Geophysical Research Letters
RESEARCH LETTER
10.1002/2016GL069835
Key Points:• The Southern Hemispherecirculation response to volcanic
forcing is a robust positive
annular mode
• Models and observations are notcontradictory, as internal variability
is large• ENSO state can explain the perceived
discrepancy between observations
and models

Supporting Information:• Supporting Information S1

Correspondence to:
M. C. McGraw,
mmcgraw@atmos.colostate.edu

Citation:
McGraw, M. C., E. A. Barnes,
and C. Deser (2016), Reconciling the observed and modeledSouthern Hemisphere circulation
response to volcanic eruptions,Geophys. Res. Lett., 43, 7259–7266,
doi:10.1002/2016GL069835.

Received 31 MAY 2016
Accepted 23 JUN 2016
Accepted article online 27 JUN 2016
Published online 12 JUL 2016

Reconciling the observed and modeled Southern Hemispherecirculation response to volcanic eruptions
Marie C. McGraw1 , Elizabeth A. Barnes1 , and Clara Deser2
1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA, 2 Climate and Global
Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado, USA

Abstract Confusion exists regarding the tropospheric circulation response to volcanic eruptions, withmodels and observations seeming to disagree on the sign of the response. The forced Southern Hemispherecirculation response to the eruptions of Pinatubo and El Chichón is shown to be a robust positive annularmode, using over 200 ensemble members from 38 climate models. It is demonstrated that the models andobservations are not at odds, but rather, internal climate variability is large and can overwhelm the forced
response. It is further argued that the state of the El Niño–Southern Oscillation can at least partially explain
the sign of the observed anomalies and may account for the perceived discrepancy between model andobservational studies. The eruptions of both El Chichón and Pinatubo occurred during El Niño events, and it
is demonstrated that the Southern Annular Mode anomalies following volcanic eruptions are weaker during
El Niño events compared to La Niña events.
1. Introduction
Volcanic eruptions constitute a large source of natural climate variability on interannual timescales and exert asubstantial inﬂuence on global climate [e.g., Santer et al., 2013; Robock, 2000, and references therein]. The sulfuric acid particles that form in the aftermath of a volcanic eruption reﬂect incoming solar radiation and absorb
terrestrial longwave radiation, leading to a warming in the lower stratosphere and cooling in the tropospherethat is often considered the signature of volcanic inﬂuence on climate [Robock, 2000]. Tropical eruptions exert
a greater inﬂuence on global climate compared to higher-latitude eruptions [e.g., Robock and Mao, 1995;
Robock, 2000], as their sulfuric acid particles are most eﬃciently distributed throughout the stratosphere
via the stratospheric overturning circulation [e.g., Trepte et al., 1993; Hitchman et al., 1994] and as the transport of volcanic aerosols to the poles acts to enhance the equator-to-pole temperature diﬀerence via bothradiative eﬀects and eﬀects on ozone chemistry [e.g., Stenchikov et al., 2002; Solomon, 1999]. Observationsof global climate anomalies in the aftermath of volcanic eruptions are limited, as only two large tropical eruptions have occurred since the beginning of the satellite era in 1979: El Chichón in April 1982 and Pinatubo
in June 1991. With such a small sample size, the evaluation of the climate response (particularly that of thetropospheric circulation) to volcanic eruptions has proven diﬃcult, with many studies often ﬁnding apparently contradictory results.
Recent research has attempted to reconcile the seemingly conﬂicting results in models and in observations.
Global climate models have been used, with mixed results, to try to isolate a forced response from internalvariability. While multiple studies found a Northern Annular Mode (NAM, the primary mode of extratropical variability in the Northern Hemisphere) state similar to observations following eruptions in individualmodel simulations [e.g., Graf et al., 1993; Stenchikov et al., 2002; Rozanov et al., 2002], other studies [e.g., Driscoll
et al., 2012] did not ﬁnd a signiﬁcant response in the model simulations performed for the Coupled Model
Intercomparison Project, phase 5 (CMIP5) [Taylor et al., 2012]. Positive NAM and North Atlantic Oscillation(NAO) responses have been observed after both tropical and extratropical volcanoes [e.g., Christiansen, 2008],
suggesting that models and observations are not presenting a consistent picture of the Northern Hemispherecirculation anomalies following volcanic eruptions.

