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| Titel |
Downscaling the Local Weather Above Glaciers in Complex Topography |
| VerfasserIn |
Johannes Horak, Marlis Hofer, Ethan Gutmann, Alexander Gohm, Mathias Rotach |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250151671
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| Publikation (Nr.) |
 EGU/EGU2017-16417.pdf |
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| Zusammenfassung |
| Glaciers have experienced a substantial ice-volume loss during the 20th century. To study
their response to climate change, process-based glacier mass-balance models (PBGMs) are
employed, which require a faithful representation of the state of the atmosphere above the
glacier at high spatial and temporal resolution.
Glaciers are usually located in complex topography where weather stations are scarce or not
existent at all due to the remoteness of such sites and the associated high cost of maintenance.
Furthermore. the effective resolution of global circulation models is too large to adequately
capture the local topography and represent local weather, which is prerequisite for
atmospheric input used by PBGMs.
Dynamical downscaling is a physically consistent but computationally expensive approach to
bridge the scale gap between GCM output and input needed by PBGMs, while
statistical downscaling is faster but requires measurements for training. Both methods
have their merits, however, a computationally frugal approach that does not rely on
measurements is desirable, especially for long term studies of glacier response to future
climate.
In this study the intermediate complexity atmospheric research model (ICAR) is employed
(Gutmann et al., 2016). It simplifies the wind field physics by relying on analytical solutions
derived with linear theory. ICAR then advects atmospheric quantities within this wind field.
This allows for computationally fast downscaling and yields a physically consistent set of
atmospheric variables.
First results obtained from downscaling air temperature, precipitation amount, relative
humidity and wind speed to 4 × 4 km2 are presented. Preliminary ICAR is applied for a six
month simulation period during five years and evaluated for three domains located in very
distinct climates, namely the Southern Alps of New Zealand, the Cordillera Blanca in Peru
and the European Alps using ERA Interim reanalysis data (ERAI) as forcing data
set.
The evaluation is based on determining the added value of the ICAR simulations - with ERAI
output as a reference – in representing the local-scale weather measured at several
automatic weather stations. For precipitation amount in particular, data by the Global
Precipitation Measurement project are used in a fuzzy verification approach. The results
indicate that ICAR provides added value for the Southern Alps of New Zealand in
the case of precipitation and relative humidity, for the Cordillera Blanca and the
European Alps for wind speed and, at certain locations in the European Alps, for
precipitation.
In order to more comprehensively investigate the physical plausibility of skill obtained for
specific weather situations, the spatio-temporal evolution of the wind field resulting from the
ICAR dynamics is analysed for individual case studies. To the authors knowledge this is the
first study that specifically investigates the multi-variable consistency of ICAR for different
climates, an important prerequisite for all applications which require multi-variable or
multi-site input.
References:
Gutmann, E., Barstad, I., Clark, M., Arnold, J., and Rasmussen, R. (2016). The Intermediate
Complexity Atmospheric Research Model (ICAR). Journal of Hydrometeorology, 17(3),
957-973. |
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