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Investigating the Influence of Anthropogenic Forcing and Natural Variability on the 2014 Hawaiian Hurricane Season

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New climate simulations suggest that the extremely active 2014 Hawaiian hurricane season was made substantially more likely by anthropogenic forcing, but that natural variability of El Niño was also partially involved.
Probability for the frequency of TCs near Hawaii between Jul and Nov simulated by a suite of simulations using FLOR. P(2) represents the probability of occurrence of a year with TC number more than or equal to near Hawaii. (a) Retrospective forecasts for TC frequency near Hawaii initialized in Jul. The black line indicates observed TC frequency, green line indicates the mean forecast value, shading indicates the confidence intervals, dots indicate values simulated by one or more ensembles. (b) Results of P(x) from the control simulations and observations. Blue bars are probability obtained by observations (1949-2014). Green bars are the results from the 1990 control simulation (500 years), whereas red bars are the results from the 1860 control simulation (2000 years). Error bars in the control simulations denote the range of minimum and maximum values of P(x), computed from each 100-year period. (c) Results of P(2) from the multi-decadal simulation. For each 20-year period, P(2) (black line) was calculated from 700 samples. Colored bars show the range of conditional P(2) induced by natural variability. For example, red bars cover P(2|AMO + ) and P(2|AMO-), namely, the range of P(2) under the conditions between positive AMO and negative AMO phases. Likewise, P(2) under the condition of positive and negative phases of PDO (green), ENSO (blue), and IPO (purple) are shown. Orange circles denote results of P(2) from the control simulations. The orange error bars show the range of minimum and maximum when P(2) is computed for each 100-year period. (d) As (c), but for P(3).
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Special Supplement to the
Bullen of the American Meteorological Society
Vol. 96, No. 12, December 2015
From A Climate Perspective
EXPLAINING
EXTREME EVENTS
OF 2014
EXPLAINING EXTREME
EVENTS OF 2014 FROM A
CLIMATE PERSPECTIVE
Editors
Stephanie C. Herring, Martin P. Hoerling, James P. Kossin,Thomas C. Peterson, and Peter A. Stott
Special Supplement to the
Bulletin of the American Meteorological Society
Vol. 96, No. 12, December 2015
AmericAn meteorologicAl Society
HOW TO CITE THIS DOCUMENT
Citing the complete report:
Herring, S. C., M. P. Hoerling, J. P. Kossin, T. C. Peterson, and P. A. Stott , Eds., 2015: Explaining Extreme Events of 2014 from
a Climate Perspective. Bull. Amer. Meteor. Soc., 96 (12), S1S172.
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DECEMBER 2015
AMERICAN METEOROLOGICAL SOCIETY |
Abstract ........................................................................................................................................................................... ii
1. Introduction to Explaining Extreme Events of 2014 from a Climate Perspective ................................1
2. Extreme Fire Season in California: A Glimpse Into the Future? ............................................................... 5
3. How Unusual was the Cold Winter of 2013/14 in the Upper Midwest? ...............................................10
4. Was the Cold Eastern Us Winter of 2014 Due to Increased Variability? ............................................15
5. The 2014 Extreme Flood on the Southeastern Canadian Prairies ........................................................ 20
6. Extreme North America Winter Storm Season of 2013/14: Roles of Radiative Forcing and the
Global Warming Hiatus ................................................................................................................................. 25
7. Was the Extreme Storm Season in Winter 2013/14 Over the North Atlantic and the United
Kingdom Triggered by Changes in the West Pacific Warm Pool? ..................................................... 29
8. Factors Other Than Climate Change, Main Drivers of 2014/15 Water Shortage in Southeast
Brazil................................................................................................................................................................... 35
9. Causal Influence of Anthropogenic Forcings on the Argentinian Heat Wave of December
2013 .....................................................................................................................................................................41
10. Extreme Rainfall in the United Kingdom During Winter 2013/14: The Role of Atmospheric
Circulation and Climate Change ................................................................................................................. 46
11. Hurricane Gonzalo and its Extratropical Transition to a Strong European Storm............................51
12. Extreme Fall 2014 Precipitation in the Cévennes Mountains ................................................................. 56
13. Record Annual Mean Warmth Over Europe, the Northeast Pacific, and the Northwest
Atlantic During 2014: Assessment of Anthropogenic Influence ..........................................................61
14. The Contribution of Human-Induced Climate Change to the Drought of 2014 in the Southern
Levant Region ................................................................................................................................................... 66
15. Drought in the Middle East and Central–Southwest Asia During Winter 2013/14............................71
16. Assessing the Contributions of East African and West Pacific Warming to the 2014 Boreal
Spring East African Drought ........................................................................................................................ 77
17. The 2014 Drought in the Horn of Africa: Attribution of Meteorological Drivers ............................ 83
18. The Deadly Himalayan Snowstorm of October 2014: Synoptic Conditions and Associated
Tre n d s ................................................................................................................................................................ 89
19. Anthropogenic Influence on the 2014 Record-Hot Spring in Korea .................................................... 95
20. Human Contribution to the 2014 Record High Sea Surface Temperatures Over the Western
Tropical And Northeast Pacific Ocean ................................................................................................... 10 0
21. The 2014 Hot, Dry Summer in Northeast Asia ....................................................................................... 105
22. Role of Anthropogenic Forcing in 2014 Hot Spring in Northern China ............................................. 111
23. Investigating the Influence of Anthropogenic Forcing and Natural Variability on the 2014
Hawaiian Hurricane Season. .......................................................................................................................11 5
