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Using climate impacts indicators to evaluate climate modelensembles: temperature suitability of premium winegrapecultivation in the United States
Noah S. Diffenbaugh • Martin Scherer
Received: 31 October 2011 / Accepted: 20 April 2012 / Published online: 18 May 2012
� Springer-Verlag 2012
Abstract We explore the potential to improve under-
standing of the climate system by directly targeting climate
model analyses at specific indicators of climate change
impact. Using the temperature suitability of premium
winegrape cultivation as a climate impacts indicator, we
quantify the inter- and intra-ensemble spread in three cli-
mate model ensembles: a physically uniform multi-mem-
ber ensemble consisting of the RegCM3 high-resolution
climate model nested within the NCAR CCSM3 global
climate model; the multi-model NARCCAP ensemble
consisting of single realizations of multiple high-resolution
climate models nested within multiple global climate
models; and the multi-model CMIP3 ensemble consisting
of realizations of multiple global climate models. We find
that the temperature suitability for premium winegrape
cultivation is substantially reduced throughout the high-
value growing areas of California and the Columbia Valley
region (eastern Oregon and Washington) in all three
ensembles in response to changes in temperature projected
for the mid-twenty first century period. The reductions in
temperature suitability are driven primarily by projected
increases in mean growing season temperature and occur-
rence of growing season severe hot days. The intra-
ensemble spread in the simulated climate change impact
is smaller in the single-model ensemble than in the
multi-model ensembles, suggesting that the uncertainty
arising from internal climate system variability is smaller
than the uncertainty arising from climate model formula-
tion. In addition, the intra-ensemble spread is similar in the
NARCCAP nested climate model ensemble and the CMIP3
global climate model ensemble, suggesting that the
uncertainty arising from the model formulation of fine-
scale climate processes is not smaller than the uncertainty
arising from the formulation of large-scale climate pro-
cesses. Correction of climate model biases substantially
reduces both the inter- and intra-ensemble spread in
projected climate change impact, particularly for the multi-
model ensembles, suggesting that—at least for some sys-
tems—the projected impacts of climate change could be
more robust than the projected climate change. Extension
of this impacts-based analysis to a larger suite of impacts
indicators will deepen our understanding of future climate
change uncertainty by focusing on the climate phenomena
that most directly influence natural and human systems.
Keywords Climate change � Climate impacts � CMIP3 �NARCCAP � RegCM3 � Winegrape
1 Introduction
Although it is clearly established that increasing concen-
trations of atmospheric greenhouse gases increase the
radiative forcing of the climate system and the global-mean
surface temperature of the planet (IPCC 2007), there
remain a number of important uncertainties about the
response of the climate system to elevated greenhouse
forcing. These uncertainties include the magnitude of the
global-scale temperature response to a given level of
forcing (e.g., Knutti et al. 2008; Knutti and Hegerl 2008;
N. S. Diffenbaugh (&) � M. Scherer
Department of Environmental Earth System Science,
Woods Institute for the Environment, Stanford University,
473 Via Ortega, Stanford, CA 94305-4216, USA
e-mail: [email protected]
N. S. Diffenbaugh
Department of Earth and Atmospheric Sciences,
Purdue Climate Change Research Center,
Purdue University, West Lafayette, IN, USA
123
Clim Dyn (2013) 40:709–729
DOI 10.1007/s00382-012-1377-1
Zickfeld et al. 2009), the response of the large-scale cli-
mate dynamics to changes in the global-scale energy bal-
ance (e.g., Neelin et al. 2006; Yamaguchi and Noda 2006;
Annamalai et al. 2007) and the response of fine-scale cli-
mate dynamics to changes in the large-scale climate
dynamics (e.g., Deque et al. 2005; Diffenbaugh et al. 2005;
Gao et al. 2006; Rauscher et al. 2008; Seneviratne et al.
2010). By influencing the magnitude and/or spatial heter-
ogeneity of climate change, each of these uncertainties
presents challenges not only for predicting future climate
change, but also for predicting the impacts of that climate
change on natural and human systems (Giorgi et al. 2008).
Here we explore the potential to improve understanding
of the climate system by directly targeting analyses of
climate model ensembles at specific indicators of climate
change impact. Although climate impacts are a product of
both the climate system and a host of non-climatic condi-
tions (e.g., Kelly and Adger 2000; Adger et al. 2004; Smit
and Wandel 2006; Diffenbaugh et al. 2007; Ahmed et al.
2009), understanding the sensitivity of natural and human
systems to climate-related stresses requires understanding
of the climate phenomena that determine those stresses, as
well as the uncertainty in the response of those climate
phenomena to changes in radiative forcing. Therefore, by
focusing on the climate phenomena that most directly
influence natural and human systems, impacts indicators
can be used as a probe for analyzing the contribution of
different sources of uncertainty to the spread in future
climate projections.
Climate change uncertainty is often partitioned between
different sources based on the experimental dimensions of
the model ensemble, such as between ‘‘model’’, ‘‘scenario’’
and ‘‘internal variability’’ uncertainty in the case of the
Coupled Model Intercomparison Project (CMIP3) (Meehl
et al. 2007a) atmosphere–ocean general circulation model
(AOGCM) ensemble (Hawkins and Sutton 2009; Hawkins
and Sutton 2010). However, although many studies have
employed multi-model ensembles to analyze potential cli-
mate change impacts (e.g., Maurer 2007; Williams et al.
2007; Ahmed et al. 2009; Loarie et al. 2009; Ahmed et al.
2010; Gao et al. 2011; Pryor and Barthelmie 2011;
Rasmussen et al. 2011), and some have attempted to par-
tition the spread in the projected impacts between climatic
and non-climatic sources (e.g., Nicholls 2004; Parry et al.
2004; Tebaldi and Lobell 2008), partitioning the spread in
the projected impacts between different sources of climate
change uncertainty has received far less attention. Of par-
ticular concern is the contribution of fine-scale climate
processes, which can play a key role in shaping climate at
the spatial and temporal scales that most directly determine
climate impacts, including the frequency and magnitude of
extreme events (e.g., Christensen and Christensen 2003;
Duffy et al. 2003; Diffenbaugh et al. 2005; White et al.
2006; Rauscher et al. 2008; Diffenbaugh and Ashfaq 2010).
Although insight has been gained into the contribution of
fine-scale processes to the spread in nested climate model
ensembles, these systematic multi-model analyses have
been focused on bulk climate metrics that are not imme-
diately translatable into the impacts domain (e.g., Deque
et al. 2005; Marengo et al. 2009).
Impacts-targeted climate model analysis can provide
particular insight into the influence of climate model biases
on the ensemble spread. Assessment of climate model
errors is routine (e.g., Randall et al. 2007), and comparison
of model biases with simulated climate change is common
in the literature (e.g., Christensen et al. 2007). However,
systematic assessment of the influence of these biases on
the simulated climate change is far less common (e.g., Hall
et al. 2008; Pierce et al. 2009; Santer et al. 2009; Ashfaq
et al. 2010a; Ashfaq et al. 2010b; Giorgi and Coppola
2011; Sun et al. 2011). The work that has been undertaken
suggests that climate model biases can substantially influ-
ence the magnitude and even sign of simulated climate
change (Hall et al. 2008; Pierce et al. 2009; Ashfaq et al.
2010a; Ashfaq et al. 2010b), including the simulated
change in the frequency and magnitude of extreme events
(Ashfaq et al. 2010a). Given the importance of critical
thresholds for climate-sensitive systems (e.g., Mote et al.
