PUBLICATIONS
Geophysical Research Letters
RESEARCH LETTER
10.1002/2017GL076440
Key Points:• Column water vapor has a bimodal
distribution, deﬁning moist-regime air
masses revealed to be synoptic inscale, with sharp margins• The margin of the moist regime
meanders; morphed satellite data
show where winds move the margin
versus where they cross it while
undergoing air mass transformation• Maintenance of the moist regime andits sharp margins implies constraints
on moist convection processes, which
are shown to be erroneous in somecurrent climate models

Correspondence to:B. E. Mapes,
mapes@miami.edu

Citation:
Mapes, B. E., Chung, E. S., Hannah, W. M.,
Masunaga, H., Wimmers, A. J., &Velden, C. S. (2018). The meandering
margin of the meteorological moist
tropics. Geophysical Research Letters, 45,
1177–1184. https://doi.org/10.1002/
2017GL076440
Received 15 NOV 2017
Accepted 14 JAN 2018
Accepted article online 17 JAN 2018
Published online 30 JAN 2018

The Meandering Margin of the Meteorological
Moist Tropics
Brian E. Mapes1 , Eui Seok Chung1 , Walter M. Hannah2
Anthony J. Wimmers4 , and Christopher S. Velden4, Hirohiko Masunaga3,

1

Rosenstiel School of Marine and Atmospheric Science, University of Miami, Coral Gables, FL, USA, 2Program for Climate
Model Development and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA, USA, 3Institute forSpace-Earth Environmental Research, Nagoya University, Nagoya, Japan, 4Space Science and Engineering Center,
University of Wisconsin–Madison, Madison, WI, USA

Abstract Bimodally distributed column water vapor (CWV) indicates a well-deﬁned moist regime in the
Tropics, above a margin value near 48 kg m 2 in current climate (about 80% of column saturation). Maps
reveal this margin as a meandering, sinuous synoptic contour bounding broad plateaus of the moist regime.
Within these plateaus, convective storms of distinctly smaller convective and mesoscales occur sporadically.
Satellite data composites across the poleward most margin reveal its sharpness, despite the crude
averaging: precipitation doubles within 100 km, marked by both enhancement and deepening of cloudiness.
Transported patches and ﬁlaments of the moist regime cause consequential precipitation events within
and beyond the Tropics. Distinguishing synoptic ﬂows that cross the margin from ﬂows that move the margin
is made possible by a novel satellite-based Lagrangian CWV tendency estimate. Climate models do notreliably reproduce the observed bimodal distribution, so studying the moist mode’s maintenance processes
and the margin-zone air mass transformations, guided by the Lagrangian tendency product, might
importantly constrain model moist process treatments.
Plain Language Summary Satellite snapshots indicate that tropical column-integrated water vapor(CWV) in the atmosphere has broad, almost-uniform moist regions, with sharp edges or margins thatmeander and move. This structure comprises a bimodal frequency distribution, an important qualitative
distinction that justiﬁes the term "regime." Deep convective storms and rainfall are largely conﬁned inside themoist regime, where they are quite sporadic. In some places, the margin of the moist regime is observed to be
moved by the wind, but in other places the wind ﬂows across the margin, with air columns undergoing a
rapid moistening as they cross the narrow marginal zone. The continued maintenance of this quasi-uniform
vapor regime (despite spotty, intense losses in rain events), and of the sharpness of its margins, implies
that convective and dynamical processes act in very concerted ways. We show that this concerted processcoupling is nontrivial, since current climate models variously lack or exaggerate the bimodality of the CWV
frequency distribution. A new data set for identifying regions of cross-margin ﬂows (air column
transformations) is offered.
1. Introduction
Tropical meteorology is arguably deﬁned by moisture (reviewed for instance in Sherwood et al., 2010), evenmore than by the weakness of the Coriolis force at low latitudes. Much of the geographical Tropics is coveredby marine deserts, where water evaporated from warm seas is lofted by cumulus clouds to be carried away on
the wind to concentrated precipitation events in the wet climates deﬁning the “deep” tropics. Even there, rain
events are sporadic and localized, but their occurrence can be strongly statistically bounded. For instance,monthly precipitation over the tropical ocean requires sea surface temperature (SST) above a “threshold”(Graham & Barnett, 1987, Johnson & Xie, 2010).

©2018. American Geophysical Union.
All Rights Reserved.

MAPES ET AL.

