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  • View in gallery

    Location map showing the Chajnantor Plateau (indicated by black star) and the two WRF domains used in this study.

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    Time series of temperature and water vapor data from the Chajnantor Plateau, 22 Jul–1 Aug 2014 (UTC): (a) air temperature (°C), (b) mixing ratio (ppmv), (c) water vapor δD (%), (d) water vapor δ18O (%). Vertical dashed line indicates time of lowest mixing ratio. Vertical solid line indicates time of lowest water vapor δ values. Mixing ratios of 500 and 2000 ppmv correspond to 0.31 and 1.2 g kg−1, respectively.

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    WRF simulated 300-hPa geopotential height (contours), temperature (color shading), and wind barbs.

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    WRF simulated 300-hPa geopotential height (contours), wind speeds in excess of 50 m s−1 in colored filled contours, vertical velocity in excess of 0.1 m s−1 enclosed by red contour.

  • View in gallery

    As in Fig. 3, but for the surface.

  • View in gallery

    Back trajectories launched from Chajnantor on 1200 UTC 26 Jul 2014. Black lines are trajectory computed from WRF output and gray line computed from NCEP GDAS. Heavy lines indicates portions of the trajectory with pressures < 350 hPa. WRF 300-hPa temperature (contours) and RH, shaded for RHice > 100% from 1200 UTC 23 Jul 2014. Path of CALIPSO instrument shown in dashed line.

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    Profile through WRF simulation at 1200 UTC 23 Jul 2014. (a) Cloud liquid water mixing ratio (kg kg−1) and (b) cloud ice mixing ratio (kg kg−1).

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    Profile of CALIPSO ice water concentration (color shading) and temperature (contours).

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    Vertical condensate distribution averaged over the inner WRF domain.

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    Vertical microphysical process rates averaged over the inner WRF domain. Deposition–sublimation of snow (Sn Dep); snow collection of cloud liquid water (Sn CLW); vapor deposition onto ice (Ice Dep); and autoconversion of cloud ice into snow (Sn Auto).

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    WRF temperature profile over South Pacific last-saturation region from 1100 UTC 23 Jul 2014.

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    (a) Water vapor δD; (b) δ18O; (c) deuterium excess vs mixing ratio for the period 1100:00 to 1800:00 UTC 26 Jul 2014. Representative curves show examples of successful solutions. Rayleigh curve for RH = 100% in heavy black line. Rayleigh curve for RHice = 115% in dashed line. Mixing line in red shows how last saturation occurred at a mixing ratio of 158 ppmv (−52°C) at an RHice of 115% and experienced mixing of 11% before reaching Chajnantor.

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Constraining Supersaturation and Transport Processes in a South American Cold-Air Outbreak Using Stable Isotopologues of Water Vapor

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  • 1 Department of Earth and Planetary Sciences, University of New Mexico, Albuquerque, New Mexico
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Abstract

In situ measurements of water vapor isotopic composition from the subtropical Chilean Andes, supported by mesoscale model simulations and diagnostic analyses, document the processes governing the transport of dry air and isotopically depleted water vapor from the midlatitudes into the subtropics during a South American cold-air surge in July 2014. On 23 July 2014, temperatures on the Chajnantor Plateau reached −18°C, among the lowest temperatures on record for the site. On 26 July 2014, water vapor δD and δ18O reached a low of −538 ± 1.6‰ and −71.7 ± 0.2‰, among the lowest values on record. Numerical simulations show that the dynamics of the event were consistent with previous studies of South American cold-air outbreaks. Back-trajectory analyses show that the isotopically depleted water vapor that reached Chajnantor on July 26 was last saturated over the South Pacific on July 23 at 300 hPa at a temperature of about −50°C under ice supersaturation with RHice of about 110%. The water vapor traveled to Chajnantor along a nearly isentropic path following saturation. Modeling of the isotopic data require condensation at temperatures between −50°C and −53°C under supersaturation with RHice between 112% and 118%, followed by less than 25% moistening during transport. These results show that measurements of water vapor isotopic composition can provide observational constraints on in-cloud processes that influence the humidity of the subtropics.

Denotes Open Access content.

Corresponding author address: Joseph Galewsky, Department of Earth and Planetary Sciences, University of New Mexico, 221 Yale Blvd. NE, Albuquerque, NM 87131. E-mail: galewsky@unm.edu

Abstract

In situ measurements of water vapor isotopic composition from the subtropical Chilean Andes, supported by mesoscale model simulations and diagnostic analyses, document the processes governing the transport of dry air and isotopically depleted water vapor from the midlatitudes into the subtropics during a South American cold-air surge in July 2014. On 23 July 2014, temperatures on the Chajnantor Plateau reached −18°C, among the lowest temperatures on record for the site. On 26 July 2014, water vapor δD and δ18O reached a low of −538 ± 1.6‰ and −71.7 ± 0.2‰, among the lowest values on record. Numerical simulations show that the dynamics of the event were consistent with previous studies of South American cold-air outbreaks. Back-trajectory analyses show that the isotopically depleted water vapor that reached Chajnantor on July 26 was last saturated over the South Pacific on July 23 at 300 hPa at a temperature of about −50°C under ice supersaturation with RHice of about 110%. The water vapor traveled to Chajnantor along a nearly isentropic path following saturation. Modeling of the isotopic data require condensation at temperatures between −50°C and −53°C under supersaturation with RHice between 112% and 118%, followed by less than 25% moistening during transport. These results show that measurements of water vapor isotopic composition can provide observational constraints on in-cloud processes that influence the humidity of the subtropics.