2005; White et al. 2006; Donner 2011), climate model
biases are likely to be important in shaping the projected
impacts of climate change. However, although investiga-
tion of such impacts clearly acknowledges the importance
of climate model biases (as evidenced by the routine
practice of ‘‘bias correcting’’ climate model fields), the
influence of model biases on the spread in the projected
impacts remains under-explored.
In the present study, we employ the temperature suit-
ability of premium winegrape cultivation as an illustrative
impacts indicator for probing the spread within and
between climate model ensembles. As discussed elsewhere
(e.g., White et al. 2006; Diffenbaugh et al. 2011a), wine-
grape suitability offers an important test case for exploring
the impacts of climate on natural and human systems.
Although premium winegrapes are grown around the
world, cultivation is restricted to a relatively small geo-
graphic area that comprises a narrow temperature space,
with excessive cold and excessive heat both limiting cul-
tivation (White et al. 2006). The narrowness of the suitable
temperature range suggests that global warming could alter
the suitability of premium growing regions if climatic
changes are sufficient to make the new temperature regime
similar to that of areas that do not currently support pre-
mium cultivation (e.g., Hayhoe et al. 2004; Jones et al.
2005; Lobell et al. 2006; White et al. 2006; Webb et al.
2008; Hall and Jones 2009). Likewise, the sensitivity to
both cold and hot conditions creates the possibility for
710 N. S. Diffenbaugh, M. Scherer
123
competing influences of global warming, with warming
potentially expanding suitable area by relieving cold lim-
itation and simultaneously contracting suitable area by
inducing heat limitation. The potential for impacts on
quality at both the hot and cold margins of the overall
suitability range raises important cost/benefit consider-
ations for decision makers (Diffenbaugh et al. 2011a).
Further, changes in the occurrence of extreme temperature
events can create impacts that are not seen in the aggre-
gated seasonal temperature changes (White et al. 2006).
Together these nuances make the temperature suitability of
premium winegrape cultivation a potentially instructive
indicator for quantifying and analyzing the spread in
ensemble climate model experiments.
2 Methods
2.1 Climate model experiments
We analyze three ensemble climate model experiments
(Table 1). The first experiment is the high-resolution nested
climate model experiment generated by Diffenbaugh and
Ashfaq (2010), which we have extended to cover the period
from 1950 through 2099 in the SRES (IPCC 2000) A1B
scenario (Giorgi et al. 2011; Diffenbaugh et al. 2011b). The
ensemble consists of five realizations of the ICTP RegCM3
high-resolution climate model (Pal et al. 2007), each nested
within a different A1B realization of the NCAR CCSM3
global climate model (Collins et al. 2006) (Table 1). The
grid follows that of Diffenbaugh et al. (2005), covering the
full contiguous U.S. with 25-km resolution in the horizontal
and 18 levels in the vertical. The physics options also follow
those of Diffenbaugh et al. (2005), and are identical
between the five ensemble members. The five members are
thus physically uniform, but differ in the large-scale
boundary conditions provided by the respective CCSM3
realizations in which the RegCM3 realizations are nested.
The differences between the five CCSM3 realizations arise
from internal climate system variability, with the five
realizations initialized from different points in the CCSM3
pre-industrial control integration and then prescribed iden-
tical transient atmospheric constituent concentrations from
the mid-nineteenth century through the late-twenty first
Table 1 Climate model ensembles
Model realization Nested model Global model Resolution of atmosphere (km or degrees) Source
RegCM3 nested model ensemble (SRES A1B scenario)
RegCM3_bES RegCM3 ncar_ccsm3_0 25 D11a
RegCM3_c RegCM3 ncar_ccsm3_0 25 D11
RegCM3_e RegCM3 ncar_ccsm3_0 25 D11
RegCM3_fES RegCM3 ncar_ccsm3_0 25 D11
RegCM3_gES RegCM3 ncar_ccsm3_0 25 D11
NARCCAP nested model ensemble (SRES A2 scenario)
CRCM_cgcm3 CRCM cccma_cgcm3_1 50 NARCCAP
CRCM_ccsm CRCM ncar_ccsm3_0 50 NARCCAP
HRM3_hadcm3 HRM3 hadcm3 50 NARCCAP
MM5I_ccsm MM5I ncar_ccsm3_0 50 NARCCAP
RCM_gfdl RegCM3 gfdl_cm2_1 50 NARCCAP
WRFG_ccsm WRFG ncar_ccsm3_0 50 NARCCAP
CMIP3 global model ensemble (SRES A2 scenario)
GFDL-CM2.1 gfdl_cm2_1 2.0� 9 2.5� NARCCAP
CGCM3.1(T47) cccma_cgcm3_1 3.7� 9 3.8� NARCCAP
CNRM-CM3 cnrm_cm3 2.8� 9 2.8� PCMDI
CSIRO-Mk3.0 csiro_mk3_0 1.9� 9 1.9� PCMDI
CSIRO-Mk3.5 csiro_mk3_5 1.9� 9 1.9� PCMDI
GISS-ER giss_model_e_r 4.0� 9 5.0� PCMDI
INGV-SXG ingv_echam4 1.1� 9 1.1� PCMDI
IPSL-CM4 ipsl_cm4 2.5� 9 3.8� PCMDI
MIROC3.2(medres) miroc3_2_medres 2.8� 9 2.8� PCMDI
ECHO-G miub_echo_g 3.7� 9 3.8� PCMDI
ECHAM5/MPI-OM mpi_echam5 1.9� 9 1.9� PCMDI
a D11 = Diffenbaugh et al. (2011b)
Using climate impacts indicators to evaluate climate model ensembles 711
123
century. Because the sub-daily 3-dimensional atmospheric
boundary conditions that are necessary for the high-reso-
lution climate model nesting were not saved by NCAR for
the CCSM3 ensemble, we re-run the atmospheric compo-
nent of CCSM3 (the CAM3 global atmospheric model),
prescribing the monthly CCSM3-simulated SST and sea ice
fields as the CAM3 boundary condition over the ocean (as
described in Diffenbaugh and Ashfaq (2010)). Changes in
temperature and precipitation induced by this ‘‘time slice’’
method are mostly not statistically significant outside of the
high latitudes, and those that are statistically significant are
mostly less than 1 �C and 0.5 mm/day in magnitude,
respectively (Ashfaq et al. 2010b).
The second experiment that we analyze is the nested
climate model ensemble generated by participants in the
North American Regional Climate Change Assessment
Program (NARCCAP) (Mearns et al. 2009). The NARC-
CAP program seeks to generate twentieth and twenty first
century high-resolution climate model simulations by
nesting 6 different RCMs within four different AOGCMs,
yielding a total of 12 different model combinations. The
nested model domain covers the full continental U.S. and
much of Canada and Mexico with 50-km resolution in the
horizontal (Table 1). Our analysis is limited to those
models and time periods for which the daily maximum and
minimum temperature data that are necessary for the pre-
mium wine temperature suitability screening have been
archived (see description of the screening criteria below).
For the NARCCAP ensemble, this includes 6 nested cli-
mate model realizations (Table 1), covering a late-twenti-
eth century period and a mid-twenty first century period of
the SRES (IPCC 2000) A2 scenario (Mearns et al. 2009;
NARCCAP 2011). For two of these model realizations
(MM5I_ccsm and RCM3_gfdl), maximum and minimum
temperature must be calculated for each day from the
archived 3-hourly temperature timeseries.