Time-resolved data help reﬁne these statistical climate constraints: instantaneous, local precipitation is sharply contingent on simultaneous column water vapor (CWV; Bretherton et al., 2004, Neelin et al., 2009, Schiro
et al., 2016). This steep dependence ﬁts the physics paradigm of self-organized criticality in carefully rescaled
logarithmic statistics (Peters & Neelin, 2006). At ﬁrst blush, a vapor-rain relationship may sound almost tautological, since rain-producing clouds are themselves columns of saturated air. But the residence time of water
vapor is a week or more, set by Earth’s temperature and surface pressure (and thus an atmospheric vapor

1177

Geophysical Research Letters10.1002/2017GL076440

capacity of a few cm liquid equivalent) and by the few mm d 1 (order
102 W m 2) vigor of its energy-water cycle. This fairly long residence timeoffers hope but no guarantee of commensurate multiday coherence andpredictability of features in the vapor ﬁeld.
Space-time precipitation data further reﬁne the view but reveal a bewilderingly broad spectrum of disturbances, right down to local triggering of
updrafts in the metastable tropical atmosphere (Schultz et al., 2000).
However, a smooth synoptic spatial scale can undeniably be glimpsed in
the water vapor ﬁeld, as sampled by passive microwave sensors on aconstellation of polar-orbiting satellites since the 1990s. Unfortunately,
the narrow swaths intermittently sampled from multiple platforms do
not immediately reveal a lucid spatial and temporal view. Since 2010, adata processing technique called “morphing” (or morphological compositing) has addressed this challenge, taking into account the main form oftime evolution in the CWV ﬁeld between sampled swaths: horizontal
Figure 1. Histograms of instantaneous CWV estimates over the oceans.
advection by lower tropospheric winds (Wimmers & Velden, 2011; see
Microwave satellite measurements on approximately 25 km footprints are
section 6). This morphing analysis has been implemented on the nearconsidered reliable within 1 mm. Nested latitude belts are indicated withcolors. The minimum at 48 mm over the geographical tropics (blue curves)
real-time data stream and yields false-color animations that are highly
deﬁnes our titular “margin” of the moist tropics.
valued by forecasters. Inspired by insights from those animations, in May
2014 we devised quantitative diagnostic outputs from the morphing
scheme (see section 6) that spotlight the nonadvective tendency terms in the 2-D CWV ﬁeld’s time evolution.
Below we show some ﬁrst looks at those tendencies, whose meaning lies in the context of a steep-sided
plateau structure observed in the CWV ﬁeld itself.

2. Linking CWV Distributions to Space-Time Structure
The frequency distribution of CWV observed over the oceanic Tropics exhibits a distinctive bimodal structure (Figure 1), with a clear minimum near 48 mm and a relatively sharp peak centered near 55 mm. This
result should be carefully distinguished from the bimodality of upper level vapor, which was shown by
Zhang et al. (2003) to spring from its very short residence time. There, sporadic injections by convection
from below leave most of the humidity values to accumulate in a dry mode, bounded below by the physical constraint of positivity. In contrast, Figure 1 is the long-lived column vapor ﬁeld, and we emphasize
its moist mode. We shall see that the present result implies that convective + dynamical processes areconspiring to play a concerted maintenance role, not just a stochastic source role in the presence of a
rapid decay.
Daily averaged data do not resolve the frequency minimum (Bretherton et al., 2004) but still show a fairly
sharp shoulder at the upper end of the distribution. Rescaling by an air temperature-dependent saturation
value (differing by about 2 K from the warm Indo-Paciﬁc to the cooler Atlantic) sharpens the distribution
structure (Neelin et al., 2009; Peters & Neelin, 2006) and is a more fundamental quantity for study underclimate changes. Here the minimally processed and direct CWV observations sufﬁce and have the virtueof simplicity.
Spatial maps of CWV and precipitation, 11 days apart, are shown in Figure 2. The CWV measurement is mostreliable against the low-emissivity backdrop of water, so land areas are masked. The nighttime scenes
depicted here (00 UTC nominal hour) have relatively minimal precipitation over land. The 48 mm contour(black) clearly conﬁnes almost all the precipitation, which appears quasi-randomly arrayed within that, inthe form of mesoscale (100 s of km) convective systems (Houze 2004). The western Indian Ocean basin isan especially interesting region for these moisture dynamics: Islands and ﬁlaments of moist, sporadically
precipitating air masses are seen to drift for considerable periods in the weak, recirculating basin-scale ﬂow,
in all seasons. The longevity of these air masses begs the question of their maintenance mechanisms, andtheir landfalls are consequential for the semiarid and arid adjacent lands. Cyclones transiting into the midlatitudes sometimes remove signiﬁcant areas of the moist-mode air mass, as suggested in the Southern
Hemisphere of Figure 2b. Meanwhile, its area is replenished by both local regrowth processes within the
Indian basin and transport from the east (clear in MIMIC-TPW animations online).