24. Anomalous Tropical Cyclone Activity in the Western North Pacific in August 2014 ................... 120
25. The 2014 Record Dry Spell at Singapore: An Intertropical Convergence Zone (ITCZ)
Drought ........................................................................................................................................................... 126
26. Trends in High-Daily Precipitation Events in Jakarta and the Flooding of January 2014 ................131
27. Extreme Rainfall in Early July 2014 in Northland, New Zealand—Was There an
Anthropogenic Influence? ........................................................................................................................... 13 6
28. Increased Likelihood of Brisbane, Australia, G20 Heat Event Due to Anthropogenic Climate
Change ..............................................................................................................................................................141
29. The Contribution of Anthropogenic Forcing to the Adelaide and Melbourne, Australia, Heat
Waves of January 2014 ................................................................................................................................ 145
30 Contributors to the Record High Temperatures Across Australia in Late Spring 2014 ............... 149
31. Increased Risk of the 2014 Australian May Heatwave Due to Anthropogenic Activity ................ 154
32. Attribution of Exceptional Mean Sea Level Pressure Anomalies South of Australia in August
2014 .................................................................................................................................................................. 158
33. The 2014 High Record of Antarctic Sea Ice Extent ................................................................................. 163
34. Summary and Broader Context .................................................................................................................... 16 8
TABLE OF CONTENTS
Sii DECEMBER 2015
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ABSTRACTStephanie C. Herring, Martin P. Hoerling, James P. Kossin, Thomas C. Peterson, and Peter A. Stott
Understanding how long-term global change affects
the intensity and likelihood of extreme weather events
is a frontier science challenge. This fourth edition of
explaining extreme events of the previous year (2014)
from a climate perspective is the most extensive yet
with 33 different research groups exploring the causes
of 29 different events that occurred in 2014. A number
of this year’s studies indicate that human-caused climate
change greatly increased the likelihood and intensity for
extreme heat waves in 2014 over various regions. For
other types of extreme events, such as droughts, heavy
rains, and winter storms, a climate change influence was
found in some instances and not in others. This year’s
report also included many different types of extreme
events. The tropical cyclones that impacted Hawaii were
made more likely due to human-caused climate change.
Climate change also decreased the Antarctic sea ice
extent in 2014 and increased the strength and likelihood
of high sea surface temperatures in both the Atlantic and
Pacific Oceans. For western U.S. wildfires, no link to the
individual events in 2014 could be detected, but the overall
probability of western U.S. wildfires has increased due to
human impacts on the climate.
Challenges that attribution assessments face include
the often limited observational record and inability of
models to reproduce some extreme events well. In
general, when attribution assessments fail to find anthro-
pogenic signals this alone does not prove anthropogenic
climate change did not influence the event . The failure
to find a human fingerprint could be due to insufficient
data or poor models and not the absence of anthropo-
genic effects.
This year researchers also considered other human-
caused drivers of extreme events beyond the usual
radiative drivers. For example, flooding in the Canadian
prairies was found to be more likely because of human
land-use changes that affect drainage mechanisms. Simi-
larly, the Jakarta floods may have been compounded by
land-use change via urban development and associated
land subsidence. These types of mechanical factors re-
emphasize the various pathways beyond climate change
by which human activity can increase regional risk of
extreme events.
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23. INVESTIGATING THE INFLUENCE OF ANTHROPOGENIC
FORCING AND NATURAL VARIABILITY ON THE 2014
HAWAIIAN HURRICANE SEASON
HiRoYuKi MuRaKaMi, gaBRiel a. veccHi, tHoMaS l. delWoRtH, KaRen paFFendoRF, RicHaRd gudgel,
liWei Jia, and FanRong zeng
Introduction. Three hurricanes approached the Hawai-
ian Islands during the 2014 hurricane season (Fig.
23.1a), which is the third largest number since 1949
(black bars in Fig. 23.1b). Previous studies suggest
that the frequency of tropical cyclones (TCs) around
Hawaii will increase under global warming (Li et al.
2010; Murakami et a l. 2013). The projected increase is
primarily associated with a northwestward shifting of
TC tracks in the open ocean southeast of the islands,
where climate models robustly predict greater warm-
ing than the other open oceans. Natural variability,
such as that associated with the El Niño–Southern
Oscillation (ENSO), also has a significant influence
on TC activity near Hawaii (Chu and Wang 1997; Jin
et al. 2014). In fact, moderate El Niño conditions were
observed during the 2014 hurricane season that might
have been favorable for TC activity near Hawaii. In
this study, we use a suite of climate experiments
to explore whether the unusually large number of
Hawaiian TCs in 2014 was made more likely by an-
thropogenic forcing or natural variability.
Methodology. We explore a suite of simulations us-
ing the Geophysical Fluid Dynamics Laboratory
(GFDL) Forecast-oriented Low Ocean Resolution
model (FLOR; Vecchi et al. 2014; see Supplementary
Material). Simulated TCs were detected using an auto-
mated tracking algorithm as proposed by Murakami
et al. (2015; see online supplemental material). For
the observational dataset, we used “best-track” data
obtained from the International Best Track Archive
for Climate Stewardship (IBTrACS; Knapp et al. 2010)
and the Unisys Corporation website (Unisys 2015) for
the period 1949–2014. We focus on TCs with tropi-
cal storm strength or stronger. For the observed sea
surface temperature (SST), we used the Hadley Center
Global Sea Ice and Sea Surface Temperature dataset
(HadISST1; Rayner et al. 2003). We define simulated/
observed TCs near Hawaii as those TCs propagating
within the coastal region of the Hawaiian Islands; that
is, the zone extending 500 km from the coastline (see
blue domain in Fig. 23.1a). We performed a prelimi-
nary investigation of the dependence of distance on
the effect of anthropogenic forcing and natural vari-
ability on TC frequency near Hawaii, which revealed
that the dependence is small qualitatively.
