scholarly journals Evaluation of a new middle-lower tropospheric CO<sub>2</sub> product using data assimilation

2012 ◽  
Vol 12 (10) ◽  
pp. 26685-26717
Author(s):  
A. Tangborn ◽  
L. L. Strow ◽  
B. Imbiriba ◽  
L. Ott ◽  
S. Pawson
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Model Simulation ◽  
Lower Troposphere ◽  
Surface Measurements ◽  
Peak Sensitivity ◽  
The Difference ◽  
Using Data ◽  
Mean Differences

Abstract. Atmospheric CO2 retrievals with peak sensitivity in the mid- to lower troposphere from the Atmospheric Infrared Sounder (AIRS) have been assimilated into the Global Modeling and Assimilation Office (GMAO) constituent assimilation system for the period 1 January 2005 to 31 December 2006. A corresponding model simulation, using identical initial conditions, circulation, and CO2 boundary fluxes was also completed. The analyzed and simulated CO2 fields are compared with surface measurements globally and aircraft measurements over North America. Surface level monthly mean CO2 values show a marked improvement due to the assimilation in the Southern Hemisphere, while less consistent improvements are seen in the Northern Hemisphere. Mean differences with aircraft observations are reduced at all levels, with the largest decrease occurring in the mid-troposphere. The difference standard deviations are reduced slightly at all levels over the ocean, and all levels except the surface layer over land. These initial experiments indicate that the retrieved channel contains useful information on CO2 in the middle to lower troposphere. However, the benefits of assimilating these data are reduced over the land surface, where concentrations are dominated by uncertain local fluxes and where the observation density is quite low. Away from these regions, the study demonstrates the power of the data assimilation technique for evaluating data that are not co-located, in that the improvements in mid-tropospheric CO2 by the sparsely distributed partial-column retrievals are transported by the model to the fixed in-situ surface observation locations in more remote areas.

scholarly journals Evaluation of a new middle-lower tropospheric CO<sub>2</sub> product using data assimilation

2013 ◽  
Vol 13 (9) ◽  
pp. 4487-4500 ◽  
Author(s):  
A. Tangborn ◽  
L. L. Strow ◽  
B. Imbiriba ◽  
L. Ott ◽  
S. Pawson
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Model Simulation ◽  
Lower Troposphere ◽  
Earth Observing System ◽  
Peak Sensitivity ◽  
The Difference ◽  
Using Data ◽  
Mean Differences

Abstract. Atmospheric CO2 retrievals with peak sensitivity in the mid- to lower troposphere from the Atmospheric Infrared Sounder (AIRS) have been assimilated into the GEOS-5 (Goddard Earth Observing System Model, Version 5) constituent assimilation system for the period 1 January 2005 to 31 December 2006. A corresponding model simulation, using identical initial conditions, circulation, and CO2 boundary fluxes was also completed. The analyzed and simulated CO2 fields are compared with surface measurements globally and aircraft measurements over North America. Surface level monthly mean CO2 values show a marked improvement due to the assimilation in the Southern Hemisphere, while less consistent improvements are seen in the Northern Hemisphere. Mean differences with aircraft observations are reduced at all levels, with the largest decrease occurring in the mid-troposphere. The difference standard deviations are reduced slightly at all levels over the ocean, and all levels except the surface layer over land. These initial experiments indicate that the used channels contain useful information on CO2 in the middle to lower troposphere. However, the benefits of assimilating these data are reduced over the land surface, where concentrations are dominated by uncertain local fluxes and where the observation density is quite low. Away from these regions, the study demonstrates the power of the data assimilation technique for evaluating data that are not co-located, in that the improvements in mid-tropospheric CO2 by the sparsely distributed partial-column retrievals are transported by the model to the fixed in situ surface observation locations in more remote areas.

Impact of Weak Coupling between Land and Atmosphere Data Assimilation Systems on Environment and Climate Change Canada’s Global Deterministic Prediction System

2019 ◽  
Vol 34 (6) ◽  
pp. 1741-1758 ◽  
Author(s):  
Maziar Bani Shahabadi ◽  
Stéphane Bélair ◽  
Bernard Bilodeau ◽  
Marco L. Carrera ◽  
Louis Garand
Keyword(s):  
Climate Change ◽  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Lower Troposphere ◽  
Forecast Model ◽  
Prediction System ◽  
Coupled Modes ◽  
Screen Level ◽  
The Impact

