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High resolution modeling of CO2 over Europe: Implications for representation errors of satellite retrievals

Pillai, D.; Gerbig, C.; Marshall, J.; Ahmadov, R.; Kretschmer, R.; Koch, T. and Karstens, U. LU (2010) In Atmospheric Chemistry and Physics 10(1). p.83-94
Abstract

Satellite retrievals for column CO2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO2 from the high resolution modeling framework WRF-VPRM, which links CO2 fluxes from a diagnostic biosphere model... (More)

Satellite retrievals for column CO2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO2 from the high resolution modeling framework WRF-VPRM, which links CO2 fluxes from a diagnostic biosphere model to a weather forecasting model at 10×10 km2 horizontal resolution. Sub-grid variability of column averaged CO2, i.e. the variability not resolved by global models, reached up to 1.2 ppm with a median value of 0.4 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO 2 fluxes as well as resolved mixing ratio of CO2, a linear model can be formulated that could explain about 50% of the spatial patterns in the systematic (bias or correlated error) component of representation error in column and near-surface CO2 during day-and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.

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author
publishing date
type
Contribution to journal
publication status
published
subject
in
Atmospheric Chemistry and Physics
volume
10
issue
1
pages
12 pages
publisher
Copernicus Gesellschaft Mbh
external identifiers
  • scopus:73949131486
ISSN
1680-7316
DOI
language
English
LU publication?
no
id
b553e73b-3abe-4192-bbab-6021cc01fbaf
date added to LUP
2016-10-13 18:35:56
date last changed
2018-06-10 05:11:13
@article{b553e73b-3abe-4192-bbab-6021cc01fbaf,
  abstract     = {<p>Satellite retrievals for column CO<sub>2</sub> with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO<sub>2</sub> via inverse techniques. However, the spatial scale mismatch between remotely sensed CO<sub>2</sub> and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO<sub>2</sub> from the high resolution modeling framework WRF-VPRM, which links CO<sub>2</sub> fluxes from a diagnostic biosphere model to a weather forecasting model at 10&amp;times;10 km<sup>2</sup> horizontal resolution. Sub-grid variability of column averaged CO<sub>2</sub>, i.e. the variability not resolved by global models, reached up to 1.2 ppm with a median value of 0.4 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO <sub>2</sub> fluxes as well as resolved mixing ratio of CO<sub>2</sub>, a linear model can be formulated that could explain about 50% of the spatial patterns in the systematic (bias or correlated error) component of representation error in column and near-surface CO<sub>2</sub> during day-and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.</p>},
  author       = {Pillai, D. and Gerbig, C. and Marshall, J. and Ahmadov, R. and Kretschmer, R. and Koch, T. and Karstens, U.},
  issn         = {1680-7316},
  language     = {eng},
  number       = {1},
  pages        = {83--94},
  publisher    = {Copernicus Gesellschaft Mbh},
  series       = {Atmospheric Chemistry and Physics},
  title        = {High resolution modeling of CO2 over Europe: Implications for representation errors of satellite retrievals},
  url          = {http://dx.doi.org/},
  volume       = {10},
  year         = {2010},
}