To assess the ability of FLOR to predict the TCs
near Hawaii, we first analyzed a retrospective sea-
sonal forecast made using FLOR initialized on 1 July
for each year of 1980–2014 (Vecchi et al. 2014; Jia et
al. 2015; see online supplemental material). Figure
23.2a shows the time series of TC number predicted
by FLOR, which reasonably predicts the interan-
nual variations of observed TC frequency (r = 0.59).
Moreover, FLOR predicted marked multidecadal
variations in the probability of TC occurrence (for
example, higher during the period 1980–94 relative
to 1995–2014), which is consistent with the observed
variability. However, FLOR underestimates TC num-
ber in the abnormal years of 1982, 2009, and 2014,
although FLOR predicts relatively larger numbers in
2009 and 2014 compared to the mean of the last two
decades (1995–2014). The deficiency in predicting the
abnormal years indicates that there may be another
forcing that is missing in the experimental setting
(for example, atmospheric initialization; aerosols). Or
New climate simulations suggest that the extremely active 2014 Hawaiian hurricane season was
made substantially more likely by anthropogenic forcing, but that natural variability of El Niño
was also partially involved.
AFFILIATIONS: MuRaKaMi , veccHi, delWoRtH, paFFen doRF,
and Jia NOAA /Geophysical Fluid Dynamics Laboratory, and
Atmospheric and Oceanic Sciences Program, Princeton University,
Princeton, New Jersey; gudge l and zen gNOAA/Geophysical
Fluid Dynamics Laboratory. Princeton, New Jersey
D O I: 10 . 117 5 / BA M S - D -1 5 - 0 0 119 .1
A supplement to this article is available online (10.1175
/BAMS-D-15-00119.2)
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these abnormal events may be unpredictable because
of random noise emerging in nature.
Hereafter, we will examine the empirical prob-
ability of exceedance for the frequency of TCs near
Hawaii during July–November as a function of TC
number using the following equation:
where x is the annual number of TCs near Hawaii.
For example, P(3) represents the probability of occur-
rence of a year with three or more TCs near Hawaii.
Effect of Anthropogenic Forcing on TCs near Hawaii.
Here we form a preliminary estimate of the impact
of anthropogenic forcing on Hawaiian TCs, com-
paring a pair of control climate simulations using
FLOR, which were run for 2000-yr (500-yr) intervals
by prescribing radiative forcing and land-use con-
ditions representative of the year 1860 (1990) (see
online supplemental material). The probability of
exceedance for seasonal Hawaiian TC frequency in
the 1990 control experiment compares much more
reasonably with the observed probability than does
the 1860 control experiment (Fig. 23.2b), although the
1990 control experiment still slightly underestimates
the observed values. The 1860 control experiment
shows substantially reduced probability relative to
the 1990 control experiment. The P(2) and P(3) from
the 1990 control experiment are about 5 and 17 times,
respectively, larger than those from the 1860 control
experiment, or a fraction of attributable risk (FAR;
Jaeger et al. 2008) of 80% and 94%, respectively. These
experiments suggest that anthropogenic forcing has
substantially changed the odds of TC seasons like
2014 near Hawaii relative to natural variability alone.
Effect of Natural Variability on TCs near Hawaii. The
black bars in Fig. 23.1b reveal substantial interannual
and decadal variations—including a relatively inac-
tive era for Hawaiian TCs for the decade prior to 2014.
Fig. 23.1. Observed TCs near Hawaii and indices of natural variability. (a) TCs in 2014. Three TCs (red: Iselle,
Julio, and Ana) approached the coastal region of Hawaii (blue). Dots denote TC genesis locations. C1 and C3
indicate categor y 1 and 3 TCs by the Saffir–Simpson hurricane wind scale, respectively. (b) Yearly variability
in the number of TCs near Hawaii during the peak season of Jul Nov for the period 1949–2014 (black bars,
number). Colored lines denote climate indices for the PDO (green), AMO (red), IPO (purple), and Niño-3.4
(blue). Units for the indices are one standard deviation. For details of the climate indices and methods used to
detect them, see the online supplemental material. (c) Regression of seasonal mean sea surface temperature
(SST) onto the number of TCs near Hawaii. Units: K number1. (d) Results of change-point analysis applied
to TC frequency near Hawaii showing the posterior probability mass function (PMF) for the year of the f irst
change point (blue) and second change point (red) under the hypothesis of two change points.
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Figure 23.1c shows the seasonal mean SST regressed
onto TC frequency near Hawaii, and reveals an El
Niño-like spatial pattern. This reflects the tendency
for an increase in Hawaiian TC activity during El
Niño: the Niño3.4 index (blue line, Supplemental Fig.
S23.2) is moderately correlated with TC frequency
(Chu and Wang 1997). Moreover, Fig. 23.1b reveals
marked multidecadal variations in the TC frequency
near Hawaii. There appear to be abrupt shifts in Ha-
waiian TC frequency in the mid-1970s and 1990s. We
applied a change-point analysis developed by Zhao
and Chu (2010) to the TC frequency time series, which
indicated that the most likely first (second) change
point was 1978 (1995) (Fig. 23.1d). The spatial pattern
Fig. 23.2. Probability for the frequency of TCs near Hawaii between Jul and Nov simulated by a suite of simula-
tions using FLOR. P(2) represents the probability of occurrence of a year with TC number more than or equal to
near Hawaii. (a) Retrospective forecasts for TC frequency near Hawaii initialized in Jul. The black line indicates
observed TC frequency, green line indicates the mean forecast value, shading indicates the confidence intervals,
dots indicate values simulated by one or more ensembles. (b) Results of P(x) from the control simulations and
observations. Blue bars are probability obtained by observations (1949–2014). Green bars are the results from
the 1990 control simulation (500 years), whereas red bars are the results from the 1860 control simulation
(2000 years). Error bars in the control simulations denote the range of minimum and maximum values of P(x),
computed from each 100-year period. (c) Results of P(2) from the multi-decadal simulation. For each 20-year
period, P(2) (black line) was calculated from 700 samples. Colored bars show the range of conditional P(2) in-
duced by natural variability. For example, red bars cover P(2|AMO+) and P(2|AMO ), namely, the range of P(2)
under the conditions between positive AMO and negative AMO phases. Likewise, P(2) under the condition of
positive and negative phases of PDO (green), ENSO (blue), and IPO (purple) are shown. Orange circles denote
results of P(2) from the control simulations. The orange error bars show the range of minimum and maximum
when P(2) is computed for each 100-year period. (d) As (c), but for P(3).
