Characteristics of Tropopause Polar Vortices Based on Observations over the Greenland Ice Sheet

Sarah M. Borg Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

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Steven M. Cavallo School of Meteorology, University of Oklahoma, Norman, Oklahoma

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David D. Turner NOAA/OAR/ESRL/Global Systems Laboratory, Boulder, Colorado

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Abstract

Tropopause polar vortices (TPVs) are long-lived, coherent vortices that are based on the dynamic tropopause and characterized by potential vorticity anomalies. TPVs exist primarily in the Arctic, with potential impacts ranging from surface cyclone generation and Rossby wave interactions to dynamic changes in sea ice. Previous analyses have focused on model output indicating the importance of clear-sky and cloud-top radiative cooling in the maintenance and evolution of TPVs, but no studies have focused on local observations to confirm or deny these results. This study uses cloud and atmospheric state observations from Summit Station, Greenland, combined with single-column experiments using the Rapid Radiative Transfer Model to investigate the effects of clear-sky, ice-only, and all-sky radiative cooling on TPV intensification. The ground-based observing system combined with temperature and humidity profiles from the European Centre for Medium-Range Weather Forecasts’s fifth major global reanalysis dataset, which assimilates the twice-daily soundings launched at Summit, provides novel details of local characteristics of TPVs. Longwave radiative contributions to TPV diabatic intensity changes are analyzed with these resources, starting with a case study focusing on observed cloud properties and associated radiative effects, followed by a composite study used to evaluate observed results alongside previously simulated results. Stronger versus weaker vertical gradients in anomalous clear-sky radiative heating rates, contributing to Ertel potential vorticity changes, are associated with strengthening versus weakening TPVs. Results show that clouds are sometimes influential in the intensification of a TPV, and composite results share many similarities to modeling studies in terms of atmospheric state and radiative structure.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sarah Borg, sarah.borg@ou.edu

Abstract

Tropopause polar vortices (TPVs) are long-lived, coherent vortices that are based on the dynamic tropopause and characterized by potential vorticity anomalies. TPVs exist primarily in the Arctic, with potential impacts ranging from surface cyclone generation and Rossby wave interactions to dynamic changes in sea ice. Previous analyses have focused on model output indicating the importance of clear-sky and cloud-top radiative cooling in the maintenance and evolution of TPVs, but no studies have focused on local observations to confirm or deny these results. This study uses cloud and atmospheric state observations from Summit Station, Greenland, combined with single-column experiments using the Rapid Radiative Transfer Model to investigate the effects of clear-sky, ice-only, and all-sky radiative cooling on TPV intensification. The ground-based observing system combined with temperature and humidity profiles from the European Centre for Medium-Range Weather Forecasts’s fifth major global reanalysis dataset, which assimilates the twice-daily soundings launched at Summit, provides novel details of local characteristics of TPVs. Longwave radiative contributions to TPV diabatic intensity changes are analyzed with these resources, starting with a case study focusing on observed cloud properties and associated radiative effects, followed by a composite study used to evaluate observed results alongside previously simulated results. Stronger versus weaker vertical gradients in anomalous clear-sky radiative heating rates, contributing to Ertel potential vorticity changes, are associated with strengthening versus weakening TPVs. Results show that clouds are sometimes influential in the intensification of a TPV, and composite results share many similarities to modeling studies in terms of atmospheric state and radiative structure.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sarah Borg, sarah.borg@ou.edu

1. Introduction

Tropopause polar vortices (TPVs) are well documented, upper-level vortices that often have significant impacts at the surface. These dynamic features are defined by closed material contours and can easily be identified as local extrema on a potential vorticity (PV) surface (Hakim and Canavan 2005).

Although the impacts of TPVs are still being discovered, upper-tropospheric disturbances originating in the Arctic have been linked to a variety of important events. TPVs have been shown to influence the formation of surface cyclones (Hoskins et al. 1985), which can then have an array of other impacts, depending on their location. There is a multitude of research describing the impacts of Arctic surface cyclones, such as sea ice breakup and transport out of the Arctic, which allows for greater human access to and through the Arctic Ocean (e.g., Simmonds and Keay 2009; Screen et al. 2011; Simmonds and Rudeva 2012; Screen et al. 2018). Arctic surface cyclones also have various effects on the Arctic surface energy budget, temperature, precipitation, and large-scale circulations (Simmonds et al. 2008; Screen et al. 2011). Examples of TPV influence on surface cyclones include the Great Arctic Cyclone of 2012 (Simmonds and Rudeva 2012), as well as 90% of extreme surface cyclones studied by Simmonds and Rudeva (2014). Additionally, Papritz et al. (2019) show that extreme cold air outbreaks in the vicinity of Fram Strait are associated with TPVs about 30% of the time, which are important factors in energy exchange throughout the Arctic climate system. Finally, TPVs can be transported equatorward by the jet stream where they can be precursors to midlatitude extratropical cyclones (Hoskins et al. 1985; Hakim et al. 1995). Midlatitude extratropical cyclones are commonly known to produce severe weather and other weather extremes, thus an increased understanding of TPVs in terms of structure and evolution could lead to improved long-term forecasting for these severe events.

Many previous studies have shown that TPVs are heavily influenced by radiation (Cavallo and Hakim 2009, 2010, 2012, 2013), with a variety of case studies and simulation-based composites used to determine this. Cavallo and Hakim (2009) set the stage for a composite study of TPVs by using a case study to show that radiation and latent heating were the prime diabatic factors in TPV evolution. A follow-up study using model output from 568 TPV cases led to the currently known composite structure of a cold-core TPV (warm-core TPVs are not a topic of this study) shown by Fig. 1, as well as a composite of radiative effects and EPV tendency from various diabatic factors inside a TPV, including radiation and latent heating (Cavallo and Hakim 2010, hereinafter CH10). The analysis showed radiative effects had the largest contribution to the total EPV tendency in the core of the TPV, strengthening the TPV by increasing EPV, while latent heating effects acted to weaken the TPV by destroying EPV beneath the core. The cold, relatively dry Arctic climate where TPVs exist supports the fact that radiation plays a more dominant role in TPV intensity changes than latent heating.

Fig. 1.
Fig. 1.

