Download Northern Hemisphere blocking frequency and duration in the CMIP5

JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 1179–1188, doi:10.1002/jgrd.50143, 2013
Northern Hemisphere blocking frequency and
duration in the CMIP5 models
Etienne Dunn-Sigouin1 and Seok-Woo Son2
Received 9 August 2012; revised 22 December 2012; accepted 26 December 2012; published 7 February 2013.
[1] Northern Hemisphere (NH) blocking climatology is examined using a subset of
climate models participating in the Coupled Model Intercomparison Project phase 5
(CMIP5). Both historical and Representative Concentration Pathway (RCP) 8.5
integrations are analyzed to evaluate the performance of the CMIP5 models and to identify
possible changes in NH blocking frequency and duration in a warmer climate. Comparison
with reanalysis data reveals that CMIP5 models can reproduce the NH blocking
climatology reasonably well, although the frequency of Euro-Atlantic blockings,
particularly those with relatively short duration, is significantly underestimated during the
cold season. In most models, overestimation of the Pacific blocking frequency is also
evident for all durations throughout the year. In comparison to historical integrations, RCP
8.5 integrations show significant decreases in blocking frequency over both the North
Pacific and north Atlantic regions, with a hint of increasing blocking frequency over
western Russia. However, there is no noticeable change in the duration of individual
blocking events for all durations.
Citation: Dunn-Sigouin, E., and S.-W. Son (2013), Northern Hemisphere blocking frequency and duration in the CMIP5
models, J. Geophys. Res. Atmos., 118, 1179–1188, doi:10.1002/jgrd.50143.
1. Introduction
[2] Atmospheric blocking is one of the most dramatic
examples of extratropical low-frequency variability. Traditionally, it refers to a synoptic-scale high-pressure system,
often accompanied by low-pressure system(s) at lower
latitudes that remains quasi-stationary for days to weeks,
interrupting the zonal flow [e.g., Rex, 1950]. Since blocking
highs are persistent and quasi-stationary by definition, they
affect surface weather and climate in a major way. For
instance, a blocking high often causes a significant increase
in surface temperature beneath it and enhanced storm
activity around it [e.g., Trigo et al., 2004]. Striking examples
are the 2003 European and 2010 Russian heat waves that
resulted from exceptionally long-lasting summer-time
blocking events. Both these events lead to record breaking
surface air temperatures and significant increases in mortality rates [Black et al., 2004; Dole et al., 2011]. In winter,
blocking highs have been identified as one of the most
important causes of cold surges in the Europe and Eurasia,
leading to an abrupt temperature drop with heavy precipitation [e.g., Park et al., 2011].
1
Department of Atmospheric and Oceanic Sciences, McGill University,
Montréal, Québec, Canada.
2
School of Earth and Environmental Sciences, Seoul National
University, Seoul, South Korea.
Corresponding author: S.-W. Son, School of Earth and Environmental
Sciences, Seoul National University, San 56-1, Sillim-9-Dong, Gwanak-Gu,
Seoul, 151-747, South Korea. ([email protected])
©2013. American Geophysical Union. All Rights Reserved.
2169-897X/13/10.1002/jgrd.50143
[3] The crucial role that blocking plays in surface weather
(especially in extreme weather) has underlined the need for
the reliable simulation of blocking events by climate models.
It is however well documented that climate models often
underestimate Northern Hemisphere (NH) blocking frequency, especially over the North Atlantic and Europe [e.g.,
D’Andrea et al., 1998, Doblas-Reyes et al., 2002, Scaife
et al., 2010, Barriopedro et al., 2010a]. While detailed causality is still an active area of research, this underestimation has
been attributed to the limited model resolution, misrepresentation of surface boundary conditions, and uncertainties in physical parameterizations that lead to the biases in the time-mean
flow and high-frequency eddies [e.g., Matsueda et al., 2009,
Scaife et al., 2010, Scaife et al., 2011].
[4] Recent studies have shown that the frequency of NH
blocking events may decrease in the future, in response to
global warming. By analyzing CMIP3 models, Barnes and
Hartmann [2010] and Barnes et al. [2012] documented a
statistically significant decrease in blocking frequency over
both the North Pacific and North Atlantic in the 21st century.
They however found no evidence in blocking duration
change. While a similar frequency change is also found in
model sensitivity tests [Matsueda et al. 2009; Sillmann and
Croci-Maspoli, 2009], different studies show slightly different results in duration change. For example, Sillmann and
Croci-Maspoli [2009] showed a possible increase in maximum blocking duration over the Euro-Atlantic region,
whereas Matsueda et al. [2009] showed a possible reduction
in long-lived blocking events.
[5] It should be noted that, despite the importance of blocking
events, only a few studies (as listed above) have documented
possible changes in blocking activities in a warmer climate.
