Interannual Variations of Stratospheric Water Vapor in MLS Observationsand Climate Model Simulations

YOSHIO KAWATANI

Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

JAE N. LEE

Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland

KEVIN HAMILTON

International Pacific Research Center, University of Hawai‘i at M�anoa, Honolulu, Hawaii

(Manuscript received 5 June 2014, in final form 23 July 2014)

ABSTRACT

By analyzing the almost-decade-long record of water vapor measurements from the Microwave Limb

Sounder (MLS) instrument on theNASAAura satellite and by detailed diagnostic analysis of the results from

state-of-the art climate model simulations, this study confirmed the conceptual picture of the interannual

variation in equatorial stratospheric water vapor discussed in earlier papers (e.g., Geller et al.). The in-

terannual anomalies in water vapor are strongly related to the dynamical quasi-biennial oscillation (QBO),

and this study presents the first QBO composite of the time–height structure of the equatorial water vapor

anomalies. The anomalies display upward propagation below about 10 hPa in a manner analogous to the

annual ‘‘tape recorder’’ effect, but at higher levels they show clear downward propagation. This study ex-

amined these variations in the Model for Interdisciplinary Research on Climate (MIROC)-AGCM and in

four models in phase 5 of the CoupledModel Intercomparison Project (CMIP5) that simulate realistic QBOs.

Diagnostic budget analysis of the MIROC-AGCM data and comparisons among the CMIP5 model results

demonstrate (i) the importance of temperature anomalies at the tropopause induced by the QBO for lower-

stratospheric water vapor variations and (ii) that upper-stratospheric water vapor anomalies are largely

driven by advection of themean vertical gradient of water content by theQBO interannual fluctuations in the

vertical wind.

1. Introduction

Although stratospheric water vapor (H2O) mixing ra-

tios are quite small, the water vapor in the stratosphere

makes a significant contribution to the global-mean radi-

ative forcing of climate (e.g., Solomon et al. 2010). In

addition to its importance as a greenhouse forcing,

stratospheric H2O is also of interest as a diagnostic of

large-scale stratospheric circulation. In a classic study,

Mote et al. (1996) analyzed satellite observations of H2O

near the equatorial stratosphere and noted a strong an-

nual cycle of mixing ratio that displayed a slow upward

phase propagation. This is consistent with a simple picture

of the stratospheric circulation in which the H2O content

of the air entering the tropical stratosphere is set by the

saturation value for the coldest temperatures experienced

as it rises through the very cold tropical tropopause. The

annual cycle of temperature near the tropical tropopause

results in significant seasonal modulation of the saturation

H2O mixing ratio, which is then reflected in the annual

cycle in the H2O concentration of air entering the

stratosphere. This annual signal is simply advected up-

ward by the slow mean upwelling associated with

Brewer–Dobson circulation near the equator, in what is

nowknownas the ‘‘tape recorder’’ effect (Mote et al. 1996).

Mote et al. (1996, 1998) noted that interannual signals

in the equatorial stratospheric H2O concentrations

could also be seen, and that the deviations from the

simple picture of the annual cycle tape recorder could be

Corresponding author address: Yoshio Kawatani, Japan Agency

for Marine-Earth Science and Technology, 3173-25 Showamachi,

Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.

E-mail: yoskawatani@jamstec.go.jp

4072 JOURNAL OF THE ATMOSPHER IC SC IENCES VOLUME 71

DOI: 10.1175/JAS-D-14-0164.1

� 2014 American Meteorological Society

largely explained as effects of the quasi-biennial oscil-

lation (QBO). The QBO is known to modulate the in-

terannual variations in tropical tropopause temperature

(Reid and Gage 1985; Randel et al. 1998, 2000, 2004;

Zhou et al. 2001). The QBO contribution to tempera-

ture variation near the tropopause may be up to 0.5K

(Randel et al. 2000), and these temperature changes

could result in significant modulation of the H2O con-

tent of the air entering the stratosphere.

Randel et al. (2004) investigated interannual varia-

tions of stratospheric H2O during 1992–2003 using

Halogen Occultation Experiment (HALOE) data from

the National Aeronautics and Space Administration

(NASA)Upper Atmospheric Research Satellite (UARS)

and found H2O interannual changes of approximately

60.3 ppmv in magnitude near the equator with a

roughly 2-yr periodicity. The anomalies can be traced

back to the tropical tropopause and can propagate ver-

tically in a manner similar to the seasonal tape recorder.

Fujiwara et al. (2010) investigated H2O variations in the

tropical lower stratosphere using balloonborne cryo-

genic frost-point hygrometer data between 1993 and

2009 during various campaigns. They identified H2O

concentration variations that are apparently associated

with the QBO in tropopause temperatures. Fujiwara

et al. also noted that the vertical gradients of H2O in the

westerly shear phase are greater than those in the east-

erly shear phase and explained this in terms of the ad-

vection by the QBO residual meridional circulation.

The QBO influences on stratospheric H2O have also

been investigated by numerical simulations, although

this avenue of research has been complicated by the fact

that most comprehensive atmospheric general circula-

tion models (AGCMs) do not simulate a QBO in the

tropical stratosphere. Giorgetta and Bengston (1999)

conducted AGCM experiments including a simple as-

similation of the observed near-equatorial stratospheric

zonal-mean winds, effectively forcing a realistic dy-

namical QBO in their model. They found evidence of

both the QBO variation in the dehydration of air rising

through the tropical tropopause and QBO modulation

of the ascent rate of tropical air. However, their model is

rather incomplete, in that it extended only to 10 hPa and

did not include the methane oxidation process that is an

important source of H2O in the real stratosphere.