S118 DECEMBER 2015
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shown in Fig. 23.1c is also similar to the low-frequency
variations of the Pacific Decadal Oscillation (PDO;
Mantua et al. 1997; green line, Supplementary Fig.
S23.3) or Interdecadal Pacific Oscillation (IPO;
Power et al. 1999; Folland et al. 2002; purple line,
Supplementary Fig. S23.4). Both indices changed sign
around 1997 (Fig. 23.1b), which may contribute to the
multidecadal variations in TC frequency (Wang et al.
2010). Moreover, Fig. 23.1c shows marked negative
SSTs in the tropical Atlantic, indicating reduced TC
frequency near Hawaii when the tropical Atlantic
is warmer. A recent study by Kucharski et al. (2011)
reported that the Atlantic warming in the late twen-
tieth century could have led to a reduction in Pacific
warming via the Walker circulation. The Atlantic
multidecadal oscillation (AMO; Delworth and Mann
2000; red line, Supplementary Fig. S23.5) index also
changed sign around 1997 (Fig. 23.1b), and this could
have caused the abrupt shift in TC frequency.
To elucidate the potential influence of the natural
variability outlined above on TC frequency near
Hawaii, we conducted 35-member ensemble multi-
decadal simulations (see online supplemental mate-
rial) from 1941 to 2040. For each 20-year period from
1941, 700 (20 × 35) samples were available to calculate
P(x). In contrast to the seasonal forecasts, because the
simulated internal variability is out of phase among
the ensemble members even with the observations),
we can estimate the conditional probability of P(x)
under any phase of natural variability. In other words,
we can estimate potential probability under any phase
of natural variability in a specific range of decades.
Here, we define a simulated/observed positive (or
negative) phase of ENSO, PDO, IPO, and AMO as
these indices exceeding one standard deviation and
estimate the amplitude of P(x) between the two phas-
es. For details of the climate indices and methods used
to detect them, see the online supplemental material.
Figures 23.2c and d summarize the results for P(2)
and P(3). Similar results were obtained for P(1) (fig ure
not shown). The black lines show P(2) and P(3) , and re-
veal a gradual increase from 1940 to 2040, indicating
that global warming generates more TCs near Hawaii,
which is consistent with the control simulations. The
colored bars denote the range of conditional prob-
ability induced by natural variability, revealing that
natural variability has considerable potentia l to influ-
ence the probability of TC frequency. The amplitude
of the bars is similar to the amplitude of the global
warming effect (that is, the difference in orange circles
in Figs. 23.2c and d), implying that internal variations
could act to either temporarily mask or substantially
amplify the impact of anthropogenic forcing on the
number of TCs near Hawaii.
Discussions and Conclusions. As shown in Fig. 23.1b, the
observed TC frequency was greater during the period
1980–94 than 1995–2014. Moreover, the observations
show positive PDO and IPO indices, as well as a
negative AMO index bet ween 1980 and 1994, whereas
these indices reversed sign between 1995 and 2014.
From Figs. 23.2c and d, it is possible that the earlier
decades (1981–2000) could have had a higher prob-
ability of TC occurrence than more recent decades
(2001–20), provided that the PDO, IPO, and AMO
indices were more favorable for TC activity during
the previous decades. Therefore, it can be concluded
that the observed multidecadal difference between
1980–94 and 1995–2014 was mainly caused by natu-
ral variability. However, the extremely large number
of TCs during the 2014 hurricane season occurred
despite the unfavorable IPO (−2.0), AMO (+0.7), and
PDO (−0.7), and moderate El Niño (+0.5). The FLOR
suggests that historical global warming could have
contributed to a substantial increase in probability of
active Hawaiian TC seasons. The evidence for this can
be shown by the composites of the years in which the
phase of natural variability is similar to 2014 case in
the control experiments. We found that P(1) from the
1990 control experiment under the condition of nega-
tive IPO, positive AMO, negative PDO, and moderate
El Niño is about 3.4 times larger than that from the
1860 control experiment (FAR = 71%). Therefore, it is
possible that global warming increased the odds of the
extremely large number of Hawaiian TCs in 2014, in
combination with the moderately favorable condition
of El Niño. The ensemble experiments with FLOR
indicate a continued increasing probability of active
seasons around Hawaii over the next few decades
[consistent with Murakami et al. (2013)]—although
there will be substantial modulation on interannual
and decadal time scales from internal variability.
ACKNOWLEDGMENT. This report was
prepared by Hiroyuki Murakami under award
NA14OAR4830101 from the National Oceanic and
Atmospheric Administration, U.S. Department of
Commerce. The statements, findings, conclusions,
and recommendations are those of the authors and
do not necessarily reflect the views of the National
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DECEMBER 2015AMERICAN METEOROLOGICAL SOCIETY |
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cal storm days (1965-2008). Geophys. Res. Lett., 37,
L07704, doi:10.1029/2010GL042487.
Zhao, X., and P.-S. Chu, 2010: Bayesian changepoint
analy sis for extreme events (typhoons, heav y rainfa ll,
and heat waves): An RJMCMC approach. J. Climate,
23, 1034–1046. doi:10.1175/2009JCL ,I2597.1.