©2016. American Geophysical Union.
All Rights Reserved.

MCGRAW ET AL.

The Southern Hemisphere response is even more muddled. For example, Roscoe and Haigh [2007] concluded
that eruptions force negative tropospheric Southern Annular Mode (SAM, the primary mode of extratropicalvariability in the Southern Hemisphere) anomalies based on a multilinear regression of the SAM index against
volcanic aerosol loading. Robock et al. [2007] found no signiﬁcant SAM anomalies in reanalysis data sets or in
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the NASA GISS-E model following eruptions, attributing what few anomalies they did see in the observations
to the concurrent El Niño event. However, Karpechko et al. [2010] composited the Southern Hemispherecirculation response to Pinatubo and El Chichón in the CMIP, phase 3 (CMIP3) models and found a positive SAM
response in both the stratosphere and the troposphere in spring and fall after the eruptions. The observed
SAM was negative following both eruptions, highlighting an apparent disagreement between models andobservations. What could be driving these diﬀerences?
Volcanic eruptions are thought to impact the tropospheric circulation by strengthening the wintertime stratospheric polar vortex and impacting planetary wave propagation [Perlwitz and Graf , 1995]. Some studies[e.g., Ottera, 2008; Stenchikov et al., 2006] argue that stratospheric polar vortices in models are generally too
strong, which could aﬀect their tropospheric responses to volcanoes. Several studies [e.g., Karpechko et al.,2010; Barnes et al., 2016] suggest that the state of the El Niño–Southern Oscillation (ENSO) could play a rolein the perceived discrepancies between modeled and observed SAMs following volcanoes.
Indeed, recent research by Lehner et al. [2016] has demonstrated the impact of ENSO state on the global meansurface temperature (GMST) response to volcanic forcing by compositing the modeled and observed GMST
responses to three tropical eruptions—Agung, El Chichón, and Pinatubo. While the full suite of CMIP5 models
leads to an overestimation of the GMST response when compared to observations, subsampling the models
according to whether they happen to simulate a concurrent El Niño event as observed improves the ﬁdelity of
the response [Lehner et al., 2016]. Barnes et al. [2016] apply a similar technique to the SAM response following
Pinatubo, ﬁnding that models with positive ENSO states had weak or negative SAM anomalies. However,Barnes et al. [2016] looked at only one eruption (Pinatubo) and analyzed only 13 CMIP5 models with a single
ensemble member each. Here we use over 200 simulations composited over two eruptions in order to more
fully evaluate the Southern Hemisphere circulation response to volcanic forcing as well as the variability ofthis response and to robustly assess the impact of ENSO in hopes of reconciling the apparent discrepancy
between the models and the observations.