Geller et al. (2002) investigated interannual variations

of stratospheric H2O associated with both the QBO and

El Niño–Southern Oscillation (ENSO) using a two-

dimensional (2D) chemistry transport model. They

showed that QBO variations in cold-point tropopause

temperature play a large role in stratospheric H2O vari-

ations, which is consistent with the work of Giorgetta and

Bengston (1999). Geller et al. (2002) also showed that the

ENSO effect produces significant variations from one

QBO cycle to another, and that the model results, in-

cluding both the QBO and ENSO effects, are improved

when judged against HALOE observations of equatorial

water vapor.

Earlier observational studies have shown that the

simple tape-recorder propagation of interannual signals

from the equatorial tropopause only explains the ob-

served water vapor anomalies up to at most 25–30 km

(;10–15 hPa) and that the upward propagation is not

apparent at higher levels (Randel et al. 1998, 2004;

Geller et al. 2002). These earlier studies also included

diagnostic and simple model investigations of the

mechanisms of interannual variability in the tropical

upper-stratospheric water vapor. Randel et al. (1998)

analyzed HALOE data from 1991 to 1997 and found

that equatorial H2O (and CH4) anomalies over the

35–45-km altitude range are correlated with anomalies

in the residual-mean vertical velocity, indicating a role

for advection of mean vertical gradients in generating

these trace-constituent variations.

Geller et al. (2002) analyzed 6 years of HALOE data

up to 50 km and showed that the interannual water va-

por anomalies slope upward with time below approxi-

mately 35 km but variations above that height show no

such slope. They also conducted experiments in a 2D

model with and without QBO residual meridional cir-

culation. By subtracting the run with no QBO transport

from the run that included the QBO transport varia-

tions, Geller et al. (2002) demonstrated that the upper-

stratospheric H2O interannual anomalies result from

the transport of the water vapor by the QBO-induced

anomalies of the residual circulation.

The discussion above indicates that considerable

progress has beenmade in characterizing and explaining

interannual variations of equatorial water vapor con-

centrations. However, the extant observational analy-

ses all had significant limitations in the data available.

Also, the simulations that have been previously ana-

lyzed have come from either simplified 2D models or

from AGCMs that lacked adequate treatment of the

upper stratosphere. Our study reported in the present

paper has been motivated by the availability of new

satellite data and more complete comprehensive nu-

merical simulations. By analyzing a long and (arguably)

higher-quality observational record and by detailed

analysis of the results from a long run of a compre-

hensive 3D model, including the methane oxidation

process, we have improved the characterization of the

QBO in equatorial water vapor. We have also con-

firmed the earlier understanding of the mechanisms

driving the QBO variations and placed it on a more

secure footing.

NOVEMBER 2014 KAWATAN I ET AL . 4073

The UARS HALOE observations have been used in

several previous studies of stratosphericH2O.TheHALOE

sampling is approximately 15 sunrise and 15 sunset mea-

surements per day, with sunrises and sunsets usually

separated in latitude. It takes about 1 month to sample

the latitude range from about 608N to 608S (Russell et al.

1993). The UARS HALOE data for trace-gas con-

centrations are available from 1991 to 2005, but until

1994 they are contaminated by the aerosol signal from

the 1991 Mt. Pinatubo eruption. In their original papers

Mote et al. (1996, 1998) analyzed the available HALOE

data but also showed that the tape-recorder signal was

clear in measurements from the UARS Microwave

Limb Sounder (MLS) instrument. In fact, the annual

cycle data for H2O appear considerably less noisy for the

MLS data shown in Mote et al. than for the HALOE

data. Unfortunately, the UARS MLS instrument oper-

ated for only 18 months and Mote et al. analyzed the

MLS H2O retrievals up to only 6.8 hPa (;35 km).

A new MLS instrument, the Earth Observing System

(EOS) Microwave Limb Sounder, is now on board

NASA’s Aura satellite, which launched in July 2004

(Waters et al. 2006). This instrument detects thermal

microwave emission from the edge of Earth’s atmo-

sphere by viewing forward along the spacecraft flight

direction. The view is scanned from the ground to about

90 km approximately every 25 s. The satellite makes

about 13 orbits per day and retrieves vertical profiles of

atmospheric temperature and composition in the verti-

cal range of 8–90 km (Livesey et al. 2006). EOS MLS

H2O data for almost 10 years are now available. In this

study, we have investigated the interannual variations in

H2O content in the equatorial stratosphere using the

long record from EOS MLS version 3.3.

We also conducted climate model simulations using

a fine-horizontal-and-vertical-resolution (T106L72) ver-

sion of the Model for Interdisciplinary Research on Cli-

mate (MIROC) AGCM to clarify the mechanism of the

observed H2O interannual variability. This model spon-

taneously simulates a rather realistic dynamical QBO in

the tropical stratosphere (Kawatani et al. 2011, 2012).

The standard version of this model includes a simple

parameterization of the effects of the methane oxidation

source of H2O, and we have conducted experiments with

and without the methane oxidation parameterization to

elucidate the mechanisms of H2O variability in the upper

stratosphere. In addition, the interannual variability of

H2O is investigated in simulations from several other

global models that were included in phase 5 of the Cou-

pled Model Intercomparison Project (CMIP5).