MAPES ET AL.

1178

Geophysical Research Letters10.1002/2017GL076440

Figure 2. Multisatellite blended CWV data (Wimmers & Velden, 2011; top color legend and black 48 mm contour, masked over land where measurements areunreliable), overlain with rainrate estimated by the TRMM 3B42 product (Huffman et al., 2007; bottom color legend in mm/d units), for 20 December 2011 (left) and
31 December 2011 (right) at 00 UTC, at contrasting phases of an intraseasonal oscillation.

3. Mean Structure of the Margin ZoneTo reveal more about the sharp spatial structure of the margin, Figure 3 shows the north-south structure ofcomposite averages of 3 years of satellite data products, centered on the most poleward 48 mm CWV value ineach swath, combining both Northern and Southern Hemispheres. This poleward edge is the easiest aspectof the meandering margin to ﬁnd and isolate algorithmically, and latitude is a convenient grid coordinate forcomposite construction, but the folded contour in Figure 2b shows its imperfection in some cases. Despite
this crude data averaging, the margin is still seen to be a relatively sharp front of approximately (100 km)
scale, separating subtropical from tropical regimes that are broadly different across approximately(1,000 km) scales. CloudSat radar echo coverage (clouds and rain, top panel) increases by at least 50% over
100 km at low levels. Sloping contours indicate a deepening of low clouds into the middle troposphere overthis distance, while high clouds overhang the subtropics at right by a few hundred km, sometimes obscuring
CloudSat Cloud Fraction composited around CWV=48 mm
14

0.25

Altitude [km]]

12

8

0.2

0.08

0.14

10

0.06
0.12

0.15
0.1

0.04

6

0.1
0.1

4

0.12
0.05

2
0
-1000

0
-800

-600

-400
-200
0
200
400
Along-track distance (poleward positive) [km]

600

800

1000

CWV (solid/on left) and Precipitation (dotted/on right)
0.3

SST

50
45

CWV

P

40

0.2

0.1

29

27
25

SST [C]CWV [mm]55

Precip [mm/h]

60

35
30
-1000

-800

-600

-400

-200

0

200

400

600

800

0
1000

23

Along-track distance (poleward positive) [km]Figure 3. Composite of A-train (Stephens et al., 2002) satellite data around the most poleward 48 mm value in each north-south grid swath. Top: Echo fraction
detected by CloudSat radar is shaded and contoured. Bottom: CWV (mm), precipitation rate P (mm/h), and SST (C) estimated from AMSR-E microwave emission
data by Remote Sensing Systems (remss.com).

MAPES ET AL.

1179

Geophysical Research Letters10.1002/2017GL076440

Pressure [hPa]v [m/s] (poleward positive) and omega [hPa/h] composited around CWV=48 mm
150

3

200

2

250
300

1

-0.4

-0.6

-0.2

400

0

0.2

0.4

0.6

0

-0.8

500
600
700

-1

0.8

-1
-1.2

850
1000
-1000

-2
-3

-800

-600

-400
-200
0
200
400
Along-track distance (poleward positive) [km]

600

800

1000

Figure 4. Composite of analyzed air velocity ﬁelds, as in Figure 3. Poleward wind is color shaded, while vertical wind is contoured. Negative contours indicate upward
motion on this hydrostatic pressure coordinate.

visible and infrared satellite views. Microwave data (lower panel) are insensitive to clouds and reveal thatmean precipitation jumps very sharply, doubling or even tripling across the origin. The steepness of the
CWV curve near the origin is another manifestation of the distributional minimum from Figure 1. Sea surface
temperature (SST), however, exhibits only a smooth latitudinal gradient in this average, since it does not follow nor anchor the daily meanderings of the margin. While the SST value of 27.5°C at the origin is consistent
with scatterplots of monthly mean climate data (Graham & Barnett, 1987; Johnson & Xie, 2010), the CWV front
is seen to meander from day to day with the weather (Figure 2).
Composite structure in vertical and poleward wind components estimated by the ERA-Interim reanalysis (Deeet al., 2011) are presented in Figure 4. Upward motion (negative values of the contoured ﬁeld) exhibits anequatorward slope with height in the lower troposphere over the approximately 200 km scale of the composite border zone (near the data’s resolution limit). The main poleward ﬂow is near 200 hPa, but slight secondary poleward ﬂows can be discerned in the middle troposphere. These data are less fundamentallyobservational than the satellite observations of Figure 3, however, so the details may not be entirely
trustworthy (Zhang et al., 2008).

