ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus PublicationsGöttingen, Germany10.5194/esd-9-187-2018Changes in tropical cyclones under stabilized 1.5 and 2.0 ∘C
global warming scenarios as simulated by the Community Atmospheric Model
under the HAPPI protocolsWehnerMichael F.mfwehner@lbl.govhttps://orcid.org/0000-0001-5991-0082ReedKevin A.https://orcid.org/0000-0003-3741-7080LoringBurlenStoneDáithíhttps://orcid.org/0000-0002-2518-100XKrishnanHarinarayanLawrence Berkeley National Laboratory, Berkeley, California 94720, USAState University of New York at Stony Brook, Stony Brook, New York 11794, USAMichael F. Wehner (mfwehner@lbl.gov)28February20189118719531October20176November201717January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://esd.copernicus.org/articles/9/187/2018/esd-9-187-2018.htmlThe full text article is available as a PDF file from https://esd.copernicus.org/articles/9/187/2018/esd-9-187-2018.pdf
The United Nations
Framework Convention on Climate Change (UNFCCC) invited the scientific
community to explore the impacts of a world in which anthropogenic global
warming is stabilized at only 1.5 ∘C above preindustrial average
temperatures. We present a projection of future tropical cyclone statistics
for both 1.5 and 2.0 ∘C stabilized warming scenarios with direct
numerical simulation using a high-resolution global climate model. As in
similar projections at higher warming levels, we find that even at these low
warming levels the most intense tropical cyclones become more frequent and
more intense, while simultaneously the frequency of weaker tropical storms is
decreased. We also conclude that in the 1.5 ∘C stabilization, the
effect of aerosol forcing changes complicates the interpretation of
greenhouse gas forcing changes.
Introduction
Changes in tropical cyclone intensity, frequency and distribution are
expected as the climate warms due to anthropogenic changes in the
composition of the atmosphere. While the development of a complete climate
theory of tropical cyclones remains elusive (Walsh et al., 2015), recent
advances in high-performance computing enable multi-decadal simulations of
climate models at tropical-cyclone-permitting resolutions. Together with
conceptual models, such numerical models are the tool of choice for
investigating projected future changes in tropical cyclones (Wehner et al.,
2017a).
Previous work has studied the impact of climate change on tropical storms
through idealized representations of future climate through uniform
increases in greenhouse gases and sea surface temperature (Walsh et al.,
2015; Wehner et al., 2015) or more realistic but more extreme cases of
warming using the Representative Concentration Pathway (RCP4.5 or RCP8.5)
scenarios (e.g., Camargo, 2013; Knutson et al., 2015; Bacmeister et al.,
2018).
The United Nations Framework Convention on Climate Change (UNFCCC)
invited the International Panel on Climate Change (IPCC) to explore the
impacts of a world in which the expected average warming remains less than or
equal to 2.0 ∘C over preindustrial levels. In particular, the UNFCCC
requested an analysis of the feasibility and impacts of a target stabilized
global mean temperature of 1.5 ∘C over preindustrial levels. The Half
a
degree Additional warming, Prognosis and Projected Impacts (HAPPI)
experimental protocol was designed in response to this request to permit
a comparison of the effects of stabilizing anthropogenic global warming at
1.5 ∘C over preindustrial levels to 2.0 ∘C (Mitchell et al., 2017).
In this paper, we present results from a high-resolution atmosphere–land
model forced by the HAPPI prescriptions of sea surface temperature (SST) and
sea ice concentration.
The HAPPI experimental protocol consists of three parts (Mitchell et al.,
2017). The “historical” part specifies observed sea surface temperatures
(SSTs) from the NOAA OI.v2 gridded monthly mean observational product
(Reynolds et al., 2002) over the period 1996–2015. An estimate of SST and sea
ice concentrations in stabilized scenarios at both 1.5 and 2.0 ∘C
is constructed from the CMIP5 (Coupled Model Intercomparison Project)
multi-model database of future climate projections under the RCP2.6 and
RCP4.5 forcing scenarios hereafter designated “HAPPI15” and “HAPPI20”. A
stabilized anthropogenic climate change to these surface forcing functions
is constant in time. By adding such a change to the observations, observed
interannual variations are preserved. As such, historical year 2006 is
directly comparable to HAPPI15 or HAPPI20 year 2106 as the date in the
stabilized scenarios is arbitrarily increased by 1 century. The original
design of the HAPPI protocols follows that of the “Climate of the 20th
Century Plus Detection and Attribution project” (C20C+) (Stone et al.,
2017) and targets large ensembles of 50 realizations or more to quantify
the differences in projections (or attribution) of extreme events in
specific years. However, at the high horizontal resolutions necessary to
simulate tropical cyclones, the computational costs of the climate model are
too high to permit such a large number of simulations and ensemble sizes are
restricted. Hence, in this study we pool results across both simulation
years and the ensembles for each part of the HAPPI experiment to isolate the
climate change signal, if any, from internal variability. As part of our
participation in the C20C+ project, we began the historical simulation
period in 1996 extending through 2015, thus permitting a more robust estimate
of present day simulated tropical cyclone statistics for comparison to the
stabilized warmer climate.