West–east cross sections of anomalous (a) temperature (K), (b) υ-wind component (m s−1), (c) EPV (PVU), and (d) relative humidity (%) constructed by CH10.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

With knowledge of the impact of radiation and latent heating, an idealized numerical experiment was set up to examine to what extent these diabatic factors affected TPV intensity change (Cavallo and Hakim 2013). Five experiments were used to isolate various mechanisms including longwave (LW) radiation, shortwave (SW) radiation, water vapor, and cloud microphysics, and analyze the resulting TPV amplitude over a 5-day period. The five experiments were set up with three that were “clear sky” (1: LW radiation only; 2: LW and water vapor; 3: LW and SW) and two that included clouds (1: LW and clouds; 2: LW, SW, and clouds) to consider the effects of latent heating. Within the first 120 h of the simulation, the TPV’s amplitude increased in all experiments. At the end of the 5-day simulation, the vortices in the cloud-included experiments had the highest amplitudes, and the experiment with LW radiation and clouds in the absence of shortwave radiation was almost 4 K higher in amplitude than the complementary experiment with shortwave included. These results suggested two important points:

  1. Vertical water vapor gradients are an important factor in TPV evolution and strengthening during clear-sky cases.

  2. Clouds can be an important factor in TPV evolution and intensification when present near the TPV’s center and near tropopause height, likely because of the increased radiative cooling they induce.

Clouds are frequently present in the Arctic (e.g., Intrieri et al. 2002; Shupe et al. 2011; Shupe 2011), often in multiple layers (e.g., Herman and Goody 1976; Intrieri et al. 2002) and with liquid present (e.g., Witte 1986; Curry et al. 1996; Shupe 2011; Bennartz et al. 2013). These cloud macro- and microphysical properties play an important role in the radiative heating rate (RHR) profiles in the troposphere, especially in Arctic locations where clouds tend to be long-lived. It has been shown that the amount of liquid water in a cloud layer has the greatest impact of all microphysical properties on the cloud’s radiative effect (Turner et al. 2018). For example, for single-layer, liquid-bearing clouds, LW cooling at cloud top dominates shortwave heating, and the LW radiative cooling rates at cloud top were 10–20 times the values for single-layer, ice-only clouds with the same total water path. Additionally, a comparison of liquid-only clouds with mixed-phase clouds in a single layer showed that the liquid in mixed-phase clouds accounts for much of the cloud-top cooling (Turner et al. 2018). Knowledge of the radiative impact of clouds in the Arctic advances our investigation of TPV evolution, since radiative cooling has been shown to be a major contributor to TPV intensification.

While several studies have offered new information about the structure, dynamics, and climatology of TPVs, all of these studies have been simulation based, and no previous studies have looked in depth at observations of TPVs, especially on a local scale. A primary goal of the present study is to verify the composite structure presented by CH10 through use of the high temporal resolution observations and associated geophysical variable retrievals. This is fundamentally important to the study of TPVs since direct measurements of TPVs have not been investigated before. It is expected here that a TPV passing over Summit should exhibit characteristics similar to those shown in Fig. 1, such as positive (negative) temperature anomalies above (below) the tropopause, positive (negative) moisture anomalies below (above) the tropopause, cyclonic winds, and positive EPV anomalies in the vortex center above the tropopause. Additionally, a secondary goal is to consider how TPVs evolve with clear-sky conditions and various cloud types in their vicinity, based on the simulated results from Cavallo and Hakim (2013). Their results suggest that water vapor gradients induced by a TPV lead to intensification of the TPV, and that cloud-top radiative cooling is a significant factor aiding in rapid intensification. This study aims to verify these results in the context of observed events.

2. Data and methods

a. Vortex intensity

The theoretical framework for characterizing a TPV’s intensity is Ertel potential vorticity (EPV) (e.g., Pedlosky 1992). EPV is given by
Π=ωaρθ,
where ρ is density, ωa is the absolute vorticity, and θ is the potential temperature. Here, a TPV’s intensity can be quantified through assessing absolute vorticity and the gradient of θ on a Π surface. Considering the EPV tendency equation to quantify intensity change, one can then note that EPV is conserved in the absence of diabatic and frictional effects (e.g., Holton 2004):
DΠDt=ωaρDθDt.
Since TPVs are based on the tropopause, which is far removed from the frictional effects of the boundary layer, a reasonable assumption is that changes in EPV contributing to TPV intensity changes are due to diabatic effects including radiation, latent heating, planetary boundary layer mixing, convection, and other mixing and diffusional processes. However, in the Arctic, there are consistently low temperatures and widespread cloudiness (e.g., Curry et al. 1996; Intrieri et al. 2002) allowing for a further breakdown of the EPV tendency equation into diabatic contributions mostly attributed to radiation and latent heating, where the dot denotes the Lagrangian time derivative:
DΠDt=ωaρ(θ˙radiation+θ˙latent heating+θ˙other).
CH10 found that no other diabatic tendencies (i.e., any processes falling into the “other” category) resulted in comparable EPV tendencies near the tropopause in the vortex core, so for this reason, previous studies have focused on radiation and latent heating as the primary diabatic contributors to intensity changes. A TPV’s specific amplitude at a given time is defined as the absolute difference between θ at the vortex core and the maximum θ on the 2-PVU surface (generally referred to as the last closed contour). Note that 1 PVU = 10−6 K kg−1 m2 s−1.

b. Data

The European Centre for Medium-Range Weather Forecasts’s (ECMWF) fifth major global reanalysis (ERA5) dataset is utilized in this study (Hersbach et al. 2020). Soundings from Summit Station, Greenland, have been assimilated into ERA5 since January 2012, making ERA5 an appropriate reanalysis dataset to use for high-temporal-resolution atmospheric state parameters over Greenland. Data are acquired at 0.25 × 0.25 grid spacing, and from them the atmospheric state data at the point closest to the location of Summit (72.6°N, 38.5°W) are extracted and used in the analysis. Data acquired from ERA5 include temperature, specific humidity, relative vorticity, geopotential height, and tropopause potential temperature.

TPV tracks are used in the analysis to visualize how a TPV evolves in shape, size, and strength as it approaches and crosses over the ground-based instruments at Summit. These tracks are acquired through TPVTrack v1.0, an open-source software package specifically designed to identify and track TPVs (Szapiro and Cavallo 2018). The goals of the tracker include representing the spatial structure of TPVs and representing time evolution including merging and splitting events. The former is achieved by using restricted regional watershed basins, which are defined by the surrounding basin that drains by steepest descent to a potential temperature minimum. Then, by using the sign of the local relative vorticity of each cell in the basin, these basins can be segmented into cyclonic and anticyclonic TPVs. A user-defined bounding contour, which for this study was restricted to the 10th percentile of boundary amplitudes, is implemented to further restrict each basin and improve isolation of the anomalies. The latter, representing time evolution of TPVs, is done by using an overlap correspondence method dominated by advection. Correspondences are classified (e.g., form, decay, split, merge, persist; major or minor) by advecting a single basin half a time step forward and another basin half a time step backward and comparing the relative overlap in the advected basins. Full tracks are then determined by connecting all major correspondences, and when no major correspondence exists before or after, the track is considered to begin or end, respectively.