S168 DECEMBER 2015
|
Table 34.1. ANTHROPOGENIC INFLUENCE
ON EVENT STRENGTH † ON EVENT LIKELIHOOD † Total
Number
of
Papers
INCREASE DECREASE NOT FOUND OR UNCERTAIN INCREASE DECREASE NOT FOUND OR UNCERTAIN
Heat
Australia (Ch . 31)
Europe (Ch .13)
S. Korea (Ch . 19)
Australia, Adelaide & Melbourne
(Ch. 29)
Australia, Brisbane (Ch.28)
Heat
Argentina (Ch. 9)
Australia (Ch. 30, Ch. 31)
Australia, Adelaide (Ch. 29)
Australia , Brisbane (Ch. 28)
Europe (Ch . 13)
S. Korea ( Ch . 19)
China (Ch. 22)
Melbourne, Australia (Ch. 29) 7
Cold Upper Midwest (Ch.3) Cold Upper Midwest (Ch.3) 1
Winter
Storms and
Snow
Eastern U.S . (Ch. 4)
N. America (Ch. 6)
N. Atlantic (Ch. 7)
Winter
Storms and
Snow
Nepal (Ch. 18)
Eastern U.S.(Ch. 4)
N. America (Ch. 6)
N. Atlantic (Ch. 7)
4
Heavy
Precipitation Canada** (Ch. 5)
Jakarta**** (Ch. 26)
United Kingdom*** (Ch. 10)
New Zealand (Ch. 27)
Heavy
Precipitation
Canada** (Ch. 5)
New Zealand (Ch. 27 )
Jakarta**** (Ch. 26)
United Kingdom*** (Ch. 10)
S. France (Ch. 12)
5
Drought
E. Africa (Ch. 16)
E. Africa* (Ch. 17)
S. Levant (Ch. 14)
Middle East and S.W. Asia
(Ch . 15)
N.E . Asia (Ch . 21)
Singapore (Ch. 25)
Drought E. Africa (C h. 16)
S. Levant (Ch. 14)
Middle East and S.W. Asia (Ch. 15 )
E. Africa* (C h . 17 )
N.E . Asia (Ch . 21)
S. E. Brazil (Ch. 8)
Singapore (Ch. 25)
7
Tro p ica l
Cyclones
Gonzalo (C h. 11)
W. Pacific (Ch. 24)
Tro p ica l
Cyclones Hawaii (Ch. 23) Gonzalo (C h. 11)
W. Pacific (Ch. 24) 3
Wildfires California (Ch. 2) Wildfires California (Ch. 2) 1
Sea Surface
Temperature
W. Tropical & N.E. Pacific (Ch. 20)
N.W. Atlantic & N .E. Pacif ic (C h. 13)
Sea Surface
Temperature
W. Tropical & N.E. Pacific
(Ch. 20)
N.W. Atlantic & N .E. Pacif ic
(Ch. 13)
2
Sea Level
Pressure S. Australia (Ch. 32) Sea Level
Pressure S. Australia (Ch. 32) 1
Sea Ice
Extent Antarctica (Ch. 33) Sea Ice
Extent Antarctica (Ch. 33) 1
TOTAL 32
Paper s that did not investigate s treng th are not listed.
†† Papers that did not investigate likelihood are not listed.
* No influence on the likel ihood of low rainf all, but human influences did re sult in higher temperatures and increased net incoming radiation at the
surface over the region most affe cted by the drought.
** An increase in spring r ainfa ll as well a s exten sive artificial pond drainage incre ased th e risk of mo re frequent severe f loods from the enhanced
rainfall.
*** Evidence for human influence was found for greater risk of UK extreme rainfall during winter 2013/14 with time scales of 10 days
*** * The stu dy of Jak arta rainf all event o f 2014 found a statistically si gnificant increase i n the prob abilit y of such r ains over the last 115 years, though
the study did not e stablish a cause.
S169
DECEMBER 2015AMERICAN METEOROLOGICAL SOCIETY |
Table 34.1. ANTHROPOGENIC INFLUENCE
ON EVENT STRENGTH † ON EVENT LIKELIHOOD † Total
Number
of
Papers
INCREASE DECREASE NOT FOUND OR UNCERTAIN INCREASE DECREASE NOT FOUND OR UNCERTAIN
Heat
Australia (Ch . 31)
Europe (Ch .13)
S. Korea (Ch . 19)
Australia, Adelaide & Melbourne
(Ch. 29)
Australia, Brisbane (Ch.28)
Heat
Argentina (Ch. 9)
Australia (Ch. 30, Ch. 31)
Australia, Adelaide (Ch. 29)
Australia , Brisbane (Ch. 28)
Europe (Ch . 13)
S. Korea ( Ch . 19)
China (Ch. 22)
Melbourne, Australia (Ch. 29) 7
Cold Upper Midwest (Ch.3) Cold Upper Midwest (Ch.3) 1
Winter
Storms and
Snow
Eastern U.S . (Ch. 4)
N. America (Ch. 6)
N. Atlantic (Ch. 7)
Winter
Storms and
Snow
Nepal (Ch. 18)
Eastern U.S.(Ch. 4)
N. America (Ch. 6)
N. Atlantic (Ch. 7)
4
Heavy
Precipitation Canada** (Ch. 5)
Jakarta**** (Ch. 26)
United Kingdom*** (Ch. 10)
New Zealand (Ch. 27)
Heavy
Precipitation
Canada** (Ch. 5)
New Zealand (Ch. 27 )
Jakarta**** (Ch. 26)
United Kingdom*** (Ch. 10)
S. France (Ch. 12)
5
Drought
E. Africa (Ch. 16)
E. Africa* (Ch. 17)
S. Levant (Ch. 14)
Middle East and S.W. Asia
(Ch . 15)
N.E . Asia (Ch . 21)
Singapore (Ch. 25)
Drought E. Africa (C h. 16)
S. Levant (Ch. 14)
Middle East and S.W. Asia (Ch. 15 )
E. Africa* (C h . 17 )
N.E . Asia (Ch . 21)
S. E. Brazil (Ch. 8)
Singapore (Ch. 25)
7
Tro p ica l
Cyclones
Gonzalo (C h. 11)
W. Pacific (Ch. 24)
Tro p ica l
Cyclones Hawaii (Ch. 23) Gonzalo (C h. 11)
W. Pacific (Ch. 24) 3
Wildfires California (Ch. 2) Wildfires California (Ch. 2) 1
Sea Surface
Temperature
W. Tropical & N.E. Pacific (Ch. 20)
N.W. Atlantic & N .E. Pacif ic (C h. 13)
Sea Surface
Temperature
W. Tropical & N.E. Pacific
(Ch. 20)
N.W. Atlantic & N .E. Pacif ic
(Ch. 13)
2
Sea Level
Pressure S. Australia (Ch. 32) Sea Level
Pressure S. Australia (Ch. 32) 1
Sea Ice
Extent Antarctica (Ch. 33) Sea Ice
Extent Antarctica (Ch. 33) 1
TOTAL 32
Paper s that did not investigate s treng th are not listed.