2. Data and Methods
2.1. Model Simulations and Reanalysis Data
The primary set of model simulations analyzed here is the 42-member Community Earth System Model(CESM1) Large Ensemble (hereafter CESM-LE) [Kay et al., 2015]. These simulations begin in 1920 and follow
the forcing protocol of the CMIP5 models [Taylor et al., 2012]. Each ensemble member has initial atmospherictemperature perturbations on the order of 10−14 ∘ C but are otherwise identical, and together, the ensemble
members allow for a robust assessment of internal variability. We analyze monthly mean zonal winds at 30pressure levels, 10 m zonal winds, and surface temperatures over two 25 year climatological base periods
centered on two tropical volcanic eruptions—the Pinatubo eruption of June 1991 and the El Chichón eruption of April 1982. We also analyze a 10-member ensemble of CESM1 “Pacemaker” simulations (hereafter
CESM-Pacemaker) [Lehner et al., 2016] in the same manner. These simulations use the identical model, forcing,and initial condition protocol as the CESM-LE, but their sea surface temperature (SST) anomalies in the eastern
tropical Paciﬁc (20∘ S–20∘ N, 180∘ W to the American coast) are nudged to observations using the NOAA
Extended Reconstruction Sea Surface Temperature, version 3 data set (ERSSTv3b), following Kosaka and Xie[2013]. In this way, the observed evolution of ENSO is maintained in each simulation, with the rest of the coupled climate system free to evolve. The CESM-LE members, by contrast, have their own internally generated,
independent sequences of ENSO events, thus ensuring a wide range of ENSO states following the volcanic
eruptions.
To evaluate the observed SAM state following El Chichòn and Pinatubo, we use zonal mean zonal winds,
surface temperature, and 10 m zonal winds from the Modern-Era Retrospective Analysis data set (MERRA)[Rienecker et al., 2011]. Finally, 155 simulations from 37 diﬀerent models in the CMIP5 Historical simulations
are compared to the CESM-LE in order to test the results across a full suite of models. Some CMIP5 models do
not have strong enough stratospheric temperature responses to volcanic forcing and were thus eliminatedfrom this analysis [see also Santer et al., 2013]; a detailed description of this evaluation can be found in the
supporting information. Signiﬁcance is assessed using a one-sided signed rank test at 95% conﬁdence unlessotherwise noted; we use the one-sided test as we hypothesize a priori that the SAM response to volcanic
forcing will be positive based on the results of Karpechko et al. [2010] and Barnes et al. [2016].
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2.2. SAM and ENSO Indices
The SAM is deﬁned as the ﬁrst empirical orthogonal function (EOF) of monthly mean zonal mean zonal wind
anomalies at each pressure level between 20∘ and 80∘ S during 1970 to 2004. The SAM time series is deﬁned
as the standardized ﬁrst principal component, with a positive annular mode index in the troposphere deﬁned
as a strengthening and poleward shift of the midlatitude jet relative to climatology. For the CESM-LE, the ﬁrst
EOF is deﬁned at each pressure level using data from all 42 members; each member’s annular mode timeseries is then calculated by projecting its zonal mean zonal wind anomalies at each time step onto the EOF
derived from the full ensemble. The SAM indices for CMIP5 are calculated in a similar manner—the ﬁrst EOF
is deﬁned at each level using data from all available simulations for each model, so that each model’s EOF andannular mode time series are calculated separately. The MERRA reanalysis SAM index time series is calculatedusing monthly mean data from 1979 to 2004. Most of this analysis is focused on November-December-January(NDJ), as these are the months of greatest response to volcanic forcing.
The ENSO phase is determined using the Niño 3.4 index. Following Lehner et al. [2016], the monthly sea surface
temperature anomalies are averaged over the Niño 3.4 region (170∘ –120∘ W, 5∘ S–5∘ N), detrended using aquadratic ﬁt and then standardized. Our results are not sensitive to the use of Niño 3 (5∘ S–5∘ N, 150–90∘ W)
or Niño 4 (5∘ S–5∘ N, 160∘ E–150∘ W) indices in place of Niño 3.4. The ENSO index is determined separately foreach member of the CESM-LE and for each simulation in CMIP5. The ENSO phase is then classiﬁed according
to the value of the detrended, standardized Niño 3.4 index. A positive ENSO (i.e., El Niño) event is deﬁned
when this value exceeds 0.5, and a negative ENSO (i.e., La Niña) event is deﬁned when this value is less than−0.5; all other years are deﬁned as neutral ENSO.
In addition to evaluating the SAM response to volcanoes, we also examine the eddy-driven jet response, as
the midlatitude jet is a dominant feature of the midlatitude circulation and is directly related to the storm
tracks. While the midlatitude jet and the SAM responses are related, calculating the jet response allows forindependent analysis of changes in jet position and jet speed [e.g., Thomas et al., 2015]. We use 10 m zonal
mean zonal winds to assess the changes in jet position and strength. Additionally, changes in the 10 m winds
can be linked to changes in surface wind stress, which is important for determining the ocean response tovolcanic forcing. This methodology is discussed in detail in the supporting information.