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DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
As a matter of fact, the future projection of NH blocking highs
was not commented in the Intergovernmental Panel on Climate
Change (IPCC) Fourth Assessment Report (AR4) [Solomon
et al., 2007] which is largely based on the CMIP3 data. This
lack of documentation partly results from availability of daily
data. Although blocking is typically examined using 500 hPa
geopotential height or upper-tropospheric dynamic fields
such as potential temperature on the 2-PVU surface or uppertropospheric PV anomalies, these data sets were not archived
in the CMIP3 in daily or subdaily resolution. Of the few studies
based on CMIP3 models, Scaife et al. [2010] and Barnes et al.
[2012] used zonal wind, assuming geostrophic balance, to characterize the NH blocking climatology in the CMIP3 models.
[6] The primary goal of this study is to examine the NH
blocking climatology in the present and future climate as
simulated by the CMIP5 models. Extending and updating previous studies, we evaluate state-of-the-art climate models in
the context of present-day blocking climatology and document
the multimodel projection of blocking frequency and duration
in the 21st century. This goal is achieved by applying a hybrid
blocking index (E. Dunn-Sigouin, S.-W. Son, and H. Lin,
Evaluation of Northern Hemisphere blocking climatology
in the Global Environment Multiscale (GEM) model, Mon.
Wea. Rev., in press, 2012), that is essentially a combination
of the two widely used blocking indices (i.e., the DoleGordon type index and Tibaldi-Molteni type index), to daily
500 hPa geopotential height fields in both historical and RCP
integrations as described below.
2. Data and Methodology
[7] The model data used in this study are historical and
RCP 8.5 runs from a subset of climate models participating
in the CMIP5 [Taylor et al., 2012]. Briefly, historical runs
are 20th century climate integrations with all observed climate forcings. RCP 8.5 runs are scenario integrations with
a rapid warming, specifically with an anthropogenic radiative forcing of 8.5 Wm 2 at 2100. All available models that
archived daily geopotential height fields are used: i.e., a total
of 17 models for historical integrations and 13 models for
RCP 8.5 integrations as of June 2012 (Table 1). If multiple
realizations are available, only the first ensemble member
is considered.
[8] All model output is first interpolated onto the lowest model
resolution, namely, 2.8∘ latitude by 2.8∘ longitude (Table 1).
Blocking statistics are then derived using this interpolated data.
To minimize the effect of internal variability in climatology, analyses are performed for a sufficiently long period. Specifically,
40 years, 1966–2005 for historical runs and 2060–2099 for
RCP 8.5 runs, are considered. Although not shown, overall
results are found to be insensitive to the choice of analysis period.
For instance, use of last 20 years, i.e., 1986–2005, yields essentially the same results but with a larger intermodel difference.
[9] The performance of the CMIP5 models is evaluated by
comparing the blocking climatology derived from historical
integrations to that of the National Centers for Atmospheric
Prediction (NCEP) and the National Center for Atmospheric
Research (NCAR) reanalysis [NNR, Kalnay et al., 1996].
Future changes in blocking activities are then quantified by
comparing historical runs with RCP 8.5 runs. All results
are shown in terms of the multimodel mean, and their significance is shown as the difference between the number of
models showing statistically significant positive differences
minus the number of models showing statistically significant
negative differences. To further illustrate intermodel difference and statistical uncertainty, individual models are also
presented in the auxiliary materials.
2.1. A Hybrid Index
[10] The blocking index employed in this study is a
hybrid index that combines the two widely used indices,
the Dole-Gordon type (DG) index [Dole and Gordon,
1983] and the Tibaldi-Molteni type (TM) index [Tibaldi and
Molteni, 1990], in a simply way (E. Dunn-Sigouin, S.-W.
Son, and H. Lin, in press, 2012). The DG index typically
identifies blocking events as persistent geopotential height
anomalies at 500 hPa. It provides 2-D (latitude-longitude) statistics of blocking highs but is known to suffer from arbitrary
blocking anomaly thresholds and possible misdetection of
quasi-stationary ridges as blocking highs. In contrast, the
TM index defines blocking events with the reversal of the meridional gradient of absolute geopotential height at 500 hPa.
Since the local gradient is evaluated about the reference latitude,
it yields blocking statistics as a function of longitude only.
Unlike the DG index, this index is applicable to short-term data
Table 1. Description of CMIP5 Models Used in This Analysis (Resolutions refer to atmospheric model resolution and horizontal resolution is approximate for spectral models)
Model
BNU-ESM
CanESM2
CMCC-CM
HadGEM2-CC
IPSL-CM5A-LR
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC5
MIROC-ESM-CHEM
MPI-ESM-LR
MPI-ESM-MR
MRI-CGCM3
NorESM1-M
BCC-CSM1-1
CCSM4
CNRM-CM5
MIROC-ESM
Group, Country
BNU, China
CCCma, Canada
CMCC, Italy
MOHC, UK
IPSL, France
IPSL, France
IPSL, France
CCSR, Japan
CCSR, Japan
MPI-M, Germany
MPI-M, Germany
MRI, Japan
NCC, Norway
BCC, China
NCAR, US
CNRM, France
CCSR, Japan
Horizontal Res. (lat. lon.)