Abstract A new ensemble-based land surface data assimilation (DA) system is coupled with the atmospheric four-dimensional ensemble-variational data assimilation (4D-EnVar) system with the goal of improving the analyses within Environment and Climate Change Canada’s Global Deterministic Prediction System. Since 2001, the sequential assimilation of surface variables is used to generate the initial conditions to launch the Global Environmental Multiscale (GEM) coupled forecast model. The work presented here is to replace the sequential DA with an independent surface DA system, the Canadian Land Data Assimilation System (CaLDAS) assimilating screen-level observations, and to compare assimilation experiments with CaLDAS run in uncoupled and weakly coupled modes. In the uncoupled mode, CaLDAS is used to initialize the forecast without interacting with the 4D-EnVar system. In the coupled mode, the analyses generated from CaLDAS and 4D-EnVar are used to initialize the forecast model. The analyses and forecasts from uncoupled and coupled runs are evaluated against surface and radiosonde observations over different subdomains to conclude the impact of coupling CaLDAS with 4D-EnVar. Results indicate a statistically significant reduction in bias and standard deviation at the surface for screen-level temperature and dewpoint temperature on the order of 0.1 K, and in the lower troposphere between 1000 and 500 hPa on the order of 0.1 dam for geopotential height and 0.1 K for air temperature and dewpoint depression in the coupled DA runs. The positive impact persists up to 5 days over some subdomains. It is concluded that the coupled DA approach generally performs better than the uncoupled version.

scholarly journals Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

2016 ◽  
Vol 9 (8) ◽  
pp. 2833-2852 ◽  
Author(s):  
Nina M. Raoult ◽  
Tim E. Jupp ◽  
Peter M. Cox ◽  
Catherine M. Luke
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Gross Primary Production ◽  
Land Surface Model ◽  
Weather Forecast ◽  
Surface Model ◽  
Land Surface Parameter ◽  
Estimation System ◽  
Using Data ◽  
The Uk

Abstract. Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate–carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model–data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

scholarly journals The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation

2021 ◽  
Vol 15 (6) ◽  
pp. 2781-2802
Author(s):  
Linlu Mei ◽  
Vladimir Rozanov ◽  
Evelyn Jäkel ◽  
Xiao Cheng ◽  
Marco Vountas ◽  
...  
Keyword(s):  
Grain Size ◽  
Land Surface ◽  
Model Simulation ◽  
Atmospheric Correction ◽  
Aircraft Measurements ◽  
Airborne Measurements ◽  
Snow Properties ◽  
Snow Particle ◽  
The Difference ◽  
Mean Square Errors

Abstract. To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m2/kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process.

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

2020 ◽  
Author(s):  
Anthony Mucia ◽  
Clément Albergel ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Jean-Christophe Calvet
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Global Land ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

&lt;p&gt;LDAS-Monde is a global Land Data Assimilation System developed in the research department of M&amp;#233;t&amp;#233;o-France (CNRM) to monitor Land Surface Variables (LSVs) at various scales, from regional to global. With LDAS-Monde, it is possible to assimilate satellite derived observations of Surface Soil Moisture (SSM) and Leaf Area Index (LAI) e.g. from the Copernicus Global Land Service (CGLS). It is an offline system normally driven by atmospheric reanalyses such as ECMWF ERA5.&lt;/p&gt;&lt;p&gt;In this study we investigate LDAS-Monde ability to use atmospheric forecasts to predict LSV states up to weeks in advance. In addition to the accuracy of the forecast predictions, the impact of the initialization on the LSVs forecast is addressed. To perform this study, LDAS-Monde is forced by a fifteen-day forecast from ECMWF for the 2017-2018 period over the Contiguous United States (CONUS) at 0.2&lt;sup&gt;o&lt;/sup&gt; x 0.2&lt;sup&gt;o&lt;/sup&gt; spatial resolution. These LSVs forecasts are initialized either by the model alone (LDAS-Monde open-loop, no assimilation, Fc_ol) or by the analysis (assimilation of SSM and LAI, Fc_an). These two sets of forecast are then assessed using satellite derived observations of SSM and LAI, evapotranspiration 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 evapotranspiration), LDAS-Monde provides reasonably accurate predictions two weeks in advance. Additionally, the initial conditions are shown to make a positive impact with respect to LAI, evapotranspiration, and deeper layers of soil moisture when using Fc_an. Moreover, this impact persists in time, particularly for vegetation related variables. Other model variables (such as runoff and drainage) are also affected by the initial conditions. Future work will focus on the transfer of this predictive information from a research to stakeholder tool.&lt;/p&gt;

scholarly journals Tropospheric CO vertical profiles deduced from total columns using data assimilation: methodology and validation

