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Titel |
Errors in modelling carbon turnover induced by temporal temperature aggregation |
VerfasserIn |
Lutz Weihermueller, Sander Huisman, Alexander Graf, Michael Herbst, Harry Vereecken |
Konferenz |
EGU General Assembly 2010
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Medientyp |
Artikel
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Sprache |
Englisch
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Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 12 (2010) |
Datensatznummer |
250032498
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Zusammenfassung |
The response of soil carbon decomposition to climate change is of great importance because
terrestrial soils act as the largest carbon sink worldwide. This large carbon pool interacts
strongly with the atmosphere and vegetation, and even small relative changes in
organic storage in the soil could constitute a significant feedback effect on greenhouse
gases in the atmosphere. CO2 modelling approaches seem to be a powerful tool to
describe the influence of changes in soil temperature and soil water content on carbon
decomposition.
It is well established that soil temperature is the most important factor driving the
decomposition of soil carbon. Most of the relationships between soil temperature and total
respiration were derived from physicochemical principles, such as the Arrhenius or from
field and laboratory experiments, The various temperature functions show similar
trends: a nonlinear positive direct relationship between temperature and respiration
irrespective of the time scale used for their development. Most modeling studies use
monthly averaged input data, especially when they are focused on the analysis of
long-term experiments and the impact of climate change on soil organic carbon (SOC)
stocks. To improve the understanding of short-term changes in soil CO2 efflux,
various authors compared instantaneous CO2 efflux data with results from numerical
models.
It is well established that the propagation of a mean temperature through a non-linear
function will not provide the same results as when the original data underlying such a mean
value are propagated through the same non-linear function before calculating the mean.
Therefore, long-term models using monthly temperature data will provide different results
when daily temperature data are used. Similarly, short-term modeling using hourly instead of
daily input data will provide different results when there is considerable variation in hourly
temperature. Unfortunately, the implications of choosing a particular temperature input
data resolution for predicting soil CO2 efflux or soil carbon loss have not been
quantified yet. Therefore, the aim of this study is to analyze the effect of different
temporal resolution of temperature input data on predicted CO2 efflux and carbon
stocks.
Additionally, we evaluate whether existing scaling techniques to derive hourly
temperature data from mean daily temperature and amplitude can be used to overcome the
decrease in accuracy associated with using daily temperature input data only.
The results indicate that averaging from hourly to daily or monthly temperatures will lead
to relative errors larger than 4 % per year for cumulative CO2 efflux, which is similar to the
measurement error for carbon stocks or chamber measurements. Instantaneous CO2 fluxes
are even more affected by temperature averaging. Daily and monthly averaging will lead to
estimation errors exceeding 20% and 25.8%, respectively. Deviations in predicted
instantaneous CO2 efflux using aggregated and reference temperature time series were larger
than 10% for 23% and 55% of the time for daily and monthly averaging, respectively. It is
also shown that a constant or daily variable temperature amplitude for rescaling
daily average temperature did not decrease the error in the predicted CO2 fluxes
when using daily or monthly mean temperature instead of hourly data. Therefore,
instantaneous fluxes are only accurately predicted when hourly temperature input is
used. |
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