∘
∘
T42 (2.8 2.8 )
T63 (2.8 ∘ 2.8 ∘)
(0.75 ∘ 0.75 ∘)
N96 (1.875 ∘ 1.25 ∘)
(1.875 ∘ 3.75 ∘)
(1.25 ∘ 2.5 ∘)
(1.875 ∘ 3.75 ∘)
T85 (1.4 ∘ 1.4 ∘)
T42 (2.8 ∘ 2.8 ∘)
T63 (1.875 ∘ 1.875 ∘)
T63 (1.875 ∘ 1.875 ∘)
TL159 (1.125 ∘ 1.125 ∘)
f19 (1.875 ∘ 2.5 ∘)
T42 (2.8 ∘ 2.8 ∘)
(0.9 ∘ 1.25 ∘)
TL127 (1.4 ∘ 1.4 ∘)
T42 (2.8 ∘ 2.8 ∘)
1180
Historical Run
RCP8.5 Run
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
in which climatology is not robustly defined. However, it is
known that blocking frequency can be significantly underestimated if the mean state is biased [Scaife et al., 2010]. Prescribing
the reference latitude is another caveat. See the studies by
Barriopedro et al. [2010b] and (E. Dunn-Sigouin, S.-W. Son,
and H. Lin, in press, 2012) for further details of the advantages
and disadvantages of these two indices.
[11] In this study, the above two approaches are combined
into a single hybrid index: i.e., a contiguous area of blocking
anomalies is identified from the 500 hPa geopotential height
field as in the DG index, and then, a reversal of the meridional gradient of geopotential height is ensured as in the TM
index. Here the anomaly, z0 , is not simply defined by the
deviation from the long-term mean. It is instead defined by
filtering out both the seasonal cycle and interannual
variability from the 500 hPa geopotential height field
[Sausen et al., 1995], then normalized by the sine of latitude,
taking the latitudinal variation of the Coriolis parameter into
account [Dole and Gordon, 1983]. As such, z0 can be
directly related with 500 hPa eddy stream function in the
context of quasi-geostrophic dynamics. More specifically,
0
z ¼ z z ^z
where z is 500 hPa geopotential height (Z) divided by the sine
of latitude,z is a running annual mean of z centered on a given
day, ^z is a mean seasonal cycle derived from z* which is a
running monthly mean of z - z centered on a given day.
[12] More specifically, the blocking events are identified
as follows. The potential blocking events are tested by
searching the closed positive contours of z0 , satisfying the
minimum amplitude (A) and spatial scale (S). They are
tracked in time, ensuring a sufficient overlap in blocking
areas (O) within 2 days. The detected anomalies are then
tested as to whether they block the flow by assuring the
reversal of the meridional gradient of the absolute 500 hPa
geopotential height on the equatorward side of the anomaly
maximum. In this regard, the anomaly field determines not
only the spatiotemporal scale of blocking highs but also their
reference latitudes. If all of these conditions are satisfied for
a consecutive period of days (D), the anomalies are finally
labeled as blocking highs.
[13] In this study, the amplitude threshold (A) is set to 1.5
standard deviation of geopotential height anomalies over
30∘ 90∘ N for a 3-month period centered at a given month.
The remaining threshold values are set to (S) = 2.5106 km2,
(O) = 50%, and (D) = 5 days, for both NNR and CMIP5 data.
See the study by (E. Dunn-Sigouin, S.-W. Son, and H. Lin, in
press, 2012) for further details.
[14] The above approach conveniently provides 2-D statistics of blocking highs, minimizing possible misdetection.
A similar approach, but applying the gradient reversal first,
was also proposed by Barriopedro et al., [2010b]. However,
a hybrid index still needs to prescribe anomaly criteria that
are subject to the analysis. Among others, the amplitude
threshold (A), a relative quantity to a given climate state,
may differ from model to model and from the present to
future climate. This could increase uncertainty in the
multimodel analyses. This issue is addressed in section 3.3.
2.2. A TM Index
[15] To examine the robustness of the findings to the
choice of blocking index, the TM index, based on the study
by Tibaldi and Molteni [1990], is also applied. It defines
blocking highs as the local instantaneous gradient reversal
in 500 hPa geopotential height field, Z. The equatorward
and poleward gradients of Z, GHGS, and GHGN are
computed at each longitude, l, for the selected reference
latitudes, ’:
GHGS ¼
Z ðl; ’0 Þ Z ðl; ’s Þ
;
’0 ’s
GHGN ¼
Z ðl; ’n Þ Z ðl; ’0 Þ
’n ’0
where
’n ¼ 77:5∘ N Δ;
’0 ¼ 60:0∘ N Δ;
’s ¼ 40:0∘ N Δ;
Δ ¼ 5:6∘ ; 2:8∘ ; 0∘ ; 2:8∘ ; or 5:6∘ :
[16] If the following two conditions are satisfied for at
least one of Δs at a given longitude, the longitude is identified as being blocked:
GHGS > 0;
GHGS < 5m= deg:
lat:;
[17] Note that the Δs are slightly modified from the original ones to match the data resolution used in the present
study and that GHGS is reduced in magnitude. The blocking
frequencies obtained with this index are compared to those
of the hybrid index in section 3.3
2.3. Variable of Interest
[18] In this study, blocking events are detected by using
500 hPa geopotential height field. This differs from the
recent approaches that utilize dynamical variables such as
the potential temperature on the dynamic tropopause [Pelly
and Hoskins, 2003] and the potential vorticity anomalies in
the upper troposphere [Schwierz et al., 2004]. Although
these variables could be more advantageous, they are not
readily available from the CMIP5 archive. More importantly,
it has been shown that most blocking events with a significant
amplitude and substantial spatial scale are reasonably well
detected in all variables [Barnes et al., 2012].