Denotes Open Access content.

Corresponding author address: Joseph Galewsky, Department of Earth and Planetary Sciences, University of New Mexico, 221 Yale Blvd. NE, Albuquerque, NM 87131. E-mail: galewsky@unm.edu

1. Introduction

a. Overview

Many models of subtropical climate invoke some degree of nonlocal mixing as a primary control on the humidity of the arid subtropical middle troposphere (Yang and Pierrehumbert 1994; Pierrehumbert 1998; Galewsky et al. 2005; Couhert et al. 2010; Pierrehumbert et al. 2006; Cau et al. 2007). In these models, the humidity of the subtropics can be thought of, to first order, in terms of the mixing of a suite of air parcels that were last saturated in different parts of the atmosphere, possibly moistened by boundary layer air that has not been processed through a cloud (Galewsky et al. 2005). Most studies that take advantage of the last-saturation framework rely on combinations of remote sensing data (Salathe and Hartmann 1997; Brogniez and Roca 2009), analysis (e.g., Sherwood 1996; Hurley and Galewsky 2010a), or on GCM output (e.g., Hurley and Galewsky 2010b; Wright et al. 2010). Measurements of humidity alone can broadly constrain the temperature of last saturation but do not provide many additional potentially useful constraints on parameters such as the degree of moistening during transport from the last-saturation point or on the microphysical processes at the point of last saturation.

Measurements of water vapor isotopic composition (e.g., Moyer et al. 1996; Webster and Heymsfield 2003; Worden et al. 2007; Sayres et al. 2010; Galewsky et al. 2007, 2011) can potentially provide such constraints, however, and the goal of this study is to use such measurements from the subtropical Andes to investigate a South American cold-air outbreak from July 2014. As we shall see, isotopic measurements made during this event contained information about the last-saturation point that were preserved during more than 3000 km of transport from the South Pacific to the subtropical Andes. While this study focuses on a small batch of measurements made during one event, it is hoped that the insight gained from this study will help to improve our interpretations of the suite of measurements made at this site, at other subtropical sites worldwide, and from remote sensing platforms.

b. South American cold-air surges

Synoptic-scale surges of cold, midlatitude air along major mountain ranges are common worldwide and have been recognized along the Andes (Marengo et al. 1997; Krishnamurti et al. 1999), Rocky Mountains (Colle and Mass 1995), the Himalaya (Wu and Chan 1995, 1997), and in Central America (Schultz and Bracken 1998). Cold-air surges, called “friaje” in Peru and “friagens” in Brazil, constitute the main source of wintertime temperature variability in subtropical South America along the Andes (Garreaud 2000) and are associated with significant economic and humanitarian impacts (Marengo et al. 1997).

The dynamics of South American cold-air surges have been extensively studied and can be understood in terms of downstream amplification of ridge–trough pairs arising from the superposition of quasi-stationary long waves and faster-moving short waves (Krishnamurti et al. 1999). The compositing analysis of Garreaud (2000) showed that the large-scale circulation at midlevels is characterized by a midlatitude wave with a ridge immediately to the west of the Andes and a downstream trough over eastern South America and the southwestern Atlantic. Geostrophic southerly winds advect cold air into the subtropics, but the flow is blocked by the high subtropical Andes, leading to an ageostrophic, terrain-parallel flow and cold-air damming. The surface cooling is thus dominated by meridional advective cooling.

The humidity of subtropical cold-air surges has received much less attention than their dynamics but are associated with an overall reduction in humidity (Marengo et al. 1997) and generate little precipitation. Given the relative frequency of these cold-air surges, their potential role in the humidity balance of subtropical South America is important. In this paper, we present measurements of atmospheric water vapor isotopic composition during a cold surge in July 2014 that brought extremely cold air to the southern South American Altiplano as well as extraordinarily isotopically depleted water vapor. The goal of this paper is to diagnose the mechanisms that set and transported the depleted water vapor to the Altiplano and place those mechanisms into the larger context of subtropical water vapor studies.