†† Papers that did not investigate likelihood are not listed.
* No influence on the likel ihood of low rainf all, but human influences did re sult in higher temperatures and increased net incoming radiation at the
surface over the region most affe cted by the drought.
** An increase in spring r ainfa ll as well a s exten sive artificial pond drainage incre ased th e risk of mo re frequent severe f loods from the enhanced
rainfall.
*** Evidence for human influence was found for greater risk of UK extreme rainfall during winter 2013/14 with time scales of 10 days
*** * The stu dy of Jak arta rainf all event o f 2014 found a statistically si gnificant increase i n the prob abilit y of such r ains over the last 115 years, though
the study did not e stablish a cause.
Paper s that did not investigate s treng th are not listed.
†† Papers that did not investigate likelihood are not listed.
* No influence on the likel ihood of low rainf all, but human influences did re sult in higher temperatures and increased net incoming radiation at the
surface over the region most affe cted by the drought.
** An increase in spring r ainfa ll as well a s exten sive artificial pond drainage incre ased th e risk of mo re frequent severe f loods from the enhanced
rainfall.
*** Evidence for human influence was found for greater risk of UK extreme rainfall during winter 2013/14 with time scales of 10 days
*** * The stu dy of Jak arta rainf all event o f 2014 found a statistically si gnificant increase i n the prob abilit y of such r ains over the last 115 years, though
the study did not e stablish a cause.
... By 2050 they will become 1-in-4-yr events. Interim (Dee et al. 2011), MERRA2 (Gelaro et al. 2017), and JRA-55 (Kobayashi et al. 2015 (Murakami et al. 2015(Murakami et al. , 2017Zhang et al. 2017). ...
... Potential influences of natural variability (e.g., ENSO, PDO, and AO) are also detected by comparing conditional P AllForc for which P AllForc is separately computed during different phases of natural variability [colored markers in Figs. 2d-f; detailed methods are given in Murakami et al. (2015)]. The potential effects of natural variability on P AllForc are measured by the lengths of the colored lines. ...
... All datasets are regridded to 1.5° × 1.5°. Observed natural indices (Niño-3 (Murakami et al. 2015(Murakami et al. , 2017Zhang et al. 2017). ...
... Potential influences of natural variability (e.g., ENSO, PDO, and AO) are also detected by comparing conditional P AllForc for which P AllForc is separately computed during different phases of natural variability [colored markers in Figs. 2d-f; detailed methods are given in Murakami et al. (2015)]. The potential effects of natural variability on P AllForc are measured by the lengths of the colored lines. ...
Article
Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2018 is a very low-resolution file. A high-resolution copy of the report is available by clicking here . Please be patient as it may take a few minutes for the high-resolution file to download.
... The ETS index (ETSI) is formed by an average over each region. Following Murakami et al. (2015), we used a probabilistic rather than a deterministic approach. We will examine the probability of the ETSI of the three regions during DJF as a function of ETSI using the following equation: ...
... We conducted new large-ensemble simulations using fully coupled models developed at GFDL that incorporate 50-km mesh atmospheric component, enabling realistic TC simulations (33,34). In order to investigate the impact of different models on the results, we conducted large-ensemble simulations with three configurations using the GFDL models (Large-Ensemble Experiments and SI Appendix, Table S1). ...
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Full-text available
Owing to the limited length of observed tropical cyclone data and the effects of multidecadal internal variability, it has been a challenge to detect trends in tropical cyclone activity on a global scale. However, there is a distinct spatial pattern of the trends in tropical cyclone frequency of occurrence on a global scale since 1980, with substantial decreases in the southern Indian Ocean and western North Pacific and increases in the North Atlantic and central Pacific. Here, using a suite of high-resolution dynamical model experiments, we show that the observed spatial pattern of trends is very unlikely to be explained entirely by underlying multidecadal internal variability; rather, external forcing such as greenhouse gases, aerosols, and volcanic eruptions likely played an important role. This study demonstrates that a climatic change in terms of the global spatial distribution of tropical cyclones has already emerged in observations and may in part be attributable to the increase in greenhouse gas emissions.
... For example, these explore how the climate change that has occurred to date may have influenced individual TCs, including storm surge (e.g., Lackmann 2015; Takayabu et al. 2015;Risser and Wehner 2017;Oldenborgh et al. 2017;Emanuel 2017;Wang et al. 2018;Patricola and Wehner 2018;Wehner et al. 2019;Keellings and Ayala 2019), with some studies performed in real time (Reed 2018). These studies can also explore how anthropogenic climate change may have influenced a particular TC season (e.g., Murakami et al. 2015Murakami et al. , 2017aMurakami et al. ,b, 2018Zhang et al. 2016). An issue with establishing confidence levels for such studies is whether a significant observed trend in a TC metric or closely related metric can be identified to support the model-estimated anthropogenic influence. ...
