Systematic Differences between the Northern and Southern Hemispheres: Warm-Frontal Ice Water Path Linked to the Origin of Extratropical Cyclones

Hanii Takahashi aJet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Catherine M. Naud bNASA-GISS, Columbia University, New York, New York

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Derek J. Posselt aJet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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George A. Duffy cDepartment of Earth and Environmental Sciences, Syracuse University, Syracuse, New York

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Abstract

Extratropical cyclones (ETCs) produce most of the winter precipitation at midlatitudes and are often associated with the most extreme winter weather events. For climate models to accurately predict the occurrence and severity of these extreme events in a changing climate, they need to accurately represent moist processes in general and ice processes in particular. To provide an observational constraint for model evaluation, because cloud cover and precipitation are prevalent in warm-frontal regions, a compositing method is applied to ice retrievals from satellite observations to explore the ice distribution across warm fronts in both hemispheres. Ice water path (IWP) and its variability are compared between Northern Hemisphere (NH) and Southern Hemisphere (SH) warm fronts for different ETC-wide characteristics, as well as for different ETC origination regions. Results reveal that warm-frontal IWP and its variability tend to be higher in the NH than the SH, even when controlling for the ETC strength and environmental precipitable water (PW). IWP differences between NH and SH are found to be primarily related to where the cyclones originate. As the intertropical convergence zone is shifted north, ETCs that originate close to the northern tropics have more PW than those that originate close to the southern tropics. This, in turn, seems to lead to larger IWP in NH frontal clouds than in the SH frontal clouds at a later time. This highlights the importance, for ice amounts generated in warm-frontal regions, of the environmental conditions that an ETC encounters during its genesis phase.

Significance Statement

Extratropical cyclones (ETCs) are responsible for most of the winter precipitation in the midlatitudes and are often associated with severe winter weather events. In order for climate models to accurately predict these extreme events in a changing climate, they need to correctly represent moist processes, especially those involving ice. To evaluate and improve these models, we apply a compositing method to satellite observations of ice profiles in warm-frontal regions, which are known for having high cloud cover and precipitation. This helps us understand the distribution of ice across warm fronts in both the Northern Hemisphere (NH) and the Southern Hemisphere (SH). We compare the ice water path (IWP) and its variability between NH and SH warm fronts, considering different characteristics of ETCs and their formation regions. Our findings show that NH warm fronts generally contain more ice, and the amount varies a lot more across warm fronts than for SH warm fronts. This is true even when accounting for the strength of the cyclones and the moisture available to them. These differences in IWP between NH and SH are found to be primarily related to the locations where the cyclones originate. As the intertropical convergence zone (ITCZ) is shifted northward, ETCs originating closer to the northern tropics tend to have more moisture available to them than those originating closer to the southern tropics. This leads to greater ice amounts in NH frontal clouds compared to SH frontal clouds at a later time. These results emphasize the importance of understanding the origin of ETCs in order to accurately characterize ice processes in warm-frontal regions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hanii Takahashi, hanii.takahashi@jpl.nasa.gov

Abstract

Extratropical cyclones (ETCs) produce most of the winter precipitation at midlatitudes and are often associated with the most extreme winter weather events. For climate models to accurately predict the occurrence and severity of these extreme events in a changing climate, they need to accurately represent moist processes in general and ice processes in particular. To provide an observational constraint for model evaluation, because cloud cover and precipitation are prevalent in warm-frontal regions, a compositing method is applied to ice retrievals from satellite observations to explore the ice distribution across warm fronts in both hemispheres. Ice water path (IWP) and its variability are compared between Northern Hemisphere (NH) and Southern Hemisphere (SH) warm fronts for different ETC-wide characteristics, as well as for different ETC origination regions. Results reveal that warm-frontal IWP and its variability tend to be higher in the NH than the SH, even when controlling for the ETC strength and environmental precipitable water (PW). IWP differences between NH and SH are found to be primarily related to where the cyclones originate. As the intertropical convergence zone is shifted north, ETCs that originate close to the northern tropics have more PW than those that originate close to the southern tropics. This, in turn, seems to lead to larger IWP in NH frontal clouds than in the SH frontal clouds at a later time. This highlights the importance, for ice amounts generated in warm-frontal regions, of the environmental conditions that an ETC encounters during its genesis phase.