This study uses the Community Atmospheric Model version 5.3 configured at a
global resolution of approximately 0.25∘, roughly equaling a grid
spacing of 28 km in tropical regions. Note that this participating model is
listed as “CAM5.1.2-0.25degree” in the HAPPI documentation
(http://portal.nersc.gov/c20c/data.html), but here it is abbreviated to ”CAM5”.
This configuration has been demonstrated to produce reasonable annual
numbers of tropical cyclones on the global scale compared to observations
(Bacmeister et al., 2014; Wehner et al., 2014; Reed et al., 2015). The
formulation of the dynamical core portion of the atmospheric model does
influence tropical cyclone counts and intensities (Reed et al., 2015). The
model used in this study used CAM5's finite-volume-based dynamical core on a
latitude–longitude grid (Lin and Rood, 1996, 1997; Lin, 2004).
Storms up to category 5 on the Saffir–Simpson scale are regularly produced,
allowing for investigation into the effects of global warming on the
distribution of tropical cyclone intensity. The relationship between maximum
wind speed and central pressure minima was also demonstrated to be realistic
(Wehner et al., 2014). However, there are significant biases in track and
cyclogenesis density, particularly in the Pacific Ocean with the model
simulating too many storms in the central North Pacific and too few in the
northwestern part of that basin.
Nonetheless, the high-resolution CAM5 can be an informative tool to explore
the change in tropical cyclone behavior in altered climates. Wehner et
al. (2015) explored tropical cyclone behavior in the four idealized climate
change configurations of the US CLIVAR Hurricane Working Group (Walsh et al.,
2015). That project compared the combined effect of a spatially uniform
2 ∘C increase applied to a climatological average of observed SST
centered at 1990 and of a doubling of atmospheric CO2 to a control 1990
simulation, as well as the separate effects of each factor. Their principal
finding was that a lower-resolution (1∘) version of the CAM5 and
methods based on the genesis potential index (Emanuel and Nolan, 2004) could
not reproduce the sign of the change in the global number of tropical
cyclones produced by the high-resolution version. Under the combined effect
of the uniform 2 ∘C SST increase and CO2 doubling, the high-resolution CAM5 reduced the annual number of tropical storms (category 0–5) from 86±4 to 70±3. However, the annual number of intense
tropical cyclones (category 4–5) increased from 10±1.7 to
12±1.7. The two separate forcing simulations revealed that most of
the reduction in the total number of tropical storms of all intensities was
caused by the change in the vertical temperature profile due to the CO2
doubling, while the increase in the number of intense tropical cyclones was
caused solely by the increased SST. The warmer SST conditions also caused
the maximum wind speeds of the most intense storms to increase and their
central pressure minima to decrease, while CO2 doubling had the opposite
effect. The peak of the zonally averaged tropical storm track density
shifted poleward by ∼2∘ in the Northern Hemisphere and
∼4∘ in the Southern Hemisphere in all three perturbed US
CLIVAR configurations. A small poleward shift (∼1∘) in
Northern Hemisphere cyclogenesis origins was exhibited in the two
simulations with warmer SSTs but not the CO2 doubling only simulation,
while all three perturbed simulations exhibited a similar shift in the
broader Southern Hemisphere cyclogenesis distribution.
The SST and sea ice perturbations imposed by the HAPPI protocols exhibit the
more realistic spatially varying SST patterns shown in Fig. 1 than the
uniform increase in the US CLIVAR experiments. In the HAPPI protocols,
warmer configurations are produced by adding monthly climatological
perturbations to the observed SSTs for each individual month, preserving the
current patterns of SST variability. The SST perturbations for the
1.5 ∘C stabilization scenario are taken directly from the
multi-model mean of CMIP5 RCP2.6 simulations (which conveniently warm by
approximately that amount on average above preindustrial temperatures).
Radiative forcings (greenhouse gas concentrations, burdens of various
aerosol species and ozone concentrations) are also taken directly from the
RCP2.6 values. The 2.0 ∘C scenario uses SST perturbations and
CO2 concentrations interpolated between CMIP5 RCP2.6 and RCP4.5
multi-model means, while other forcings remain the same as for the
1.5 ∘C scenario. Sea ice concentrations are computed using an
adapted version of the method described in Massey (2018) by using
observations of SST and ice to establish a linear relationship between the
two fields for the time period 1996–2015 and are consistent with the HAPPI
prescribed SST fields. Details are further described in Mitchell et al. (2017).
