river flows over most of Canada. The GEM-Hydro model mainly consists of the GEM-Surf component to represent surface processes, and of the Watroute model to represent river and lake routing, in order to perform streamflow simulations and forecasts. The surface component of GEM-Hydro can simulate 5 different types of surfaces.&amp;#160; Currently, the water tile consists of a very simple algorithm which, in terms of water balance, consists of producing runoff fluxes simply equal to precipitation minus evaporation. This runoff over water surfaces is then provided as input, along with runoff and drainage generated over other surface tiles, to the Watroute model. The Watroute version used in GEM-Hydro currently only represents major lakes (area greater than 100km&lt;sup&gt;2&lt;/sup&gt;) along the river networks, and does not represent the impact that small lakes can have on streamflow, which mainly consists in slowing down runoff before it reaches the main streams of the network.&lt;/p&gt;&lt;p&gt;Recently, the Canadian Small Lake Model (CSLM) was implemented in the surface component of GEM-Hydro to represent the energy and water balance over water tiles more accurately. So far, CSLM simulations have been shown promising in terms of evaporation, ice cover, absolute and dew point temperature simulations, compared with the former algorithm used over water. However, the impact of CSLM on the resulting streamflow simulations performed with GEM-Hydro has not been evaluated yet. This study aims first at evaluating the impact of CSLM on streamflow simulations, and secondly at testing different CSLM configurations as well as different coupling strategies with Watroute, with the objective of finding the best set up for the prediction of streamflow in Canada. For example, overland runoff generated by the land tile can be provided to the water tile of the same grid point in different ways, and the outflow computed at the outlet of the water tile can be computed with different parameters. Moreover, different outflow computations have to be taken into account depending on if the water tile of a grid point represents subgrid-scale lakes, or if on the contrary it belongs to a lake spanning over multiple model grid points.&lt;/p&gt;&lt;p&gt;To do so, different GEM-Hydro open-loop simulations have been performed on the Lake of the Woods watershed, located in Canada, with and without CSLM to represent water tiles. The CSLM configurations leading to the best results are presented here. CSLM simulations are also evaluated in terms of surface fluxes, to ensure that the main purpose of the model, which is to improve surface fluxes to ultimately improve atmospheric forecasts, is preserved, compared to the default configuration of the model. Ideas for further improving the coupling between the GEM-Hydro surface and routing components, in terms of lake processes, are also presented and will be tested in future work.&lt;/p&gt;

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