The third experiment that we analyze is the global climate
model ensemble generated by participants in the Coupled
Model Intercomparison Project (CMIP3) (Meehl et al.
2007a). The CMIP3 archive contains simulations from 25
AOGCMs. We have been able to obtain daily maximum and
minimum temperature data for the late-twentieth century and
mid-twenty first century of the A2 scenario from 11
AOGCMs via the NARCCAP and CMIP3 archives (Table 1).
(We refer to these 11 AOGCMs as the CMIP3 ensemble). The
horizontal atmospheric resolution in these AOGCMs ranges
from 1.1� latitude 9 1.1� longitude (INGV-SXG) to 4.0�latitude 9 5.0� longitude (GISS-ER) (Table 1).
2.2 Wine suitability
We use the premium wine temperature suitability screening
of White et al. (2006) and Diffenbaugh et al. (2011a) as a
climate impacts indicator. The screening criteria are based
on empirical relationships between temperature variables
and premium winegrape cultivation, with the recognition
that other factors in addition to temperature also influence
cultivation. We determine the temperature suitability at
each grid point for each year. For a grid point to be des-
ignated as suitable in a given year, all of the following
criteria must be met: (1) During the growing season (GS;
April 1–October 31), the heat accumulation in growing
degree days (GDD; base 10 �C) must fall between 850
GDD and 2700 GDD, the mean temperature must fall
between 13 and 20 �C, the mean diurnal temperature range
(DTR; calculated as the daily maximum temperature minus
the daily minimum temperature) must not exceed 20 �C,
and the number of severe hot days (defined as days with
maximum temperature above 35 �C) must not exceed
30 days; (2) During the ripening season (RS; August 15 to
October 15), the mean DTR must not exceed 20 �C; and
(3) During the fall (September 1 to November 30), winter
(December 1 to February 28) and spring (March 1 to May
31), the total number of severe cold days (defined as days
with minimum temperature below -6.7 �C for fall and
spring and as days with minimum temperature below
-12.2 �C for winter) must not exceed 14 days combined
across all three seasons (fall, winter and spring).
These requirements use updated heat accumulation and
severe hot day thresholds from Diffenbaugh et al. (2011a).
The updated thresholds are based on temperature analyses
reported in Jones et al. (2010) and Diffenbaugh et al.
(2011a), respectively, and allow for a wider temperature
tolerance than the limits of 14 severe hot days, 1111 GDD,
and 2499 GDD that were used in White et al. (2006). For
climate model simulations that are not based on a Grego-
rian calendar, the cumulative metrics (GDD, total severe
hot days, and total severe cold days) are standardized to the
Gregorian calendar so that seasonal totals can be compared
between models that utilize different calendars.
2.3 Bias correction
We compare present and future temperature suitability in
the three climate model ensembles using both the original
climate model fields (‘‘uncorrected’’) and climate model
fields that have had monthly temperature biases corrected
to observational values (‘‘corrected’’). As in Diffenbaugh
et al. (2011a), the bias correction is performed using the
quantile-based method of Ashfaq et al. (2010a) and Ashfaq
et al. (2010b). The method generates a daily timeseries of
maximum and minimum temperatures at each grid point by
correcting the errors in the monthly-scale temperature
values. The correction is made for each quantile of each
calendar month in the baseline and scenario periods,
meaning that the warmest simulated January is corrected
712 N. S. Diffenbaugh, M. Scherer
123
by the error in the warmest simulated January, the second
warmest January is corrected by the error in the second
warmest January, and so on for all quantiles of each month
of the calendar. The method is thereby constructed to
preserve the simulated monthly- and daily-scale changes in
temperature at each grid point. As in Ashfaq et al. (2010a),
we perform the correction using the PRISM observational
monthly-mean daily maximum and minimum temperature
fields (Daly et al. 2000). Following both Ashfaq et al.
(2010a) and Diffenbaugh et al. (2011a), we first interpolate
the climate model and PRISM fields to a common 1/8th-
degree grid. Although the bias correction is performed at
the monthly scale, the baseline simulation of daily-scale
statistics is substantially improved by the correction
(Ashfaq et al. 2010a).
We compare the suitability screening in the three cli-
mate model ensembles with that in the NCEP North
American Regional Reanalysis (NARR) (Mesinger et al.
2006). The NARR reanalysis data are available with a
resolution of 32 km for the years 1979 through 2011. We
correct the biases in the NARR temperature fields using
the same approach as we use for the climate model
ensembles.
The quantile-based bias correction requires the time-
series to be of equal lengths in the baseline and scenario
periods. Our comparison of the climate model ensembles
and the reanalysis is therefore limited by the availability of
the different datasets. Based on the overlap of available
time period, we select 20 years to be our period of record.
Given the availability of the different datasets, we correct
the 20-year period from 1979 to 1998 in the NARR
reanalysis, and the 20-year periods from 1976 to 1995 and
2046 to 2065 in the three climate model ensembles.
Although the A2 and A1B emissions scenarios are not
identical over this period, the twenty first century cumu-
lative CO2 emissions are very similar in the two scenarios
[e.g., 731 and 893 GtC in 2050 and 2060, respectively, of
the A1B scenario and 729 and 912 GtC in 2050 and 2060,
respectively, of the A2 scenario (IPCC 2000)], and the
projected global warming over this mid-twentyfirst-century
period is indistinguishable (Meehl et al. 2007b).
As with the bias corrected datasets, all uncorrected
datasets are interpolated to a common 1/8th-degree grid
prior to analysis. In calculating the respective ensemble
means, we first calculate the temperature metrics for each
realization in the ensemble, and then calculate the mean of
the metrics across the realizations. In addition to compar-
ing the three climate model ensembles, we also create a
‘‘Superensemble’’ that includes projected mid-twenty first
century changes from all 22 realizations in the three cli-
mate model ensembles. This Superensemble allows us to
compare the mean and spread of the members of the
respective uncorrected and corrected ensembles with the
mean and spread of all members of all uncorrected and
corrected ensembles.
3 Results and discussion
3.1 Temperature suitability in the late-twentieth
century climate
The corrected and uncorrected datasets produce similar
overall patterns of late twentieth century temperature
suitability in the continental U.S., with peak suitability
over the Pacific Coast and over a mid-latitude belt of the
eastern U.S., along with less than 10 % suitability over
most of the central, southeastern and northeastern U.S.
(Fig. 1). However, the regional- and local-scale patterns of
temperature suitability are substantially different between
the respective uncorrected and corrected datasets. Notable
discrepancies include (1) suitability of less than 30 % over
the Columbia Valley in eastern Washington and Oregon in
the uncorrected NARCCAP and CMIP3 ensembles, (2)
suitability of less than 50 % over most of coastal Oregon
and the mid-latitude belt of the eastern U.S. in the uncor-
rected NARCCAP ensemble, (3) suitability of greater than
60 % over much of the Central Valley in California in the
uncorrected RegCM3 and CMIP3 ensembles, (4) suitability
of less than 20 % over much of coastal southern California
in all three uncorrected climate model ensembles, and (5)
suitability of greater than 95 % over large areas of the
western U.S. in the uncorrected NARR dataset.
In contrast to the uncorrected datasets, the four bias
corrected datasets produce nearly identical temperature
suitability patterns for the late twentieth century (Fig. 1).