Although they represent a smaller perturbation to the climate system
than the US CLIVAR experiment, the HAPPI experiment is more physically
consistent in terms of the relationship of the SST change to radiative
forcing changes and in the distribution of sea ice in the high latitudes,
permitting the HAPPI simulations to be more widely applicable to phenomena
outside of the tropics.
The CAM5 simulations performed for the HAPPI project consist of five
realizations of the historical period plus six realizations of each
stabilization scenario. One of the historical realizations is incomplete due
to computer resource limitations, resulting in 96 simulated years for this
part of the dataset. Sixty simulated years were produced for both the 1.5
and 2.0 ∘C stabilization scenarios. Data products are freely available
with further information provided at www.portal.nersc.gov/c20c.
Simulated tropical cyclones are identified and tracked with the Toolkit for
Extreme Climate Analysis (TECA2.) available for download and installation at
https://github.com/LBL-EESA/TECA using the methods described in
Knutson et al. (2007).
The temporal average of the imposed change (∘C) in sea
surface temperature as prescribed by the HAPPI protocols: (a) 1.5 ∘C
stabilization, (b) 2.0 ∘C stabilization.
Another critical difference between the HAPPI and the US CLIVAR experimental
protocols is the aerosol forcing. While the US CLIVAR protocols had no
specified changes to aerosols, the HAPPI protocols set aerosol forcings to
the end of the 21st century levels under the RCP2.6 scenario for both
stabilization scenarios. Hence, there is a substantial reduction in the
aerosol forcing in the stabilization simulations compared to the historical
simulations. Dunstone et al. (2013) indirectly found a substantial reduction
in Atlantic tropical storms by varying aerosol forcing in the UK MetOffice
climate model HadGEM2-ES at a resolution of 1.2∘× 1.9∘. In the CAM5
simulations presented here, we used its bulk aerosol model to prescribe
aerosol concentrations rather than emissions in order to reduce the
computational burden (Kiehl et al., 2000). Huff et al. (2017) established
that CAM5 does exhibit sensitivity to aerosol formulation in the simulated
number and intensity distribution of tropical cyclones in the simulated
current climate. However, the HAPPI protocol does not establish a controlled
investigation of the effects of the aerosol forcing reduction in the
stabilized scenarios, nor have we performed such simulations yet. Figure 2
shows the percent change in total aerosol optical depth in the visible band
comparing the historical and 2.0 ∘C stabilization simulations averaged
over all years and realizations. Significant decreases are evident over most
of the Northern Hemisphere and the tropics. Results from the 1.5 ∘C
stabilization simulations are the same.
Percent difference between the stabilized 2 ∘C scenario and
the historical simulation of the total aerosol optical depth in the visible
band.
Results
As in the US CLIVAR idealized experiments, the global number of intense
tropical cyclones (category 4 and 5) is substantially increased in the
warmer climates of the HAPPI stabilization scenarios, with a statistical
significance higher than the 1 % level as shown in Fig. 3. Also as in
the idealized warming experiments, the number of tropical storms (category 0) is substantially decreased in a warmer climate. However, the effect on
the total number of named storms of all intensities (category 0–5) is
subtler in the HAPPI simulations. For this version of CAM5, the global
annual number of category 0 to 5 storms is 73.4±0.91 in the
historical ensemble
The historical annual global tropical storm
counts over all categories differ from the 1990 climatological simulations
of Wehner et al. (2015) for three reasons: (1) SSTs are slightly different,
(2) the version of CAM5 is a more recent release (CESM v1.2.2 vs. v1.0.3) and (3)
there are subtle differences in the implementation of the tracking algorithm.
. In the
1.5 ∘C stabilization scenario, this number is only reduced to
72.5±1.2, which is not significant at a 10 % significance level.
However, in the 2.0 ∘C stabilization scenario, a further reduction to
67.5±1.3 is realized, which is significant at the 1 % level. In the
cooler stabilization scenario, the decrease in category 0 storms is roughly
offset by the increase in intense storms, leading to the insignificance of
the change in the total number of storms. In the warmer scenario, the yet
larger decrease in category 0 causes the change in the total number of
storms to be more significant. In both stabilization scenarios, the changes
from the historical simulation in category 1, 2 and 3 storms are not
statistically significant above the 5 % level. Differences between the
1.5 and 2 ∘C stabilization scenarios are only highly significant
in the decrease by category for the number of the weakest category of
storms. Importantly, the differences in the number of intense tropical
cyclones between the two warming scenarios are not statistically significant
in this study. The results presented in Fig. 3 are repeated numerically in
Table 1. Basin-specific results are tabulated in the Supplement.