The tracker was previously applied to ERA-Interim 6-hourly data over the Northern Hemisphere, north of any user-specified latitude, but for this study the tracker is applied to ERA5 hourly data north of 30°N for 2012–14. We chose to limit the data to latitudes above 30°N to reduce the dataset over which to run the tracker, and the years were chosen due to data quality and data availability from ground-based instruments at Summit Station. Meteorological inputs to the tracker are zonal wind, meridional wind, relative vertical vorticity, and potential temperature on the tropopause. Once tracks are identified, tracks are then filtered for specific use in this project. The first filter applied restricts vortices to at least a 2-day lifetime, a TPV requirement, providing 11 765 TPVs. The second is a spatial filter since the interest of the study is restricted to the single point location of Summit Station. Because of this, we restricted tracks to TPVs that passed within 40 km of Summit at each TPV’s closest point, which provided a dataset of 40 TPVs. This threshold guarantees that the core of the TPV must come within one ERA model grid point of Summit. The dataset includes 12 TPVs in spring (March, April, and May), 9 in summer (June, July, and August), 13 in autumn (September, October, and November), and 6 in winter (December, January, and February).

The Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit (ICECAPS) Project has been part of an ongoing effort to characterize cloud and atmospheric state properties in the Arctic (Shupe et al. 2013). This project began in 2010 and provides a rich resource for probing the atmosphere above the 3250-m-high Greenland Ice Sheet. Instrumentation at Summit Station is modeled after other Arctic observatories such as the Atmospheric Radiation Measurement site at Barrow, Alaska (now known as Utqiaġvik), and the NOAA–Canadian Network for Detection of Atmospheric Change (CANDAC) in Eureka, Canada (Shupe et al. 2013). Cloud and atmospheric state properties are measured continuously by a large number of remote sensing instruments that are present at Summit Station including twice-daily radiosonde launches.

The Shupe–Turner (ST) algorithm retrieves cloud properties from the ground-based remote sensors at Summit that can be used to understand radiative fluxes along with a range of other applications (Shupe et al. 2015). This retrieval algorithm was initially evaluated using a 2-yr observational microphysics dataset from Barrow and is specific to Arctic observatories, making it unique from many cloud property retrieval systems in the past (Shupe et al. 2015). The ST algorithm was designed to retrieve cloud properties from Arctic clouds, and qualitative comparisons of the radar and lidar backscatter and liquid water paths between Summit and Barrow show that the two are similar. Thus, we chose to use this retrieval algorithm here, since all of the needed inputs were available in the ICECAPS dataset. The algorithm relies on the strengths of various instruments working together in concert to fill in missing pieces of the cloud-property puzzle. It utilizes data from the Millimeter Cloud Radar (MMCR; Kollias et al. 2016), polarization sensitive micropulse lidar (MPL; Campbell et al. 2002), Atmospheric Emitted Radiance Interferometer (AERI; Knuteson et al. 2004), microwave radiometer (MWR; Cadeddu et al. 2013), ceilometer, and radiosondes. Each instrument has its weaknesses, for example, the MPL is attenuated very easily in the vertical, especially by optically thick clouds and liquid layers. For this reason, it is important to include observations from the MMCR, which is not attenuated as easily and provides a better estimate of cloud-top height. A detailed explanation of how the observations from these instruments are combined to identify the cloud properties (ice and liquid water contents, effective radius of the ice and liquid) as a function of time and height above the instrument suite is provided in Shupe et al. (2015).

c. Methods

In this study, we use profiles of RHR to gain insight into the diabatic heating of the atmosphere, which could contribute to the strengthening or weakening of the TPV, depending on the location of the heating. We use the atmospheric state and cloud properties described in section 2b for these calculations. The RHR profiles are computed using the Rapid Radiative Transfer Model (RRTM; Mlawer et al. 1997), which uses a correlated-k approach to account for gaseous absorption. The temperature and water vapor profiles are specified, and the standard subarctic winter atmosphere is used to specify all other trace gases (e.g., methane, ozone, carbon dioxide). Because the surface around Summit is all ice, surface emissivity is held constant at 0.95 for all calculations. The layering in RRTM uses 90-m increments from the surface to 10 km, and then decreases, with subsequent height levels being 20% thicker up to a maximum height of 70 km. Only LW RHR calculations are performed, because there is little shortwave radiative heating given a high-albedo surface and small (or negative) solar elevation angles. Although only LW RHR is considered here, note that, when present, shortwave heating slows the development of TPVs; however, previous studies show that shortwave radiative heating provides relatively small contributions to TPV evolution (e.g., Cavallo and Hakim 2013).

For each case in our study, we perform multiple sets of calculations (Table 1), wherein the RHRs are computed over a 5-day period (3-day period for all sky) centered at the TPV overpass to compute the anomalies in both the RHR profiles and atmospheric state variables. The 5-day mean temperature and water vapor (calculated for each vertical layer) for each case are used as the background profiles of temperature and water vapor, respectively. The all-sky case (experiment 1, Table 1) uses the observed cloud properties retrieved by the ST algorithm. We make several clear-sky RHR calculations (experiments 2–6), wherein no clouds are included in the RRTM calculations at all; these are performed to isolate the impact of the evolving temperature and water vapor profiles on the development of the TPV. Two additional RHR calculations are made, one in which only ice clouds are included (i.e., if there are liquid clouds present, they are ignored) and one in which only liquid clouds are included; these are experiments 7 and 8, respectively. A summary of these computational experiments is given in Table 1.

Table 1.

Table summarizing the experiments run for this study using the RRTM and different forcings. An X represents the presence of the forcing in the respective experiment.

Table 1.

In addition, the composite time–height structure from the 40 TPVs is created to compare with the composite structure derived from model simulations (Fig. 1). Anomalous temperature, relative humidity, meridional wind component (υ), RHR, and EPV time–height cross sections are created by plotting 24-h periods of each variable centered on the hour of the TPV’s closest point to Summit. Standardized anomalies are used, which are found by subtracting the 5-day, clear-sky mean in each vertical layer from the actual value at a given time, then dividing by the standard deviation of the values in that vertical layer. Standardization of anomalies is used to assist in representation and analysis of the anomalies. The goal is to compare what would be considered the “core” of the TPV to the outside (i.e., before/after the passage), which informs us whether the core is being influenced differently than the surrounding environment, leading to intensity changes. Composites of RHR properties are only created for the clear-sky cases due to the varied nature of the clouds in and around TPVs in the 40 cases compiled as well as the lack of cloud data for 25% of cases caused by data availability/quality issues that prevented the ST algorithm from being computed for the case.