3. Results
3.1. Blocking Climatology
[19] Figure 1a presents NH blocking climatology from
40-year long NNR data [see also the study by (E. DunnSigouin, S.-W. Son, and H. Lin, in press, 2012). Blocking
frequency is shown as the number of days per year a blocked
area occupies each grid point. As widely documented in the
literature, two active regions of blocking occurrence emerge:
North Pacific (hereafter PA blocking) and Europe-northeastern Atlantic (EA blocking). The latter generally exhibits
higher frequency than the former, although the opposite is
true during the summer (Figure 2a). EA blocking also shows
a more zonally elongated geographical distribution than PA
blocking, with a long tail to western Russia. In certain seasons, blocking frequencies over western Russia are separated
from those over the North Atlantic and western Europe (e.g.,
November in Figure 2a) and are referred to as Ural blocking.
[20] The blocking climatology, derived from 17 CMIP5 historical runs, is illustrated in Figures 1b–1c. It is evident that the
CMIP5 models can reproduce the overall geographical distribution of the NH blocking activities reasonably well. Noticeable
biases are however present in frequency (Figure 1c). Most of all,
EA blocking frequency is underestimated by about 30%. This is
common to all the models analyzed in this study (Figure S1)
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DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
Figure 1. Climatology of NH annual-mean blocking frequency: (a) NNR, (b) historical multimodel
mean, (c) historical multimodel mean minus NNR, (d) RCP 8.5 multimodel mean, and (e) RCP 8.5 minus
historical multimodel mean. Units are number of blocked days per year. Contour intervals in Figures 1a,
1b, and 1d and Figures 1c and 1e are 4 and 2 days per year, respectively. Shaded areas in Figure 1c denote
the number of models with significant positive bias minus the number of models with significant negative
bias at the 95% level using a two-tailed Student’s t test. Shaded areas in Figure 1e are the same as in
Figure 1c except for significant model changes.
Figure 2. Seasonal cycle of the NH blocking frequency as a function of longitude: (a) NNR, (b)
historical multimodel mean, (c) historical multimodel mean minus NNR, (d) RCP 8.5 multimodel mean,
and (e) RCP 8.5 minus historical multimodel mean. Units are days per month. Contour intervals in
Figures 2a, 2b, and 2d and Figures 2c and 2e are 1 and 0.5 days per month, respectively. Shaded areas
in Figure 2c denote the number of models with significant positive bias minus the number of models with
significant negative bias at the 95% level using a two-tailed Student’s t test. Shaded areas in Figure 2e are
the same as in Figure 2c except for significant model changes.
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DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
and consistent with previous modeling studies [e.g., D’Andrea
et al., 1998, Scaife et al., 2010]. In contrast, PA blocking
frequency is overestimated by the models over broad regions.
While this bias is less robust than EA blocking bias (see shading
in Figure 1c in midlatitudes; see also Figure S1), it is still
significant particularly on the poleward side of the climatological blocking frequency maxima. A similar overestimation was
also reported in a recent study by (E. Dunn-Sigouin, S.-W.
Son, and H. Lin, in press, 2012) as well as in a few climate models participating in the CMIP3 [Scaife et al., 2010].
[21] The above model biases in blocking frequency might
be partly caused by the model resolution. It is well known
that high-frequency eddy forcing, which is one of the most
important maintenance mechanisms of blocking highs [e.
g., Shutts, 1983; Nakamura et al., 1997], is very sensitive
to the model resolution. Matsueda et al., [2009], for
instance, presented more accurate EA blocking climatology
in a higher resolution model integration. However, a direct
comparison between IPSL-CM5A-MR and IPSL-CM5ALR, whose difference is only model resolution (Table 1),
shows a negligible difference in EA blocking frequency
(Figures S1c–S1d). A moderate increase in horizontal
resolution instead leads to a slight decrease in EA blocking
frequency. This result supports the finding of Scaife et al.