By construction, the bias corrected datasets have identical
long-term monthly-mean temperatures. Therefore, any
discrepancies in suitability must arise from discrepancies in
the distribution of daily maximum and minimum temper-
atures. The most prominent discrepancy in suitability
between the three bias corrected climate model ensembles
occurs over the Columbia Valley, with the corrected
NARCCAP ensemble exhibiting suitability of less than
80 % and the corrected RegCM3 and CMIP3 ensembles
exhibiting suitability of greater than 75 %. The suitability
exhibited by the corrected NARR dataset over the
Columbia Valley more closely matches that of the cor-
rected RegCM3 and CMIP3 ensembles than that of the
corrected NARCCAP ensemble.
3.2 Changes in temperature suitability in response
to the mid-twenty first century climate
All three corrected ensembles produce very similar patterns
of projected changes in suitability for the mid-twenty first
Using climate impacts indicators to evaluate climate model ensembles 713
123
century period (Fig. 2). These changes include decreases of
up to 20 suitable years (out of a possible 20) over much of
the area of California that exhibits at least 95 % suitability
(19 years) in the late-twentieth century climate, along with
decreases of up to 16 years over the Columbia Valley. In
addition, much of the mid-latitude belt of the eastern U.S.
also exhibits decreases in suitability in the mid-twenty first
century period, including decreases of at least 14 years over
most areas that show at least 80 % suitability (16 years) in
the late-twentieth century climate. Notable differences in
the corrected ensembles (relative to the respective uncor-
rected ensembles) include reversal of the sign of projected
change over the Columbia Valley from positive (in the
uncorrected ensembles) to negative (in the corrected
ensembles), elimination of positive changes over the Wil-
lamette Valley in western Oregon (including reversal of
CM
IP3
Reg
CM
3N
AR
CC
AP
NA
RR
Bias Corrected Uncorrected
0 10020 40 60 80
%
Fig. 1 Percentage of suitable years for premium winegrape cultiva-
tion in the current climate in the NARR reanalysis and the
NARCCAP, RegCM3, and CMIP3 ensembles. Given the availability
of the different datasets, we analyze the 20-year period from 1979 to
1998 in the NARR reanalysis, and the 20-year period from 1976 to
1995 in the three climate model ensembles. Left panels show
suitability for bias corrected datasets. Right panels show suitability
for uncorrected datasets
714 N. S. Diffenbaugh, M. Scherer
123
sign in the NARCCAP ensemble), and reduction in the
spatial extent of negative changes over California.
Many of the largest projected future decreases in the
corrected ensembles occur over areas that currently repre-
sent important fractions of U.S. premium winegrape pro-
duction (Fig. 2) (Hodgen 2008). For example, the twenty
first century reductions in suitable years exceed 70 % of
the baseline suitable years over the northern and southern
Pacific coasts of California in all three corrected ensem-
bles, including at least 90 % reduction over the northern
Pacific coast in the NARCCAP and RegCM3 ensembles
and at least 90 % reduction over the southern Pacific coast
in the RegCM3 ensemble. Similarly, decreases over much
of the Columbia Valley exceed 50 % of the baseline suit-
able years in the NARCCAP and RegCM3 ensembles, and
exceed 40 % in the CMIP3 ensemble.
Bias Corrected Uncorrected
CM
IP3
Reg
CM
3N
AR
CC
AP
Sup
eren
sem
ble
0-10-20 10 20
years
Fig. 2 Change in suitable years for premium winegrape cultivation in
the NARCCAP, RegCM3, and CMIP3 ensembles, as well as the
combined Superensemble. Changes are calculated 2046–2065 minus
1976–1995. Positive changes reflect increasing suitability. Negative
changes reflect decreasing suitability. Left panels show suitability for
bias corrected datasets. Right panels show suitability for uncorrected
datasets
Using climate impacts indicators to evaluate climate model ensembles 715
123
It is also notable that many of the largest projected
increases in suitable years occur over what are presently
the cool margins of temperature suitability. These include
widespread increases of at least 16 years (out of a possible
20) over higher-elevation areas of the western U.S. that are
adjacent to lower-elevation areas of high twentieth-century
suitability, and widespread increases of at least 8 years
over higher-latitude areas of the eastern U.S. that are
adjacent to lower-latitude areas of high twentieth-century
suitability (Fig. 1). These changes suggest a shift in
suitability upward in elevation and poleward in latitude as
cold limitation is removed in response to mid-twenty first
century warming.
3.3 Causes of mid-twenty first century changes
in temperature suitability
The projected mid-twenty first century decreases in overall
temperature suitability in the corrected ensembles are
associated with increases in the number of years in which
0-10-20 10 20
years
Severe Cold Days Severe Hot Days
Growing Season DTR Ripening Season DTR
Heat Accumulation Growing Season Temperature
Fig. 3 Change in the number of years in which the overall suitability
is limited by individual screening criteria in the bias corrected
Superensemble. Changes are calculated 2046–2065 minus
1976–1995. Positive changes reflect increasing limitation by an
individual criterion, and decreasing overall suitability. Negativechanges reflect decreasing limitation by an individual criterion, and
increasing overall suitability
716 N. S. Diffenbaugh, M. Scherer
123
the overall suitability is limited by the mean growing
season temperature and/or the number of severe hot days
(Fig. 3). For example, most areas of California that exhibit
decreases in overall suitability of at least 16 years in the
Superensemble also exhibit increases of at least 16 years in
limitation by mean growing season temperature (Figs. 2, 3).
In addition, areas of northern California that show
decreases in suitability of at least 16 years in the Supe-
rensemble also show increases of at least 14 years in lim-
itation by growing season hot days. Areas of the Columbia
Valley that exhibit decreases in overall suitability of at
least 8 years likewise exhibit increases of at least 10 years
in limitation by growing season hot days, while areas of the
eastern U.S. that show decreases in suitability of at least
12 years show increases of at least 16 years in limitation
by mean growing season temperature. The increase in the
number of years limited by an individual factor can be
greater than the decrease in total suitable years if other
individual criteria limit the baseline suitability. Indeed,
while large areas of the U.S. exhibit increases of at least
14 years in limitation by heat accumulation in the mid-
twenty first century period, the fact that other criteria limit
the late-twentieth century suitability to near-zero (Fig. 1)
causes the mid-twenty first century change in suitability
over those areas to also be near-zero (Fig. 2).
Areas of the western U.S. that show mid-twenty first
century increases in overall temperature suitability are
associated primarily with decreases in limitation by mean
growing season temperature and severe cold days (with
some areas also exhibiting decreased limitation by growing
season heat accumulation), while areas of the eastern U.S.
that show increased suitability are associated primarily with
decreases in limitation by severe cold days (Fig. 3). How-
ever, a number of high elevation areas of the western U.S.
and northerly areas of the eastern U.S. that exhibit decreases
in limitation by growing season heat accumulation and
mean growing season temperature in the bias corrected
ensembles also show no change in limitation by severe cold
days (Fig. 3). This continued limitation by severe cold days
during the twenty first century period limits the change in
overall temperature suitability over these areas, in spite of
increasing growing season suitability (Fig. 2).