4. Air mass Transformations Versus Moving the Margin
Does the wind blow the margin, or blow through it? The answer must include both. Animations of themargin’s meanders and excursions (web-search MIMIC TPW) make clear that these are largely pushed bywinds from day to day. But winds must cross the margin too, because otherwise the convergence of ﬂow
in the moisture-bearing lower troposphere would collapse the moist region to inﬁnitesimal size. The sharpness of the margin suggests that air columns undergo a relatively rapid moistening process as they crossit. Since moistening rates exceeds surface evaporation, horizontal convergence (or equivalently, verticaladvection) of moisture must be involved, perhaps as a response to bottom-heavy convective heating whiletemperature gradients remain weak (e.g., Chikira, 2014), translating to a negative value of gross moist stability(e.g., Raymond et al., 2009).
Dispersion of the column integral by sheared winds make the concept of “air columns” rather loose, but thatis precisely what makes the mode-and-margin structure of CWV interesting: the column integral of an
unmaintained passive tracer with a broad surface source, several km depth, and a residence time of a week
would not exhibit such structure.
Figure 5 shows this study’s novel diagnostic for distinguishing margin-crossing air masses: the Lagrangian
CWV Tendency (LCT in the nomenclature of Hannah et al., 2016). Here we estimate this LCT as an analysis tendency (AT) within the data assimilation process that morphing embodies: It is the tendency required to explain
how the evolution observed in the satellite data differs from that predicted by a horizontal advection-only procedure (see section 6 for more details). In the moisture budget, LCT represents the sum of all nonhorizontaladvection terms: surface evaporation, plus the sum of cloud processes and vertical advection, called “column
process” by Chikira (2014). Because the radically spotty vapor sink of precipitation is closely compensated byvertical advection by the equally spotty upward motion induced by the associated latent heat release, this

MAPES ET AL.

1180

Geophysical Research Letters10.1002/2017GL076440

Figure 5. CWV (blue 48 mm contour) and daily-mean Lagrangian tendency (shading; red maximum is 5 mm/d) for 28 September 2016. The black contour is CWV
1 day later, on 29 September. Blue curved arrows show 1 day trajectories of 850 hPa winds over the day, computed from MERRA-2 reanalysis. Inset images
corresponding to the black boxes show the visible character of cloud ﬁelds on the indicated days, from Suomi NPP VIIRS, overlaid with the 46–50 mm CWV range
from GCOM-W1/AMSR2 in transparent colors (color bar in upper right inset). These cloud and vapor images can be reproduced exactly, interacted with, andexpanded to other locations and times at https://go.nasa.gov/2EPmqh2.

direct observation of their sum is a valuable quantity that cannot be reliably constructed from individual
estimates of those subcomponents. Here we display a daily mean AT, because intersatellite calibrationdifferences cause unphysical AT signals in this merged product from multiple sun-synchronous satellites,
which vanish in the daily average.
In Figure 5, strong positive values of LCT on 28 September 2016 (dark red shading at bottom), indicative of air
columns undergoing strong moistening, are seen to occur near the margin (blue and black contours are
CWV = 48 mm on 28 and 29 September, respectively). Indeed, the 48 mm contour is seen to have migrated
south with time, even though the 850 hPa winds were northward. Bands of positive LCT are also seen in thewest, straddling the moist belt from 2S to 8S, maintaining its width over the day despite convergent northsouth inﬂowing winds. Meanwhile, along the equator, LCT is small, and correspondingly the 48 mm contour
moved eastward in a day (blue to black) more than half the length of the 1 day 850 hPa wind trajectories(curved arrows).
Cloud images for the boxed areas in Figure 5 allow a glimpse of the hypothesis that bottom-heavy heating inshallow precipitating convection is active in strong LCT events. In both boxes, cloud streaks support thegeneral correctness of the analyzed low-level wind direction, and the position and movement of the
48 mm margin (rainbow colorations). Along the equator, where dry air is sweeping eastward with time,
the shallow cumulus ﬁelds are modest in coverage. In contrast, in the strong-LCT box to the south, vigorous
convection is seen near the rainbow-colored margin zone, especially on 29 September. The differenceemphasizes that there are many ﬂavors of moist convection (Stevens, 2005). The cloud ﬁelds in Figure 5
are consistent with our process hypothesis, at least in this rather arbitrary sample, selected as the most illustrative day in the Indian Ocean in the ﬁrst month of the AT data we have examined in this way. Causality
remains unclear: Did forced (adiabatic) ascent moisten and cool the air columns by advection, leading to
the vigorous convective development seen at lower right on 29 September? Or did convection there

MAPES ET AL.