[2011] that EA blocking frequency is more sensitive to
surface boundary conditions than atmospheric model resolution. Matsueda et al. [2009] also indicated that the PA
blocking frequency could be overestimated in a highresolution model integration. However, no systematic
relationship between PA blocking bias and model resolution
is observed throughout the models (Figure S1). This is largely
consistent with (E. Dunn-Sigouin, S.-W. Son, and H. Lin, in
press, 2012) who showed that PA blocking frequency could
be overestimated even in a coarse resolution model integration where high-frequency eddy activities are underrepresented. They suggested that the biases in time-mean flow
could result in model biases in blocking frequency [see also
Doblas-Reyes et al., 2002].
[22] The seasonal cycles of blocking frequency are illustrated in Figures 2a–2b. It presents the evolution of monthly
mean NH blocking frequency as a function of longitude. The
number of blocked days are simply counted along a given
longitude band from 30c to 90∘ N for a given month. The
longitudinal distribution of blocking frequency and its
seasonality is reasonably well reproduced in the multimodel
mean. Underestimation of EA blocking frequency is largely
confined to the cold season, whereas overestimation of PA
blocking frequency is found throughout most of the year
(Figure 2c). It is also found that, although the models
successfully reproduce the summertime peak in PA blocking, it is delayed by a month from August to September.
[23] Figures 3a–3b show the annual mean number of blocking events as a function of duration. The EA and PA blocking
events, defined over 0–90∘ N and 25∘W 42∘E and 0–90∘N
and 151∘E 220∘W, respectively, are separately illustrated
along with a total number of blocking events over whole
NH. In general, the number of blocking events exhibits an
exponential decrease with duration. Comparison of CMIP5
Figure 3. Annual mean frequency of blocking events as a function of duration: (a) NNR, (b) historical
multimodel mean, (c) historical multimodel mean minus NNR, (d) RCP 8.5 multimodel, and (e) RCP 8.5
minus historical multimodel mean. Black, blue, and red bars denote blocking events over the NH, the EA,
and PA sectors, respectively. Units are (Figures 3a–3d) number of events per year and (Figure 3e) percent
of events per year, respectively. See the text for further details. Shaded bars in (Figure 3c) denote durations
where the number of models with significant positive bias minus the number of models with significant
negative bias at the 95% level using a two-tailed Student’s t test is greater than 3. Shaded bars in
(Figure 3e) are the same as in (Figure 3c) except for significant model changes.
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DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
to NNR data, however, reveals that, although not robust, shortlived blocking events, duration shorter than 9 days, are generally underrepresented by the models while long-lived ones are
somewhat overrepresented (see black bars in Figure 3c). The
bias in short-lived blocking events is mostly due to EA blocking events (see blue bars in Figure 3c). In most durations, PA
blocking events show overestimation.
[24] What causes the model biases in blocking climatology?
Although identification of exact cause(s) is beyond the scope
of the present study, it is worthwhile to examine the possible
relationship between high-frequency eddies and blocking
events in the models. As addressed earlier, it is well documented that high-frequency eddies are one of the most important
mechanisms of blocking formation and maintenance
[Nakamura et al., 1997; Cash and Lee, 2000]. Any biases in
high-frequency eddies, regardless of their origins (e.g., coarse
model resolution, unrealistic physical parameterizations,
misrepresentation of external forcings, etc), could hence result
in biases in blocking frequency and duration.
[25] Figures 4a–4b present high-frequency eddy activities
in the NNR and CMIP5 models, quantified by the standard
deviation of 500 hPa geopotential height anomalies with
periods shorter than 7 days. It can be seen that the CMIP5
models successfully reproduce the spatial distribution of
high-frequency eddy activities. However, it is also evident that
eddy activities are significantly underestimated in high latitudes and slightly overestimated in midlatitudes, indicating
equatorward biases in the extratropical storminess particularly
at the exit regions of the westerly jets over the two basins. The
underestimated eddy activities in the North Atlantic are consistent with the EA blocking frequency biases on the poleward
side of the climatological blocking frequency maximum
(compare Figures 1a–1c and 4a–4c), indicating that misrepresentation of high-frequency eddies is likely one of the culprits
of the EA blocking biases. However, a similar consistency is
not found with PA blocking biases. This is even clearer if
one examines individual models (see Figure S4). This result
suggests that misrepresentation of high-frequency eddies
alone would not be able to explain model biases in blocking
frequency and duration. Other factors, such as time-mean flow
and low-frequency variability, likely also play a role.
[26] To examine the possible impact of time-mean flow
biases on PA blocking frequency biases, the analyses are
extended to low-frequency energetics. This is motivated by
the fact that blocking biases in the models are in qualitative
agreement with those in low-frequency eddy activities, quantified by the standard deviation of 500 hPa geopotential height
anomalies with periods longer than 10 days (not shown). A
number of studies have shown that barotropic energy conversion from the time-mean flow to low-frequency eddies (BTC)
is one of the major sources of low-frequency eddy kinetic
energy in the exit regions of the westerly jets, where blocking
forms most frequently [e.g., Simmons et al., 1983; Sheng and
Derome, 1991ab]. In the midlatitudes, it is shown by Simmons
et al. [1983] that BTC is approximated by
0 2
0 2
BTC ’ Eru ¼ u v
1 @u
1 @u
u0 v0
acos’ @l
a @’
(1)
where u and v are the zonal and meridional wind components, respectively; overbars represent a time average, primes
denote deviations from the time mean; l is the longitude; ’ is
the latitude; and a is the radius of the earth. The E-vector is
defined as in the study by Hoskins et al. [1983].