2. Background

a. Stable isotopic composition of water vapor

The relative abundance of stable isotopologues such as , , and is controlled by a variety of processes and records the history of evaporation, condensation, and mixing in a given air parcel (Noone 2012). Isotopic measurements are reported relative to standard mean ocean water (SMOW) in per mil (‰) using δ-notation where, for the ratio,
e1
where is the ratio in standard mean ocean water.
The starting point for most analyses of water vapor isotopic composition is the Rayleigh distillation model, an idealization in which condensate is removed immediately from an ascending air parcel. The equation describing this process is
e2
where is the isotopic mixing ratio for HDO or another heavy isotopologue, q is the mixing ratio, T is the temperature, and α is the temperature-dependent equilibrium fractionation factor between vapor and condensate.
If two air parcels mix, the resulting parcel will be less depleted in heavy isotopes than would be expected from a parcel at the same mixing ratio subject to Rayleigh distillation alone (Galewsky and Hurley 2010). The mixing ratio q of the mixed parcel is the weighted average of the mixing ratio of the two parcels:
e3
where f is the mixing fraction. The δ value of the mixed parcel is not a simple weighted fraction of the two parcels, though, because the resulting ratio of heavy to light isotopic abundance is given by
e4

Mixing can occur between any number of air masses, and those air masses need not lie on the Rayleigh curve, so a broad region above the Rayleigh curve is thus accessible via mixing.

The Rayleigh paradigm presented above is valid for relative humidity of 100%, but the deposition of vapor onto ice particles typically requires conditions that are supersaturated with respect to ice (Meyers et al. 1992). Jouzel and Merlivat (1984) showed that condensation in fully glaciated clouds around Antarctica entails an isotopic kinetic effect that reduces the temperature-isotope gradient and presented a simple theoretical framework that we use below for computing the isotopic composition in such conditions. They introduced a kinetic fractionation factor :
e5
where is the saturation over ice at the ice–vapor interface, is the equilibrium fractionation coefficient with respect to ice, and is the ratio of diffusivity of to the heavy isotopologue.

Ciais and Jouzel (1994) developed a model for mixed-phase clouds around Antarctica and found that the results were not especially sensitive to the amount of liquid or solid that remains in the cloud. Bolot et al. (2013) showed that mixed-phase processes in deep tropical convection, especially the balance between direct freezing of liquid droplets versus the vapor deposition in the Wegener–Bergeron–Findeisen process, may influence water vapor isotopic composition differently from the stratiform clouds that were the focus of Jouzel and Merlivat (1984) and Ciais and Jouzel (1994).

The deuterium-excess parameter is defined as and has been extensively studied in precipitation (Gat 1996) but somewhat less so in water vapor. In the free troposphere, the deuterium-excess increases with altitude, especially above 400 hPa (Blossey et al. 2010; Bony et al. 2008). To first order, this increase is a straightforward consequence of Rayleigh distillation, but condensation under supersaturated conditions can lower the deuterium excess of water vapor (Samuels-Crow et al. 2014).

Several studies have investigated water vapor isotopic composition in the marine boundary layer, which represents an upper bound of water vapor isotopic composition. Some of the highest δ values have been reported from the Mediterranean, with δD as high as −70‰ and δ18O as high as −12‰ (Gat et al. 2003). Uemura et al. (2008) presented a transect of marine boundary layer water vapor from the South Pacific (as far south as 66°S) to about 30°S. Their δD values ranged from a low of −174‰ to a high of −96‰ and δ18O values that ranged from −25‰ to −12‰. Steen-Larsen et al. (2014) measured marine boundary layer water vapor isotopic composition in Bermuda (32°N) for over a year and found average δD values of −80.8‰ and average δ18O values of −11.81‰. The deuterium excess in marine boundary layer water vapor ranges from about −5‰ to about 34‰ (Uemura et al. 2008; Gat et al. 2003).

In the free troposphere, water vapor isotopic composition varies substantially, with previous in situ measurements of δD reaching as low as −540‰ and δ18O as low as −65‰ in the middle troposphere (Galewsky et al. 2011) and δD averaging −650‰ in the upper troposphere (Sayres et al. 2010).

b. The Chajnantor Plateau

The Atacama Large Millimeter Array (ALMA) astronomical observatory (Wootten and Thompson 2009) is located on the Chajnantor Plateau (Fig. 1) at an altitude of 5 km and is one of the driest sites on Earth’s surface, outside of Antarctica, with median precipitable water vapor (PWV) of 1.2 mm (Giovanelli and Darling 2001). There are strong seasonal variations in PWV, averaging less than 1 mm between March and December and frequently exceeding 10 mm during the summertime December–February (DJF) months. There is a modest diurnal cycle in PWV, which is highest in the late afternoon, with a rapid decrease after sunset. Giovanelli and Darling (2001) showed that the diurnal cycle of PWV lags the insolation cycle by about 4 h with an amplitude of about 20% around the median PWV values.

Fig. 1.
Fig. 1.

Location map showing the Chajnantor Plateau (indicated by black star) and the two WRF domains used in this study.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

Galewsky et al. (2011) presented the first water vapor isotopic measurements from the site and showed that dry air from the upper tropical troposphere (UT) reaches the surface at Chajnantor, with mixing ratios as low as 215 ppmv and water vapor δD values as low as −540‰, values that are consistent with aircraft measurements from the upper troposphere (Sayres et al. 2010). Galewsky et al. (2011) further showed that this dry UT air mixes with moister air from the middle and lower troposphere to set the wintertime humidity over the southern Altiplano.

