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| Titel |
What is the importance of climate model bias when projecting the impacts of climate change on land surface processes? |
| VerfasserIn |
M. Liu, K. Rajagopalan, S. H. Chung, X. Jiang, J. Harrison, T. Nergui, A. Guenther, C. Miller, J. Reyes, C. Tague, J. Choate, E. P. Salathé, C. O. Stöckle, J. C. Adam |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 11, no. 10 ; Nr. 11, no. 10 (2014-05-16), S.2601-2622 |
| Datensatznummer |
250117412
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| Publikation (Nr.) |
 copernicus.org/bg-11-2601-2014.pdf |
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| Zusammenfassung |
Regional climate change impact (CCI) studies have widely involved
downscaling and bias correcting (BC) global climate model (GCM)-projected
climate for driving land surface models. However, BC may cause uncertainties
in projecting hydrologic and biogeochemical responses to future climate due
to the impaired spatiotemporal covariance of climate variables and a
breakdown of physical conservation principles. Here we quantify the impact
of BC on simulated climate-driven changes in water variables
(evapotranspiration (ET), runoff, snow water equivalent (SWE), and water
demand for irrigation), crop yield, biogenic volatile organic compounds
(BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN)
export over the Pacific Northwest (PNW) region. We also quantify the impacts
on net primary production (NPP) over a small watershed in the region (HJ-Andrews). Simulation results from the coupled ECHAM5–MPI-OM model with A1B
emission scenario were first dynamically downscaled to 12 km resolution with
the WRF model. Then a quantile-mapping-based statistical downscaling model
was used to downscale them into 1/16° resolution daily climate data
over historical and future periods. Two climate data series were generated,
with bias correction (BC) and without bias correction (NBC). Impact models
were then applied to estimate hydrologic and biogeochemical responses to
both BC and NBC meteorological data sets. These impact models include a
macroscale hydrologic model (VIC), a coupled cropping system model
(VIC-CropSyst), an ecohydrological model (RHESSys), a biogenic emissions model
(MEGAN), and a nutrient export model (Global-NEWS).
Results demonstrate that the BC and NBC climate data provide consistent
estimates of the climate-driven changes in water fluxes (ET, runoff, and
water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop
yield, and river DIN export over the PNW domain. However, significant
differences rise from projected SWE, crop yield from dry lands, and HJ-Andrews's ET between BC and NBC data. Even though BC post-processing has no
significant impacts on most of the studied variables when taking PNW as a
whole, their effects have large spatial variations and some local areas are
substantially influenced. In addition, there are months during which BC and
NBC post-processing produces significant differences in projected changes,
such as summer runoff. Factor-controlled simulations indicate that BC
post-processing of precipitation and temperature both substantially
contribute to these differences at regional scales.
We conclude that there are trade-offs between using BC climate data for
offline CCI studies versus directly modeled climate data. These trade-offs
should be considered when designing integrated modeling frameworks for
specific applications; for example, BC may be more important when considering
impacts on reservoir operations in mountainous watersheds than when
investigating impacts on biogenic emissions and air quality, for which VOCs
are a primary indicator. |
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