This paper is arranged as follows. Section 2 describes

observational data and provides a description of the

model. Section 3 analyzes the interannual variation of

MLS H2O. Section 4 investigates the interannual vari-

ation of equatorial water vapor concentration as simu-

lated in the MIROC model. Section 5 compares the

interannual variations of water vapor concentration in

the equatorial stratosphere in four CMIP5 global

models. Section 6 summarizes the study and provides

concluding remarks.

2. Observational data and model description

a. MLS observation

Monthly-mean EOS Aura MLS H2O concentration

data from August 2004 to January 2014, derived from

latest version 3.3 (v3.3) of daily-mean observations, are

analyzed in this study. Extensive assessment has been

conducted for MLS v2.2 H2O product through valida-

tion studies (Lambert et al. 2007; Read et al. 2007). For

MLS v2.2 H2O data, the single-profile precision is about

0.2–0.3 ppmv (4%–9%) in the stratosphere and the ac-

curacy is estimated to be 0.2–0.5 ppmv (4%–11%) for

the pressure range 68–0.01 hPa (Lambert et al. 2007).

This precision is not achieved in the lower-stratosphere-

and-upper-troposphere region with values of 10%–20%

from 121 to 82.5 hPa (Read et al. 2007). The MLS v3.3

H2O product is expected to be about 0.2–0.3 ppmv

wetter than the v2.2 product in the pressure range 82.5–

0.1 hPa (Livesey et al. 2011). For pressures greater than

21 hPa, the precisions of the two versions are nearly

identical. The H2O data mapped into a 48 (latitude) 3 88(longitude) grid from 146.8 to 0.46 hPa (29 levels) are an-

alyzed here. The vertical resolution is about 2.5km at 316–

215hPa, 3.0 km at 100–1.0hPa, and 3.4km above 1hPa.

b. General circulation model

We use a version of the MIROC-AGCM almost

identical to that described in Kawatani et al. (2011). This

version of the model has a horizontal resolution of T106

spectral truncation that corresponds to a grid interval of

approximately 120 km (about 1.1258 in latitude and

longitude). The model uses 72 vertical numerical levels

(L72) with the top boundary at 1.2 hPa (;47 km). The

vertical resolution is close to 550m from about 300 up to

5 hPa, which should provide adequate representation

of mean-flow interaction with vertically propagating

waves. Starting at 4.5 hPa, the model includes an artifi-

cial damping in a ‘‘sponge layer.’’ The topographic

gravity wave parameterization of McFarlane (1987) is

employed, but no parameterization of nonstationary

gravity wave effects is included. Hence, the simulated

QBO is driven by explicitly resolved waves in themodel.

The parameterization of the methane oxidation pro-

cess used in the European Centre for Medium-Range

4074 JOURNAL OF THE ATMOSPHER IC SC IENCES VOLUME 71

Weather Forecasts (ECMWF) is included in the model.

Methane oxidation is a primary process of H2O pro-

duction in the middle atmosphere. The chemical source

in the water vapor mass mixing ratio tendency equation

is expressed as k(Q2 q), where k is a rate (specified as

a function of pressure), Q is a parameter set at 4.25 31026 (corresponding to 6.8 ppmv), and q is the model

H2O mass mixing ratio [see ECMWF (2013) for more

details and references therein]. Our T106 MIROC

AGCM integrations reported in this paper were con-

ducted with annually repeating sea surface temperatures

(SSTs) based on present-day observed climatology

(Kawatani et al. 2011).

We conducted two model integrations: (i) a control

run with the methane oxidation parameterization and

(ii) another run without the methane oxidation source

for water vapor. The model in each case was integrated

for 30 years after a spinup to equilibrate the mean

stratospheric H2O concentrations.

3. Interannual variation of MLS H2O

Figure 1 shows the time–height cross section of the

Aura MLS H2O volume mixing ratio averaged over

128S–128N.We show data only to 0.46 hPa (a little higher

than stratopause level) because the main purpose of this

study is to investigate stratospheric interannual H2O

variations and particularly those associated with the

QBO (which becomes quite weak above 1 hPa; Hamilton

1981; Baldwin et al. 2001; Baldwin and Gray 2005). The

minimum annual-mean H2O mixing ratio is observed

near the tropical tropopause, and it is clear that the ver-

tical gradient of annual-mean H2O concentration is pos-

itive and becomes larger above about 10hPa. This

vertical stratification of the mean H2O concentration

agrees with earlier observations (e.g., Randel et al. 2004).

The annual cycle in Fig. 1 is interpreted as resulting from

more upward H2O transport from June to October and

less upward H2O transport from December to April, be-

cause of the seasonal temperature cycle at the tropical

tropopause and consequent variations in saturationmixing

ratios at the tropical tropopause (Mote et al. 1996, 1998).

Figure 2a shows the frequency power spectra of MLS

H2O in 128S–128N as a function of height. Before cal-

culating the power spectra, the linear trend was re-

moved. There are three major spectral peaks in the

stratosphere—at 6, 12, and 20–40 months—that corre-

spond to semiannual, annual, and QBO variability, re-

spectively. These three peaks are evident at all heights,

but they are weaker near 10 and 0.5 hPa. In the tropo-

sphere and near the tropopause around 70–100 hPa,

rather than a QBO peak, one finds variability spread

over a broader spectral range, possibly indicating the

significance of ENSO variations on this region of the

tropical atmosphere. The procedure to extract inter-

annual variability is as follows: the mean seasonal cycle

(i.e., annual cycle) is calculated using data from August

2004 to January 2014, and then the values are subtracted

FIG. 1. Time–height cross section of monthly- and zonal-mean

MLS H2O over 128S–128N. The color interval is 0.3 (0.5) ppmv for

values less than (larger than) 4.5 ppmv.