The University of New Mexico has been making nearly continuous measurements of water vapor isotopic composition and ozone at the ALMA site since 2012, and the present study is based on this dataset. Other studies based on this dataset have focused on the isotopic composition of water vapor in stratospheric air intrusions (Galewsky and Samuels-Crow 2014), water vapor deuterium excess (Samuels-Crow et al. 2014), and summertime water vapor transport processes to the southern South American Altiplano (Galewsky and Samuels-Crow 2015).

3. Methods

a. Data

Mixing ratio and isotopic composition were measured using a Picarro L2130 analyzer. This system uses newer spectroscopic techniques that yield significantly improved precision and less drift in measurements of δ values than earlier Picarro analyzers such as the L1115 and L1102. At 2500 ppmv, the precision of the instrument is better than 0.5‰ for δD and 0.1‰ for δ18O when averaged for 100 s. Two secondary standards were injected an average of three times per month into a vaporizer operated at 140°C prior to delivery to the instrument to monitor instrument drift, and the measurements presented here were corrected for the minimal drift encountered. The analyzer was housed at the ALMA Observatory’s Central Weather Station, which is a heated hut situated at 5-km altitude near the central cluster of the observatory. Outside air was introduced into the analyzer from a Teflon line mounted on the roof of the weather station, about 5 m from the ground surface. The line was unheated, but condensation in the line is not an issue at the very low relative humidity on the Chajnantor Plateau. Measurements were made every 30 s and the results presented here are 5- and 30-min averages.

The two secondary standards (NM-3 and Antarctic snow, ANT) were calibrated to international standards at the University of New Mexico and were chosen because they span a broad range of isotopic composition (‰, ‰; ‰, ‰). Standard values did not vary systematically during the study period.

The Picarro analyzer has a systematic bias in δ values at low mixing ratio. Prior to deployment this concentration bias was quantified at Picarro, Inc. following methods described by Galewsky et al. (2011) and Johnson et al. (2011). The 1 − σ uncertainty in 5-min averages of measurements increased at lower mixing ratio but was 1.6‰ and 0.2‰ in δD and δ18O at 200 ppmv, respectively; and 0.3‰ and 0.07‰ in δD and δ18O at 1000 ppmv, respectively. The concentration bias correction was based on the linear relationship between delta values and the inverse of the mixing ratio (1/q) determined during this experiment using the technique described in Johnson et al. (2011).

The isotopic data presented here are supplemented with routine surface meteorological measurements obtained at the Atacama Pathfinder Experiment (APEX), an observatory on the Chajnantor Plateau that is located about 1 km from the CWS.

Owing to the kinetic effects associated with the sublimation of snow, persistent snow cover can impact measurements of water vapor isotopic composition, and even though several astronomical observatories on the Chajnantor Plateau collect routine surface meteorological data, none collect surface snowfall or snowpack data. Satellite estimates of snow cover can provide some constraints, however, and daily snow cover averaged over the Chajnantor Plateau, derived from the MODIS instrument on the Terra satellite, show that there was no snow cover on the Chajnantor Plateau during the 23–26 July period studied here.

b. Modeling

The processes governing the cold-air outbreak and the associated isotopically depleted water vapor are diagnosed with numerical simulations computed using the Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) version 3.6.1. The model was run with a 300 × 175 gridpoint outer domain with grid spacing of 36 km and a 298 × 172 gridpoint inner domain over the South Pacific with a grid spacing of 12 km (Fig. 1). Both domains had 50 vertical levels. To explore the distribution of supersaturation conditions, we used the Thompson microphysics scheme (Thompson et al. 2004, 2008), along with the radiative schemes of Iacono et al. (2008), the convective parameterization of Kain (2004), the PBL scheme of (Hong et al. 2006), and the Noah land surface parameterization (Ek et al. 2003). The initial and boundary conditions were provided by the NCEP Final Operational Model Global Analysis product. Back-trajectory analysis was performed using the HYSPLIT model (Draxler and Hess 1998) driven by the WRF output and by the NCEP global analysis.

The Thompson microphysics scheme has six classes and predicts number concentrations for ice and rain. The Thompson scheme has been tested in simulations of glaciated and mixed-phase clouds over the Southern Ocean (Morrison et al. 2010; Huang et al. 2014) and has been shown to have good skill in simulating high-altitude cloud structure and phase, although Huang et al. (2014) showed that the scheme tends to underpredict low-altitude clouds over the Southern Ocean. A sensitivity study of the influence of microphysics parameterization on the present simulations and isotopic interpretations would likely be fruitful but is beyond the scope of the current study.