3. Case-study results

a. Overview and description

We first examine a case study of a TPV existing from 17 to 31 July 2012, chosen on the basis of its direct track over Summit (with its proximity to Summit at its closest point being 17.6 km at 2300 UTC 24 July 2012; Fig. 2) and the presence of mixed-phase clouds above Summit in the vicinity of the TPV’s core. The estimated radius of the TPV is 339 km at its closest point and averages about 350 km during the ±12 h surrounding its passage. For this study, the radius is defined as the distance between the location of the minimum potential temperature on the 2-PVU surface (i.e., the core) and the outermost contour of potential temperature, which defines the TPV’s amplitude. Note that a cyclonic radius can be defined in a number of equally effective ways, such as those outlined by Simmonds (2000) and Rudeva and Gulev (2007), all of which produce a different result. During a 12-h period, including ±6 h surrounding the TPV’s closest passage, the net amplitude of the TPV increases by ~2 K from about 15 to 17 K with many hourly fluctuations in amplitude apparent from the reanalysis. The core potential temperature remains steady at ~298 K (Fig. 3).

Fig. 2.
Fig. 2.

Potential temperature contours on the 2-PVU surface at (a) 1300, (b) 1800, and (c) 2300 UTC 24 Jul 2012 and (d) 0400 UTC 25 Jul 2012; (c) represents the time when the TPV’s core was the closest to Summit (title highlighted in blue). Contour intervals are 2 K. The red contour on each is representative of the TPV’s innermost core potential temperature contour at the given interval.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

Fig. 3.
Fig. 3.

Line plots showing temporal evolution of the TPV’s amplitude (blue), core potential temperature (green), and last closed contour potential temperature (green dashed). The vertical red line represents the time of the TPV’s closest passage, at 2300 UTC 24 Jul 2012. The red shading represents ±6 h from the TPV’s closest passage.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

Optically thin, low-level ice clouds that contain liquid layers exist at Summit as early as 1100 UTC 24 July 2012 (Fig. 4). Ice precipitation increases at 2000 UTC, while liquid is still present at cloud top, at the same time the TPV core reaches Summit (recall Fig. 2). From 2200 to 2300 UTC, the ice precipitation is accompanied by a greater liquid content before the cloud dissipates around 0000 UTC 25 July 2012 (Fig. 4).

Fig. 4.
Fig. 4.

Time–height cross sections showing (a) ice water concentration (mg m−3) and (b) liquid water concentration (g m−3) at Summit over the time period of the passing TPV on 24 Jul 2012. The height axis is in terms of kilometers AGL. The light-blue line represents the height of the 2-PVU surface at Summit from ERA5 (i.e., the tropopause height), and the vertical black line represents the time at which the TPV’s core was closest to Summit.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

b. All-sky and clear-sky RHR

Radiative properties of this TPV are investigated in a series of experiments described in Table 1, beginning with an overview of the full radiative forcing experiment (i.e., experiment 1). LW cooling rates are greater in the presence of clouds (Fig. 5a) and RHR values of less than −5 K day−1 (down to −99 K day−1) exist where notable vertical gradients in optical depth are present, such as between ice and liquid layers from 1500 to 1800 UTC and near cloud top from 2100 to 2300 UTC. Background RHR values remain mostly between −1 and −2 K day−1, which is consistent with LW clear-sky RHR values found by Turner et al. (2018). Anomalous values of RHR show enhanced cooling in the regions described, with positive anomalies in RHR values below cloud bases and in small regions below areas of liquid inside ice clouds (Fig. 5b).

Fig. 5.
Fig. 5.

Results from experiments 1 and 2 in Table 1 showing the time–height cross section of (a) all-sky, LW RHR centered at 2300 UTC 24 Jul 2012; (b) standardized anomalies of the all-sky, LW RHR; (c) clear-sky, LW RHR centered at 2300 UTC 24 Jul 2012; and (d) standardized anomalies of the clear-sky, LW RHR. The height axis is in terms of kilometers AGL. The black vertical line represents the time at which the TPV’s core was closest to Summit. The blue line represents the height of the 2-PVU surface at Summit from ERA5. Black contours represent liquid clouds, and white contours represent ice clouds.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

To better isolate the radiative processes occurring in this case, we look into the clear-sky properties of the TPV (i.e., experiment 2). Clear-sky RHR magnitudes are small throughout the time period in consideration with values no lower than −3 K day−1 (Fig. 5c). Clear-sky RHR anomaly values are mostly consistent throughout the time period (Fig. 5d), implying that clear-sky RHR values are not influencing the TPV core differently than the surrounding environment, and hence not likely contributing to the TPV intensity changes.

c. RHR from background temperature, background water vapor, and perturbations of each

Clear-sky RHR is small, however we can still glean information by breaking this down into water vapor contribution and temperature contribution (i.e., experiments 5 and 6 in Table 1). Both water vapor and temperature contributions are small (Figs. 6a,b); however, water vapor does show a higher contribution below the tropopause, as expected, due to the decrease in water vapor concentration above the tropopause. This can be seen when considering the difference between RHR from water vapor and RHR from temperature, where water vapor has much more influence on the clear-sky RHR below the tropopause as opposed to temperature above (Fig. 6c). This fact will be discussed in greater detail in later sections when considering RHR and EPV tendency composites broken down by forcing. RHR values in the lowest levels are consistent with water vapor gradients from the mean water vapor in the profile, while the mean temperature contribution is small (Fig. 6d).

Fig. 6.
Fig. 6.

Results from experiments 5 and 6 in Table 1 showing time–height cross sections of LW RHR values from (a) water vapor perturbations only, (b) temperature perturbations only, and (c) experiment 5 minus experiment 6, centered at 2300 UTC 24 Jul 2012. The height axis is in terms of kilometers AGL. The black vertical line represents the time at which the TPV’s core was closest to Summit. The blue line represents the height of the 2-PVU surface at Summit from ERA5. (d) Results from experiments 3 and 4 in Table 1, showing the heating-rate profile from the combined mean water vapor and mean temperature (blue), and the mean temperature only (purple) in each layer over a 5-day period centered at 2300 UTC 24 Jul 2012.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

d. RHR from liquid and ice

While experiments 2–6 show clear-sky LW cooling at the tropopause during the time when the TPV is over Summit, these values are small relative to RHR associated with clouds (cf. Fig. 6 with Fig. 5a). In addition, recall the small anomalous clear-sky cooling rates at the tropopause during the entire analysis period (Fig. 5d), which make it unlikely that clear-sky radiative effects contribute to the 12-h net increase in amplitude that this TPV experiences. Next, we focus on the radiative effects of the clouds present during this TPV’s passage in consideration of whether these clouds assisted in the intensification of the TPV.