Figure 4. Climatology of annual mean standard deviation of 500 hPa eddy geopotential height with
periods shorter than 7 days: (a) NNR, (b) historical multimodel mean, (c) historical multimodel mean
minus NNR, (d) RCP 8.5 multimodel mean, and (e) RCP 8.5 minus historical multimodel mean. Contour
intervals in Figure s 4a, 4b, and 4d and Figures 4c and 4e are 5 and 2 m, respectively. Shaded areas in
Figures 4c and 4e denote the number of models with significant positive bias minus the number of models
with significant negative bias at the 95% level using a two-tailed Student’s t test.
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DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
[27] Figures 5a–5c present the climatological E-vectors
superimposed on the climatological geostrophic zonal wind
in the NNR and CMIP5 models and their difference. Model
biases in zonal wind exhibit a dipole pattern about the axis
of the jets. This pattern resembles that of high-frequency
eddies (compare Figures 4c and 5c), indicating equatorward
biases both in high-frequency eddies and the time-mean
flow. Stretching deformation, @u=@l, also exhibits significant biases. In midlatitudes, negative biases in zonal wind
centered over the eastern North Pacific causes a reduced
@
u=@l over the central North Pacific where climatological
E-vectors are negative. This enhances local BTC, likely
leading to an overestimation of PA blocking frequency on
the equatorward side of the PA blocking maximum
(Figure 1c). However, high-latitude biases in zonal wind
and E-vectors are rather weak and not consistent with a
significant overestimation of blocking frequency over
northeastern Siberia and Alaska. This result suggests that
the energy transfer from the time-mean flow is not likely a
cause of the PA blocking frequency biases in the models.
[28] It is noteworthy that EA blocking frequency biases are
consistent with BTC. The zonal wind is underestimated on the
poleward side of the Atlantic jet axis. This enhances @u=@l
over broad regions of the North Atlantic where E-vectors are
directed westward, resulting in a reduced BTC.
[29] Alternatively, PA blocking biases could be caused by
unrealistic low-frequency variability in the model. An example
is the variability associated with El Niño-Southern Oscillation
(ENSO). It is documented that North Pacific blocking highs
tend to develop more frequently during La Niña winters than
during El Niño winters [Renwick and Wallace, 1996; Chen
and van den Dool, 1997]. This suggests that PA blocking
frequency could be overestimated in coupled models if La Niña
events are overrepresented. Figures 6a–6b show the composite
blocking frequency difference between DJF winters during La
Niña and El Niño conditions for the NNR and CMIP5 historical
integrations. Here, La Niña and El Niño conditions are
identified when the DJF-mean detrended Nino3.4 index is
smaller than -0.5 K and larger than 0.5 K, respectively. The
Nino3.4 index is obtained from the ERSST.V3B (observation
used for the NNR) and individual models’ sea surface temperature (SST). Both composites show increased PA blocking
frequencies during La Niña years, consistent with previous
findings. However, PA blocking frequency increase in the
CMIP5 models is biased equatorward and more importantly
weaker than that in the NNR. The probability distribution
function of Nino3.4 index in fact shows less frequent La Niña
events in the models (Figure 6c). This result suggests that model
biases in PA blocking frequency are not directly caused by
ENSO-related low-frequency variability in the models.
[30] In summary, EA blocking biases in the models are likely
caused by the biases in high-frequency eddies and time-mean
flow. However, the cause(s) that drive PA blocking biases is
not clear. This result is not surprising as the formation and
maintenance of blocking highs are highly nonlinear, involving
various interactions among mean flow, low-frequency eddies,
and high-frequency eddies. Further investigation is needed.
3.2. Future Changes
[31] With model biases described above in hand, this section
examines potential changes in blocking frequency and duration
in a warmer climate. The multimodel mean climatology, derived
from 40 years (2060–2099) of RCP 8.5 runs, is presented in
Figure 5. Climatological geostrophic zonal wind (contours) and low-frequency E-vectors:(a) NNR, (b)
historical multimodel mean, (c) historical multimodel mean minus NNR, (d) RCP 8.5 multimodel mean,
and (e) RCP 8.5 minus historical multimodel mean. Contour intervals in Figures 5a, 5b, and 5d and
Figures 5c and 5e are 3 and 0.5 ms 1, respectively. Shaded areas in Figures 5c and 5e denote the number
of models with significant positive bias minus the number of models with significant negative bias at the
95% level using a two-tailed Student’s t test.
