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| Titel |
Methodological aspects of a pattern-scaling approach to produce global fields of monthly means of daily maximum and minimum temperature |
| VerfasserIn |
S. Kremser, G. E. Bodeker, J. Lewis |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 1 ; Nr. 7, no. 1 (2014-01-30), S.249-266 |
| Datensatznummer |
250115541
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| Publikation (Nr.) |
 copernicus.org/gmd-7-249-2014.pdf |
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| Zusammenfassung |
| A Climate Pattern-Scaling Model (CPSM) that simulates global patterns of
climate change, for a prescribed emissions scenario, is described. A CPSM
works by quantitatively establishing the statistical relationship between a
climate variable at a specific location (e.g. daily maximum surface
temperature, Tmax) and one or more predictor time series (e.g. global
mean surface temperature, Tglobal) – referred to as the
"training" of the CPSM. This training uses a regression model to derive fit
coefficients that describe the statistical relationship between the predictor
time series and the target climate variable time series. Once that
relationship has been determined, and given the predictor time series for any
greenhouse gas (GHG) emissions scenario, the change in the climate variable
of interest can be reconstructed – referred to as the "application" of the
CPSM. The advantage of using a CPSM rather than a typical atmosphere–ocean
global climate model (AOGCM) is that the predictor time series required by
the CPSM can usually be generated quickly using a simple climate model (SCM)
for any prescribed GHG emissions scenario and then applied to generate global
fields of the climate variable of interest. The training can be performed
either on historical measurements or on output from an AOGCM. Using model
output from 21st century simulations has the advantage that the climate
change signal is more pronounced than in historical data and therefore a more
robust statistical relationship is obtained. The disadvantage of using AOGCM
output is that the CPSM training might be compromised by any AOGCM
inadequacies. For the purposes of exploring the various methodological
aspects of the CPSM approach, AOGCM output was used in this study to train
the CPSM. These investigations of the CPSM methodology focus on monthly mean
fields of daily temperature extremes (Tmax and Tmin). The
methodological aspects of the CPSM explored in this study include
(1) investigation of the advantage gained in having five predictor time
series over having only one predictor time series, (2) investigation of the
time dependence of the fit coefficients and (3) investigation of the
dependence of the fit coefficients on GHG emissions scenario. Key conclusions
are (1) overall, the CPSM trained on simulations based on the Representative
Concentration Pathway (RCP) 8.5 emissions scenario is able to reproduce AOGCM
simulations of Tmax and Tmin based on predictor time series from
an RCP 4.5 emissions scenario; (2) access to hemisphere average land and
ocean temperatures as predictors improves the variance that can be explained,
particularly over the oceans; (3) regression model fit coefficients derived
from individual simulations based on the RCP 2.6, 4.5 and 8.5 emissions
scenarios agree well over most regions of the globe (the Arctic is the
exception); (4) training the CPSM on concatenated time series from an
ensemble of simulations does not result in fit coefficients that explain
significantly more of the variance than an approach that weights results
based on single simulation fits; and (5) the inclusion of a linear time
dependence in the regression model fit coefficients improves the variance
explained, primarily over the oceans. |
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