4. Results

a. Data

Figure 2 shows the 2-m air temperature, water vapor mixing ratio, and isotopic composition measured on the Chajnantor Plateau between 22 and 31 July 2014. The minimum temperature during this period was −18°C and was reached 1045 UTC 23 July (Fig. 2a; dashed line). In the 5-yr period from 1 August 2009 to 1 August 2014, only two days on the Chajnantor Plateau had colder temperatures: 25 May 2014 (−18.2°C) and 29 June 2011 (−20°C). Temperatures rebounded on 25 July and continued to warm through the end of the month.

Fig. 2.
Fig. 2.

Time series of temperature and water vapor data from the Chajnantor Plateau, 22 Jul–1 Aug 2014 (UTC): (a) air temperature (°C), (b) mixing ratio (ppmv), (c) water vapor δD (%), (d) water vapor δ18O (%). Vertical dashed line indicates time of lowest mixing ratio. Vertical solid line indicates time of lowest water vapor δ values. Mixing ratios of 500 and 2000 ppmv correspond to 0.31 and 1.2 g kg−1, respectively.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

The minimum water vapor δ values occurred on 26 July, 3 days after the temperature minimum (Figs. 2c,d; solid line). Water vapor δD reached a minimum of −538 ± 1.6‰ at 1135 UTC, which was the same time as δ18O reached a minimum of −71.7 ± 0.2‰ (Fig. 2c). The deuterium excess for the very low δ values was 35 ± 4‰, which is a relatively low value for the arid subtropics (Samuels-Crow et al. 2014). These were the lowest δ values measured since the present project began in July 2012 and are comparable to the lowest δ values reported by Galewsky et al. (2011), although the uncertainties on the present measurements are about an order of magnitude smaller than in the previous study. The isotopic measurements exhibit a marked diurnal cycle beginning on 25 July, with a range of nearly 100‰ in δD and nearly 20‰ in δ18O.

The mixing ratio at the time of the minima in δ values was 195 ± 4 ppmv (Fig. 2b), or 0.12 g kg−1. This was not the lowest mixing ratio observed during this period, which was 177 ± 3.5 ppmv (0.11 g kg−1) and occurred about 24 h earlier. These were very dry conditions for the Chajnantor Plateau, with the 24-h-average dewpoint measured at APEX of −39°C within the 1% of driest days during the 2009–14 period.

b. Large-scale circulation

Having presented the surface observations from this period, we now turn toward understanding the large-scale circulation that gave rise to these very low water vapor δ values. We are especially interested in identifying the last-saturation region associated with these measurements with the aim of determining the extent to which the isotopic measurements preserved information about the last-saturation point and the degree of moistening during transport to Chajnantor.

On 22 July, the upper-level circulation was characterized by a prominent midlatitude wave train, with a trough–ridge pair to the west of the Pacific coast of South America and another trough nearly centered over the Andes, and extending south from the subtropical Andes (Fig. 3a). The trough over the Andes amplified on 23–24 July (Figs. 3b,c) and was bounded upstream and downstream by jets with winds in excess of 50 m s−1 along the west coast of South America, with a region of ascent over the South Pacific downstream of the westernmost trough (Fig. 4) and to the north of the eastern jet. By 25 July (Fig. 3d), the winds across the subtropical Andes returned to a more zonal pattern.

Fig. 3.
Fig. 3.

WRF simulated 300-hPa geopotential height (contours), temperature (color shading), and wind barbs.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

Fig. 4.
Fig. 4.

WRF simulated 300-hPa geopotential height (contours), wind speeds in excess of 50 m s−1 in colored filled contours, vertical velocity in excess of 0.1 m s−1 enclosed by red contour.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

At low levels (Fig. 5), a migrating high pressure system strengthened the anticyclone off the coast of southern Chile on 22 and 23 July (Figs. 5a,b). By 23–24 July (Figs. 5b,c), temperatures dropped below −15°C in the subtropical Andes and below freezing across much of Argentina and Chile as a strong (1032 hPa) surface continental anticyclone developed just to the east. This was associated with the development of a low-level trough off the southeastern coast of South America. By 25 July (Fig. 5d), the high pressure centers weakened and the continental high pressure drifted to the east.

Fig. 5.
Fig. 5.

As in Fig. 3, but for the surface.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

c. Last-saturation region and transport pathway

To constrain the source area for the very low water vapor δ values that reached Chajnantor, we used back-trajectory analysis using the HYSPLIT model driven by the WRF output and NCEP GDAS datasets. Figure 6 shows trajectories launched from the Chajnantor Plateau at 1200 UTC 26 July 2014. All trajectories pass through a region of simulated in excess of 105% over the South Pacific near 50°S at temperatures of around −50°C at a pressure of 300 hPa on 23 July. Representative trajectories are shown, but the ensemble of trajectories launched from locations near Chajnantor and at different elevations all show a very similar pathway. The trajectories from the high region over the South Pacific to Chajnantor are largely isentropic, mostly confined to the 310-K isentrope (not shown), and the relative humidity along the trajectories never approached saturation after leaving the South Pacific. Along these isentropic trajectories, the temperature increased from −50°C over the South Pacific to −11°C over Chajnantor. Despite the warming of nearly 40°C during isentropic descent from 300 to 550 hPa, this event still constituted a significant cold-air outbreak in the region.