2014 ◽  
Vol 7 (9) ◽  
pp. 3035-3057 ◽  
Author(s):  
L. El Amraoui ◽  
J.-L. Attié ◽  
P. Ricaud ◽  
W. A. Lahoz ◽  
A. Piacentini ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Data Sets ◽  
Vertical Profiles ◽  
Night Time ◽  
Chi Square ◽  
Independent Observations ◽  
The Mean ◽  
The Difference ◽  
Using Data ◽  
Total Column

Abstract. This paper presents a validation of a method to derive the vertical profile of carbon monoxide (CO) from its total column using data assimilation. We choose version 3 of MOPITT CO total columns to validate the proposed method. MOPITT products have the advantage of providing both the vertical profiles and the total columns of CO. Furthermore, this version has been extensively validated by comparison with many independent data sets, and has been used in many scientific studies. The first step of the paper consists in the specification of the observation errors based on the chi-square (χ2) test. The observations have been binned according to three types: over land during daytime, over land during night-time, and over sea. Their respective errors using the χ2 metric have been found to be 8, 11 and 7%. In the second step, the CO total columns, with their specified errors, are used within the assimilation system to estimate the vertical profiles. These are compared to the retrieved profiles of MOPITT V3 at global and regional scales. Generally, the two data sets show similar patterns and good agreement at both scales. Nevertheless, total column analyses slightly overestimate CO concentrations compared to MOPITT observations. The mean bias between both data sets is +15 and +12% at 700 and 250 hPa, respectively. In the third step, the assimilation of total column has been compared to the assimilation of MOPITT vertical profiles. The differences between both analyses are very small. In terms longitude–latitude maps, the mean bias between the two data sets is +6 and +8% at the pressure levels 700 and 200 hPa, respectively. In terms of zonal means, the CO distribution is similar for both analyses, with a mean bias which does not exceed 12%. Finally, the two analyses have been validated using independent observations from the aircraft-based MOZAIC program in terms of vertical profiles over eight airports. Over most airports, both analyses agree well with aircraft profiles. For more than 50% of recorded measurements, the difference between the analyses and MOZAIC does not exceed 5 ppbv (parts per billion by volume).

scholarly journals Multiple steady-states in the terrestrial atmosphere-biosphere system: a result of a discrete vegetation classification?

2007 ◽  
Vol 4 (5) ◽  
pp. 707-714 ◽  
Author(s):  
A. Kleidon ◽  
K. Fraedrich ◽  
C. Low
Keyword(s):  
Land Surface ◽  
Initial Conditions ◽  
Steady States ◽  
Multiple Steady States ◽  
Atmospheric Conditions ◽  
Vegetation Productivity ◽  
Positive Feedbacks ◽  
Terrestrial Atmosphere ◽  
The Difference ◽  
Global Vegetation

Abstract. Multiple steady states in the atmosphere-biosphere system can arise as a consequence of interactions and positive feedbacks. While atmospheric conditions affect vegetation productivity in terms of available light, water, and heat, different levels of vegetation productivity can result in differing energy- and water partitioning at the land surface, thereby leading to different atmospheric conditions. Here we investigate the emergence of multiple steady states in the terrestrial atmosphere-biosphere system and focus on the role of how vegetation is represented in the model: (i) in terms of a few, discrete vegetation classes, or (ii) a continuous representation. We then conduct sensitivity simulations with respect to initial conditions and to the number of discrete vegetation classes in order to investigate the emergence of multiple steady states. We find that multiple steady states occur in our model only if vegetation is represented by a few vegetation classes. With an increased number of classes, the difference between the number of multiple steady states diminishes, and disappears completely in our model when vegetation is represented by 8 classes or more. Despite the convergence of the multiple steady states into a single one, the resulting climate-vegetation state is nevertheless less productive when compared to the emerging state associated with the continuous vegetation parameterization. We conclude from these results that the representation of vegetation in terms of a few, discrete vegetation classes can result (a) in an artificial emergence of multiple steady states and (b) in a underestimation of vegetation productivity. Both of these aspects are important limitations to be considered when global vegetation-atmosphere models are to be applied to topics of global change.

scholarly journals Data assimilation of satellite-retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF's Composition-IFS

2015 ◽  
Vol 15 (9) ◽  
pp. 5275-5303 ◽  
Author(s):  
A. Inness ◽  
A.-M. Blechschmidt ◽  
I. Bouarar ◽  
S. Chabrillat ◽  
M. Crepulja ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Assimilation ◽  
Nitrogen Dioxide ◽  
Initial Conditions ◽  
Stratospheric Ozone ◽  
Lower Troposphere ◽  
Atmospheric Composition ◽  
Reactive Gases ◽  
The Impact ◽  
Total Column