3. Results
3.1. Southern Hemisphere Circulation Response to Volcanoes
Figure 1 displays the NDJ CESM-LE and CESM-Pacemaker zonal mean zonal wind anomalies following the
Pinatubo eruption. Figures 1a and 1b highlight contrasting patterns simulated by two members of the
CESM-LE; simulation 14 shows a strongly positive SAM (Figure 1a), while simulation 32 shows a strongly
negative SAM (Figure 1b), highlighting the contribution of internal variability in any one realization. The
ensemble mean of CESM-LE (Figure 1c), indicative of the forced response to Pinatubo, shows a forced positive
SAM response, with positive zonal wind anomalies at 60∘ S and negative zonal wind anomalies at 40∘ S. This
response is robust and signiﬁcant, with over 70% of the simulations agreeing on the sign of the SAM response(Figure 1d).
The Northern Hemisphere also demonstrates large internal variability, with a positive annular mode response
evident in simulation 14 and a negative response evident in simulation 32 (Figures 1a and 1b). Unlike theSAM, the forced NAM response is not signiﬁcant (Figure 1d). Barnes et al. [2016] ﬁnd that the CMIP5 Northern
Hemisphere circulation anomalies following Pinatubo do not project well onto the zonal mean NAM but do
exhibit robust poleward shifts of the midlatitude jet when separated into North Paciﬁc and North Atlantic
sectors. We focus on the Southern Hemisphere for the remainder of this study.
The forced (e.g., ensemble mean) response in the CESM-Pacemaker runs (Figure 1e) is similar to that of the
CESM-LE, showing a positive SAM, albeit one with weaker magnitude. The CESM-Pacemaker ensemble meanalso shows positive zonal wind anomalies in the subtropics and tropics that are not present in the CESM-LE.
The diﬀerences between the forced responses in the CESM-Pacemaker and CESM-LE ensembles are highlighted in Figure 1f. The hemispheric symmetry of the anomalies in Figure 1f are indicative of the circulation
response to ENSO, emphasizing that ENSO may play a role in the diﬀerences between the CESM-Pacemakerand CESM-LE responses (see discussion in section 3.3).
The results shown in Figure 1 indicate that the simulated forced response to Pinatubo is a robust positive
SAM but that large internal variability exists. We explore this further by plotting histograms of the NDJ SAM
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Figure 1. Zonal mean zonal wind anomalies in NDJ after Pinatubo from the CESM-LE for (a) simulation 14,(b) simulation 32, and (c) the ensemble mean of all 42 simulations. (d) Agreement in sign of the zonal wind anomalies
following Pinatubo across the 42 simulations. (e) As in Figure 1c but for the ensemble mean of the 10 CESM-Pacemakersimulations. (f ) Figure 1e minus Figure 1c. In Figure 1d, stippling indicates agreement on the sign of the anomaly at 95%
conﬁdence using a binomial distribution. In Figure 1f, stippling indicates signiﬁcance at 95% conﬁdence according to adiﬀerence of means test.

index in years with and without volcanoes for the CESM-LE and CESM-Pacemaker simulations (Figure 2). The
NDJ SAM index is calculated for all years from 1970 to 2004; the red lines show the SAM indices in the years
following the eruptions of Pinatubo and El Chichón (1991–1992 and 1982–1983), while the blue lines show
the SAM indices in all other years. In the CESM-LE (solid lines), the post-volcano SAM distribution is morepositive than the SAM distribution in years without volcanoes. The two SAM distributions are signiﬁcantly
diﬀerent from each other at 95% conﬁdence according to the two-sample Kolmogorov-Smirnov test (K-S test).
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The CESM-Pacemaker simulations (dashedlines) diﬀer much less, and the diﬀerence
is not signiﬁcant, hinting at the impacts
of ENSO variability on the SAM response.
Analagous ﬁgures based on jet positionand strength present a complementary picture of the circulation response,
showing a poleward shift and strengthening of the surface zonal winds in response to volcanic forcing (see supporting
information).

Figure 2. The distribution of NDJ 700 hPa SAM indices for years
without volcanoes (blue lines) and years with volcanoes (red lines)
for the CESM-LE simulations (solid lines) and the CESM-Pacemakersimulations (dashed lines). The observed mean NDJ SAM indicesfollowing El Chichòn and Pinatubo are shown in the dashed andsolid black vertical lines, respectively.

The observed NDJ SAM indices following
Pinatubo and El Chichón (black lines) areboth negative but well within the rangeof variability found in the CESM-LE and
CESM-Pacemaker simulations. Thus, theobserved SAM responses to the Pinatubo
and El Chichón eruptions are not at odds
with the modeled forced response tothese eruptions, since internal variability
can overwhelm the forced response.