Fig. 6.
Fig. 6.

Back trajectories launched from Chajnantor on 1200 UTC 26 Jul 2014. Black lines are trajectory computed from WRF output and gray line computed from NCEP GDAS. Heavy lines indicates portions of the trajectory with pressures < 350 hPa. WRF 300-hPa temperature (contours) and RH, shaded for RHice > 100% from 1200 UTC 23 Jul 2014. Path of CALIPSO instrument shown in dashed line.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

The trajectory analysis thus indicates that last saturation for the very low water vapor δ values that reached Chajnantor on 26 July occurred on 23 July high over the South Pacific under conditions of supersaturation with respect to ice. This water vapor was transported to the subtropical Andes by the strong winds that developed along the flanks of the deep trough that was centered over the Andes.

The conditions at the last-saturation site should, at least to some extent, be preserved in the isotopic measurements. Before returning to those measurements, we focus in more detail on the simulated microphysical processes in the last-saturation region. A profile of the WRF cloud liquid water and cloud ice fields across the last-saturation area shows that the liquid water is largely confined below 900 hPa (Fig. 7a), but some plumes of supercooled water extend to about 700 hPa. The top of the cloud ice is at between 300 and 350 hPa at temperatures ranging from −40°C to nearly −60°C. A fortuitous passage of the CALIPSO instrument on 23 July crossed over the back-trajectory site near 50°S at 2000 UTC and shows ice cloud in this region between 7 and 10 km, with temperatures between 210 and 240 K (Fig. 8), largely consistent with the WRF simulations.

Fig. 7.
Fig. 7.

Profile through WRF simulation at 1200 UTC 23 Jul 2014. (a) Cloud liquid water mixing ratio (kg kg−1) and (b) cloud ice mixing ratio (kg kg−1).

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

Fig. 8.
Fig. 8.

Profile of CALIPSO ice water concentration (color shading) and temperature (contours).

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

A budget of the microphysical fields and tendencies from the Thompson scheme in the inner domain over the South Pacific can provide some additional context for the last-saturation region. The maximum cloud ice mixing ratio was centered at 300 hPa, near the last-saturation region for the isotopic measurements (Fig. 9). The most abundant solid phase was snow, which extended from the near surface up to 200 hPa, with a peak in snow mixing ratio at 650 hPa. No rainwater extended above 660 hPa, and no cloud liquid water or graupel extended above 550 hPa.

Fig. 9.
Fig. 9.

Vertical condensate distribution averaged over the inner WRF domain.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

Heterogeneous nucleation of ice in the upper troposphere (not shown) followed by vapor deposition was the dominant process for generating cloud ice above 500 hPa (Fig. 10). The average RH at which vapor deposition on ice and snow occurred within the model was 104% and as high as 125%. Autoconversion of ice into snow does not entail any isotopic fractionation and largely balanced the cloud ice production in the upper troposphere. Deposition of vapor onto snow was the dominant process for generating solid phase condensate above 700 hPa, while snow collection of cloud liquid water dominated the production of the ice phase in the lower troposphere.

Fig. 10.
Fig. 10.

Vertical microphysical process rates averaged over the inner WRF domain. Deposition–sublimation of snow (Sn Dep); snow collection of cloud liquid water (Sn CLW); vapor deposition onto ice (Ice Dep); and autoconversion of cloud ice into snow (Sn Auto).

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

The trajectory analysis, along with an analysis of the simulated microphysical processes, suggests that the very low δ values measured on Chajnantor were set within a fully glaciated cloud under conditions that were supersaturated with respect to ice. We now return to the isotopic data to evaluate the extent to which these processes were preserved in the observations and to explore how we can use simple models applied to the isotopic data to generate constraints on the last-saturation conditions.

d. Isotopic modeling

The picture that emerges from the data and analysis thus far is that the very low δ values measured on Chajnator on 26 July were largely set in the upper troposphere over the South Pacific on 23 July. The water vapor from this region was transported to Chajnantor along an isentropic path by the very strong winds on the upstream limb of the trough. WRF modeling suggests that the last-saturation region was associated with ice-supersaturated conditions. The goal of this section is to focus on the isotopic data itself to determine the extent to which these processes may have been preserved in the data.

Of course, some simple constraints can be obtained just by considering the low mixing ratios measured on Chajnantor. Here, 195 ppmv corresponds to dewpoint of −45°C at 550 hPa (the altitude of Chajnantor) or −50°C at 300 hPa (the last-saturation altitude indicated by back-trajectory analysis). These values are consistent with the simulated temperatures within the last-saturation region, suggesting that minimal moistening was required during transport to Chajnantor. On the other hand, a last-saturation temperature of perhaps −60°C is not out of the question, which would correspond to 65 ppmv and a more substantial degree of moistening during transport.