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Full-text available
Since the Eighth International Workshop on Tropical Cyclones (IWTC-8), held in December 2014, progress has been made in our understanding of the relationship between tropical cyclone (TC) characteristics, climate and climate change. New analysis of observations has revealed trends in the latitude of maximum TC intensity and in TC translation speed. Climate models are demonstrating an increasing ability to simulate the observed TC climatology and its regional variations. The limited representation of air-sea interaction processes in most climate simulations of TCs remains an issue. Consensus projections of future TC behavior continue to indicate decreases in TC numbers, increases in their maximum intensities and increases in TC-related rainfall. Future sea level rise will exacerbate the impact of storm surge on coastal regions, assuming all other factors equal. Studies have also begun to estimate the effect on TCs of the climate change that has occurred to date. Recommendations are made regarding future research directions. Keywords: tropical cyclone, climate change, climate variability
... We employed a probabilistic approach to examine the exceedance probability of an event that quantifies the frequency of tropical depressions (FRE), similar to previous studies [25][26][27]: ...
Article
Full-text available
Fourteen tropical depressions formed in the North Indian Ocean during 2018-the most active season since 1986 and the second most active season since 1980. Among the 14 tropical depressions during 2018, seven developed into cyclonic storms, with five intensifying into severe cyclonic storms-three of which became very severe cyclonic storms. The sea surface temperature anomaly associated with El Niño appears to have played a minor role in shaping this extreme event (i.e., the 14 tropical depressions in the North Indian Ocean). Using large ensemble experiments performed by the Community Earth System Model developed by the National Center for Atmospheric Research, we detected an important role potentially played by anthropogenic forcing in increasing the risk of the 14 tropical depressions in the North Indian Ocean that were observed in the active 2018 season. Moreover, the projection experiments suggest a rising frequency of tropical depressions in the second half of the 21st century.
... In spite of uncertainties in the data, given the potential for very substantial impacts on lives and property, the evidence presented here and in Kossin 3 point towards an important need for further study. For example, it is possible to perform "attribution without formal detection" studies using controlled numerical simulations 22,23 . One such related study 24 has recently found that the tropical-cyclone translation speed of Atlantic hurricanes is reduced, on average, under global warming scenarios, which adds to the balance of evidence for a slowdown and a physical connection to human activities. ...
... In spite of uncertainties in the data, given the potential for very substantial impacts on lives and property, the evidence presented here and in Kossin 3 point towards an important need for further study. For example, it is possible to perform "attribution without formal detection" studies using controlled numerical simulations 22,23 . One such related study 24 has recently found that the tropical-cyclone translation speed of Atlantic hurricanes is reduced, on average, under global warming scenarios, which adds to the balance of evidence for a slowdown and a physical connection to human activities. ...
Article
Full-text available
Abstract The authors analyze the global statistics of tropical cyclones undergoing extratropical transition (ET) in the Forecast‐oriented Low Ocean Resolution version of CM2.5 with Flux Adjustment (FLOR‐FA). The cyclone phase space (CPS) is used to diagnose ET. A simulation of the recent historical climate is analyzed and compared with data from the Japanese 55‐year Reanalysis (JRA‐55), and then a simulation of late 21st century climate under Representative Concentration Pathway 4.5 is compared to the historical simulation. When CPS is applied to the FLOR‐FA output in the historical simulation, the results diverge from those obtained from JRA‐55 by having an unrealistic number of ET cases at low latitudes, due to the presence of strong local maxima in the upper‐level geopotential. These features mislead CPS into detecting a cold core where one is not present. The misdiagnosis is largely corrected by replacing the maxima required by CPS with the 95th percentile values, smoothing the CPS trajectories in time, or both. Other climate models may contain grid‐scale structures akin to those in FLOR‐FA and, when used for CPS analysis, require solutions such as those discussed here. Comparisons of ET in the projected future climate with the historical climate show a number of changes that are robust to the details of the ET diagnosis, though few are statistically significant according to standard tests. Among them are an increase in the ET fraction and a reduction in the mean latitude at which ET occurs in the western North Pacific.
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Tropical cyclones (TCs) can have severe impacts on Australia. These include extreme rainfall and winds, and coastal hazards such as destructive waves, storm surges, estuarine flooding, and coastal erosion. Various aspects of TCs in the Australian region have been documented over the past several decades. In recent years, increasing emphasis has been placed on human‐induced climate change effects on TCs in the Australian region and elsewhere around the globe. However, large natural variability and the lack of consistent long‐term TC observations have often complicated the detection and attribution of TC trends. Efforts have been made to improve TC records for Australia over the past decades, but it is still unclear whether such records are sufficient to provide better understanding of the impacts of natural climate variability and climate change. It is important to note that the damage costs associated with tropical cyclones in Australia have increased in recent decades and will continue to increase due to growing coastal settlement and infrastructure development. Therefore, it is critical that any coastal infrastructure planning and engineering decisions, as well as disaster management decisions, strongly consider future risks from tropical cyclones. A better understanding of tropical cyclones in a changing climate will provide key insights that can help mitigate impacts of tropical cyclones on vulnerable communities. An objective assessment of the Australian TCs at regional scale and its link with climate variability and change using improved and up‐to‐date data records is more imperative now than before. This article is categorized under: • Paleoclimates and Current Trends > Modern Climate Change
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A new high-resolution Geophysical Fluid Dynamics Laboratory (GFDL) coupled model [the High-Resolution Forecast-Oriented Low Ocean Resolution (FLOR) model (HiFLOR)] has been developed and used to investigate potential skill in simulation and prediction of tropical cyclone (TC) activity. HiFLOR comprises high-resolution (~25-km mesh) atmosphere and land components and a more moderate-resolution (~100-km mesh) sea ice and ocean component. HiFLOR was developed from FLOR by decreasing the horizontal grid spacing of the atmospheric component from 50 to 25 km, while leaving most of the subgrid-scale physical parameterizations unchanged. Compared with FLOR, HiFLOR yields a more realistic simulation of the structure, global distribution, and seasonal and interannual variations of TCs, as well as a comparable simulation of storm-induced cold wakes and TC-genesis modulation induced by the Madden–Julian oscillation (MJO). Moreover, HiFLOR is able to simulate and predict extremely intense TCs (Saffir–Simpson hurricane categories 4 and 5) and their interannual variations, which represents the first time a global coupled model has been able to simulate such extremely intense TCs in a multicentury simulation, sea surface temperature restoring simulations, and retrospective seasonal predictions.