3.2. Comparison With CMIP5
An advantage of using the CESM-LE isthat all simulations use the same model,thus isolating internal variability from model structural uncertainty. However, to demonstrate that the forced
SAM signal is robust across a range of models and not just a feature of CESM1, we perform similar analysis
on 155 simulations from 37 CMIP5 models. Figure 3 shows the evolution of the SAM index for the 24 months
following the two volcanic eruptions in the CESM-LE (Figure 3a) and CMIP5 (Figure 3b). In both ﬁgures, theevolution of the mean SAM index across all simulations (black line) is compared to the observed SAM anomalies following Pinatubo (solid red line) and El Chichón (dashed red line). Both the CESM-LE and the CMIP5
models show a positive SAM response in the 2–7 months following an eruption; in many months, the sign ofthe response is signiﬁcant at 95% conﬁdence (black dots). In both CMIP5 and the CESM-LE, internal variability(gray shading) is larger than the forced SAM anomalies. The CESM-Pacemaker ensemble mean (light blue line

Figure 3. Evolution of monthly 700 hPa SAM indices over the 24 months following a volcanic eruption (month zero)for (a) the CESM-LE and (b) the CMIP5 models. The black curves show the ensemble mean values, with black dots
indicating months in which the sign of the response is positive and signiﬁcant at 95% conﬁdence. The light blue curvein Figure 3a denotes the CESM-Pacemaker ensemble mean. The red lines indicate the observed SAM indices following
Pinatubo (solid) and El Chichón (dashed). The gray shading indicates the 95th percentile range of SAM indices acrossall model runs.

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Figure 4. Mean NDJ SAM index as a function of ENSO state for (a) CESM-LE and CESM-Pacemaker simulations and(b) CMIP5. Years following volcanoes are in red, while all other years are in blue. Shaded boxes indicate that the medianvalue of the SAM index in years with volcanoes is signiﬁcantly diﬀerent from that in years without volcanoes at 95%
conﬁdence. The numbers inside the boxes list the number of simulations in each ENSO state. The whiskers show the95th percentile range across the simulations. The horizontal gray boxes in Figure 4a indicate the observed values of the
NDJ SAM index after El Chichòn (E) and Pinatubo (P) based on MERRA reanalysis.

in Figure 3a) lacks the magnitude and robustness of the positive SAM response in the CESM-LE, with SAM
indices near zero following the eruption. This is likely due to the positive ENSO state of the CESM-Pacemakerruns (as will be discussed in the next section). Finally, the observed SAM indices following Pinatubo (solid red)and El Chichón (dashed red) diﬀer from the forced responses in the CESM-LE and CMIP5 ensembles, and fromeach other, but they largely fall inside the 95th percentile of the SAM responses for both model ensembles(gray shading), once more highlighting the dominance of internal climate variability.
3.3. Impact of ENSO State
Both Pinatubo and El Chichón occurred during positive ENSO events [see Lehner et al., 2016, Figure 1]. Whilesome research indicates that an El Niño might actually be more likely in the 1 to 2 years following a large
eruption [e.g., Adams et al., 2003; Emile-Geay et al., 2008; Stevenson et al., 2016], the positive ENSO events after
El Chichón and Pinatubo were already underway before the eruptions occurred. Therefore, questions regarding this causality are set aside for the purposes of this study, and we focus our analysis on the year immediately
following the eruption. We emphasize that while previous studies [e.g., Karpechko et al., 2010; Barnes et al.,2016] have hinted at a relationship between ENSO state and the SAM response to volcanic forcing, we use
a larger sample of model simulations (52 CESM1 and 155 CMIP5 composited over two eruptions—over 200
simulations in total) for a robust assessment of any connections between ENSO state and the SAM phase.
Figure 4 shows the NDJ SAM indices in 1982–1983 and 1991–1992 (red boxes) and all other years (blue boxes)
as a function of ENSO state for both the CESM-LE (Figure 4a) and CMIP5 (Figure 4b). Regardless of ENSO state,years following major tropical volcanic eruptions show a more positive median SAM index (black lines) thanyears without major tropical eruptions in both CMIP5 and the CESM-LE. In the CESM-LE, this positive SAM
response is much stronger (and signiﬁcant at 95% conﬁdence) in simulations with negative or neutral ENSO
states compared to simulations with positive ENSO states (including the CESM-Pacemaker runs), which show amuch smaller and insigniﬁcant increase in the SAM index. The CMIP5 SAM responses are similar to the CESM-LE
responses during positive and negative ENSO states; however, the CMIP5 response during neutral ENSO is
weaker than that of the CESM-LE. Note that when all 207 simulations are analyzed together (i.e., combining
CMIP5 and the CESM-LE), the SAM is signiﬁcantly more positive in years with major tropical eruptions than inyears without, regardless of ENSO state (see supporting information Figure S3). These results are consistent
with Figures 2 and 3 and demonstrate that the forced response to tropical eruptions is a positive SAM.
Going further, we ﬁnd that the SAM index following eruptions in simulations with a negative ENSO state is signiﬁcantly larger than the SAM index in simulations with a positive ENSO state (compare red boxes in Figure 4).
This diﬀerence is signiﬁcant at 95% conﬁdence for the CESM-LE and CMIP5 combined analysis and at 90%
conﬁdence for the CESM-LE and CMIP5 individually. Thus, while years following eruptions all exhibit more positive NDJ SAM indices when compared to all other years, this positive SAM anomaly is largest during negative
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ENSO conditions. One might expect that this diﬀerence is due to a simple oﬀset of the circulation response
to volcanic eruptions by the response to ENSO [e.g., L’Heureux and Thompson, 2006]. However, if this werethe case, one would expect that the SAM responses for positive and negative ENSO states would be diﬀerent
even in years without volcanoes, but this is not evident (compare blue boxes in Figure 4). Thus, the reasons forthe diﬀerences between the SAM anomalies during positive and negative ENSO states are likely more complicated than a simple oﬀset due to the ENSO response. Recent results by Welhouse et al. [2016] emphasize
the diﬀerent mechanisms of the SAM-ENSO teleconnection in summer versus winter, further highlighting thecomplexities of the SAM-ENSO relationship.
The observed SAM indices (horizontal gray boxes in Figure 4a) following both the El Chichón and Pinatubo
eruptions fall below the median model SAM indices, but within the internal variability exhibited for neutral
and positive ENSO states in the CESM-LE and CMIP5 (Figure 4). It is worth noting that the observed SAM
anomaly after El Chichón is just outside the natural variability of the response for simulations with negative
ENSOs in both CMIP5 and the CESM-LE, while the observed SAM anomaly following Pinatubo is just inside
this range. This suggests that the observed SAM anomaly following El Chichón may have been unlikely in ayear with a negative ENSO event. Finally, the CESM-Pacemaker simulations (Figure 4a) show a weaker SAM
response to volcanic forcing compared to the CESM-LE simulations (see also Figure 1f )—a weaker responsethat is likely due, at least in part, to the positive ENSO states of all 20 CESM-Pacemaker simulations.