The deuterium-excess values for the measurements was 35 ± 4‰, which is consistent with relatively high ice supersaturation (Samuels-Crow et al. 2014). Moistening after the last saturation can mask the deuterium-excess values and can thus confound interpretations of supersaturation, so the goal of our isotopic modeling is to try to separate these processes and find a single set of parameters that simultaneously matches the mixing ratio, δD, δ18O, and deuterium-excess data.

Given the analysis thus far, we expect that the very low δ values measured on Chajnantor are controlled by condensation, potentially under supersaturated conditions, possibly followed by some degree of moistening during transport from the last-saturation site to the subtropical Andes. In addition, the initial isotopic composition of the water vapor in the boundary layer, prior to ascent, and the temperature at the lifted condensation level may also control the final δ values.

We used a genetic algorithm approach (Beasley et al. 1993; Galewsky and Samuels-Crow 2014) to find model parameters that fit the observations, with the specific aim of constraining the supersaturation at the point of last condensation and the degree of moistening that occurred during transport to Chajnantor. The approach taken here is to focus on the 7-h period beginning at the time the lowest δ value was measured. During this period the mixing ratio increased from 195 ppmv (0.12 g kg−1) to 510 ppmv (0.31 g kg−1) while the δD and δ18O values increased from −538‰ and −71.7‰ to −453‰ to −57.4‰, respectively.

For each candidate model, we generate a mixing curve that passes through the lowest water vapor δ values at the measured mixing ratio of 195 ppmv but is free to continue to a drier and more isotopically depleted endmember. We seek a dry endmember for that mixing curve such that the curves in δD and δ18O and the deuterium excess all end at the same mixing ratio and on the same supersaturation curve. There are many curves that meet that goal, but we can use the results to constrain several parameters of interest. The model parameters are 1) the mixing ratio of the dry endmember of the mixing curve, 2) the relative humidity under which condensation occurred to set the dry endmember, 3) the boundary layer water vapor isotopic composition, and 4) the lifted condensation level (LCL). The temperature profile used for the Rayleigh distillation calculation was derived from the WRF output over the South Pacific (Fig. 11).

Fig. 11.
Fig. 11.

WRF temperature profile over South Pacific last-saturation region from 1100 UTC 23 Jul 2014.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

Figure 12 shows the 5-min-averaged measurements of water vapor δD and δ18O of water vapor for the 7-h period following the lowest δ values and a representative model that simultaneously explains the mixing ratio and isotopic measurements, including deuterium excess. The results are nonunique, but several constraints emerge from this analysis.

Fig. 12.
Fig. 12.

(a) Water vapor δD; (b) δ18O; (c) deuterium excess vs mixing ratio for the period 1100:00 to 1800:00 UTC 26 Jul 2014. Representative curves show examples of successful solutions. Rayleigh curve for RH = 100% in heavy black line. Rayleigh curve for RHice = 115% in dashed line. Mixing line in red shows how last saturation occurred at a mixing ratio of 158 ppmv (−52°C) at an RHice of 115% and experienced mixing of 11% before reaching Chajnantor.

Citation: Journal of the Atmospheric Sciences 72, 5; 10.1175/JAS-D-14-0352.1

The observations can be explained in terms of condensation down to mixing ratios of 120 to 195 ppmv (0.07 to 0.12 g kg−1), corresponding to last-saturation temperatures of between −50° and −53°C at an ice supersaturation with between 112% and 118%. No consistent solution can be obtained in the limiting case of = 100%, indicating that some degree of ice supersaturation is required to explain the observations. Consistent solutions can be obtained that require no moistening at all between the point of last saturation and Chajnantor, but solutions can be obtained for last saturation at mixing ratios as low as 120 ppmv. The initial isotopic composition of the air mass, including the deuterium excess, are relatively unimportant in setting the final isotopic composition. A broad range of boundary layer isotopic compositions and deuterium-excess values can yield results consistent with the measurements on Chajnantor. Similarly, consistent solutions can be obtained for a wide range of reasonable LCLs.

To assess the potential for uncertainties in the measurements to influence the results, we ran 50 simulations in which the data were randomly perturbed, by up to ±40 ppmv in mixing ratio, ±10‰ in δD, and ±1‰ in δ18O, a range that substantially exceeds the measurement uncertainties. The basic results presented above are robust to such uncertainties; in particular, the range of ice supersaturation and the degree of mixing appear to be robust estimates.

The results of this simple isotopic modeling exercise are consistent with the WRF results from the last-saturation region but show how those parameters can be extracted from the isotopic data with a relatively simple modeling framework. The results also show, perhaps somewhat remarkably, that the imprint of in-cloud processes was preserved across more than 3000 km of transport from the far South Pacific to the subtropical Andes.