1181

Geophysical Research Letters10.1002/2017GL076440

Figure 6. Column water vapor frequency distributions like Figure 1 in 3-hourly data from three uncoupled atmospheric models in the Climate Model
Intercomparison Project archive (Taylor et al., 2012).

induce vertical advection by its latent heating? Finer temporal discernment will be required, but with theseAT ﬁelds as a guide it should be possible to build more incisive, statistically signiﬁcant characterizations of theconvective weather of the margin zone.
We interpret the AT ﬁeld in most locations as an estimate of LCT, although perusal suggests it still has somedata artifacts. While these are easily discerned by eye (as satellite swath- and sweep-shaped patches), they
are not trivial to remove by algorithm, so blind averaging of this data set (version 4) should be avoided until
more robust screening methods are developed. Two years of AT data are available in our repository, at thiswriting, and another 1.5 years are being processed.

5. Summary and Discussion
We showed that moist air masses in the maritime Tropics comprise a distinct atmospheric regime (i.e., a modeof the CWV frequency distribution), with sharp margins (a minimum, Figure 1). An associated steep statisticalgradient of average rainrate with CWV (variously called a “transition,” “onset,” or “pickup” in the literature) is ajump in the probability of sporadic but intense convectively driven rains, including the associated stratiform(Houze 1997) component (Ahmed & Schumacher, 2015). Animated maps clarify that the sharp statistical gradient is spatially synoptic and temporally coherent over days, in the form of many-degree-scale plateaus(Figure 2) whose edge fronts are as narrow as 100 km even in highly averaged observations (Figures 3 and 4).
Air columns crossing into the tropical plateau of high CWV sometimes undergo rapid moistening but in othersituations winds move the position of the margin (Figure 5). The large-scale ﬂow or thermodynamic proﬁlesthat cause these different situations remain unknown. Vigorous but shallow precipitating convection, whose
bottom-heavy heating proﬁle can drive efﬁcient column moistening (e.g., Chikira, 2014; Mapes, 2001;
Raymond et al., 2009), appears to be implicated in the air mass transformation process. Fields from initialized
global weather models may clarify if those models can reproduce these processes, or at least if their analyzed
thermodynamic and dynamic environments are appropriately different between advected versus crossed
segments of the moisture margin (even if their convection schemes fail to distinguish the shallow-deepconvection spectrum properly). A related but distinct question is: how are the plateau CWV values maintainedin the face of a spotty precipitation sink? Again, the sum of precipitation and advection by latent heatingcoupled updrafts (Chikira’s “column process”) is certainly involved, but the causality needs elaboration.
These processes present an incisive, holistic test of the intimately interacting proﬁles of lapse rate, vapor,subgrid moist processes, and divergent ﬂow response to bursts of latent heating. Figure 6 indicates that contemporary climate models can indeed fail or succeed at this test, in diverse ways, making this an incisive
example of a “process-oriented diagnostic” (Kim et al., 2014) or perhaps even an “emergent constraint”(Allen & Ingram, 2002).
Conditional sampling of cloud scenes based on LCT and/or CWV plateau geometry will further illuminate
these mechanisms and sharpen the associated modeling constraint. Like any fundamental local features ofnature, these sharp but meandering margins invite reconsiderations of all averaging-blurred low-frequency
envelope dynamics in the moist tropics, from intraseasonal variability to greenhouse warming. Do large-scale
tropical moisture changes occur via simple right-left shifts of the CWV frequency distribution? Or might they
instead involve changes in the distribution; that is, changes in the area of the moist plateau (mode), for

MAPES ET AL.