FIG. 2. Frequency power spectra of MLS H2O mixing ratio av-

eraged over 128S–128N as a function of height for (a) all compo-

nents and (b) deseasonalized and smoothed components. The

shaded intervals are 3, 6, 9, 18, 36, 72, 144, and 288 31023 ppmv2month.

NOVEMBER 2014 KAWATAN I ET AL . 4075

from the raw data. The resulting series were then smoothed

by taking 5-month running means (e.g., Kawatani and

Hamilton 2013). Figure 2b shows the spectra for the

deseasonalized and smoothed time series. Most compo-

nents extracted by this method are concentrated in pe-

riods of 20–40 months in the stratosphere—that is, the

QBO period’s ranges—while components with periods

longer than 40 months and those around 8–11 months

remain in the troposphere and tropopause regions.

Figure 3 shows vertical profiles of the time-mean

128S–128N MLS H2O mixing ratio, along with the total

standard deviation and standard deviations due to the

annual cycle and due to interannual components. The

MLS H2O mixing ratio in the tropics reaches its mini-

mum at 82.5 hPa, at or just above the tropopause, and it

increases with height in the stratosphere. The cold-point

tropopause typically lies between 100 and 82 hPa, and

the mean water vapor seen in earlier HALOE obser-

vations also shows a minimum at 82 hPa (Randel et al.

2004). The annual cycle is dominant in the upper tro-

posphere and lower stratosphere, while interannual

variability becomes comparable to annual variability in

the upper stratosphere.

Figure 4 illustrates the time–height cross section of the

interannual anomaly of MLS H2O in 128S–128N com-

pared with the observed deseasonalized and smoothed

(5-month running mean) zonal wind over Singapore

from August 2004 to January 2014 [data from Kunze

(2014)]. Note that blue colors correspond to positive

H2O anomalies. Inspection shows this period included

roughly 4.5 cycles of both the wind QBO and the dom-

inant interannual variation of H2O in the stratosphere.

Upward-propagating anomalies are clearly seen from

the lower stratosphere to themiddle stratosphere, and in

their rather uniform upward propagation they resemble

the annual tape-recorder signal apparent in the water

vapor in this altitude range (e.g., Figure 1). The inter-

annual H2O anomalies display more variability than the

wind signals, which could reflect the presence of other

sources of interannual variability for H2O, including

ENSO (Geller et al. 2002).

At higher levels—say, above 10–15hPa—the anomalies

in H2O seem to propagate downward. The interannual

variability in the upper and lower stratosphere appear

either unrelated or perhaps have a phase cancellation

around 10hPa (note the power spectrum of interannual

anomalies has minimum values around 10hPa; Fig. 2).

To isolate the effects of the QBO on interannual

variations of H2O, a composite based on the phase of the

zonal wind QBO was computed. Month 0 of the com-

posite is taken to be when the zonal winds at 30 hPa in

the deseasonalized and smoothed Singapore wind series

changes from westerly to easterly. Composite values

were then computed for 618 months around these zero

months (i.e., February 2007, May 2009, and September

2011; see Fig. 4b).

FIG. 3. Vertical profiles derived from monthly time series of MLS water vapor mixing ratio

averaged over 128S–128N. (a) Long-term-mean profile and (b) standard deviation due to all

(black), annual (red), and interannual (blue) components.

4076 JOURNAL OF THE ATMOSPHER IC SC IENCES VOLUME 71

The time–height cross section of the QBO composite

for H2O mixing ratio is shown in Fig. 5. While a com-

posite based on just three cycles cannot remove all the

extraneous signals, the QBO effect on H2O is quite

apparent in Fig. 5. Below about 20 hPa, the upward-

propagating tape-recorder signal is clearly seen as dis-

cussed in previous studies (Randel et al. 1998, 2004;

Giorgetta and Bengtsson 1999; Geller et al. 2002). The

difference between the maximum and minimum anom-

alies around the tropopause is approximately 0.25 ppmv.

The anomalies in Fig. 5 propagate vertically between

100 and 20 hPa with an estimated mean speed of about

8.5 kmyr21 (;0.27mms21), similar to the propagation

speed of the annual cycle in H2O (Mote et al. 1998;

Niwano et al. 2003) and also to other estimates of

the mean upwelling in the tropical lower stratosphere

(Rosenlof 1995).

In the upper stratosphere, downward-propagating

signals are apparent. Seen together, the upward-

propagating and downward-propagating signals form

a ‘‘boomerang’’ pattern in the time–height plot. In ear-

lier studies of satellite data, Randel et al. (1998) and

Geller et al. (2002) noted that the regular upward

propagation of the water vapor anomalies in the lower

stratosphere was not seen above about 30 km, but they

did not characterize further the downward propagation

of the anomalies. With our analysis of about 10 years of

MLS observations, the upper-stratospheric variations

are better characterized and the dominant downward

propagation of QBO-related water vapor anomalies

there is quite clear.

FIG. 4. (a) Time–height cross section of the 128S–128N interannual anomaly of MLS water

vapor mixing ratio and (b) observed deseasonalized and smoothed zonal wind over Singapore

fromAugust 2004 to January 2014, provided by the Free University of Berlin (FUB). The color

intervals are (a) 0.05 ppmv and (b) 5m s21. For H2O, blue colors correspond to positive values

(more water vapor).