LW RHRs are enhanced because of ice clouds that are present during the TPV’s passage (Fig. 7). From 1800 to 0000 UTC, radiative cooling within the ice cloud increases (Fig. 7a), with RHR values less than −5 K day−1 (down to −57 K day−1 for ice and −99 K day−1 for liquid) present in the cloud and throughout the layer between the surface and the tropopause. This cloud extends slightly above the tropopause and the cooling rates within the cloud overlap the tropopause while the TPV’s core is directly over Summit. While liquid layers exist near the surface for many hours before the TPV’s closest point, liquid also exists at the top of the ice cloud that is present from 1800 to 0000 UTC (Fig. 7b), providing further enhancement of radiative cooling at the tropopause in the TPV core.

Fig. 7.
Fig. 7.

Results from experiments 7 and 8 in Table 1 showing time–height cross sections of LW RHR values from (a) ice in clouds only and (b) liquid in clouds only, centered at 2300 UTC 24 Jul 2012. The height axis is in terms of kilometers AGL. The black vertical line represents the time at which the TPV’s core was closest to Summit. The blue line represents the height of the 2-PVU surface at Summit from ERA5. Black contours represent liquid clouds, and white contours represent ice clouds.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

Given the results of this case study, we conclude that clouds greatly enhanced the radiative cooling during the analysis period from 1100 UTC 24 July to 1100 UTC 25 July 2012. Of more importance is that this enhanced cooling is found across the tropopause, which is hypothesized to be important for intensification of TPVs. During the time the TPV core was the closest to Summit, enhanced cloud-top cooling from both ice and liquid clouds existed at the tropopause (Fig. 5a), where RHR anomalies had large negative values from approximately 1900 to 2300 UTC (Fig. 5b), as opposed to the weak positive anomalies at the tropopause noted in the clear-sky case.

EPV tendency is not directly analyzed as part of this case study, but one can draw conclusions about the EPV creation from the RHR and RHR anomaly plots. When considering the all-sky case (Figs. 5a,b), from nearly 1900 UTC to 0000 UTC the vertical gradient in heating rate across the tropopause is mostly positive, implying a positive contribution to EPV tendency. This suggests a strengthening of the TPV by increasing EPV below the tropopause. By referring back to Fig. 2, the TPV core is very close to Summit even at the 1900 UTC point, which supports that clouds at Summit during this time may have already begun to influence the TPV. In this example, it is likely that this cloud-top radiative cooling in the TPV core contributed to the average hourly amplitude increase seen over a 12-h period.

While we do not show the SW heating experiment for this case, it acts to slightly offset the effect from LW radiation alone; however, the impact on the TPV itself is not expected to be significant given the results of Cavallo and Hakim (2013). For there to be a significant impact on the TPV, there would need to be a horizontally varying difference in LW and SW heating between the core and outer edge of the vortex.

4. Composite study results

a. Observed atmospheric state structure

While the case study illustrates how clouds are potentially relevant for TPV intensification, we now turn to a composite study to gain a general understanding of TPV structure and clear-sky radiative effects. CH10 and CH13 show that clear-sky effects are major contributors to the strengthening of a TPV, making it important and relevant to consider the radiative impact of such effects while ignoring clouds. The composites are created using time–height cross sections of the 40 TPVs in our dataset over a ±24-h period of each one’s closest passage to Summit. This 40-TPV dataset includes many more weak than strong TPVs, and the average size of the TPVs ranges from 50 to nearly 400 km in radius (Fig. 8). There is a lot of variability in the TPV tracks; however, on average the TPVs in this dataset have a southwest to northeast motion (not shown). The composites show anomalously warm temperatures above the composite tropopause and anomalously cool temperatures below (Fig. 9a), analogous to the temperature structure noted in CH10. The strongest anomalies, both positive and negative, are between −3 and 0 h from closest passage, coincident with the lowest point on the composite tropopause. The weakest temperature anomalies exist as early as −21 h and as late as +18 h. Similar time–height cross sections show anomalously low values of relative humidity near the composite tropopause (Fig. 9b). The negative relative humidity anomalies are the greatest just above the tropopause, at values near −30% just before 0 h. Negative anomalies of around −5% exist as early as −18 h and as late as +21 h, similar to the anomalous temperature findings. The negative anomalies extend from about 2 km above ground level (AGL) to nearly 7 km AGL in the vertical at times from −6 to +6 h, and small positive relative humidity anomalies exist for a short time before the TPV is closest to Summit.

Fig. 8.
Fig. 8.

Distribution of 24-h average of (a) amplitude (K) and (b) radius (km) for the 40 TPVs used in composite calculations. The 24-h average is computed using ±12 h on each side of the TPV’s closest time to Summit.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

Fig. 9.
Fig. 9.

Composite time–height cross section of (a) anomalous temperature (K), (b) anomalous relative humidity (%), (c) anomalous υ-wind component (m s−1), and (d) absolute vorticity (s−1) for ±24 h of a TPV’s closest passage to Summit, including TPVs passing Summit during the time period from March 2012 to October 2014. The blue line represents the composite tropopause, and the gray lines represent the minimum and maximum tropopause height at each time. Solid and dashed contours represent values of the positive and negative standardized anomaly, respectively, for (a)–(c).

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

Composite anomalies of the v-wind component show anomalous wind speeds, or cyclonic wind around the vortex core (Fig. 9c). Between −24 and −3 h the υ-wind component anomaly composite shows strong southerly winds. The strongest southerly wind anomalies are centered on the tropopause between −22 and −16 h and exceed 6 m s−1. For TPVs moving from west to east across Summit, this implies that an anomalously strong southerly wind is present on the east side of the vortex as early as 24 h before its core reaches Summit. The υ-wind component composite may also imply that on average, Summit is already within the last closed contour of potential temperature defining the TPV as early as 24 h before the core reaches Summit. This may be a consequence of several very large TPVs in the dataset, or several very slow-moving TPVs. Alternatively, for the same west–east-moving TPVs, the strongest negative anomalies (northerly winds) exist on the west side (i.e., after the TPV has passed), and the negative anomalies are not as strong or long-lived as the positive, only reaching values around −3 m s−1 between the hours of +6 and +21. Weak positive anomalies of the v-wind component also exist near the composite TPV core; however, these small positive anomalies (near +2 m s−1) exist above the tropopause rather than centered on it. The presence of exceptionally strong positive anomalies before the TPV’s passage may be due in part to the presence of significant ridging before a TPV’s passage; however, in order to diagnose this, a case-by-case synoptic overview would need to be considered.