1182

Geophysical Research Letters10.1002/2017GL076440

instance due to changes in synoptic eddies that sculpt and erode it, as evoked by Figure 2? How might global
warming-driven widening of the dynamical tropics (Seidel et al., 2007) affect the synoptic export of tropicalmoist air masses (Allen & Mapes, 2017; Knippertz et al., 2013), with implications in the tropics as well as in thereceiving latitudes (e.g., Shields & Kiehl, 2016)? Feature analysis, not just ﬁeld analysis, may be needed toaddress such questions. In addition to somewhat arbitrarily deﬁned entities such as the popular “atmosphericrivers,” we suggest that geophysically fundamental features with parsimonious deﬁnitions (like a bimodal
distribution’s internal minimum) should also be targets of such research.
Observationally, robust and cloud-insensitive retrieval of CWV over the complex emission backdrop of landremains a frontier. As the challenges are met, morphing approaches may become applicable across the coastline. Experimental morphed CWV products over land are now available (http://tropic.ssec.wisc.edu/real-time/
mtpw2), but the land ﬁelds are much less coherent in appearance than over water (whether rightly or
wrongly). The “ingredients” of convective activity over land are more diverse and more variable (Doswell
et al., 1996), so CWV is likely less determinative and may also occur in less coherent and uniform patches,as its evaporative source is also more heterogeneous. At some stage, output grids from well-calibrated
weather models assimilating diverse data sets through long time windows may surpass raw data and simpler
techniques like morphing. But at present, lessons accessible from these fairly direct observations and a lighttouch of modeling (horizontal time-to-space conversion only) remain to be revealed and understood.

6. Methods
Data used in Figure 3, the composite around the 48 mm “margin” of Figure 3, are from the A-Train of satellites(Stephens et al., 2002) with local overpass times of 1:30 (a.m. and p.m.), covering the period July 2006 throughDecember 2009. The CloudSat 2B-GEOPROF (Marchand et al., 2008) product in Figure 3 was provided by the
CloudSat Data Processing Center (www.cloudsat.cira.colostate.edu). The AMSR-E retrievals of CWV, precipitation, and SST, masked over water only, were obtained from Remote Sensing Systems (remss.com). All satellite
data were resampled onto a 0.25° latitude-longitude grid before processing. Circulation data in Figure 4 arefrom the ERA-Interim reanalysis (Dee et al., 2011), interpolated linearly to the satellite data’s grid mesh andoverpass time from 6-hourly data. Data were used over 25°S–25°N only, so the poleward most 48 mm value
omits any possible high CWV values poleward of 25° latitude. Precipitation estimates on Figure 2 are from the
TRMM 3B42 product (Huffman et al. 2007).
The morphed (MIMIC-TPW) CWV product in Figures 2 and 5 (Wimmers & Velden, 2011) uses an older water
vapor retrieval than the AMSR-E results in Figure 3, with some clipping of high values, but values are similar
within a very few mm. The analysis tendencies in Figure 5 are a new (since May 2014) by-product of MIMICTPW’s construction. In the MIMIC process, CWV swath data from multiple satellites have their pixels translated
in a time-to-space conversion exercise, according a speciﬁed two-dimensional vector wind ﬁeld (a layeraveraged wind from 1,000 to 700 hPa from the GFS weather forecast model, detailed in Wimmers & Velden,2011). At each product deﬁnition hour, in each spatial grid cell, the nearest-in-time translated observations
at that location are averaged to produce the CWV product (colored in Figure 2 and contoured on Figure 5).
The new Analysis Tendency (AT) ﬁeld (colored in Figure 5) is derived as the difference of these same twonearest-in-time values at each location, divided by the difference between the observation times. This AT
measures the deviation of the observations from the ﬁrst guess by a model that solves the horizontal advection equation. If the data and horizontal advection procedure are correct, the AT therefore represents the sumof all the other terms in the CWV budget: a quantity called (horizontally) Lagrangian CWV tendency (LCT) by
Hannah et al. (2016). LCT equals surface evaporation minus precipitation plus vertical advection. AT is thus
LCT plus all errors in the data and in the advection procedure. Data errors include swath-shaped stripes
due to intersatellite calibration differences, which vanish upon daily averaging since orbits are sunsynchronous, and occasional swath-shaped outliers in the AT distribution of uncertain origin that we haveclipped before daily averaging. Errors in the advecting velocity show up as AT dipoles across CWV gradients,
especially in places where temporal “jumps” of features are seen occasionally in the MIMIC-TPW animations.
Visually, we see only mild and rare evidence of this ﬁngerprint of advection errors, so the daily mean AT product is interpreted in Figure 5 as being almost equal to LCT.
Model data in Figure 6 are from the CMIP-5 archive (Taylor et al., 2012) and were downloaded from the Earth
System Grid Federation server (https://esgf-node.llnl.gov/projects/esgf-llnl/). The models shown (CNRM-CM5,

MAPES ET AL.

