Work Description

Title: Data in support of the study "Using space-based observations and Lagrangian modeling to evaluate urban carbon dioxide emissions in the Middle East" Open Access Deposited

h
Attribute Value
Methodology
  • Column-averaged dry-air mole fraction of CO2 (XCO2) data from the OCO-2 satellite, for days and cities of interest, were binned by latitude at a resolution of 0.1 degrees. Using the X-STILT model, we simulated corresponding data points. Simulated enhancements due to near-field emissions were found by convolving X-STILT footprints with global CO2 emissions inventory data. The background contributions to total XCO2 were estimated using CarbonTracker data, with biases corrected through comparison to observed OCO-2 background values. Overpass-level scaling factors for each inventory were estimated by dividing the sum of OCO-2 enhancements for each overpass by the sum of the modeled enhancements. These overpass-level scaling factors were combined using a bootstrap method to find city-level mean scaling factors. The city-level mean scaling factors were used to estimate scaled emissions for each city. 90% confidence intervals were also evaluated using the bootstrap method. City-level estimates were summed.
Description
  • This data set supports a study that seeks to evaluate global fossil fuel CO2 emissions inventory representations of CO2 emissions of five cities in the Middle East, and assess the ability of satellite observations to inform this evaluation. Improved observational understanding of urban CO2 emissions, a large and dynamic global source of fossil CO2, can provide essential insights for both carbon cycle science and mitigation decision making. In this study we compare three distinct global CO2 emissions inventory representations of urban CO2 emissions for five Middle Eastern cities (Riyadh, Mecca, Tabuk, Jeddah, and Baghdad) and use independent satellite observations from the Orbiting Carbon Observatory-2 (OCO-2) satellite to evaluate the inventory representations of afternoon emissions. We use the column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model to account for atmospheric transport and link emissions to observations. We compare XCO2 simulations with observations to determine optimum inventory scaling factors. Applying these factors, we find that the average summed emissions for all five cities are 100 MtC/y (50-151, 90% CI), which is 2.0 (1.0, 3.0) times the average prior inventory magnitudes. The total adjustment of the emissions of these cities comes out to ~7% (0%, 14%) of total Middle Eastern emissions (~700 MtC/y). We find our results to be insensitive to the prior spatial distributions in inventories of the cities’ emissions, facilitating robust quantitative assessments of urban emission magnitudes without accurate high-resolution gridded inventories.

  • There are three files included in this data set, and all data are in tab-delimited form. The first file, xco2_lat.zip, contains 26 separate text files, each named by the city and date of the corresponding OCO-2 overpass. Each of these 26 files includes overpass-specific data, with modeled and observed XCO2 values binned by 0.1 degree of latitude. The file overpass_scaling_factors.txt provides the scaling factors for each overpass used in this study. The file city_estimates.txt provides the scaled emissions estimates for each city (or sum of cities) as well as the lower and upper bounds of the 90% confidence intervals, for each inventory.
Creator
Depositor
  • egyang@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
  • National Aeronautics and Space Administration (NASA)
ORSP grant number
  • F039072, F052836
Keyword
Citations to related material
  • Yang, E. G., Kort, E. A., Wu, D., Lin, J. C., Oda, T., Ye, X., & Lauvaux, T. (2020). Using space‐based observations and Lagrangian modeling to evaluate urban carbon dioxide emissions in the Middle East. Journal of Geophysical Research: Atmospheres, 125, e2019JD031922. https://doi.org/10.1029/2019JD031922
Resource type
Curation notes
  • Updated the citation to the article in the "citation to related material" field on March 27, 2020
Last modified
  • 11/17/2022
Published
  • 03/20/2020
DOI
  • https://doi.org/10.7302/rbjy-7e98
License
To Cite this Work:
Yang, E. G., Kort, E. A., Wu, D., Lin, J. C., Oda, T., Ye, X., Lauvaux, T. (2020). Data in support of the study "Using space-based observations and Lagrangian modeling to evaluate urban carbon dioxide emissions in the Middle East" [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/rbjy-7e98

Relationships

This work is not a member of any user collections.

Files (Count: 4; Size: 107 KB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
readme.txt 2020-03-16 2020-03-16 6.12 KB Open Access
xco2_lat.zip 2020-03-10 2020-03-10 97.8 KB Open Access
overpass_scaling_factors.txt 2020-03-10 2020-03-10 2.68 KB Open Access
city_estimates.txt 2020-03-10 2020-03-10 649 Bytes Open Access

=================
README
=================

Purpose:
This is the README and guide for the data repository referenced in
the publication ‘Using space-based observations and Lagrangian
modeling to evaluate urban carbon dioxide emissions in the Middle
East’ by E. G. Yang, E. A. Kort, D. Wu, J. C. Lin, T. Oda, X. Ye,
and T. Lauvaux (2020).