FIG. 5. Composite of the QBO in interannual variation of 128S–128N average H2O where month 0 corresponds to the westerly-to-

easterly transition of the zonal wind at 30 hPa. The color interval is

0.05 ppmv.

NOVEMBER 2014 KAWATAN I ET AL . 4077

4. Interannual variation of H2O in theMIROC-AGCM

Wenow turn to results from the longMIROC-AGCM

simulations described in section 2. As noted earlier, the

model has fine vertical resolution up to 5 hPa, but

coarser resolution and an artificial damping are imposed

higher up. So, we show results only up to 5 hPa. The red

curve in Fig. 6a shows the vertical profile of the mean

H2O mixing ratio averaged over 128S–128N in the con-

trol simulation, and it is compared with the MLS

observed result (black curve). The basic pattern of

minimum water vapor concentration near 80–90 hPa

with rising values above that point is seen in both the

model and MLS data. However, the minimum is deeper

in the observations (;3.7 vs ;4.1 ppmv in the model)

and the vertical gradient is larger in the observations at

least above approximately 70 hPa. The simulated long-

term annual-mean and zonal-mean temperature in the

equatorial region is nearly identical to that in the In-

terim ECMWF Re-Analysis (ERA-Interim). However,

the MIROC-AGCM generally has warm biases in the

coldest tropical tropopause temperatures, which occur

over the equatorial western Pacific (Holton andGettelman

2001; Zhou et al. 2004), compared with ERA-Interim. The

MIROC-AGCM has approximately 1-K warm biases

around there, corresponding to a saturation mixing ratio

bias of about 0.5ppmv. The blue curve in Fig. 6a shows

results from the MIROC simulation without the methane

oxidation source [note thatGiorgetta andBengtsson (1999)

also had no methane oxidation source]. In this case the

vertical gradient of H2O concentration is actually neg-

ative above 70 hPa.

Figure 6b shows the standard deviations of the annual

cycle of the H2Omixing ratios as a function of height for

the two model experiments compared with the MLS

observations. The model results significantly under-

estimate the variance above about 40 hPa. Figure 6c

shows the standard deviation of the interannual anom-

alies, again comparing the twomodel runs with theMLS

observations. The interannual standard deviation is

smaller in the model than in the MLS observations at all

altitude ranges. Possible contributors to the smaller in-

terannual variation in the modeled water vapor mixing

ratio include the use of climatological SSTs and an am-

plitude of the simulated dynamical QBO that is some-

what smaller than observed (Kawatani et al. 2011).

Figures 7a–d show the QBO composite of zonal-mean

zonal wind, temperature, residual vertical velocity, and

H2O mixing ratio in the MIROC T106 control simula-

tions. For each variable, the results presented are aver-

aged over 128S–128N. The procedure for making the

composite is the same as for that described in section 3

for observations (Fig. 5) but a total of 13 cycles from the

30-yr simulations are averaged for themodel. Themodel

simulates a QBO-like oscillation in the zonal wind with

a period close to 24 months (Fig. 7a). The simulated

QBO amplitude is smaller than that in the real world,

especially in the lower stratosphere. Given the pre-

sumed role of the cold-point tropopause in dehydration

of air entering the stratosphere, it is of interest to char-

acterize the model QBO at that level (83 hPa in the

FIG. 6. Vertical profiles of (a) the 128S–128N mean H2O and its standard deviation due to (b) annual and (c) interannual components.

Profiles of MLS and model simulation with and without methane parameterization are drawn by black, red, and blue lines, respectively.

Intervals of the abscissa are (a) 0.5, (b) 0.2, and (c) 0.05 ppmv.

4078 JOURNAL OF THE ATMOSPHER IC SC IENCES VOLUME 71

model). TheQBOamplitude at 80hPa calculated using the

FUB monthly-mean winds for Singapore (1.48N) during

the 2000s is about 4.1ms21, whereas the simulated QBO

amplitude at 83hPa for 1.78S–1.78N is about 1.2ms21. The

MIROC model-simulated QBO in temperature near the

tropopause is 60.2Kat 83hPa and 60.1Karound 100hPa

(Fig. 7b), whereas it is known to range up to about 60.5K

in the real world (Randel et al. 2000). The variations of

MLSH2Omixing ratio at 83 hPa in the QBO composite

are about 60.25 ppmv (Fig. 5), while those in the model

are about 60.06 ppmv (Fig. 7d). Although the simu-

lated QBO variations in zonal wind, temperature, and

H2O are smaller than those from observations, the

overall qualitative characteristics are similar in obser-

vations and the model simulation.

The QBO component of residual vertical velocity is

downward (upward) when the vertical shear of theQBO

zonal wind is positive (negative) (Fig. 7c), consistent

with expectations (e.g., Plumb and Bell 1982). The

simulated H2O mixing ratio variation shows a similar

boomerang pattern as that seen in theMLS data (Fig. 5):

there are upward-propagating signals from the upper

troposphere to the middle stratosphere and downward-

propagating signals in the upper stratosphere. The tran-

sition between upward and downward propagation in the

model is around 15–20hPa, which is a little lower down

than in the MLS observations.

