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| Titel |
Reliable, robust and realistic: the three R's of next-generation land-surface modelling |
| VerfasserIn |
I. C. Prentice, X. Liang, B. E. Medlyn, Y.-P. Wang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 15, no. 10 ; Nr. 15, no. 10 (2015-05-29), S.5987-6005 |
| Datensatznummer |
250119768
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| Publikation (Nr.) |
 copernicus.org/acp-15-5987-2015.pdf |
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| Zusammenfassung |
| Land-surface models (LSMs) are increasingly called upon to represent not
only the exchanges of energy, water and momentum across the land–atmosphere
interface (their original purpose in climate models), but also how
ecosystems and water resources respond to climate, atmospheric environment,
land-use and land-use change, and how these responses in turn influence
land–atmosphere fluxes of carbon dioxide (CO2), trace gases and other
species that affect the composition and chemistry of the atmosphere.
However, the LSMs embedded in state-of-the-art climate models differ in how
they represent fundamental aspects of the hydrological and carbon cycles,
resulting in large inter-model differences and sometimes faulty predictions.
These "third-generation" LSMs respect the close coupling of the carbon and
water cycles through plants, but otherwise tend to be under-constrained, and
have not taken full advantage of robust hydrological parameterizations that
were independently developed in offline models. Benchmarking, combining
multiple sources of atmospheric, biospheric and hydrological data, should be
a required component of LSM development, but this field has been relatively
poorly supported and intermittently pursued. Moreover, benchmarking alone is
not sufficient to ensure that models improve. Increasing complexity may
increase realism but decrease reliability and robustness, by increasing the
number of poorly known model parameters. In contrast, simplifying the
representation of complex processes by stochastic parameterization (the
representation of unresolved processes by statistical distributions of
values) has been shown to improve model reliability and realism in both
atmospheric and land-surface modelling contexts. We provide examples for
important processes in hydrology (the generation of runoff and flow routing
in heterogeneous catchments) and biology (carbon uptake by species-diverse
ecosystems). We propose that the way forward for next-generation complex
LSMs will include: (a) representations of biological and hydrological
processes based on the implementation of multiple internal constraints; (b)
systematic application of benchmarking and data assimilation techniques to
optimize parameter values and thereby test the structural adequacy of
models; and (c) stochastic parameterization of unresolved variability,
applied in both the hydrological and the biological domains. |
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