The composite of absolute vorticity for ±24 h of the composite TPV’s passage shows positive values with a maximum centered around the 0 h (Fig. 9d). This implies that the vorticity is dominated by the cyclonic nature of the flow and therefore EPV will project most strongly with the heating rates in the core of the TPV.

In general, the composite temperature, relative humidity, and meridional wind component agree with results presented in CH10 with regard to the composite structure of TPVs (Fig. 1 vs Fig. 9). Both sources show anomalously warm temperatures above the tropopause and anomalously cool temperatures below; however, CH10 show stronger anomalies with values reaching +5.5 and −8.5 K. While relative humidity anomalies from this study show agreement with CH10 in terms of anomalously dry air above the tropopause, there is not an indication of anomalous moisture present near the surface below the TPV core as in the modeled composite from CH10 (Fig. 1d). This may be influenced by the lower moisture of the atmosphere above Summit in comparison to other Arctic locations (e.g., Serreze et al. 2012; Shupe et al. 2013); however, a general increase in moisture near the surface would still be expected based on simulations done in previous studies of TPVs. Anomalous moisture does exist during early hours in the composite from this study in agreement with the anomalous moisture present in midlevels on the east side of the modeled composite TPV from CH10 (Fig. 1d). This could possibly be due to northward advection of relatively warm, moist air by the TPV, as it is coincident with the timing of the composite TPV’s strongest southerly winds. Further analysis is needed to determine whether this anomalous moisture is in fact due to the presence of the TPV. The results presented here show cyclonic motion around the core of the vortex in terms of υ, in agreement with CH10. Two differences, however, are the strength of the anomalies and the symmetry of the anomalies. The modeling composite in CH10 shows a symmetric cyclonic structure (Fig. 1b), with equal anomalous winds on both sides of the vortex core reaching values close to ±16 m s−1. The observational composite at Summit does not indicate symmetric northerly and southerly winds (Fig. 9c), and the anomalies are less than half the value of the anomalies from the modeling composite (Fig. 1b). The modeling composite also does not show an anomalous southerly wind component above the tropopause at the location of the core as the observational composite does (cf. Fig. 1b to Fig. 9c). We note that there are many possible reasons for these differences, including inconsistent TPV movement past a fixed location relative to other TPVs (i.e., each TPV’s movement is unique), and background flow that may be altered by the complex topography of Greenland. It is also important to note that the sample used for the composites in this study is relatively small (40 TPVs) and includes a larger portion of weak TPVs than strong TPVs (i.e., 31 out of 40 TPVs in the dataset have average 24-h amplitudes less than 10 K as shown in Fig. 8a), likely reducing the strength of the expected signal.

b. Observed radiative properties

Composites of RHR properties are only created for the clear-sky cases for two main reasons. Cloud features are highly variable in and around TPVs in the 40 cases compiled. The standard deviation of EPV tendency values surrounding the tropopause for all-sky cases is 3.5 times that of the clear-sky EPV tendency (not shown). In addition to this variability, we lack cloud data for 25% of cases because of missing data in one or more of the data streams needed for the cloud retrievals.

The composites are broken down into strengthening cases and weakening cases based on the average hourly amplitude change over the ±6-h period from the TPV’s closest passage. For this analysis, there are 17 strengthening cases and 22 weakening cases, with one case neglected because of a 0-K amplitude change. To better understand the effect of radiative cooling from each contributor, composites are created on the basis of experiments 2 (clear sky), 5 (water vapor perturbation contribution), and 6 (temperature perturbation contribution).

1) Strengthening cases

For strengthening TPVs, the clear-sky anomaly composite shows positive anomalies of RHR on and around the tropopause for the entire time period considered; however, the largest anomalies are found in the period from −6 to +9 h (Fig. 10a). When considering only the water vapor perturbation, positive RHR anomalies are found mostly above the tropopause, likely due to the larger relative change in water vapor concentration above the tropopause (Fig. 10b). In the temperature perturbation contribution composite, negative anomalies are found above the tropopause for the entire period, with the strongest of those existing from −6 to 0 h, and weak positive anomalies exist below the tropopause at all times (Fig. 10c). As opposed to the clear-sky and water vapor contribution RHR anomaly cases, the tropopause in the temperature perturbation RHR anomaly composite nearly coincides with the zero line between positive and negative anomalies. While negative anomalies exist above the tropopause in this composite, these anomalies are weak in comparison to positive anomalies from the water vapor perturbation composite, which leads to the positive anomalies present in much of the clear-sky anomaly composite (Fig. 10a). In the clear-sky and water vapor contribution to RHR cases, RHR anomalies are negative near the surface throughout much of the time leading up to the TPV’s passage (at hour 0); however, positive anomalies grow in strength at and above the tropopause as the TPV approaches Summit.

Fig. 10.
Fig. 10.

Composite time–height cross section of (a) anomalous RHR for clear sky (experiment 2), (b) anomalous RHR for water vapor contributions (experiment 5), (c) anomalous RHR for temperature contributions (experiment 6), (d) anomalous EPV tendency for clear sky, (e) anomalous EPV tendency for water vapor contributions, and (f) anomalous EPV tendency for temperature contributions, based on ±24 h of a TPV’s closest passage, considering only TPVs that strengthened on average over a ±6-h period. Anomalies are measured in standard deviations from the mean heating rate.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

Recall from Eq. (2) that positive vertical gradients in RHR across the tropopause provide positive contributions to EPV tendency; however, the contributions from absolute vorticity make EPV tendency anomalies much noisier than the RHR anomalies. Positive anomalies of EPV tendency below the tropopause act to strengthen TPVs by increasing EPV in the troposphere beneath the TPV core. The clear-sky experiment for composite anomalous EPV tendency shows this pattern (from −9 to +6 h; roughly 3–4 km AGL), but the magnitude of the tendency is small (Fig. 10d). The largest contribution to clear-sky EPV anomalies is from the water vapor contribution case (Fig. 10e). Strong positive anomalies exist directly below the tropopause and extend downward under the TPV’s core, implying that the water vapor contribution is acting to enhance EPV below the tropopause and strengthen the vortex. An important feature to note is the general negative nature of anomalies on the tropopause for all times outside the vortex “core” in the water vapor contribution case. The water vapor composite of RHR shows an increased vertical gradient in RHR across the tropopause throughout the TPV core as compared to the “outside” of the vortex, which provides a positive contribution to EPV creation, acting to strengthen the vortex (i.e., generally, for the times from −12 to +12 there is a stronger positive gradient in RHR across the tropopause than times outside this range). The temperature contribution to RHR anomalies shows the opposite, that is, that there is a negative vertical gradient in heating rate, which acts to reduce the magnitude of the EPV tendency and weaken the vortex. The direct effect of this is not obvious in the EPV anomalies, as the temperature contribution is weak, making the EPV tendencies noisy with no prominent signal (Fig. 10f). Note that EPV tendencies from water vapor and temperature do not directly sum to the clear-sky total as is the case in the RHR composites. Nonlinear effects can have an impact on the resulting EPV tendencies. Based on the clear-sky composites of both RHR and EPV tendency, the water vapor contribution has a larger effect on the total clear-sky RHR values than temperature, allowing these TPVs to strengthen due to clear-sky water vapor effects rather than weaken by clear-sky temperature effects.