The climate model biases influence the projected twenty
first century change in temperature suitability by influ-
encing the ‘‘suitability margin’’, or the difference between
the threshold criterion value and the late-twentieth century
criterion value (Figs. 4, 5, 6). For example, mid-twenty
first century increases in heat accumulation, mean growing
season temperature, and severe hot days are less than 60 %
of the suitability margin over the Columbia Valley in all
three uncorrected ensembles. As a result, none of the three
uncorrected ensembles exhibits mid-twenty first century
decreases in temperature suitability over the Columbia
Valley (Fig. 2). However, although the bias correction does
not alter the projected change in monthly temperature, the
bias correction does alter the magnitude of the projected
change in the temperature variables relative to the
respective suitability margins, with the change in severe
hot days exceeding 100 % of the suitability margin over
much of the Columbia Valley in all three corrected
ensembles (Figs. 4, 5, 6). This alteration of the magnitude
of projected change relative to the suitability margin causes
a reversal in the sign of suitability change over the
Columbia Valley between the uncorrected and corrected
datasets (Fig. 2). Likewise, bias correction increases the
area of California over which the baseline suitability
margin is zero in all three ensembles (Figs. 4, 5, 6), con-
tracting the area that shows suitability for the late-twentieth
century period (Fig. 1), and thereby also reducing the area
in California that exhibits loss of suitability in response to
mid-twenty first century warming (Fig. 2).
3.4 Multi-model spread in projected mid-twenty first
century changes in temperature suitability
The climate model bias correction reduces discrepancies in
projected changes in temperature suitability between the
three climate model ensembles, including reducing inter-
ensemble differences in the magnitude of suitability
decreases over California and the mid-latitude belt of the
eastern U.S., and reducing inter-ensemble differences in the
magnitude of suitability increases over the Pacific North-
west and the Northeast (Fig. 2). In addition, the bias cor-
rection reduces the intra-ensemble spread in projected
change in suitability over most areas of the U.S. (Fig. 7).
The CMIP3 ensemble exhibits the greatest reduction in
intra-ensemble spread, with standard deviations of greater
than 6 years occurring over much of the U.S. in the
uncorrected dataset but only over areas of the southwestern
U.S. and mid-latitude belt of the eastern U.S. in the cor-
rected dataset. Intra-ensemble standard deviations of greater
than 6 years are similarly confined in the bias corrected
NARCCAP dataset, despite being widespread in the western
and eastern U.S. in the uncorrected dataset. Although the
RegCM3 ensemble exhibits substantially smaller intra-
ensemble standard deviation than the other two ensembles,
it does exhibit reduced spread in the bias corrected dataset
relative to the uncorrected dataset over most areas of the
U.S. A notable exception is the Columbia Valley, which
exhibits greater intra-ensemble spread in the corrected
versions of both the NARCCAP and RegCM3 ensembles.
The bias correction narrows the inter- and intra-
ensemble spread in overall temperature suitability in part
by narrowing the spread in projected change in individual
temperature screening criteria (Figs. 8, 9, 10, 11). Over
California, the mid-twenty first century change in area that
Using climate impacts indicators to evaluate climate model ensembles 717
123
is suitable in the mean growing season temperature crite-
rion ranges from reduction of less than 20 % to reduction
of more than 80 % in the uncorrected CMIP3 realizations,
and from gain of less than 10 % to reduction of more than
50 % in the uncorrected NARCCAP realizations (Fig. 8).
Similarly, the change in area that is suitable in the severe
hot day criterion ranges from no change to reduction of
more than 70 % in the uncorrected CMIP3 realizations, and
from reduction of less than 20 % to reduction of more than
70 % in the uncorrected NARCCAP realizations. However,
in all three bias corrected ensembles, changes in the suit-
able area over California are confined to reductions of less
than 40 % for both mean growing season temperature and
severe hot days.
Other regions show similar reductions in inter- and
intra-ensemble spread in projected change in individual
temperature screening criteria, particularly for growing
season heat accumulation and mean growing season
NARCCAP
Sev
ere
Hot
Day
sH
eat A
ccum
ulat
ion
Gro
win
g S
easo
n Te
mpe
ratu
re
Bias Corrected Uncorrected
0 20 8040 60 100 150 200
%
Fig. 4 Change in growing season heat accumulation, mean temper-
ature and severe hot days in the NARCCAP ensemble, expressed as a
percent of the suitability margin. The suitability margin is the
difference between the hot threshold value and the value in the
1976–1995 period, and therefore quantifies the maximum change that
would still allow for premium wine grape suitability. For a grid piont
with 25 hot days in the 1976–1995 period and 27 hot days in the
2040–2065 period, the suitability margin would be 5 days [the
difference between the hot threshold value (30 days) and the value in
the 1976–1995 period (25 days)], and the change in growing season
hot days (2 days) would be 40 % as a percentage of the suitability
margin. Areas that are above the warm limit of the suitability range in
the 1976–1995 period are shown in grey. The ensemble average
percentage is calculated by averaging each suitability metric and its
respective margin across the ensemble before calculating the average
change as a percentage of the average suitability margin. Left panelsshow changes for bias corrected datasets. Right panels show changes
for uncorrected datasets
718 N. S. Diffenbaugh, M. Scherer
123
temperature (Figs. 9, 10, 11). For example, over both
western Oregon/Washington and eastern Oregon/Wash-
ington, the range of projected change in suitable area in the
mean growing season temperature criterion exceeds 100,
200 and 1,200 percentage points in the uncorrected Reg-
CM3, CMIP3 and NARCCAP ensembles (respectively),
but is confined to less than 40 percentage points in all three
corrected ensembles (Figs. 9, 10). Likewise, over the
eastern U.S., the projected change in suitable area in the
growing season heat accumulation criterion ranges from
reductions of greater than 30 % to gains of greater than
30 % in the uncorrected CMIP3 and NARCCAP ensem-
bles, but is confined to reductions of less than 30 % in all
three corrected ensembles (Fig. 11). In contrast, eastern
Oregon/Washington and the eastern U.S. both exhibit
substantial spread in the projected change in suitable area
for the severe hot and cold day criteria in both the uncor-
rected and corrected ensembles (Figs. 10, 11).
Given the spread in projected mid-twenty first century
change exhibited by the corrected ensembles over eastern
Oregon/Washington for both overall temperature suitability
(Fig. 7) and severe hot and cold suitability (Fig. 9), we
examine the reliability of the bias-corrected temperature
criteria over that region (Figs. 12, 13). All three corrected
ensembles exhibit strong statistical agreement with the bias
corrected NARR dataset for growing season heat accu-
mulation, mean growing season temperature, and growing
season DTR, with some realizations also exhibiting strong
RegCM3
Sev
ere
Hot
Day
sH
eat A
ccum
ulat
ion
Gro
win
g S
easo
n Te
mpe
ratu
re
Bias Corrected Uncorrected
0 20 8040 60 100 150 200
%
Fig. 5 As in Fig. 4 but for the RegCM3 ensemble
Using climate impacts indicators to evaluate climate model ensembles 719
123
agreement for ripening season DTR (Fig. 12). However, all
three corrected ensembles show considerably weaker
agreement for severe hot and cold day occurrence, with the
NARCCAP- and CMIP3-simulated severe cold day values
showing the weakest agreement with the corrected NARR
dataset.
The regional-mean severe cold day bias ranges from -5
to 11 days over eastern Oregon-Washington in the cor-
rected CMIP3 ensemble (Fig. 13). The smaller NARCCAP
and RegCM3 ensembles exhibit exclusively positive
regional-mean biases, with the corrected NARCCAP real-
izations exhibiting biases from 5 to 11 days, and the cor-
rected RegCM3 realizations exhibiting biases from 4 to
7 days. In all three corrected ensembles, the most negative
severe cold day biases are associated with the least
negative mid-twenty first century changes in severe cold
day occurrence, while the most positive severe cold day
biases are associated with the most negative mid-twenty
first century changes. This relationship could be expected
given that the number of severe cold days is zero-bounded,
and that the projected mid-twenty first century period is
warmer than the late-twentieth century period: for a
reduction in severe cold day occurrence in response to
mean warming, negative biases artificially decrease the
reduction in severe cold days that is possible before
reaching zero, while positive biases artificially increase the
reduction that is possible.