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DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
40
40
35
c)
35
30
Frequency (%)
Frequency (%)
30
25
20
15
25
20
15
10
10
5
5
0
−4
d)
−3
−2
−1
0
1
2
3
0
−4
4
Nino3.4 index (deg K)
−3
−2
−1
0
1
2
3
4
Nino3.4 index (deg K)
Figure 6. DJF composite blocking frequency for El Niño minus La Niña winters: (a) NNR and (b) historical
multi-model mean. Contour intervals are 1 day per year. Shaded areas in Figure 6a denote significant differences, and shaded areas in Figure 6b denote the number of models with significant positive differences minus
the number of models with significant negative differences at the 95% level using a two-tailed Student’s t test.
(Figures 6c and 6d) PDF of DJF-mean de-trended Nino3.4 index for observations (thick black), historical
multimodel mean (thick green), and RCP8.5 multimodel mean (thick orange). Thin green and orange lines
in Figures 6c and 6d denote individual models for historical and RCP8.5 integrations.
Figure 1d and compared with its counterpart derived from
40 years of historical runs (1966–2005) in Figure 1e. Only 13
models that have both historical and RCP 8.5 runs are used to
make a direct comparison (see right-most column in Table 1).
[32] The geographical distribution of blocking frequency
in the future climate is quite similar to the one in the present
climate (Figure 1d). However, blocking frequency in a
warmer climate is slightly decreased over the northeastern
Pacific and northwestern Atlantic (Figure 1e). While the
decrease is rather weak, it is found in about half of the
models (see also Figure S5). This result is in agreement with
previous findings [Matsueda et al., 2009; Sillmann and
Croci-Maspoli, 2009; Barnes et al., 2012].
[33] In contrast to blocking frequency changes over the North
Pacific and North Atlantic, blocking frequency over eastern
Europe-Russia is predicted to increase in the future (Figure 1e).
This increase of the so-called Ural blocking frequency is not
robust across the models; however, a general tendency is
observed in a number of models (Figure S5). In some models
(e.g., HadGEM2-CC), the Ural blocking frequency change is
statistically significant and even larger than PA blocking
frequency change. A hint of the increased high-frequency eddy
activities upstream of the Ural region (Figure 4e) further
supports the potential increase in Ural blocking frequency.
[34] The seasonal cycle of the NH blocking activities in
the RCP 8.5 integrations is compared with their historical
counterparts in Figures 2d–2e. It is found that the reduced
blocking frequencies over the North Pacific and North
Atlantic, and the increased frequencies over eastern
Europe-Russia, occur primarily during the fall and winter
seasons, although they extend into spring over the North
Pacific (see also Figure S6). Figures 3d–3e further present
blocking frequency in a warm climate as a function of
duration and its relative difference from a past climate. In
Figure 3e, the relative change is computed by differencing
both normalized frequency distributions and plotted with a
percentage difference. This is to illustrate changes in duration, independent from changes in the number of events.
Figure 3e shows no significant changes in the relative number of blocking events for all durations (see also Figure S7).
This suggests that blocking frequency changes (Figures 1e
and 2e) are largely the result of changes in the number of
blocking events rather than changes in the duration of individual events.
[35] It is not clear why PA and EA blocking frequencies are
decreased in RCP 8.5 integrations. Although high-frequency
eddy activities are predicted to decrease over the east and west
coasts of the North America (Figures 4d–4e), an overall
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DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
resemblance to blocking frequency changes (Figures 1d–1e) is
rather weak. The mean flow change likely contributes to the
EA blocking frequency change. As shown in Figures 5d–5e,
the Atlantic jet is predicted to weaken in a warm climate especially at the entrance regions of the jet. This causes increased
stretching deformation over the North Atlantic. Since E-vectors are directed westward there, it implies weaker BTC (equation (1)), supporting weaker low-frequency variability over the
North Atlantic. This contrasts to the mean flow change over
eastern North Pacific, which is not consistent with PA blocking change. Reduced PA blocking frequency may instead
result from more frequent El Niño events (or less frequent
La Niña events) in RCP8.5 integrations. In fact, Nino3.4 index
shows a weak hint of enhanced El Niño events in RCP8.5 integrations (Figure 6d). However, investigation of individual
models does not show a robust relationship between ENSO
frequency change and PA blocking frequency change in a
warm climate. Further studies are needed.
3.3. Sensitivity Tests
[36] The robustness of the above findings is extensively
tested by changing the data resolution, the threshold value of
the blocking index, the anomaly definition, and the blocking
index itself. As described in section 2, all analyses are performed
using interpolated data. The interpolation from high to moderate
resolutions (e.g., T42), however, could result in an underestimation of blocking events as it smoothens the anomaly field. To
examine the sensitivity to the data resolution, overall analyses
are repeated using raw data for the selected models. The results
are found to be insensitive to the data resolution (not shown).