2) Weakening cases

The RHR anomaly composites for the weakening cases show some notable differences from the strengthening cases. In the clear-sky composite for weakening cases, much of the anomalous positive RHR just below the tropopause from −13 to +3 h comes from water vapor perturbations, which can be seen in the composite for RHR from water vapor (Figs. 11a,b), where large positive anomalies exist between −3 and 0 h. The composite of RHR anomaly from temperature contributions for the weakening cases shows a similar pattern to that of the strengthening cases, with negative anomalies above the tropopause and positive below (Fig. 11c); however, this signal is much stronger than that of the strengthening cases.

Fig. 11.
Fig. 11.

As in Fig. 10, but considering only TPVs that weakened on average over a ±6-h period.

Citation: Journal of Applied Meteorology and Climatology 59, 11; 10.1175/JAMC-D-20-0004.1

Similar to the strengthening cases, vertical RHR gradients have a large impact on the resulting anomalous EPV tendencies. Weak negative anomalies of EPV tendency are found along the tropopause in the clear-sky case (Fig. 11d), which contribute to a general weakening of the core in the composite vortex. In the water vapor contribution case, there are two areas with opposing RHR gradients acting on the EPV tendency (see Fig. 11b). The first is a positive vertical gradient of RHR from the surface to the maximum positive anomalous RHR, which exists below the tropopause. The second is above the tropopause where RHR anomalies are decreasing vertically even though they remain positive in the stratosphere. While the positive gradient of RHR below the tropopause clearly leads to the positive EPV anomalies from −3 to 0 h at roughly 3 km AGL, the second gradient does not have such a clear effect on EPV at the tropopause (Fig. 11e). It may be true that in the case of weakening TPVs, water vapor contributions still act to strengthen the vortex, however; their effects are overcome by other processes.

The negative anomalies present above the tropopause in the RHR anomaly composite from temperature are similar in structure to those in the strengthening case, but stronger and shifted slightly downward to better overlap the tropopause. This contributes to a broad area of negative anomalous EPV throughout the time period (Fig. 11f) that is much more prominent in the weakening cases than in the strengthening cases. This is expected since the temperature contribution to RHR generally reduces the EPV tendency because the vertical gradient in RHR is negative across the tropopause. In the weakening cases, the temperature contribution is an important factor in opposing any positive EPV tendency that water vapor creates. Ultimately, combining the two effects and considering the clear-sky RHR anomalies, the influence of the negative vertical gradient in RHR across the tropopause from temperature is much greater than in the strengthening cases, leading to a net negative anomalous EPV in the clear-sky composite, consistent with a weakening vortex.

5. Summary and conclusions

Given previous studies showing the importance of water vapor gradients and cloud-top radiative cooling in the intensification of modeled TPVs, it was hypothesized that the water vapor gradient induced by TPVs, as well as cloud-top radiative cooling, would aid in the intensification of TPVs. While many studies have analyzed the effects of these factors on the evolution of TPVs, those studies have primarily been based on simulations of TPVs and the ability to use controlled variables. This study focuses on observations of 40 TPVs, a unique approach that has not been previously exploited to examine the hypothesis.

A case study was chosen with a direct path over Summit to fully utilize the cloud observatory located there and investigate the associated cloud and radiative scene. An analysis of radiative features of the TPV in the case study showed RHR anomalies were positive in and around the TPV’s core when clouds were not included in the RHR calculation, with a weak vertical RHR gradient likely not contributing greatly to strengthening the vortex. When clouds were included in the RHR calculation, however, there was strong radiative cooling from clouds present at the tropopause in the TPV’s center. This TPV had an overall strengthening trend on average, and since clear-sky RHR values did not suggest strong intensification, this strengthening was attributed to cloud-top LW radiative cooling in the TPV’s core.

Composites of anomalous temperature, relative humidity, and winds among the 40 cases available allowed a comparison between the currently accepted structure of a TPV based on simulations (CH10) and that of one based on observations. Temperature composites showed positive (negative) anomalies above (below) the composite tropopause (Fig. 9), in agreement with composites presented by CH10 (Fig. 1). The relative humidity composite showed negative anomalies above the tropopause, but the anomalously moist tropospheric conditions seen in CH10 are absent here. This may be due to outside environmental factors, such as frequent advection of dry air at low levels, which acts to dry out any abnormal moisture induced by the TPV. This may also be due to the relatively dry nature of Summit in general during all seasons (Serreze et al. 2012; Shupe et al. 2013) and Summit’s high altitude making it difficult to advect moisture in. Similarly, the υ-wind component composites showed the cyclonic environment that a TPV encompasses; however, the TPVs seen over Summit are notably less symmetric than those from CH10. There are many possible reasons that could lead to the differences over Summit, such as the relatively low amplitudes of the sample of TPVs considered, ridging before the TPV’s passage, the obscure shapes that may characterize naturally occurring TPVs as opposed to modeled TPVs, and compositing cases that are too different to consider as one group (seasonal differences, the radii are too varied, etc.). In addition, blocking of the zonal flow induced by the Greenland Ice Sheet, or “Greenland blocking” (e.g., Hanna et al. 2016; Luo et al. 2019) may be responsible for obscuring the expected west-to-east motion of TPVs, leading to asymmetries and variations from the well-behaved TPVs in model simulations.

Composite results for RHR and EPV tendency were broken down by cases that strengthened or weakened on average over 12 h. Considering the RHR anomalies, Cavallo and Hakim (2013) suggested that anomalous cooling just below the tropopause and heating above are present due to the water vapor gradient in the TPV core. While the cooling was small, the general trend in the strengthening cases was the positive vertical gradient in RHR across the tropopause, leading to a positive contribution to EPV tendency. EPV tendency composites for the strengthening cases show areas along the tropopause with positive EPV tendencies, which is expected for strengthening TPVs. RHR composites for weakening cases show a similar scene with large areas of positive anomalies near the tropopause. In the weakening cases, however, there is an obvious difference in the location of the strongest positive anomalies as compared to the RHR composites of the strengthening cases with maximum positive anomalies in RHR below the tropopause, creating a negative vertical gradient in RHR across the tropopause and contributing negatively to EPV tendency. Composites of EPV tendency for weakening cases show slightly negative values on the tropopause for the clear-sky total. The water vapor contribution to this composite presents a positive EPV tendency anomaly below and near the tropopause, which theoretically would act to strengthen the vortex, however the temperature contribution shows negative EPV tendency anomalies on the tropopause over much of the time period. The temperature contribution in these cases, along with the possibility of external factors (e.g., clouds) may be the cause of general weakening. It is also important to note that RHR is not the only factor leading to the resulting EPV (e.g., absolute vorticity is another factor).