The regional-mean severe hot day bias ranges from -2
to 2 days in the corrected CMIP3 ensemble (Fig. 13). As
with the severe cold day bias, the smaller NARCCAP and
CMIP3
Sev
ere
Hot
Day
sH
eat A
ccum
ulat
ion
Gro
win
g S
easo
n Te
mpe
ratu
re
Bias Corrected Uncorrected
0 20 8040 60 100 150 200
%
Fig. 6 As in Fig. 5 but for the CMIP3 ensemble
720 N. S. Diffenbaugh, M. Scherer
123
RegCM3 ensembles exhibit exclusively positive regional-
mean biases, with the corrected NARCCAP realizations
exhibiting biases from 1 to 3 days, and the corrected
RegCM3 realizations exhibiting biases from 0 to 2 days.
The relationship between late twentieth century bias and
mid-twenty first century change is far less coherent for
severe hot days than severe cold days. However, as with
severe cold day occurrence, the corrected CMIP3 and
NARCCAP ensembles exhibit larger ranges in mid-twenty
first century change in severe hot day occurrence
(6–21 days and 7–17 days, respectively) than the RegCM3
ensemble (13–19 days).
3.5 Implications of projected mid-twenty first century
changes in temperature suitability for premium
winegrape cultivation
The corrected ensembles exhibit very similar pattern and
magnitude of changes in premium winegrape temperature
suitability (Fig. 2). Of particular interest from the per-
spective of climate change impacts, all three corrected
ensembles project substantial decreases in temperature
suitability over much of the area of high production in
California and the Columbia Valley (in eastern Oregon and
Washington), and smaller decreases over the Willamette
Bias Corrected Uncorrected
CM
IP3
Reg
CM
3N
AR
CC
AP
Sup
eren
sem
ble
0 2 4 6
Fig. 7 As in Fig. 2, but for the
standard deviation in change in
suitable years for premium
winegrape cultivation
Using climate impacts indicators to evaluate climate model ensembles 721
123
Valley (in western Oregon). These decreases in suitability
are primarily associated with increases in excessive mean
growing season temperature and excessive occurrence of
growing season severe hot days. In addition, projected
increases in suitable years in the corrected ensembles are
concentrated in areas that do not currently exhibit high
temperature suitability.
The results are broadly similar to those reported by
White et al. (2006) analyzing a single model, single real-
ization climate model experiment for the late-twenty first
century in the A2 scenario, and those reported by Dif-
fenbaugh et al. (2011a) analyzing a single model, multiple
realization climate model experiment for the early-twenty
first century in the A1B scenario. For example, all three
studies report substantial decreases in suitability in the
California
Severe Cold Days Severe Hot Days
Growing Season DTR Ripening Season DTR
Heat Accumulation Growing Season Temperature
change in area (%) change in area (%)
change in area (%) change in area (%)
change in area (%) change in area (%)0 40 80-40-800 40 80-40-80
Ens
embl
e
N
R
C
N
R
C
Ens
embl
e
N
R
C
N
R
C
Ens
embl
e
N
R
C
N
R
C
-80 0 40 80-40
Same RCM, Different GCM
Same GCM, Different RCM
-80 0 40 80-40
-80 0 40 80-40-80 0 40 80-40
Fig. 8 The projected change in area that is suitable in each of the
temperature screening criteria in the California region (34–42�N,
118–125�W; California points only). Each realization is shown from
the NARCCAP (N), RegCM3 (R) and CMIP3 (C) ensembles.
Changes are calculated 2046–2065 minus 1976–1995, and are
presented as area normalized to the total area of the region in
percentage. Circles denote bias corrected data and diamonds denote
uncorrected data. Red symbols denote the NARCCAP ensemble, bluesymbols denote the RegCM3 ensemble, and black symbols denote the
CMIP3 ensemble. The orange bars show the range of values for the
NARCCAP realizations that use the same nested climate model but
different global climate models. The purple bars show the range of
values for the NARCCAP realizations that use the same global
climate model but different nested climate models
Oregon-Washington West
Severe Cold Days Severe Hot Days
Growing Season DTR Ripening Season DTR
Heat Accumulation Growing Season Temperature
change in area (%) change in area (%)
change in area (%) change in area (%)
change in area (%) change in area (%)0 200 400-200-4000 200 400-200-400
Ens
embl
e
N
R
C
N
R
C
Ens
embl
e
N
R
C
N
R
CE
nsem
ble
N
R
C
N
R
C
0 40 80-40-80 0 40 80-40-80
Same RCM, Different GCM
Same GCM, Different RCM
0 40 80-40-800 40 80-40-80
Fig. 9 As in Fig. 8 but for the Oregon-Washington West region
(42–49�N, 121–25�W; Oregon and Washington points only). Changes
that are larger than ±400 % are shown as ±400 %
722 N. S. Diffenbaugh, M. Scherer
123
high-value areas of California and the Columbia Valley of
eastern Oregon and Washington, along with increases in
suitability in the Puget Sound region of western Wash-
ington. In addition, both the current analysis and that of
White et al. (2006) exhibit decreases in suitability in the
mid-latitude belt of the eastern U.S. and increases in suit-
ability in the northeastern U.S. The most notable differ-
ences in the results reported here and those reported by
White et al. (2006) are increases in suitability that occur
along the coast of northern California and southern and
central Oregon in the results of White et al. (2006). This
contrast results from the fact that our analysis yields higher
baseline suitability in most coastal gridpoints (Fig. 1) than
is seen in that of White et al. (2006), a discrepancy that
itself is likely due to the fact that White et al. used a 1-km
gridded observational dataset that captures greater spatial
heterogeneity of the present coastal temperature field than
the 1/8-degree gridded observational dataset used in our
multi-model bias correction.
The primary advance in the current study relative to those
of White et al. (2006) and Diffenbaugh et al. (2011a) is the
use of multiple climate models with different formulations
and resolutions. The fact that the different corrected
ensembles show very similar patterns and magnitudes of
change (Fig. 2) suggests that the projected impacts are
robust to variations in model formulation of large- and fine-
scale climate processes, and to internal climate system
variability. This robustness is particularly strong for what
Oregon-Washington East
Severe Cold Days Severe Hot Days
Growing Season DTR Ripening Season DTR
Heat Accumulation Growing Season Temperature
change in area (%) change in area (%)
change in area (%) change in area (%)
change in area (%) change in area (%)
Ens
embl
e
Ens
embl
es
N
R
C
N
R
C
Ens
embl
e
N
R
C
N
R
C
Ens
embl
e
N
R
C
N
R
C
0 200 400-200-4000 200 400-200-400
0 200 400-200-400
0 40 80-40-80
Same RCM, Different GCM
Same GCM, Different RCM
Ens
embl
es
0 40 80-40-80
0 40 80-40-80
Fig. 10 As in Fig. 8 but for the Oregon-Washington East region
(42–49�N, 117–121�W; Oregon and Washington points only).