From the model results, a detailed budget for the

near-equatorial H2O can be calculated. We regard the

time series of any quantity as being composed of a long-

termmean, a component related to the dynamical QBO,

and everything else (including the annual cycle and non-

QBO-related interannual variations). So, for example,

the H2O mixing ratio consists of long-term mean q,

QBO component q0, and others, Rq, so

q5 q1 q01Rq . (1)

In practice the overbar is a time mean over the whole

record and the prime indicates the component that is

isolated by the QBO compositing procedure outlined

above. We also divide the residual-mean vertical ve-

locity [w* in the transformed Eulerian-mean (TEM) for-

malism (cf. Andrews et al. 1987); hereinafter, just denoted

asw] into a time mean, QBO, and other components. The

zonal-meanH2Obudget for theQBOcomponent of water

vapor mixing ratio is then expressed as

›q0

›t52w

›q0

›z2w0›q

›z1 residual , (2)

where ‘‘residual’’ includes the effects of quadratic terms

involving the annual cycle and other components as well

as effects of meridional advection and chemical sources

or sinks as they might project onto the QBO. The first

term on the right-hand side expresses the mean advec-

tion of the QBO variation of H2O mixing ratio by mean

FIG. 7. Composite of the QBO in interannual variation of 128S–128N zonal-mean (a) zonal wind, (b) temperature, (c) residual

vertical velocity, and (d),(e) H2O for (a)–(d) the control simulation

and (e) the run without amethane oxidation parameterization. The

color contours are 61, 63, 65, 610, and 615m s21 for (a); 60.1,60.2, 60.4, 60.8, and 61.2K for (b); 60.01, 60.05, 60.1, 60.15,

and 60.2m s21 for (c); and 60.01, 60.02, 60.04, 60.06, and

60.08 ppmv for (d),(e).

NOVEMBER 2014 KAWATAN I ET AL . 4079

upwelling. The second term on the right-hand side in-

dicates the advection of the mean vertical gradient of

H2O by the QBO component of vertical velocity.

Figures 8a and 8c show the time variation of the H2O

budget terms through the composite QBO cycle in the

control run. Results for 5–15 and 30–50 hPa are shown.

At 30–50 hPa, the actual tendency in the H2O mixing

ratio is mainly accounted for by the advection of QBO

H2O anomalies by the mean upwelling (2w›q0/›z).Conversely, at 5–15 hPa, the total tendency is driven by

QBO variation in vertical velocity advecting the mean

gradient (2w0›q/›z) and is opposed by the advection of

QBO mixing ratio anomalies by the mean upwelling.

Below 15 hPa, the QBO-related water vapor anomalies

propagate upward via the familiar tape-recorder effect,

while above 15 hPa the water vapor anomalies display

a downward propagation characteristic of the propaga-

tion of the dynamical QBO itself.

Figure 7e is as in Fig. 7d, but for the simulation without

the parameterization of methane oxidation, as discussed

in section 2 above. The zonal wind, temperature, and

residual vertical velocity composites (not shown) in this

case are nearly identical to those in the control simulation,

which includes the methane oxidation parameterization.

The H2O mixing ratio composite in Fig. 7e is similar to

that in the control run up to about 20hPa, but quite dif-

ferent at higher levels. In fact, the positive and negative

phases around 5–15hPa in Fig. 7e are opposite to those

in the composite from the simulation with the methane

oxidation source (Fig. 7d).

Figures 8b and 8d present the same budget terms as

Figs. 8a and 8c, but for the simulation without methane

oxidation. At 30–50 hPa, the dominant term that drives

the H2O tendency is the advection of QBOmixing ratio

anomalies by the mean upwelling, just as that in the

simulation with themethane oxidation source. Note that

the effect of the methane oxidation source in the lower

stratosphere is expected to be negligible (the photo-

chemical lifetime of H2O is very long at the tropopause;

Brasseur and Solomon 1984). At 5–15hPa, budget anal-

ysis is more complicated. The biggest term is the advec-

tion of the mean by the QBO vertical wind, but this is

FIG. 8. Time variation of the terms in the budget of 128S–128N zonal-mean water vapor mixing ratio in the QBO

composite derived using the T106 MIROC-AGCM model results. Shown are the tendency of H2O mixing ratio

(black), the advection of the QBO component by mean upwelling (red), the advection of the mean H2O vertical

gradient by the QBO component of upwelling (blue), and the residual needed to balance the budget (purple), at

(a),(b) 5–15 and (c),(d) 30–50 hPa in model simulations (a),(c) with a methane oxidation source and (b),(d) without

methane oxidation. Units are 13 1029 ppmv s21.

4080 JOURNAL OF THE ATMOSPHER IC SC IENCES VOLUME 71

opposed by both the advection by the mean vertical wind

and the residual term, leaving a small net tendency. In the

simulation without the methane oxidation source, the

sign of the vertical gradient of mean H2O (›q/›z) in

the stratosphere is opposite to that in the control simu-

lation (Fig. 6a) and this leads to very different results for

the QBO in water vapor concentration above 15hPa.