1183

Geophysical Research Letters10.1002/2017GL076440

HadGEM2-A, and MRI-CGCM3) were arbitrarily selected as the ﬁrst three we found with availability of 3-hourly
outputs of CWV. The distributions are shown for 1 year (2008s prescribed observed SST and sea ice
conditions).
Acknowledgments
This work was supported by award
NA13OAR4310156 from NOAA, U.S.
Department of Commerce, and by
NASA-NEWS program grant
NNX15AD11G. W. M. H.’s work wasadditionally supported under the auspices of the U.S. Department of Energy
by Lawrence Livermore National
Laboratory under contract DE-AC5207NA27344. The statements, ﬁndings,
conclusions, and recommendations do
not necessarily reﬂect the views of theseagencies. The paper’s unique data product (LCT, shaded in Figure 5) may beobtained from the repository at http://
weather.rsmas.miami.edu/repository/
entry/show?entryid=30df8ea5-9e2e4e90-af2b-a1f79f347586; please contact
the lead author for questions and clariﬁcations. MIMIC TPW data in Figure areavailable in our AMIE-DYNAMO ﬁeld
campaign archive at http://weather.
rsmas.miami.edu/repository/entry/
show?entryid=5c0355aa-bcc1-4b90808f-48ecc03b7989. All other data arefrom readily accessible public
repositories.

MAPES ET AL.