Changes that are larger than ± 400 % are shown as ± 400 %
East
Severe Cold Days Severe Hot Days
Growing Season DTR Ripening Season DTR
Heat Accumulation Growing Season Temperature
change in area (%) change in area (%)
change in area (%) change in area (%)
change in area (%) change in area (%)
Ens
embl
e
N
R
C
N
R
C
Ens
embl
e
N
R
C
N
R
C
Ens
embl
e
N
R
C
N
R
C
0 40 80-40-80
0 200 400-200-400
Same RCM, Different GCM
Same GCM, Different RCM
0 40 80-40-80
0 40 80-40-80
0 40 80-40-80
0 40 80-40-80
Fig. 11 As in Fig. 8 but for the East region (36–47�N, 65–87�W;
U.S. points only). Changes that are larger than ± 400 % are shown
as ± 400 %
Using climate impacts indicators to evaluate climate model ensembles 723
123
are presently the high-value areas of California, which show
decreases in suitability that are substantially larger than the
intra-ensemble spread for all three climate model ensembles
(Figs. 2, 7). In contrast, the decreases in suitability over the
Columbia Valley are smaller as a fraction of the intra-
ensemble spread (Figs. 2, 7).
Although the temperature screening criteria are intended
to capture both mean seasonal temperature conditions and
severe temperature events, our treatment of the influence of
temperature on premium winegrape cultivation does exhi-
bit important limitations. For example, the different var-
ietals grown throughout the U.S. exhibit a diversity of
tolerances within the broad screening criteria (White et al.
2006; Jones and Goodrich 2008; Jones et al. 2010;
Diffenbaugh et al. 2011a). As a result, there may be
impacts on wine quality that occur within the broad crite-
ria. For example, an area such as the Willamette Valley in
western Oregon, which exhibits little change in overall
Severe Cold Days
Severe Hot Days
Growing Season DTR
Ripening Season DTR
Heat Accumulation
Growing Season Temperature
Oregon-Washington East
Correlation
Correlation
Correlation
Sta
ndar
d D
evia
tion
(Nor
mal
ized
)S
tand
ard
Dev
iatio
n (N
orm
aliz
ed)
NARCCAP RegCM3
CMIP3
Fig. 12 Taylor diagram for the temperature screening criteria over
the Oregon-Washington East region in the corrected NARCCAP,
RegCM3 and CMIP3 ensembles for the 1979–1995 period, using the
corrected NARR dataset as the reference field. The time period for
comparison with the observational data is governed by the start of the
NARR availability (1979). The Taylor diagram (Taylor 2001) is used
to compare the pattern statistics between a test field and a reference
field. A point’s azimuthal position in the diagram indicates the
correlation between the test field (in this case the climate model field)
and the reference field (in this case the corrected NARR field). A
point’s distance from the origin denotes the standard deviation of the
test field normalized by the standard deviation of the reference field.
A point’s distance from the reference point (‘‘REF’’) provides the
normalized root mean square error between the test field and the
reference field. The closer a point in the diagram is located to the
REF-point on the x axis, the closer the statistical agreement between
the pattern of the test (climate model) and reference (reanalysis) field.
(See Taylor (2001) and Hegglin et al. (2010) for illustrative
examples).
724 N. S. Diffenbaugh, M. Scherer
123
temperature suitability over the range of global warming
explored here (Fig. 2; (Diffenbaugh et al. 2011a), could
experience a shift away from conditions that are most
suitable for cool varietals and towards conditions that are
most suitable for intermediate varietals (Diffenbaugh et al.
2011a). Likewise, as discussed by Diffenbaugh et al.
(2011a), there are a number of actions that can be taken in
the field, winery and marketplace that could potentially
decrease the impacts of the projected temperature changes.
For example, pruning, trellising and irrigation practices can
each be designed to increase tolerance of severe heat in
existing plantings. Likewise, measures can be taken in the
winery after winegrapes are harvested in order maintain
wine quality, and in the marketplace after wines are made
in order to maintain a perception of wine quality. It is also
possible to change row orientation and/or varietals at a
current growing location or to develop new growing
locations, although new plantings can require substantial
real and opportunity costs.
An additional limitation is that the scale of our modeling
and analysis is not able to capture all of the heterogeneity
in temperature that is experienced by growers in the U.S.
For example, the topographic complexity of California
creates complex ‘‘micro-climates’’ that can create sub-
stantial local-scale variability in temperature (White et al.
2006). As a result, a range of winegrape varietals can be
found planted within a given region or even on an indi-
vidual vineyard, depending on physiographic conditions
such as slope, aspect, and proximity to water bodies.
Although the RegCM3 and NARCCAP nested model
ensembles capture the topographic complexity of the U.S.
at higher horizontal resolution than the CMIP3 global
model ensemble (Table 1), the topographic complexity that
shapes these microclimates is not fully resolved in any of
the climate model experiments. Further, for areas near the
coast, changes in growing conditions are also likely to be
influenced by changes in the coastal atmosphere and ocean
circulations. Theoretical arguments (Bakun 1990) and
high-resolution climate model experiments (Snyder et al.
2003; Diffenbaugh et al. 2004) suggest that elevated
greenhouse forcing could enhance coastal upwelling in the
California Current by enhancing the land-sea temperature
contrast and strengthening alongshore winds. Observa-
tional evidence suggests twentieth century trends towards
increasing strength of coastal winds (Bakun 1990) and
cooler coastal temperatures (Lebassi et al. 2009) along the
California coast, but decreasing coastal fog frequency
along the Pacific coast (Johnstone and Dawson 2010). In
addition, the response of the land-sea breeze and coastal
fog production will be strongly influenced by the suite of
ocean processes that determine coastal sea surface tem-
peratures (e.g., Mendelssohn and Schwing 2002), which
are very poorly resolved in the current generation of global
climate models (e.g., Large and Danabasoglu 2006; Ashfaq
et al. 2010b).
Finally, temperature is not the only environmental
influence on premium winegrape cultivation and, as noted
in the study of White et al. (2006), areas that have been
identified as highly suitable from a temperature perspective
may face other climate-related challenges, such as from
water availability (as in the arid southwestern U.S.), or
from excessive humidity and heavy precipitation (as in the
Pacific Northwest). Processes controlling precipitation are
less well represented in climate models than processes
controlling surface air temperature, resulting in greater
uncertainty in regional- and local-scale precipitation
changes, particularly over the near-term decades in which
internal climate system variability dominates the spread in
Severe Hot DaysSevere Cold Days
bias in corrected dataset-6 -3 0 3 6 9 12 10-1-2 2 3 4
6
9
12
15
18
21-6
-9
-12
-15
-18
-21F
utur
e m
inus
Pre
sent
NARCCAP RegCM3 CMIP3
Oregon-Washington East
bias in corrected dataset
Fig. 13 Change in severe cold
days and severe hot days over
the Oregon-Washington East
region in the corrected
NARCCAP, RegCM3 and
CMIP3 ensembles, using the
corrected NARR dataset as the
reference field. Changes are
calculated from the area-
weighted average in the
2046–2065 and 1976–1995
periods and are plotted for each
member, along with the member
bias in the 1979–1995 period
Using climate impacts indicators to evaluate climate model ensembles 725
123
climate model projections of precipitation over the western
U.S. (Hawkins and Sutton 2010). However, given that
much of the water consumed for agriculture in the western
U.S. is delivered and stored as snowpack (Mock 1996;
Hamlet and Lettenmaier 1999), the prospect of tempera-
ture-driven shifts towards decreased snow-to-precipitation
ratio and earlier snowmelt timing (Leung and Ghan 1999;
Hamlet et al. 2005; Maurer and Duffy 2005; Mote et al.