Global annual number of tropical cyclones by Saffir–Simpson scale
for the historical (blue), 1.5 ∘C stabilization scenario (gray) and
2 ∘C stabilization scenario (red). Error bars are the standard errors
based on interannual variability. Blue: historical. Gray: 1.5∘
stabilization. Red: 2.0∘ stabilization.
Differences in CAM5 simulated global annual tropical storm counts
by Saffir–Simpson scale between the two HAPPI stabilization scenarios,
the historical simulation and each other. Differences that are statistically
significant at the 1 % level are in bold, while those at the 10 % level
are in italics.
Saffir–Simpson0–5012345HAPPI15 minus historical-0.9-4.5-0.40.20.62.11.2HAPPI20 minus historical-5.9-7.2-1.0-0.4-0.11.41.2HAPPI20 minus HAPPI15-5.0-2.6-0.5-0.5-0.7-0.60.1
Average storm track length, duration and mean translational speed are shown
for the HAPPI scenarios as a function of maximum lifetime intensity on the
Saffir–Simpson scale in Fig. 4. Weak storms (category 0) show no
substantial changes in track length, translational speed or duration among
the three ensembles of CAM5 simulations and this result is consistent with
the US CLIVAR experiments (Wehner et al., 2015). While these three metrics
show increases for category 2–4 storms in the 1.5 ∘C stabilization
scenario compared to the historical simulations, those increases are
attenuated in the warmer 2.0 ∘C stabilization scenario. However, the
most intense storms (category 5) exhibit consistent increases in track
length and duration on average as the climate system warms. Translational
speed (here averaged over the entire storm duration) increases in all three
ensembles with storm intensity but the differences among scenarios are
complex. Notably, while increases in average translational speed in the
warmer scenarios are simulated for storms in the middle of the
Saffir–Simpson scale, decreases are simulated for the most intense category.
While all of the differences in Fig. 4 are statistically significant well
above the 1 % level due to the large number of storms tracked, subtle
changes in the experimental design, including changes in SST pattern or
aerosol forcing, might alter these results. Better quantification of this
type of structural uncertainty will require further developments in high-performance computing technologies to permit more diverse experiments.
(a) Average tropical storm track length (km) for the HAPPI
scenarios as a function of maximum intensity on the Saffir–Simpson scale.
(b) Average tropical storm track duration (days) for the HAPPI scenarios
as a function of maximum intensity on the Saffir–Simpson scale. (c)
Average tropical storm track speed (km h-1) for the HAPPI scenarios as a
function of maximum intensity on the Saffir–Simpson scale. Blue: historical.
Gray: 1.5∘ stabilization. Red: 2.0∘ stabilization.
The zonal average of the normalized density of storm tracks of all
intensities for the HAPPI scenarios is shown in Fig. 5a.
As mentioned above, CAM5 is known to have a significant bias in the genesis
location of Pacific tropical storms although the total number, both in that
basin and globally, is not far from observed records. More detailed but
somewhat noisy maps of track density differences among the HAPPI scenarios
are shown Fig. S1 in the Supplement. Integrating over all longitudes,
as in Fig. 5, damps this noise, revealing a poleward shift in the warmer
HAPPI scenarios compared to the historical simulations. In the Northern
Hemisphere, there is a tendency for a substantially larger normalized
density of storm tracks poleward of 25∘ N in both the Atlantic and Pacific
Ocean basins (see Fig. S1). This may partially explain the increased track
lengths and durations shown in Fig. 4. With warmer temperatures,
conditions that can sustain tropical storm wind speeds extend poleward.
Although not considered here, there is potential for an anthropogenic
influence on the transition to extratropical characteristics of storms that
undergo them (Liu et al., 2017; Zarzycki et al., 2017). In the Southern
Hemisphere, Fig. 5 reveals that normalized storm track density is a
narrower function of latitude in the warmer HAPPI scenarios. Figure S1
reveals that this is mainly due to a change in the location of simulated
tropical storms in the southern Indian Ocean. In both hemispheres,
differences between the 1.5 and 2.0 ∘C stabilization scenarios are
smaller and noisier, making any differences in track density between them
difficult to interpret. The statistical significance of the larger
differences in normalized track density between the historical and warmer
stabilized scenarios is very high as assessed by a comparison of the standard
errors.
The zonal average of the normalized cyclogenesis density for tropical storms
of all intensities is shown in Fig. 5b. Again, more
detailed but noisy maps of cyclogenesis density differences among the
HAPPI scenarios are shown in Fig. S2. In the Northern
Hemisphere, a much smaller poleward shift than for track density starting at
about 15∘ N is simulated in the warmer HAPPI scenarios compared to the
historical simulations. Figure S2 suggests that much of this change is
coming from the Atlantic Ocean, but these cyclogenesis differences are not as
compelling as they are for the tropical storm tracks. In the Southern
Hemisphere, the cyclogenesis changes are similar to the track changes in
both Fig. 5 and the Supplement. Hence, we can conclude that the shifts in
Southern Hemisphere tracks are mainly a result of cyclogenesis shifts that
are mostly in the southern Indian Ocean.