References
Ahmed, F., & Schumacher, C. (2015). Convective and stratiform components of the precipitation-moisture relationship. Geophysical Research
Letters, 42, 10,453–10,462. https://doi.org/10.1002/2015GL066957
Allen, M. R., & Ingram, W. J. (2002). Constraints on future changes in climate and the hydrologic cycle. Nature, 419(6903), 224–232. https://doi.
org/10.1038/nature01092
Allen, T. L., & Mapes, B. E. (2017). The late spring Caribbean rain-belt: climatology and dynamics. International Journal of Climatology, 37,
4981–4993. https://doi.org/10.1002/joc.5136
Bretherton, C. S., Peters, M. E., & Back, L. E. (2004). Relationships between water vapor path and precipitation over the tropical oceans. Journalof Climate, 17(7), 1517–1528. https://doi.org/10.1175/1520-0442(2004)017%3C1517:RBWVPA%3E2.0.CO;2
Chikira, M. (2014). Eastward-propagating intraseasonal oscillation represented by Chikira-Sugiyama cumulus parameterization. Part II:
Understanding Moisture Variation under Weak Temperature Gradient Balance. Journal of the Atmospheric Sciences, 71, 615–639.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., … Vitart, F. (2011). The ERA-Interim reanalysis: Conﬁguration andperformance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/
10.1002/qj.828
Doswell, C. A. III, Brooks, H. E., & Maddox, R. A. (1996). Flash ﬂood forecasting: An ingredients-based methodology. Weather and Forecasting,11(4), 560–581. https://doi.org/10.1175/1520-0434(1996)011%3C0560:FFFAIB%3E2.0.CO;2
Graham, N. E., & Barnett, T. P. (1987). Sea surface temperature, surface wind divergence, and convection over tropical oceans. Science,
238(4827), 657–659. https://doi.org/10.1126/science.238.4827.657
Hannah, W. M., Mapes, B. E., & Elsaesser, G. S. (2016). A Lagrangian view of moisture dynamics during DYNAMO. Journal of the Atmospheric
Sciences, 73(5), 1967–1985. https://doi.org/10.1175/JAS-D-15-0243.1
Houze, R. A. Jr. (1997). Stratiform precipitation in regions of convection: A meteorological paradox? Bulletin of the American MeteorologicalSociety, 78(10), 2179–2196. https://doi.org/10.1175/1520-0477(1997)078%3C2179:SPIROC%3E2.0.CO;2
Houze, R. A. Jr. (2004). Mesoscale convective systems. Reviews of Geophysics, 42. RG4003, doi:https://doi.org/10.1029/2004RG000150
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., … Stocker, E. F. (2007). The TRMM multisatellite precipitation analysis(TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at ﬁne scales. Journal of Hydrometeorology, 8(1), 38–55. https://doi.org/10.1175/JHM560.1
Johnson, N. C., & Xie, S.-P. (2010). Changes in the sea surface temperature threshold for tropical convection. Nature Geoscience, 3, 842–845.
Kim, D., Xavier, P., Maloney, E., Wheeler, M., Waliser, D., Sperber, K., … Liu, H. (2014). Process-oriented MJO simulation diagnostic: Moisturesensitivity of simulated convection. Journal of Climate, 27(14), 5379–5395. https://doi.org/10.1175/JCLI-D-13-00497.1
Knippertz, P., Wernli, H., & Gläser, G. (2013). A global climatology of tropical moisture exports. Journal of Climate, 26(10), 3031–3045. https://doi.org/10.1175/JCLI-D-12-00401.1
Mapes, B. E. (2001). Water’s two height scales: The moist adiabat and the radiative troposphere. Quarterly Journal of the Royal MeteorologicalSociety, 127(577), 2353–2366. https://doi.org/10.1002/qj.49712757708
Marchand, R., Mace, G. G., Ackerman, T., & Stephens, G. (2008). Hydrometeor detection using Cloudsat—An Earth-orbiting 94-GHz cloudradar. Journal of Atmospheric and Oceanic Technology, 25(4), 519–533. https://doi.org/10.1175/2007JTECHA1006.1
Neelin, J. D., Peters, O., & Hales, K. (2009). The transition to strong convection. Journal of the Atmospheric Sciences, 66(8), 2367–2384. https://doi.org/10.1175/2009JAS2962.1
Peters, O., & Neelin, J. D. (2006). Critical phenomena in atmospheric precipitation. Nature Physics, 2(6), 393–396. https://doi.org/10.1038/
nphys314
Raymond, D. J., Sessions, S. L., Sobel, A. H., & Fuchs, Z. (2009). The mechanics of gross moist stability. Journal of Advances in Modeling Earth
Systems, 1(3), 9. https://doi.org/10.3894/JAMES.2009.1.9
Schiro, K. A., Neelin, J. D., Adams, D. K., & Lintner, B. R. (2016). Deep convection and column water vapor over tropical land versus tropicalocean: A comparison between the Amazon and the tropical western Paciﬁc. Journal of the Atmospheric Sciences, 73(10), 4043–4063.
https://doi.org/10.1175/JAS-D-16-0119.1
Schultz, D. M., Schumacher, P. N., & Doswell, C. A. III (2000). The intricacies of instabilities. Monthly Weather Review, 128(12), 4143–4148.
https://doi.org/10.1175/1520-0493(2000)129%3C4143:TIOI%3E2.0.CO;2Seidel, D. J., Fu, Q., Randel, W. J., & Reichler, T. J. (2007). Widening of the tropical belt in a changing climate. Nature Geoscience, 1(1), 21–24.
https://doi.org/10.1038/ngeo.2007.38
Sherwood, S. C., Roca, R., Weckwerth, T. M., & Andronova, N. G. (2010). Tropospheric water vapor, convection, and climate. Reviews ofGeophysics, 48, RG2001. https://doi.org/10.1029/2009RG000301
Shields, C. A., & Kiehl, J. T. (2016). Atmospheric river landfall-latitude changes in future climate simulations. Geophysical Research Letters, 43,8775–8782. https://doi.org/10.1002/2016GL070470
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., … Mitrescu, C. (2002). The Cloudsat mission and the A-Train. A new
dimension of space-based observations of clouds and precipitation. Bulletin of the American Meteorological Society, 83, 1771–1790. https://doi.org/10.1175/BAMS-83-12-1771
Stevens, B. (2005). Atmospheric moist convection. Annual Review of Earth and Planetary Sciences, 33(1), 605–643. https://doi.org/10.1146/
annurev.earth.33.092203.122658
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American MeteorologicalSociety, 93(4), 485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
Wimmers, A. J., & Velden, C. S. (2011). Seamless advective blending of total precipitable water retrievals from polar-orbiting satellites. Journalof Applied Meteorology and Climatology, 50(5), 1024–1036. https://doi.org/10.1175/2010JAMC2589.1
Zhang, C., Mapes, B. E., & Soden, B. J. (2003). Bimodality in tropical water vapour. Quarterly Journal of the Royal Meteorological Society,129(594), 2847–2866. https://doi.org/10.1256/qj.02.166
Zhang, C., Nolan, D. S., Thorncroft, C. D., & Nguyen, H. (2008). Shallow meridional circulation in the tropical atmosphere. Journal of Climate,21(14), 3453–3470. https://doi.org/10.1175/2007JCLI1870.1

1184