2005; Maurer 2007; Rauscher et al. 2008) suggests that
premium winegrape cultivation may require increasing
tolerance of severe heat and decreased water availability in
the coming decades.
4 Conclusions
Our results suggest that global warming that is projected to
occur by the mid-twenty first century could substantially
displace the geographic distribution of optimal temperature
conditions for the cultivation of premium winegrapes, with
the areas that currently account for most of the U.S. pro-
duction experiencing growing conditions that are substan-
tially warmer than present, and the optimal temperature
conditions in the future occurring in areas that are currently
at the cool margin of premium winegrape suitability.
Although previous research has suggested similar wine-
grape impacts using single-model climate modeling
frameworks (e.g., White et al. 2006; Diffenbaugh et al.
2011a), this is the first such national-scale analysis using a
multi-model ensemble approach. The agreement between
those previous studies and the multi-model analysis pre-
sented here suggests that the previously reported results are
robust.
Comparing the spread within and between different
climate model ensembles can help to constrain the mag-
nitude of different sources of uncertainty in future climate
change (e.g., Deque et al. 2005; Giorgi et al. 2008;
Hawkins and Sutton 2009, 2010). Like most multi-model
ensembles created to date, the three ensembles analyzed
here are not perfectly uniform in their experimental design
(e.g., Table 1). However, despite the fact that they do not
form a perfect experiment, some insight about climate
change uncertainty can be drawn from comparing the intra-
and inter-ensemble variations.
For example, comparison of the three ensembles shows
that the RegCM3 ensemble exhibits the smallest intra-
ensemble spread in the impact of mid-twenty first century
climate change on premium winegrape temperature suit-
ability (Figs. 7, 8, 9, 10, 11). Because the five RegCM3
realizations are physically uniform, this result suggests
that—for the temperature suitability examined here—the
uncertainty arising from internal climate system variability
is smaller than the uncertainty arising from climate model
formulation. In addition, the 6-member NARCCAP
ensemble and the 11-member CMIP3 ensemble exhibit
similar mid-twenty first century intra-ensemble spread over
the regions of high temperature suitability (Figs. 7, 8, 9, 10,
11). Further, in most cases, the range between realizations
using the same nested model but different AOGCMs is
equal to or less than the range between realizations using
different nested models but the same AOGCM (Figs. 8, 9,
10, 11). Although the population size is small (the 6
NARCCAP realizations are nested within only 4 AOGCM
realizations; Table 1), these results suggest that the
uncertainty arising from the model formulation of fine-
scale climate processes is not smaller than the uncertainty
arising from the model formulation of large-scale climate
processes.
Comparison of the three ensembles also shows that bias
correction reduces the inter-ensemble spread, along with
the intra-ensemble spread within the NARCCAP and
CMIP3 ensembles (Figs 2, 7, 8, 9, 10, 11). These results
suggest that the climate model biases dominate the multi-
model spread, particularly through the suitability margin
between baseline temperature values and critical tempera-
ture thresholds (Figs. 4, 6). The fact that climate model
biases have been found not to dominate the multi-model
regional temperature change (Giorgi and Coppola 2011)
highlights the importance of using climate impacts indi-
cators to evaluate the spread in climate model ensembles,
as the simulated climate change and the simulated climate
change impacts may show different sensitivities to model
biases.
Identification and quantification of the importance of
climate model biases is certainly not new. Indeed, the
importance of bias correction has long been recognized in
the climate modeling community, and is used particularly
prominently in hydrologic impacts work (e.g., Maurer and
Duffy 2005; Maurer 2007) and seasonal climate prediction
(e.g., Landman and Goddard 2002; Baigorria et al. 2007).
What has received less attention is the importance of cli-
mate model biases in shaping the model agreement in
simulated climate change impacts. In our approach, the
simulated monthly-scale temperature change between the
present and future periods is preserved in each model
realization. As a result, the model differences in simulated
temperature change are also preserved. These model dif-
ferences can be substantial, and understanding the causes
of such differences has received considerable attention in
the literature (e.g., Meehl et al. 2007b). However, to the
best of our knowledge, this is the first attempt to system-
atically evaluate the contributions of intra-model variabil-
ity, inter-model differences, and climate model bias to the
spread in the simulated impacts of climate change. The fact
that the corrected ensembles show very similar results over
most of the U.S. even though the model differences in the
726 N. S. Diffenbaugh, M. Scherer
123
simulated temperature changes are preserved suggests
that—at least for some systems—the projected climate
change impacts could be more robust than the projected
climate change.
In addition, the fact that some areas do show substantial
variation between the corrected ensembles helps to identify
sources of particular uncertainty in future climate change
impacts. In our analyses, variations in the corrected
ensembles are seen most prominently in areas where errors
in the temperature extremes persist after bias correction,
such as for severe hot and cold days over eastern Oregon-
Washington (Figs. 12, 13). These errors further highlight
the importance of continued efforts to improve the climate
model representation of extreme events. Specifically, the
fact that the three corrected climate model ensembles have
identical mean monthly temperature values in the baseline
period but exhibit substantial variation in the occurrence of
extreme hot and cold events suggests that the processes that
dictate daily temperature variability require further under-
standing. This is likely to also be a prominent need for
precipitation, as changes in the extremes of precipitation
are not always linearly correlated with changes in the mean
of the precipitation distribution (e.g., Wehner 2004).
It is important to consider that this study examines only
one impacts indicator for one human system, and that this
indicator is based only on surface air temperature.
Although the cultivation of premium winegrapes exhibits
well-developed relationships with temperature, the contri-
bution of different sources of uncertainty to the overall
spread in the response to enhanced radiative forcing is
likely to vary between climate-sensitive systems. While it
is clearly not feasible to conduct a case study of every
conceivable climate-sensitive system, our finding that cli-
mate model bias dominates the spread in projected climate
change impact should be tested across a range of impacts
indicators. By increasing the focus on the climate phe-
nomena that most directly influence natural and human
systems, extension of this analysis to a larger suite of
impacts indicators will help to deepen our understanding of
the mechanisms and uncertainty of future climate change.
Acknowledgments We thank two anonymous reviewers for
insightful and constructive comments. We wish to thank the North
American Regional Climate Change Assessment Program (NARC-
CAP) for providing the NARCCAP data used in this paper. NARC-
CAP is funded by the National Science Foundation (NSF), the U.S.
Department of Energy (DoE), the National Oceanic and Atmospheric
Administration (NOAA), and the U.S. Environmental Protection
Agency Office of Research and Development (EPA). We acknowl-
edge the modeling groups, the Program for Climate Model Diagnosis
and Intercomparison (PCMDI) and the WCRP’s Working Group on
Coupled Modelling (WGCM) for their roles in making available the
WCRP CMIP3 multi-model dataset. Support of the CMIP3 dataset is
provided by the Office of Science, U.S. Department of Energy. We
thank the National Centers for Environmental Prediction (NCEP) for
providing access to the North American Regional Reanalysis (NARR)
dataset, and the PRISM Climate Group and Oregon State University
for providing access to the PRISM observational temperature dataset.
Our RegCM3 climate model experiments were generated and stored
using computing resources provided by the Rosen Center for
Advanced Computing (RCAC) at Purdue University, and our analyses
of all datasets were performed using computing resources provided
by the Center for Computational Earth and Environmental Sci-
ence (CEES) at Stanford University. The research reported
here was supported by NSF award 0955283 and NIH award
1R01AI090159-01.
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