(a) Zonally averaged normalized tropical storm track density for
the HAPPI scenarios. (b) Zonally averaged normalized tropical storm
genesis density for the HAPPI scenarios. Blue: historical. Gray: 1.5∘
stabilization. Red: 2.0∘ stabilization.
The annual accumulated cyclonic energy (ACE) is shown in Fig. 6 for the
historical and HAPPI stabilization scenarios both globally and by the major
ocean basins with tropical cyclone activity. ACE is a measure of the annual
kinetic energy contained in tropical storms and is obtained by squaring the
maximum sustained surface wind in the system every 6 h and summing it
up for the year
(http://www.cpc.ncep.noaa.gov/products/outlooks/background_information.shtml).
Comparison with an observational estimate taken from
Maue (2011) suggests that the model is overactive by this measure of
tropical cyclone activity, although differences in the methods with which tracks
and wind speeds are calculated could explain some of the biases shown in
Fig. 6. Globally, ACE is mainly increased in the 1.5 ∘C stabilization
scenario by the increase in the number of intense tropical cyclones.
Increases in average storm duration also lead to in the increase in ACE.
However, as the total number of storms is significantly decreased in the
2.0 ∘C stabilization scenario, ACE is decreased compared to the cooler
stabilization scenario. The global changes are dominated by similar changes
in the North Atlantic and Northeast Pacific. Changes in the Northwest
Pacific do not exhibit large changes but CAM5 has a significant cyclogenesis
location bias in the Pacific Ocean that may be relevant. While the total
number of simulated North Pacific storms is a reasonable representation of
observations (Wehner et al., 2014), Northwestern Pacific storms originate too
far to the east, causing cyclogenesis and track densities to be too high in
the central Pacific; this is the focus of current research to be presented
elsewhere. Also of note is that ACE in the Southern Hemisphere does not
change despite the cyclogenesis and track changes discussed above.
Average annual accumulated cyclonic energy (ACE) for the
historical and HAPPI stabilization scenarios for all named storms by basin
and globally for intense tropical cyclones only. Units: 1
ACE = 104 knots. Green: observations. Blue: historical. Gray: 1.5∘ stabilization.
Red: 2.0∘ stabilization. Error bars are standard errors based on
interannual variability.
Figure 7 shows the relationship between peak wind speeds and central
pressure minima at the time of maximum intensity for the three HAPPI
ensembles. As there are no changes to the model configuration among the
simulations other than forcing conditions, this relationship does not
significantly change other than the appearance of combinations of wind speed
and pressure at the very highest simulated intensities in the warmer
simulations that do not occur in the historical simulation. The peak wind
speed and central pressure minima relationship is controlled by the
mechanical constraints of gradient wind balance, storm size and Coriolis
force (Chavas et al., 2017; Chavas, private communication, October 2017). The small
poleward shift in the track density (Fig. 5) and subtle structural changes
in wind speed radii discussed below are not large enough to change this
relationship. Warmer temperatures do change the distribution of peak wind
speeds and central pressure minima (Fig. 3) but do not appear to
substantially change how they co-vary. We note, however, that model
resolution and structure may influence the simulation of this relationship,
thus requiring that evaluation of the effect of forcing changes on tropical
storm statistics only be done with simulations from the same version of
the climate model.
Scatterplot of minimum central pressure (hPa) versus maximum wind
speed (m s-1) at the time of maximum intensity for the HAPPI simulations.
Blue: historical. Gray: 1.5∘ stabilization. Red: 2.0∘
stabilization. Solid lines are quadratic fits to the data.
A definition of the physical size of tropical storms has recently been
developed by Chavas et al. (2015) by defining an approximate radius at
specified wind speeds. Figure 8 shows average Chavas radii for the
historical and HAPPI stabilization scenarios. Radii are calculated every
3 h over the duration of every tracked storm for the threshold wind
speeds defining the Saffir–Simpson categories and for the storm
maximum wind speeds. Each relevant radius is calculated for all storms. For
instance, we calculate six Chavas radii for a category 5 storm (one for each
Saffir–Simpson threshold) as all six Saffir–Simpson wind speeds are present at
some point in such storms. Likewise, only a single Chavas radius for a
category 0 storm and the higher wind speeds are not realized. The CAM5 HAPPI
simulations exhibit about a 5 % increase in category 0 Chavas radii and a
smaller (2–3 %) increase in category 1 Chavas radii in the warmer
stabilized climates. Little change in Chavas radii is simulated for more
intense wind speeds except for category 5 storms in the 2 ∘C
stabilization scenario that experience an 8 % increase in Chavas radius.