[37] The sensitivity to the amplitude threshold, (A) in
section 2, is also tested. The (A) is a relative quantity and
varies from model to model. This introduces uncertainty in
multimodel analyses as each run would have different
minimum threshold of blocking anomalies. A sensitivity test
is performed by applying the multimodel mean (A) to each
model integration. Although not shown, no significant sensitivity is observed. This is because (A) does not vary much among
the models. Since the 1.5 standard deviation level is a somewhat
arbitrary choice, a sensitivity test is also performed by varying
the standard deviation level of (A). Again, the results are qualitatively insensitive to the choice of standard deviation level.
[38] Although the anomaly z0 , defined in section 2, filters
out the long-term trend in the data, uncertainty in the anomaly definition in transient climate simulations still remains.
To minimize the possible biases introduced by the expansion
of the troposphere in a warming climate, overall analyses are
repeated with the global daily mean 500 hPa geopotential
height removed from each daily anomaly z0 . The results are
found to be insensitive to this choice of anomaly definition.
[39] A sensitivity test is extended to the blocking index itself.
As introduced in section 2, the TM index is applied to the
CMIP5 data and the results are summarized in Figure 7. Since
the TM index takes different approach as compared to the
hybrid index and detects blocking events only as a function of
longitude, direct comparison is not straightforward. However,
as illustrated in Figure 7b, the model biases and future changes
in blocking frequency are qualitatively similar to those presented in Figures 1c and 1e. More specifically, the overestimation of the PA blocking frequency and the underestimation of
the EA blocking frequency in the CMIP5 historical runs are evident. Additionally, an overall decrease in blocking frequency
over the two basins is consistent with the result derived from
a hybrid index, although the change in Ural blocking frequency
is much weaker. Overall, this suggests that the results presented
in this study are reasonably robust to the type of blocking index.
4. Conclusions
[40] This study examines the NH blocking climatology as
simulated by the CMIP5 models. Both historical and RCP 8.5
runs are examined to evaluate the performance of the CMIP5
models in comparison to NNR and to identify possible changes
in blocking frequency and duration in a warmer climate.
[41] Comparison to reanalysis data reveals that the CMIP5
models can reproduce the NH blocking climatology reasonably
well. It is however found that the EA blocking frequency is
generally underestimated in most models during the cold
Figure 7. Longitudinal distribution of annual mean blocking frequency derived from the TM index: (a)
thick black, green, and orange lines denote NNR, historical multimodel mean, and RCP8.5 multimodel
mean, respectively. (b) Thick green and orange lines denote historical multimodel mean minus NNR
and RCP8.5 minus historical multimodel mean. In both panels, thin green and orange lines denote individual models for historical and RCP8.5 integrations.
1187
DUNN-SIGOUIN AND SON: CMIP5 BLOCKING
season. This is mostly due to the short-lived blocking events
with duration shorter than 9 days. In contrast, the PA blocking
frequency is largely overestimated throughout the year for
almost all durations. Although this result contrasts to previous
modeling studies which have typically documented underestimation of the blocking frequency over both the North Pacific
and Atlantic in a moderate-resolution climate model integration
[e.g., D’Andrea et al., 1998;, Doblas-Reyes et al., 2002;
Matsueda et al., 2009; Barriopedro et al., 2010a], a similar
result is documented for the latest operational model (E. DunnSigouin, S.-W. Son, and H. Lin, in press, 2012) and for certain
models participating in the CMIP3 [Scaife et al., 2010].
[42] In comparison to historical integrations, the RCP8.5
integrations show a weak hint of reduced blocking frequency
over the North Pacific and North Atlantic especially during
the fall and winter. This result largely confirms previous
findings that are based on different data and different blocking
indices [Matsueda et al., 2009; Sillmann and Croci-Maspoli,
2009; Barnes and Hartmann, 2010; Barnes et al., 2012]. In
contrast to North Pacific and North Atlantic blocking events,
blocking events over eastern Europe and Russia, the so-called
Ural blocking, are predicted to marginally increase in a
warmer climate. There is, however, no systematic change in
the duration of individual blocking events for all durations.
[43] The causes of model biases and future changes of
blocking frequency are not clear especially over the North
Pacific. While further analyses are needed, preliminary
analyses indicate that underestimation of blocking frequency
over the Euro-Atlantic and a future decrease in blocking
frequency over the North Atlantic are consistent with model
biases and predicted changes in high-frequency eddy activities and time-mean flow. A similar consistency, however, is
not observed over the North Pacific, indicating that model
biases and predictions of PA blocking events in the CMIP5
models are likely associated with multiple factors. Further
studies are needed to identify exact causal relationship.
[44] Acknowledgments. We thank Junhyung Bae and Joowan Kim for
their assistances in processing model data. We acknowledge the World
Climate Research Programme’s Working Group on Coupled Modelling,
which is responsible for CMIP, and thank the climate modeling groups
(listed in Table 1 of this paper) for producing and making available their
model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support
and led development of software infrastructure in partnership with the Global
Organization for Earth System Science Portals. The work of S.W. Son was
supported by research settlement fund for the new faculty member of SNU.
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