Consistent assimilation of multiple data streams in a carbon cycle data assimilation system
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-10-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/9/3569/2016/gmd-9-3569-2016.pdf |
| Summary: | 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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| ISSN: | 1991-959X 1991-9603 |