The increase in weak-wind-speed Chavas radii may be due to the change in the
track density discussed above. The increased tracked tropical
storms at higher latitudes are likely to be in the lower categories and may
be starting their extratropical transition but still maintaining high
winds. The increase in category 5 Chavas radii in only the warmer of the two
HAPPI stabilizations currently lacks an explanation. Planned simulations of
this version of CAM5 with the so-called unHAPPI protocols (stabilized at
3 and 4 ∘C above preindustrial levels) may provide some insight
into these aspects of change in storm structure.
Chavas radii at different wind speeds selected as the definitions
of the Saffir–Simpson categories (km) for the HAPPI simulations. Blue:
historical. Gray: 1.5∘ stabilization. Red: 2.0∘ stabilization.
Conclusions
The Half a degree Additional warming, Prognosis and Projected Impacts
(HAPPI) experimental protocol was designed to rapidly inform the
Intergovernmental Panel on Climate Change about the differences between
stabilized climate at 1.5 and 2.0 ∘C above preindustrial global
temperatures. However, it does not isolate all of the effects of forcing
changes required to stabilize the climate from the present day conditions.
In particular, the effect of sulfate aerosol reductions in the atmosphere
has a nonlocal effect in the HAPPI simulations and has been demonstrated to
be important to assessing changes in tropical cyclones (Huff et al., 2017)
and heat waves (Wehner et al., 2017b). As the radiative forcing changes due
to CO2 between the historical and 1.5 ∘C scenarios may be
smaller than the forcing changes due to aerosols, the CO2 effects in
tropical storms may be comparable or even smaller due to the aerosol effects at
this stabilization level.
It is fair to say that the simulated differences in tropical cyclone statistics
between the 1.5 and 2.0 ∘C stabilization scenarios as defined by
the HAPPI protocols are small. Indeed, both warmer climates produce fewer
tropical storms over all intensities in the global sense and the reduction
increases as the sea surface temperature (SST) becomes warmer. Also, the
most intense storms become more intense in both warmer SST configurations
with the highest peak wind speeds and lowest central pressure minima
simulated in the warmer of the two stabilizations.
Given the similarities between the two HAPPI scenarios and the importance of
aerosol forcings, a more complete understanding of tropical storm frequency
in aggressively stabilized climates requires detailed descriptions of the
changes in those forcings. This would be particularly critical in
geoengineering schemes relying on solar radiation management. However, as
found by Bacmeister et al. (2018) in their comparison of RCP4.5 to RCP8.5,
major uncertainties in the pattern of SST changes also pose a significant
challenge in accurately projecting future tropical storm frequency.
Changes in other important characteristics of tropical cyclone behavior are
subtler. Both warmer climate conditions considered here project significant
changes in the poleward density of tropical storm tracks compared to the
historical simulations, but the differences between them are not likely to be
highly significant. Also, changes in accumulated cyclonic energy (ACE),
storm duration, track length and translational speed are complex with the
differences clearly evident for only the most intense storms. Finally, some
properties of tropical cyclones are not significantly altered in warmer
climates, most notably the robust relationship between maximum wind speeds
and central pressure minima.
The tracking software used in this study is the Toolkit
for Extreme Climate Analysis (TECA2.) available for download and installation at
https://github.com/LBL-EESA/TECA. Data for this study total 23 TB and are currently available for
download from the tape storage archive at the National Energy Research Supercomputing
Center via anonymous wget scripts provided via the following DOI link:
https://doi.org/10.25342/HAPPI_TC_2018. Because of the large
dataset size, wget transfers of the entire database will likely be
slow, and interested parties should contact the authors for faster access.
As data transfer technologies serving this database evolve, faster alternatives
will be offered via the DOI link.
The Supplement related to this article is available online at https://doi.org/10.5194/esd-9-187-2018-supplement.
The authors declare that they have no conflict of interest.
This article is part of the special issue “The Earth system at a global
warming of 1.5 ∘C and 2.0 ∘C”. It is not associated with a conference.
Acknowledgements
The work at LBNL was supported by the Regional and Global Modeling Program
as part of the Calibrated and Systematic Characterization, Attribution, and
Detection of Extremes project (CASCADE). LBNL is operated for the Department
of Energy's Office of Science under contract number DE-AC02-05CH11231. This
document was prepared as an account of work sponsored by the United States
Government. While this document is believed to contain correct information,
neither the United States Government nor any agency thereof, nor the Regents
of the University of California, nor any of their employees, makes any
warranty, express or implied, or assumes any legal responsibility for the
accuracy, completeness, or usefulness of any information, apparatus,
product, or process disclosed, or represents that its use would not infringe
privately owned rights. Reference herein to any specific commercial product,
process, or service by its trade name, trademark, manufacturer, or
otherwise, does not necessarily constitute or imply its endorsement,
recommendation, or favoring by the United States Government or any agency
thereof, or the Regents of the University of California. The views and
opinions of authors expressed herein do not necessarily state or reflect
those of the United States Government or any agency thereof or the Regents
of the University of California.