4. Conclusions
The Southern Hemisphere circulation response to volcanic forcing is a robust positive Southern Annular Mode
(SAM) in both the CESM1 Large Ensemble and the CMIP5 models. However, due to the large internal variability,
this forced response may be obscured in any individual simulation. At least some of this internal variability
can likely be attributed to the state of ENSO: negative ENSO states exhibit a stronger SAM anomaly, while
positive ENSO states exhibit a weaker SAM anomaly following eruptions. Internal variability coupled with thepositive ENSO states observed after both El Chichón and Pinatubo places the observations into context. We
argue that the models and observations are not in opposition—the observed SAM indices fall well within
the distribution of modeled SAM indices, especially when accounting for ENSO state. Ultimately, while thevolcanoes examined here do produce a forced tropospheric circulation response in the Southern Hemisphere,
this response does not appear strong enough to overwhelm other sources of internal climate variability.
Acknowledgments
We thank Tingting Fan and Adam
Phillips at the National Center
for Atmospheric Research (NCAR)
for conducting and providing uswith the Pacemaker simulations
and assisting us in obtaining the
CESM1 Large Ensemble Data andHaibo Liu and the Lamont-Doherty
Earth Observatory for obtaining theCMIP5 data. We also thank Flavio
Lehner for helpful discussions andfeedback and the two anonymous
reviewers for their comments andsuggestions. The MERRA data usedin this study have been provided
publicly by the Global Modeling andAssimilation Oﬃce (GMAO) at NASA
Goddard Space Flight Center throughthe NASA GES DISC online archive(http://disc.sci.gsfc.nasa.gov/daac-bin/
DataHoldings.pl). M.C.M. and E.A.B.
were supported, in part, by the
Climate and Large-scale Dynamics
Program of the National Science
Foundation under grant 1419818.
NCAR is sponsored by the National
Science Foundation.

MCGRAW ET AL.

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