Although most results from this study have agreed with previous studies or theoretical expectations, we note that differences in analysis techniques may lead to some discrepancies between this study and others in the literature. The use of composites to analyze a general structure based on a dataset of TPVs would be improved by using a larger sample size or a different sample with more controlled variables. For example, by choosing a dataset of only TPVs with amplitudes and radii above a certain threshold, features may be more apparent in composites. However, true isolation of variables, such as those outlined in Table 1, is difficult when using observations, since the presence of clouds will influence the atmospheric state of the environment even in the clear-sky calculations. In terms of TPVs, all cases are unique in many ways, for example, their speed, direction of movement, size, shape, season, and strength. Composite structures and radiative features will be influenced by such differences without a method for controlling these variables, so analyses may be improved or new details may be uncovered by compositing on the basis of specific features. To do this, a much larger sample size is needed in order to more robustly sample the spectrum of TPVs. With this in mind, the analysis done here could be improved by making use of other Arctic observatories to include many more cases. TPV cases here were limited to years with data available from ICECAPS as well as cases that crossed Summit in close enough proximity to even use this data. These limitations significantly reduced the number of cases available for analysis. By involving other Arctic observatories with similar observing instruments, this analysis could be expanded to include many more TPV observations in future studies and consider cloud properties in more depth.

Acknowledgments

This work was supported by The National Science Foundation Grants 1304692 and 1314358. The authors thank Dr. Greg McFarquhar for additional comments and discussion with regard to this work and Matthew Shupe for data processing that made this analysis possible. The authors also thank the three anonymous reviewers for insightful comments that have greatly improved this paper.

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  • Fig. 1.

    West–east cross sections of anomalous (a) temperature (K), (b) υ-wind component (m s−1), (c) EPV (PVU), and (d) relative humidity (%) constructed by CH10.

  • Fig. 2.

    Potential temperature contours on the 2-PVU surface at (a) 1300, (b) 1800, and (c) 2300 UTC 24 Jul 2012 and (d) 0400 UTC 25 Jul 2012; (c) represents the time when the TPV’s core was the closest to Summit (title highlighted in blue). Contour intervals are 2 K. The red contour on each is representative of the TPV’s innermost core potential temperature contour at the given interval.

  • Fig. 3.

    Line plots showing temporal evolution of the TPV’s amplitude (blue), core potential temperature (green), and last closed contour potential temperature (green dashed). The vertical red line represents the time of the TPV’s closest passage, at 2300 UTC 24 Jul 2012. The red shading represents ±6 h from the TPV’s closest passage.

  • Fig. 4.

    Time–height cross sections showing (a) ice water concentration (mg m−3) and (b) liquid water concentration (g m−3) at Summit over the time period of the passing TPV on 24 Jul 2012. The height axis is in terms of kilometers AGL. The light-blue line represents the height of the 2-PVU surface at Summit from ERA5 (i.e., the tropopause height), and the vertical black line represents the time at which the TPV’s core was closest to Summit.

  • Fig. 5.

    Results from experiments 1 and 2 in Table 1 showing the time–height cross section of (a) all-sky, LW RHR centered at 2300 UTC 24 Jul 2012; (b) standardized anomalies of the all-sky, LW RHR; (c) clear-sky, LW RHR centered at 2300 UTC 24 Jul 2012; and (d) standardized anomalies of the clear-sky, LW RHR. The height axis is in terms of kilometers AGL. The black vertical line represents the time at which the TPV’s core was closest to Summit. The blue line represents the height of the 2-PVU surface at Summit from ERA5. Black contours represent liquid clouds, and white contours represent ice clouds.

  • Fig. 6.

    Results from experiments 5 and 6 in Table 1 showing time–height cross sections of LW RHR values from (a) water vapor perturbations only, (b) temperature perturbations only, and (c) experiment 5 minus experiment 6, centered at 2300 UTC 24 Jul 2012. The height axis is in terms of kilometers AGL. The black vertical line represents the time at which the TPV’s core was closest to Summit. The blue line represents the height of the 2-PVU surface at Summit from ERA5. (d) Results from experiments 3 and 4 in Table 1, showing the heating-rate profile from the combined mean water vapor and mean temperature (blue), and the mean temperature only (purple) in each layer over a 5-day period centered at 2300 UTC 24 Jul 2012.

  • Fig. 7.

    Results from experiments 7 and 8 in Table 1 showing time–height cross sections of LW RHR values from (a) ice in clouds only and (b) liquid in clouds only, centered at 2300 UTC 24 Jul 2012. The height axis is in terms of kilometers AGL. The black vertical line represents the time at which the TPV’s core was closest to Summit. The blue line represents the height of the 2-PVU surface at Summit from ERA5. Black contours represent liquid clouds, and white contours represent ice clouds.

  • Fig. 8.

    Distribution of 24-h average of (a) amplitude (K) and (b) radius (km) for the 40 TPVs used in composite calculations. The 24-h average is computed using ±12 h on each side of the TPV’s closest time to Summit.

  • Fig. 9.

    Composite time–height cross section of (a) anomalous temperature (K), (b) anomalous relative humidity (%), (c) anomalous υ-wind component (m s−1), and (d) absolute vorticity (s−1) for ±24 h of a TPV’s closest passage to Summit, including TPVs passing Summit during the time period from March 2012 to October 2014. The blue line represents the composite tropopause, and the gray lines represent the minimum and maximum tropopause height at each time. Solid and dashed contours represent values of the positive and negative standardized anomaly, respectively, for (a)–(c).

  • Fig. 10.

    Composite time–height cross section of (a) anomalous RHR for clear sky (experiment 2), (b) anomalous RHR for water vapor contributions (experiment 5), (c) anomalous RHR for temperature contributions (experiment 6), (d) anomalous EPV tendency for clear sky, (e) anomalous EPV tendency for water vapor contributions, and (f) anomalous EPV tendency for temperature contributions, based on ±24 h of a TPV’s closest passage, considering only TPVs that strengthened on average over a ±6-h period. Anomalies are measured in standard deviations from the mean heating rate.

  • Fig. 11.

    As in Fig. 10, but considering only TPVs that weakened on average over a ±6-h period.

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