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

MacBean, Natasha; Peylin, Philippe; Chevallier, Frédéric; Scholze, Marko LU and Schürmann, Gregor (2016) In Geoscientific Model Development 9(10). p.3569-3588
Abstract

Data assimilation methods provide a rigorous statistical framework for constraining parametric uncertainty in land surface models (LSMs), which in turn helps to improve their predictive capability and to identify areas in which the representation of physical processes is inadequate. 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 for the carbon cycle component of LSMs. We give particular consideration to the assumptions associated with the type of inversion... (More)

Data assimilation methods provide a rigorous statistical framework for constraining parametric uncertainty in land surface models (LSMs), which in turn helps to improve their predictive capability and to identify areas in which the representation of physical processes is inadequate. 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 for the carbon cycle component of LSMs. We give particular consideration to the assumptions associated with the type of inversion algorithm that are typically used when optimising global LSMs-namely, Gaussian error distributions and linearity in the model dynamics. We explore the effect of biases and inconsistencies between the observations and the model (resulting in non-Gaussian error distributions), and we examine the difference between a simultaneous assimilation (in which all data streams are included in one optimisation) and a step-wise approach (in which each data stream is assimilated sequentially) in the presence of non-linear model dynamics. In addition, we perform a preliminary investigation into the impact of correlated errors between two data streams for two cases, both when the correlated observation errors are included in the prior observation error covariance matrix, and when the correlated errors are ignored. We demonstrate these challenges 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. Finally, we provide some perspectives and advice to other land surface modellers wishing to use multiple data streams to constrain their model parameters.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Geoscientific Model Development
volume
9
issue
10
pages
20 pages
publisher
Copernicus Gesellschaft Mbh
external identifiers
  • scopus:84990039980
  • wos:000385387500003
ISSN
1991-959X
DOI
10.5194/gmd-9-3569-2016
language
English
LU publication?
yes
id
283998f0-e37d-4980-be6d-cb6b5fec7965
date added to LUP
2016-10-21 08:29:01
date last changed
2017-08-06 05:12:25
@article{283998f0-e37d-4980-be6d-cb6b5fec7965,
  abstract     = {<p>Data assimilation methods provide a rigorous statistical framework for constraining parametric uncertainty in land surface models (LSMs), which in turn helps to improve their predictive capability and to identify areas in which the representation of physical processes is inadequate. 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 for the carbon cycle component of LSMs. We give particular consideration to the assumptions associated with the type of inversion algorithm that are typically used when optimising global LSMs-namely, Gaussian error distributions and linearity in the model dynamics. We explore the effect of biases and inconsistencies between the observations and the model (resulting in non-Gaussian error distributions), and we examine the difference between a simultaneous assimilation (in which all data streams are included in one optimisation) and a step-wise approach (in which each data stream is assimilated sequentially) in the presence of non-linear model dynamics. In addition, we perform a preliminary investigation into the impact of correlated errors between two data streams for two cases, both when the correlated observation errors are included in the prior observation error covariance matrix, and when the correlated errors are ignored. We demonstrate these challenges 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. Finally, we provide some perspectives and advice to other land surface modellers wishing to use multiple data streams to constrain their model parameters.</p>},
  author       = {MacBean, Natasha and Peylin, Philippe and Chevallier, Frédéric and Scholze, Marko and Schürmann, Gregor},
  issn         = {1991-959X},
  language     = {eng},
  month        = {10},
  number       = {10},
  pages        = {3569--3588},
  publisher    = {Copernicus Gesellschaft Mbh},
  series       = {Geoscientific Model Development},
  title        = {Consistent assimilation of multiple data streams in a carbon cycle data assimilation system},
  url          = {http://dx.doi.org/10.5194/gmd-9-3569-2016},
  volume       = {9},
  year         = {2016},
}