Work at Stony Brook University was supported by the Department of Energy's
Office of Science under contract number DE-SC0016605.
These simulations were performed using resources of the National Energy
Research Scientific Computing Center, a DOE Office of Science User Facility
supported by the Office of Science of the US Department of Energy, also
under contract no. DE-AC02-05CH11231.
Edited by: Ben Kravitz
Reviewed by: two anonymous referees
ReferencesBacmeister, J. T., Wehner, M. F., Neale, R. B., Gettelman, A., Hannay, C.,
Lauritzen, P. H., Caron, J. M., and Truesdale, J. E.: Exploratory High-Resolution
Climate Simulations using the Community Atmosphere Model (CAM), J. Climate,
27, 3073–3099, 10.1175/JCLI-D-13-00387.1, 2014.Bacmeister, J. T., Reed, K. A., Hannay, C., Lawrence, P. J., Bates, S. C.,
Truesdale, J. E., Rosenbloom, N. A., and Levy, M. N.: Projected changes in
tropical cyclone activity under future warming scenarios using a
high-resolution climate model, Clim. Change, 146, 547–560, 10.1007/s10584-016-1750-x, 2018.Camargo, S. J.: Global and Regional Aspects of Tropical Cyclone
Activity in the CMIP5 Models, J. Climate, 26, 9880–9902,
10.1175/JCLI-D-12-00549.1, 2013.Chavas, D. R., Lin, N., and Emanuel, K.: A model for the complete radial
structure of the tropical cyclone wind field. Part I: Comparison with
observed structure, J. Atmos. Sci., 72, 3647–3662,
10.1175/JAS-D-15-0014.1, 2015.Chavas, D. R., Reed, K. A., and Knaff, J. A.: Physical understanding of the
tropical cyclone wind-pressure relationship, Nat. Comm., 8, 1–11,
10.1038/s41467-017-01546-9, 2017.Dunstone, N. J., Smith, D. M., Booth, B. B. B., Hermanson, L., and Eade, R.:
Anthropogenic aerosol forcing of Atlantic tropical storms, Nat. Geosci., 6,
534–539, 10.1038/ngeo1854, 2013.
Emanuel, K. and Nolan, D. S.: Tropical cyclone activity and the global climate system,
Preprints, 26th Conf. on Hurricanes and Tropical Meteorology,
Miami, FL, Am. Meteorol. Soc., 240–241, 2004.
Huff, J. J. A., Reed, K. A., Bacmeister, J. B., and Wehner, M. F.: Evaluating the
Influence of CAM5 Aerosol Configuration on Simulated Tropical Cyclones in
the North Atlantic, J. Adv. Model. Earth Syst., in review, 2017.Kiehl, J. T., Schneider, T. L., Rasch, P. J., Barth, M. C., and Wong, J.:
Radiative forcing due to sulfate aerosols from simulations with the National
Center for Atmospheric Research Community Climate Model, Version 3, J.
Geophys. Res., 105, 1441–1457, 10.1029/1999JD900495, 2000.Knutson, T. R., Sirutis, J. J., Garner, S. T., Held, I., and Tuleya, R. E.:
Simulation of the Recent Multidecadal Increase of Atlantic Hurricane
Activity Using an 18-km-Grid Regional Model, B. Am. Meteorol. Soc., 88,
1549–1565, 10.1175/BAMS-88-10-1549, 2007.Knutson, T. R., Sirutis, J. J., Zhao, M., Tuleya, R. E., Bender, M., Vecchi, G. A.,
Villarini, G., and Chavas, D.: Global Projections of Intense Tropical
Cyclone Activity for the Late Twenty-First Century from Dynamical
Downscaling of CMIP5/RCP4.5 Scenarios, J. Climate, 28, 7203–7224,
10.1175/JCLI-D-15-0129.1, 2015.
Lin, S.-J.: A “vertically Lagrangian” finite-volume dynamical core
for global models, Mon. Weather Rev., 132, 2293–2307, 2004.
Lin, S.-J. and Rood, R. B.: Multidimensional flux-form
semi-Lagrangian transport scheme, Mon. Weather Rev., 124, 2046–2070, 1996.
Lin, S.-J. and Rood, R. B.: An explicit flux-form semi-Lagrangian
shallow water model on the sphere, Q. J. Roy. Meteorol. Soc., 123,
2477–2498, 1997.Liu, M., Vecchi, G. A., Smith, J. A., and Murakami, H.: The Present-Day
Simulation and Twenty-First-Century Projection of the Climatology of
Extratropical Transition in the North Atlantic, J. Climate, 30, 2739–2756,
10.1175/JCLI-D-16-0352.1, 2017.