5. Interannual variations of H2O in CMIP5 models

We have investigated the variability of stratospheric

equatorial H2O in long-term climate simulations for

four of the coupled ocean–atmosphere global models

whose results are available through CMIP5. CMIP5 in-

cluded four models that simulate a reasonable QBO in

the equatorial zonal wind (Kawatani and Hamilton

2013). Specifically, these are the Max Planck Institute

Earth System Model, medium resolution (MPI-ESM-

MR; Roeckner et al. 2006; Schmidt et al. 2013), the

Hadley Centre Global Environment Model, version 2 -

Carbon Cycle (HadGEM2-CC; Collins et al. 2011; Jones

et al. 2011; Martin et al. 2011), the Model for In-

terdisciplinary Research on Climate, Earth System

Model, Chemistry Coupled (MIROC-ESM-CHEM),

and the Model for Interdisciplinary Research on Cli-

mate, Earth System Model (MIROC-ESM; Watanabe

et al. 2011). For each of the four models, we analyzed

a single realization of the CMIP5 ‘‘historical’’ run

(forced with observed greenhouse gas and aerosol con-

centrations; Taylor et al. 2012) from 1950 to 1999. Since

the response to changing climate forcing is not an issue

for this paper, the first step in our analysis of each of the

CMIP5 model time series was to remove any linear

trend over the 50 years considered.

The left panels in Fig. 9 show the QBO composite of

H2O for each of the four CMIP5 models. The top

boundary of all four CMIP5 models considered is

roughly 0.01 hPa, but we show results only up to 0.5 hPa.

Note that all these models include some representation

of the methane oxidation process and simulate positive

vertical gradients of mean H2O (right panels). All four

models clearly show the boomerang structures of H2O

anomalies seen in ourMIROCT106AGCM control run

(cf. Fig. 7d), with the transition from upward propaga-

tion to downward propagation occurring near 20 hPa.

The CMIP5 model data archive does not include the

information needed to calculate the TEM residual

vertical velocity, so we cannot repeat the budget analysis

as shown in MIROC-AGCM (Fig. 8). However, the

availability of several different models with QBO sim-

ulations allows comparisons to be made that can shed

light on themechanisms driving interannual water vapor

variations. We focus on the relation of the QBO water

vapor variations (as revealed by our QBO compositing

procedure) with the temperature variations near the

tropopause and with the mean vertical water vapor

gradient in the upper stratosphere.

One feature in which themodels differ markedly is the

simulated mean equatorial water vapor profile in the

region above about 20 hPa. Twomodels, MPI-ESM-MR

(Fig. 9a) and MIROC-ESM (Fig. 9d), have vertical gra-

dients in this region that are reasonably similar to those

observed by MLS (although the models have an overall

dry bias throughout the equatorial stratosphere). The

other two models, HadGEM2-CC (Fig. 9b) and MIROC-

ESM-CHEM (Fig. 9c), have unrealistically small vertical

gradients above 20hPa. In the upper stratosphere, models

with stronger H2O vertical gradients simulate stronger

downward-propagating QBO anomalies compared with

models with weaker gradients. Although TEM vertical

velocity fields for these CMIP5 models are not known,

these results are at least consistent with our view that the

mean H2O vertical gradient plays a key role in generating

downward-propagating water vapor anomaly signals.

Figure 10 shows the time variation of the QBO com-

posite temperature at 100hPa, and water vapor at 100 and

70hPa in each of the CMIP5model simulations and in our

T106 MIROC-AGCM control simulation (note that

choice of levels to analyze was constrained by requiring

data availability for all four CMIP5 models). It is clear

that models with larger QBO temperature variability at

100hPa have larger QBO water vapor concentration var-

iability at 100hPa, and there seems to be little phase lag

between the QBO temperature variations and the water

vapor variations. This is consistent with the notion that

simple cold trapping determines the water vapor mixing

ratios at 100hPa. Figure 10c shows the same composites

for the QBO, but for water vapor at 70hPa. The QBO

water vapor signals are similar to those seen at 100hPa but

are somewhat smaller anddelayed by about 2months. This

would be consistent with the usual tape-recorder effect,

assuming some dilution of the upward-propagating air

near the equator.

These intermodel comparisons clarify the important

role ofQBOtropopause temperature variations in upward-

propagating H2O anomalies in the lower stratosphere and

that of the mean H2O vertical gradient in downward-

propagating anomalies in the upper stratosphere, which

support our conclusions obtained from our MIROC-

AGCM experiments.

6. Summary and concluding remarks

The classic studies ofMote et al. (1996, 1998) show the

seasonal cycle of water vapor mixing ratio in the equa-

torial lower stratosphere can be explained by introduction

NOVEMBER 2014 KAWATAN I ET AL . 4081

FIG. 9. (left) Composite of the QBO in the interannual variation of H2O

mixing ratio for (a) MPI-ESM-MR, (b) HadGEM2-CC, (c) MIROC-ESM-

CHEM, and (d) MIROC-ESM from 1950 and 1999 in the historical run.

(right) Profile of mean H2O from each model (red) with that from MLS

observations (black). The color contours are 60.01, 60.02, 60.04, 60.08,

and 60.12 ppmv.

4082 JOURNAL OF THE ATMOSPHER IC SC IENCES VOLUME 71

of variations in the saturation mixing ratio of air passing

upward through the cold-point tropopause; these varia-

tions are then advected upward by the mean vertical

upwelling. As noted in these early papers, this basic tape-

recorder mechanism also can account for the upward

propagation of QBO-related anomalies that are observed

to appear near the tropopause, a conclusion supported by

the modeling study of Giorgetta and Bengtsson (1999).

The early observational andmodeling work was extended

to the equatorial upper stratosphere by Geller et al.

(2002), who pointed out that the interannual fluctuations

in the upper stratosphere could not be explained by the

tape recorder and must depend on the interannual vari-

ations of the transport circulation itself.