Abstract. Daily global analyses and 5-day forecasts are generated in the context of the European Monitoring Atmospheric Composition and Climate (MACC) project using an extended version of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The IFS now includes modules for chemistry, deposition and emission of reactive gases, aerosols, and greenhouse gases, and the 4-dimensional variational data assimilation scheme makes use of multiple satellite observations of atmospheric composition in addition to meteorological observations. This paper describes the data assimilation setup of the new Composition-IFS (C-IFS) with respect to reactive gases and validates analysis fields of ozone (O3), carbon monoxide (CO), and nitrogen dioxide (NO2) for the year 2008 against independent observations and a control run without data assimilation. The largest improvement in CO by assimilation of Measurements of Pollution in the Troposphere (MOPITT) CO columns is seen in the lower troposphere of the Northern Hemisphere (NH) extratropics during winter, and during the South African biomass-burning season. The assimilation of several O3 total column and stratospheric profile retrievals greatly improves the total column, stratospheric and upper tropospheric O3 analysis fields relative to the control run. The impact on lower tropospheric ozone, which comes from the residual of the total column and stratospheric profile O3 data, is smaller, but nevertheless there is some improvement particularly in the NH during winter and spring. The impact of the assimilation of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) is small because of the short lifetime of NO2, suggesting that NO2 observations would be better used to adjust emissions instead of initial conditions. The results further indicate that the quality of the tropospheric analyses and of the stratospheric ozone analysis obtained with the C-IFS system has improved compared to the previous "coupled" model system of MACC.

scholarly journals The retreival of snow properties from SLSTR/Sentinel-3 – part 2: results and validation

2020 ◽  
Author(s):  
Linlu Mei ◽  
Vladimir Rozanov ◽  
Evelyn Jäkel ◽  
Xiao Cheng ◽  
Marco Vountas ◽  
...  
Keyword(s):  
Grain Size ◽  
Land Surface ◽  
Model Simulation ◽  
Atmospheric Correction ◽  
Aircraft Measurements ◽  
Airborne Measurements ◽  
Snow Properties ◽  
Snow Particle ◽  
The Difference

Abstract. To evaluate the performance of eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in part 1 of the companion paper, this manuscript applies the XBAER algorithm on the Sea and Land Surface Temperature Radiometer (SLSTR) and Ocean and Land Colour Instrument (OLCI) instruments onboard Sentinel-3. Snow properties: Snow Grain Size (SGS), Snow Particle Shape (SPS), and Specific Surface Area (SSA) are derived under cloud-free conditions. XBAER derived snow properties are compared to other existing satellite products and validated by ground-based/aircraft measurements. Cloud screening is performed by standard XBAER algorithm synergistically using OLCI and SLSTR instruments both onboard Sentinel-3. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) Aerosol Optical Thickness (AOT) and aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a Look-Up-Table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN simulated surface directional reflectances at 0.55 and 1.6 μm. The SSA is derived for a given SGS and SPS pair. XBAER derived SGS, SPS and SSA have been validated using in-situ measurements from the recent campaign SnowEx17 during February 2017. The comparison of the retrieved SGS with the in-situ data shows a relative difference between XBAER-derived SGS and SnowEx17 measured SGS of less than 4 %. The difference between XBAER-derived SSA and SnowEx17 measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonable-explained by the SnowEx17 observed snow particle shapes. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R = 0.82 and R = 0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability of the aircraft track of PAMARCMiP campaign. The comparison between XBAER-derived SGS results and MODIS Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonable-explained by campaign-based and laboratory investigations, indicating reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities of XBAER-derived SGS and SSA over both Greenland and Arctic-wide agree with the snow metamorphism process.

scholarly journals Contributions of weakly coupled data assimilation-based land initialization to interannual predictability of summer climate over Europe

2021 ◽  
pp. 1-55
Author(s):  
Pengfei Shi ◽  
Bin Wang ◽  
Yujun He ◽  
Hui Lu ◽  
Kun Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Potential Predictability ◽  
Climate System Model ◽  
Climate Predictability ◽  
Summer Climate ◽  
Weakly Coupled ◽  
Coupled Data Assimilation

AbstractLand surface is a potential source of climate predictability over the Northern Hemisphere mid-latitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a DRP-4DVar-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe.

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