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| Titel |
Investigating the role of background and observation error correlations in improving a model forecast of forest carbon balance using four dimensional variational data assimilation. |
| VerfasserIn |
Ewan Pinnington, Eric Casella, Sarah Dance, Amos Lawless, James Morison, Nancy Nichols, Matthew Wilkinson, Tristan Quaife |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250126463
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| Publikation (Nr.) |
 EGU/EGU2016-6189.pdf |
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| Zusammenfassung |
| Forest ecosystems play an important role in sequestering human emitted carbon-dioxide from
the atmosphere and therefore greatly reduce the effect of anthropogenic induced climate
change. For that reason understanding their response to climate change is of great
importance. Efforts to implement variational data assimilation routines with functional
ecology models and land surface models have been limited, with sequential and Markov
chain Monte Carlo data assimilation methods being prevalent. When data assimilation has
been used with models of carbon balance, background “prior” errors and observation errors
have largely been treated as independent and uncorrelated. Correlations between background
errors have long been known to be a key aspect of data assimilation in numerical weather
prediction. More recently, it has been shown that accounting for correlated observation errors
in the assimilation algorithm can considerably improve data assimilation results and
forecasts. In this paper we implement a 4D-Var scheme with a simple model of forest carbon
balance, for joint parameter and state estimation and assimilate daily observations
of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux
site in Hampshire, UK. We then investigate the effect of specifying correlations
between parameter and state variables in background error statistics and the effect of
specifying correlations in time between observation error statistics. The idea of
including these correlations in time is new and has not been previously explored
in carbon balance model data assimilation. In data assimilation, background and
observation error statistics are often described by the background error covariance
matrix and the observation error covariance matrix. We outline novel methods for
creating correlated versions of these matrices, using a set of previously postulated
dynamical constraints to include correlations in the background error statistics and a
Gaussian correlation function to include time correlations in the observation error
statistics. The methods used in this paper will allow the inclusion of time correlations
between many different observation types in the assimilation algorithm, meaning that
previously neglected information can be accounted for. In our experiments we compared
the results using our new correlated background and observation error covariance
matrices and those using diagonal covariance matrices. We found that using the new
correlated matrices reduced the root mean square error in the 14 year forecast of daily
NEE by 44 % decreasing from 4.22 g C m−2 day−1 to 2.38 g C m−2 day−1. |
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