These previous studies characterized interannual

variations of water vapor concentrations, but the sup-

porting evidence from models and observations in each

case has some significant limitations. In the present

study, we revisited this issue of interannual variations in

equatorial water vapor through application of recent

satellite data and results from several state-of-the-art

comprehensive global simulation models. We use nearly

10 years of observations from the MLS instrument on

the NASA Aura satellite, which allowed us to make

a three-cycle QBO composite keyed off the dynamical

QBO, and it provides amuch nicer view of the systematic

QBO-related variations than can be seen in any of the

earlier published observational records of equatorial

stratospheric H2O anomalies. We investigated the time–

height structure of interannual variations in equatorial

H2O concentration using these longer and higher-quality

observational records. From the upper troposphere to the

middle stratosphere, H2O concentration anomalies were

found to propagate upward in a manner analogous to the

seasonal ‘‘tape recorder’’ (Mote et al. 1996), which is

consistent with previous observational and modeling

studies (Randel et al. 1998, 2004; Giorgetta and Bengtsson

1999; Geller et al. 2002). On the other hand, clear

downward-propagating anomalies are found above about

10–15hPa.

We examined the interannual equatorial stratospheric

water vapor variations in the control integrations con-

ducted with a fine-horizontal-and-vertical-resolution

(T106L72) version of the MIROC-AGCM and in four

models in the CMIP5 that are known to simulate fairly

realistic dynamical QBOs (Kawatani et al. 2011, 2012;

Kawatani and Hamilton 2013). We showed that the

global models all simulate somewhat realistic interannual

water vapor variations in the equatorial stratosphere. In

particular, the model-simulated H2O concentration dis-

plays the same basic ‘‘boomerang’’ pattern as the MLS

data with rather uniform upward propagation from the

tropopause to some midstratospheric level and down-

ward propagation of anomalies at higher levels. It is ap-

parent that the interannual water vapor anomalies in

both models and observations are dominated by the fa-

miliar stratospheric QBO.

The detailed data available from the high-resolution

MIROC-AGCM simulation allowed a budget analysis

of the zonal mean H2O mixing ratio based on a QBO

compositing procedure. This showed that the upward

propagation in the equatorial lower stratosphere is in-

deed caused by the mean advection of interannual water

content anomalies induced by the QBO at the tropo-

pause, while the downward propagation is primarily due

to the advection of the mean vertical gradient of water

content by the QBO fluctuations in vertical wind. We are

also able to demonstrate the central role of themeanH2O

vertical gradient in the downward propagation with our

experiment with the methane oxidation source turned

FIG. 10. Time variation of the QBO composite (a) temperature and (b),(c) H2O mixing ratio at (a),(b) 100 and (c) 70 hPa in

MIROC-AGCM (black), MPI-ESM-MR (red), HadGEM2-CC (blue), MIROC-ESM-CHEM (yellow), and MIROC-ESM (green).

NOVEMBER 2014 KAWATAN I ET AL . 4083

off. The importance of these two mechanisms had been

proposed earlier byGeller et al. (2002) and Fujiwara et al.

(2010), but the pictures that we present are more com-

plete and our conclusion is more secure.

We also analyze QBO-related water vapor variations

using the four CMIP5 models that simulate a reasonable

QBO. The models with larger tropopause temperature

anomalies induced by the QBO have larger lower-

stratospheric water vapor anomalies, while the models

with stronger mean H2O vertical gradients display stron-

ger upper-stratospheric water vapor variations. The in-

termodel comparisons support our conclusions from our

MIROC-AGCM simulations.

The high-resolution MIROC model results for the

QBO in H2O concentration shown here, while quali-

tatively similar to observations, do display significant

differences. These are likely related in large part to

deficiencies in the mean water vapor simulation and in

the detailed structure of the simulated dynamical QBO.

Efforts to improve the model in these respects should be

continued. Also, while we have produced a reasonably

straightforward picture for the nature and causes of the

QBO-related water vapor concentration anomalies,

more observational and modeling research could help

also explain those interannual variations that are not

directly related to the QBO, including variations that

may be linked to ENSO variability in the troposphere.

Acknowledgments. The authors thank Drs. A. Noda,

S. Watanabe, and N. Eguchi for their valuable sugges-

tions on this study.We also express our gratitude to Prof.

M. A. Geller and an anonymous reviewer for construc-

tive comments on the original manuscript. This work

was supported by the Environment Research and

Technology Development Fund (2A-1201) of the Min-

istry of the Environment, Japan; and by Grant-in-Aid

for Scientific Research B (26287117) from the Japan

Society for the Promotion of Science. This research was

also supported by the Japan Agency for Marine-Earth

Science and Technology (JAMSTEC) through its spon-

sorship of research at the International Pacific Research

Center and byNOAA throughGrantNA11NMF4320128.

This research was also supported by the NASA Living

With a Star Targeted Research and Technology Program

(NNH10ZDA001N-LWSTRT). The MIROC model

simulation was conducted using the JAMSTEC Earth

Simulator. The GFD Dennou Library and GrADS

were used to draw the figures. We acknowledge the

World Climate Research Programme’s Working Group

on Coupled Modelling, which is responsible for CMIP,

and we thank the climate modeling groups for making

available their model output. For CMIP the U.S. De-

partment of Energy’s Program for Climate Model

Diagnosis and Intercomparison provides coordinating

support and led development of software infrastructure

in partnership with the Global Organization for Earth

System Science Portals. We also acknowledge the Data

Integration and Analysis System (DIAS) Fund for

National Key Technology from MEXT.

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