Massey, N.: Generating sea ice patterns and uncertainty from coupled
climate models, J. Geophys. Res., in preparation, 2018.Maue, R. N.: Recent historically low global tropical cyclone activity,
Geophys. Rev. Lett., 38, L14803, 10.1029/2011GL047711, 2011.Mitchell, D., AchutaRao, K., Allen, M., Bethke, I., Beyerle, U., Ciavarella,
A., Forster, P. M., Fuglestvedt, J., Gillett, N., Haustein, K., Ingram, W.,
Iversen, T., Kharin, V., Klingaman, N., Massey, N., Fischer, E., Schleussner,
C.-F., Scinocca, J., Seland, Ø., Shiogama, H., Shuckburgh, E., Sparrow, S.,
Stone, D., Uhe, P., Wallom, D., Wehner, M., and Zaaboul, R.: Half a degree
additional warming, prognosis and projected impacts (HAPPI): background and
experimental design, Geosci. Model Dev., 10, 571–583,
10.5194/gmd-10-571-2017, 2017.Reed, K. A., Bacmeister, J. T., Rosenbloom, N. A., Wehner, M. F., Bates, S. C.,
Lauritzen, P. H., Truesdale, J. E., and Hannay, C.: Impact of the
dynamical core on the direct simulation of tropical cyclones in a
high-resolution global model, Geophys. Res. Lett., 42, 3603–3608,
10.1002/2015GL063974, 2015.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W. C.:
An improved in situ and satellite {SST}
analysis for climate, J. Climate, 15, 1609–1625, 2002.Stone, D. A., Christidis, N., Folland, C., Perkins-Kirkpatrick, S.,
Perlwitz, J., Shiogama, H., Wehner, M. F., Wolski, P., Cholia, S., Krishnan, H.,
Murray, D., Ang'elil, O., Beyerle, U., Ciavarella, A., Dittus, A., and Quan,
X.-W.: Experiment design of the International CLIVAR C20C+ Detection and
Attribution Project. Weather and Climate Extremes, in preparation, 2017.Walsh, K. J. E., Camargo, S., Vecchi, G., Daloz, A. S., Elsner, J., Emanuel, K.,
Horn, M., Lim, Young-K., Roberts, M., Patricola, C., Scoccimarro, E., Sobel, A.,
Strazzo, S., Villarini, G., Wehner, M., Zhao, M., Kossin, J., LaRow, T., Oouchi, K.,
Schubert, S., Wang, H., Bacmeister, J., Chang, P., Chauvin, F., Jablonowski, C.,
Kumar, A., Murakami, H., Ose, T., Reed, K., Saravanan, R., Yamada, Y.,
Zarzycki, C., Vidale, P.-L., Jonas, J., and Henderson, N.: Hurricanes and
climate: the U.S. CLIVAR working group on hurricanes, B. Am. Meteorol.
Soc., 96, 997–1017, 10.1175/BAMS-D-13-00242.1, 2015.Wehner, M. F., Reed, K., Li, F., Prabhat, Bacmeister, J., Chen, C.-T.,
Paciorek, C., Gleckler, P., Sperber, K., Collins, W. D., Gettelman, A.,
and Jablonowski, C.: The effect of horizontal resolution on simulation quality
in the Community Atmospheric Model, CAM5.1, J. Model. Earth Syst., 06,
980–997, 10.1002/2013MS000276, 2014.Wehner, M. F., Prabhat, Reed, K., Stone, D., Collins, W. D., and Bacmeister,
J.: Resolution dependence of future tropical cyclone projections of
CAM5.1 in the US CLIVAR Hurricane Working Group idealized configurations, J.
Climate, 28, 3905–3925, 10.1175/JCLI-D-14-00311.1, 2015.
Wehner, M. F., Reed, K. A., and Zarzycki, C. M.: High-Resolution
Multi-Decadal Simulation of Tropical Cyclones, Chapter 8 in Hurricanes and
Climate Change, edited by: Collins, J. and Walsh, K., Springer, 187–207, 2017a.Wehner, M., Stone, D., Mitchell, D., Shiogama, H., Fischer, E., Graff, L. S.,
Kharin, V. V., Sanderson, B., and Krishnan, H.: Changes in extremely hot days
under stabilized 1.5 ∘C and 2.0 ∘C global warming scenarios as
simulated by the HAPPI multi-model ensemble, Earth Syst. Dynam., submitted, 2017b.Zarzycki, C. M., Thatcher, D. R., and Jablonowski, C.: Objective tropical
cyclone extratropical transition detection in high-resolution reanalysis and
climate model data, J. Adv. Model. Earth Syst., 9, 130–148,
10.1002/2016MS000775, 2017.