Repository Created by:
E. G. Yang, University of Michigan.
Department of Climate & Space Sciences & Engineering.
March 15, 2020

=================
Contact
=================

Emily G. Yang (egyang@umich.edu); Eric A. Kort (eakort@umich.edu)

=================
Research summary
=================

Improved observational understanding of urban CO2 emissions, a large and
dynamic global source of fossil CO2, can provide essential insights for both
carbon cycle science and mitigation decision making. Here we compare
representations of urban CO2 emissions by three distinct global CO2 emissions
inventories (FFDAS, ODIAC, and EDGAR) for five Middle Eastern cities (Riyadh,
Mecca, Tabuk, Jeddah, and Baghdad) and use independent satellite observations
from the Orbiting Carbon Observatory-2 (OCO-2) satellite to evaluate the
inventory representations of afternoon emissions. We use the column version of
the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model to account
for atmospheric transport and link emissions to observations. The X-STILT
model outputs are coupled with the emissions inventories to simulate XCO2.
We compare these XCO2 simulations with the satellite observations to determine
optimum inventory scaling factors. Applying these factors, we find that the
average summed emissions for all five cities are 100 MtC/y (50-151, 90% CI),
which is 2.0 (1.0, 3.0) times the average prior inventory magnitudes. The total
adjustment of the emissions of these cities comes out to ~7% (0%, 14%) of total
Middle Eastern emissions (~700 MtC/y). We find our results to be insensitive to
the prior spatial distributions in inventories of the cities’ emissions,
facilitating robust quantitative assessments of urban emission magnitudes
without accurate high-resolution gridded inventories.

=================
Files description
=================

For all data files, data are provided in tab-delimited format, with missing
values denoted as NA.

There are 26 files included in xco2_lat.zip. Each is named by the city
and date of the corresponding OCO-2 overpass. All units are in ppm
except for the first column, lat.

Variables included in the 26 datafiles are the following:
lat: latitudinal bin (degrees north)
tot_oco2: full XCO2 from OCO-2 binned by 0.1 degree
tot_ffdas: full modeled XCO2, sum of columns tot_bg & enhance_ffdas
tot_odiac: full modeled XCO2, sum of columns tot_bg & enhance_odiac
tot_edgar: full modeled XCO2, sum of columns tot_bg & enhance_edgar
tot_agg: full modeled XCO2, sum of columns tot_bg & enhance_agg
mod_bg: modeled background using CarbonTracker
tot_bg: sum of mod_bg and bias correction using OCO-2 tails
enhance_oco2: OCO-2 enhancements, difference of tot_oco2 and tot_bg
enhance_ffdas: simulated enhancement using FFDAS
enhance_odiac: simulated enhancement using ODIAC
enhance_edgar simulated enhancement using EDGAR
enhance_agg simulated enhancement using aggregated ODIAC
unc_oco2 uncertainty of OCO-2 data

The file overpass_scaling_factors.txt provides the scaling factors for each
overpass used in this study. Rows are named by the overpass city and date, and
the variables are the following:
ffdas_sf FFDAS scaling factor
odiac_sf ODIAC scaling factor
edgar_sf EDGAR scaling factor
agg_sf aggregated ODIAC scaling factor
unc fractional uncertainty

The file city_estimates.txt provides the scaled emissions estimates for each
city (or sum of cities) as well as the lower and upper bounds of the 90%
confidence intervals, for each inventory. Rows are named by city name or
"summed". The variables are the following (all in units of MtC/y):
ffdas_est FFDAS scaled estimate
ffdas_min FFDAS lower bound
ffdas_max FFDAS upper bound
odiac_est ODIAC scaled estimate
odiac_min ODIAC lower bound
odiac_max ODIAC upper bound
edgar_est EDGAR scaled estimate
edgar_min EDGAR lower bound
edgar_max EDGAR upper bound
agg_est aggregated ODIAC estimate
agg_min aggregated ODIAC lower bound
agg_max aggregated ODIAC upper bound

=================
Data sources
=================
The OCO-2 data are hosted at the OCO-2 data archive maintained at the NASA
Goddard Earth Science Data and Information Services Center, and can be found
at: https://disc.gsfc.nasa.gov/datasets?project=OCO. Version 8 data acquired
on November 01, 2017.
The most recent version of FFDAS (v2.2) data can be found at:
http://ffdas.rc.nau.edu/Data.html. Version 2014b data were provided by Kevin
Gurney on September 26, 2016.
The ODIAC emission data product is archived at the Center for Global
Environmental Research, National Institute for Environmental Studies (NIES),
Japan (http://db.cger.nies.go.jp/dataset/ODIAC/DL_odiac2017.html). Version
ODIAC2017 acquired on January 08, 2018.
EDGAR is provided and archived by the European Commission, Joint Research
Centre (JRC)/Netherlands Environmental Assessment Agency (PBL)
(http://edgar.jrc.ec.europa.eu/overview.php?v=432_GHG&SECURE=123). Version
4.3.2 acquired on January 09, 2018.

=================
Related publication
=================

Yang, E. G., Kort, E. A., Wu, D., Lin, J. C., Oda, T., Ye, X., & Lauvaux, T.
(2020). Using space-based observations and Lagrangian modeling to evaluate
urban carbon dioxide emissions in the Middle East. Journal of Geophysical
Research: Atmospheres, XXX(XXX), XXXXX. https://doi.org/XXXX. Forthcoming.

=================
Recommended citation for data
=================

Yang, E. G., Kort, E. A., Wu, D., Lin, J. C., Oda, T., Ye, X., & Lauvaux, T.
(2020). Using space-based observations and Lagrangian modeling to evaluate
urban carbon dioxide emissions in the Middle East [Data set]. Retrieved from
University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/rbjy-7e98.

Download All Files (To download individual files, select them in the “Files” panel above)

Best for data sets < 3 GB. Downloads all files plus metadata into a zip file.

Files are ready   Download Data from Globus
Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus

Remediation of Harmful Language

The University of Michigan Library aims to describe its collections in a way that respects the people and communities who create, use, and are represented in them. We encourage you to contact us anonymously if you encounter harmful or problematic language in catalog records or finding aids. More information about our policies and practices is available at Remediation of Harmful Language.