5. Discussion

This study presents an analysis of only one event, but it provides a stepping-off point for further consideration of how extratropical processes can influence the humidity of the subtropics. Much of our recent understanding of subtropical humidity has been influenced by advection-condensation models (e.g., Sherwood et al. 2010 and citations therein) that assume conservation of mixing ratio since the point of last saturation. Many advection-condensation studies do not include the potential impact of supersaturation conditions on humidity. Ice formation in the upper troposphere typically requires supersaturations over ice on the order of tens of percent, so the inclusion of such processes may be expected to yield a corresponding increase of water vapor at the point of last saturation. The modeling study of Gettelman and Kinnison (2007) showed that the inclusion of supersaturation in a global model induced an increase in relative humidity throughout the upper troposphere, an increase in mixing ratios in the extratropics and stratosphere, and a decrease in relative humidity and mixing ratios in the deep tropical and subtropical middle and lower troposphere. The simulated increase in humidity in the upper troposphere and stratosphere is likely a straightforward consequence of the reduced degree of condensation when condensation occurs under supersaturation. The decrease in humidity in the subtropical middle troposphere with the inclusion of supersaturation may be due to condensation occurring at a higher and colder temperature under supersaturation than in the control simulation in which saturation occurred at a relative humidity of 100%, but this was not fully diagnosed in their study. The present study shows that the conditions of ice supersaturation at the point of last saturation can be preserved during transport into the subtropics and can thus impact the humidity at long distances from the last-saturation region. The frequency with which supersaturated conditions impact the humidity of the subtropics is not constrained in the current study, but a careful study of the climatology of the Chajnantor data may help to further quantify the role of supersaturation in influencing the humidity of the subtropics.

The degree of moistening during transport from the last-saturation region is difficult to constrain from measurements of mixing ratio alone, but the joint measurements of isotopic composition and mixing ratio do provide some constraints. The current study shows that moistening of up to about 25% is permitted by the data. At the mixing ratios encountered in this study, this corresponds to a very small increase in mixing ratio. Indeed, part of the reason why the low δ values were preserved was because there was very little moistening during transport. A useful complement to the current study would be to identify a period during which mixing was a dominant process. In principle, this could be identified by δ values that are substantially higher than expected for Rayleigh distillation at a given mixing ratio. Periods associated with strong mixing are unlikely to be easily linked to a single upstream last-saturation point and may reflect greater mixing between different upstream sources. In such cases, retroplume techniques (e.g., Stohl et al. 2002) may be more useful for identifying diverse source areas than the back-trajectory techniques used here.

Perhaps one of the more surprising results of this study is the relative insensitivity of the results to the boundary layer isotopic composition. Many studies of water vapor isotopic composition have linked downstream isotopic measurements to boundary layer processes (Steen-Larsen et al. 2014, 2013; Kurita 2011), but such influence is not evident in the current dataset. Instead, it appears that in-cloud processes were more important than initial conditions in setting the isotopic composition measured at Chajnantor. Most likely this is due to the extreme degree of dehydration encountered by air parcels reaching the upper troposphere. It would be useful to explore this relative sensitivity in moister settings or in the moist summertime on Chajnantor.

6. Conclusions

The goal of this study was to explore the processes that set the low mixing ratio and water vapor δ values measured on the Chajnantor Plateau during a cold-air outbreak on 23–26 July 2014. On 23 July 2014, some of the coldest temperatures on record affected the Chajnantor Plateau in the northern Chilean Andes. Three days later, on 26 July 2014, some of the lowest water vapor δ values on record were measured on the Chajnantor Plateau. Numerical simulations show that the dynamics of the event were consistent with previous studies of South American cold-air outbreaks. Back-trajectory analysis of the numerical simulations show that the highly isotopically depleted water vapor that reached Chajnantor on 26 July was last saturated over the South Pacific on 23 July at 300 hPa at a temperature of about −50°C under ice supersaturation with about 110%. The water vapor traveled to Chajnantor along a nearly isentropic path following saturation. The deep trough centered over the west coast of South America provided a nearly direct isentropic path from the South Pacific to the Chajnantor Plateau, thus providing the dynamical link between the cold-air outbreak and the isotopically depleted water vapor that reached the subtropical Andes.

Modeling of the isotopic data is consistent with the meteorological simulations and analysis. The isotopic data require condensation at temperatures between −50° and −53°C under supersaturation with between 112% and 118%, followed by less than 25% moistening air during transport. These results show that measurements of water vapor isotopic composition can provide observational constraints on in-cloud processes that influence the humidity of the subtropics.

Acknowledgments

This study is based on data collected at the Atacama Large Millimeter/Submillimeter Array (ALMA), an international astronomy facility which is a partnership of Europe, North America, and East Asia in cooperation with the Republic of Chile. We thank the staff of ALMA for their generous support of this project, especially Richard Hills, David Rabanus, Joaquin Penroz, and Jim Murray. We also thank Kimberly Samuels-Crow, Dylan Ward, Alex Lechler, Alec Tunner, and Lauren Vargo for field assistance and Chris Rella, Danthu Vu, and Kate Dennis from Picarro, Inc. for their technical support. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation. This project was supported by NSF-AGS Award 1158582 to JG.

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