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The El Niño Southern Oscillation (ENSO) creates strong variations in sea surface temperature in the eastern equatorial Pacific, leading to major climatic and societal impacts. In particular, ENSO influences the yearly variations of tropical cyclone (TC) activities in both the Pacific and Atlantic basins through atmospheric dynamical factors such as vertical wind shear and stability. Until recently, however, the direct ocean thermal control of ENSO on TCs has not been taken into consideration because of an apparent mismatch in both timing and location: ENSO peaks in winter and its surface warming occurs mostly along the Equator, a region without TC activity. Here we show that El Niño--the warm phase of an ENSO cycle--effectively discharges heat into the eastern North Pacific basin two to three seasons after its wintertime peak, leading to intensified TCs. This basin is characterized by abundant TC activity and is the second most active TC region in the world. As a result of the time involved in ocean transport, El Niño's equatorial subsurface 'heat reservoir', built up in boreal winter, appears in the eastern North Pacific several months later during peak TC season (boreal summer and autumn). By means of this delayed ocean transport mechanism, ENSO provides an additional heat supply favourable for the formation of strong hurricanes. This thermal control on intense TC variability has significant implications for seasonal predictions and long-term projections of TC activity over the eastern North Pacific.
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A hierarchical Bayesian framework is developed to identify multiple abrupt regime shifts in an extreme event series. Specifically, extreme events are modeled as a Poisson process with a gamma-distributed rate. Multiple candidate hypotheses are considered, under each of which there presumably exist a certain number of abrupt shifts of the rate. A Bayesian network involving three layers-data, parameter, and hypothesis-is formulated. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is developed to calculate posterior probability for each hypothesis as well its associated within-hypothesis parameters. Based on the proposed RJMCMC algorithm, a simulated example is designed to illustrate the effectiveness of the method. Subsequently, the algorithm is applied to three real, rare event time series: the annual typhoon counts over the western North Pacific (WNP), the annual extreme heavy rainfall event counts at the Honolulu airport, and the annual heat wave frequency in the Chicago area. Results indicate that the typhoon activity over the WNP is very likely to have undergone a decadal variation, with two change points occurring around 1972 and 1989 characterized by the active 1960-71 epoch, the inactive 1972-88 epoch, and the moderately active 1989-2006 epoch. For the extreme rainfall case, only one shift around 1970 is found and heavy rainfall frequency has remained stationary since then. There is no evidence that the rate of the annual heat wave counts in the Chicago area has had any abrupt change during the past 50 years.
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A global high-resolution (∼40 km) atmospheric general circulation model (ECHAM5 T319) is used to investigate the change of tropical cyclone frequency in the North Pacific under global warming. A time slice method is used in which sea surface temperature fields derived from a lower-resolution coupled model run under the 20C3M (in which historical greenhouse gases in 20th century were prescribed as a radiative forcing) and A1B (in which carbon dioxide concentration was increased 1% each year from 2000 to 2070 and then was kept constant) scenarios are specified as the lower boundary conditions to simulate the current and the future warming climate, respectively. A significant shift is found in the location of tropical cyclones from the western to central Pacific. The shift to more tropical cyclones in the central and less in the western Pacific is not attributable to a change in atmospheric static stability, but to a change in the variance of tropical synoptic-scale perturbations associated with a change in the background vertical wind shear and boundary layer divergence.
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We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1° latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5° latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reduced-space optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The sea ice fields are made more homogeneous by compensating satellite microwave-based sea ice concentrations for the impact of surface melt effects on retrievals in the Arctic and for algorithm deficiencies in the Antarctic and by making the historical in situ concentrations consistent with the satellite data. SSTs near sea ice are estimated using statistical relationships between SST and sea ice concentration. HadISST1 compares well with other published analyses, capturing trends in global, hemispheric, and regional SST well, containing SST fields with more uniform variance through time and better month-to-month persistence than those in GISST. HadMAT1 is more consistent with SST and with collocated land surface air temperatures than previous NMAT data sets.
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Full-text available
The analysis of a series of regionally coupled ocean-atmospheric simulations suggests that the Atlantic warming that has occurred in the 20th century may have reduced the concomitant warming in the eastern tropical Pacific. The Pacific response to the Atlantic warming shows La Nina-like features even in the presence of greenhouse gas (GHG) forcing. The physical mechanism for the Atlantic warming influence on the tropical Pacific is a change in the Walker circulation that results in easterly surface wind anomalies in the central-west Pacific. Coupled ocean-atmosphere processes then amplify the signal. The possibility of an Atlantic Ocean induced cooling of the eastern tropical Pacific is complementary to the hypothesis that the GHG forcing itself may have caused the observed relative eastern Pacific cooling. It is argued that the uncertainties in the projected future mean state in the Pacific may be partly due to the competition of the GHG induced warming and the Atlantic induced cooling.