Significance Statement

Extratropical cyclones (ETCs) are responsible for most of the winter precipitation in the midlatitudes and are often associated with severe winter weather events. In order for climate models to accurately predict these extreme events in a changing climate, they need to correctly represent moist processes, especially those involving ice. To evaluate and improve these models, we apply a compositing method to satellite observations of ice profiles in warm-frontal regions, which are known for having high cloud cover and precipitation. This helps us understand the distribution of ice across warm fronts in both the Northern Hemisphere (NH) and the Southern Hemisphere (SH). We compare the ice water path (IWP) and its variability between NH and SH warm fronts, considering different characteristics of ETCs and their formation regions. Our findings show that NH warm fronts generally contain more ice, and the amount varies a lot more across warm fronts than for SH warm fronts. This is true even when accounting for the strength of the cyclones and the moisture available to them. These differences in IWP between NH and SH are found to be primarily related to the locations where the cyclones originate. As the intertropical convergence zone (ITCZ) is shifted northward, ETCs originating closer to the northern tropics tend to have more moisture available to them than those originating closer to the southern tropics. This leads to greater ice amounts in NH frontal clouds compared to SH frontal clouds at a later time. These results emphasize the importance of understanding the origin of ETCs in order to accurately characterize ice processes in warm-frontal regions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hanii Takahashi, hanii.takahashi@jpl.nasa.gov

1. Introduction

Extratropical cyclones (ETCs) and their associated fronts are dominant features of the midlatitudes: their passage is often associated with strong winds, large moisture gradients, and temperature changes. They are the main source of precipitation in the extratropics (Hawcroft et al. 2012; Catto et al. 2012), and play a central role in controlling the meridional transport of energy and moisture from the equator to the poles (e.g., Trenberth and Stepaniak 2003; Shaw et al. 2016). Therefore, they have a significant influence not only on local weather, but also on the global climate. To have confidence in projections of how these systems may change in a future climate, particularly the most extreme events, it is of great importance for climate models to realistically represent cloud and precipitation in these systems. However, the accurate representation of moist processes in general circulation models (GCMs) has been a long-standing challenge. Issues in this representation might explain why the latest generation of GCMs still show biases in their representation of cyclones, and more specifically high-intensity cyclones (Priestley et al. 2020). Possibly related to this, and of importance for projections of high-impact events, there are still discrepancies across model simulations on how atmospheric river precipitation might respond to climate change (Shields et al. 2023).

The bulk of cloud and precipitation in extratropical cyclones occurs in a comma shaped region, the head of which encompasses the center, where sea level pressure is minimum, extending to the east along the warm front and equatorward along the cold front. Precipitation and clouds in the warm-frontal region are formed through the ascent of a poleward flow of relatively warm and moist air, the warm conveyor belt (Browning and Harrold 1969; Carlson 1980; Wernli and Davies 1997; Eckhardt et al. 2004; Madonna et al. 2014; Pfahl et al. 2014). The warm conveyor belt airstream originates in the warm sector boundary layer, in advance of the cold front. As a result, the amount of precipitation in the cyclone is strongly dependent on the processes that occur in the warm conveyor belt, and more specifically ice processes. In turn, through the release of latent heat, these processes can impact the cyclone’s dynamics and intensity (e.g., Joos and Wernli 2012; Igel and van den Heever 2014; Binder et al. 2016, 2023). GCMs are still experiencing difficulties in the representation of ice and ice microphysics, and these deficiencies lead to large uncertainties in climate simulations (e.g., Morrison et al. 2015). The dynamic and thermodynamic features of ETC clouds and precipitation (Catto 2016) have been studied extensively. More recently, observations of cloud type and precipitation in warm conveyor belts were analyzed for five Northern Hemisphere winters and provided important information on the vertical structure of ice and liquid distributions (Binder et al. 2020). However, to our knowledge, observations of ice content in ETCs have not been systematically explored on a global scale. The fraction of liquid versus frozen cloud and precipitation, their influence on the radiation budget, and their relationships to the properties of the surface and environment, are still not well known, which makes it hard to clarify whether the representation of liquid and ice clouds in ETCs is well represented in global models (e.g., Waliser et al. 2009). Earlier work has used a reanalysis to better understand ice processes in warm conveyor belts (Wernli et al. 2016) but to fully overcome the challenge a detailed observation-based study of ice amounts in the frontal zones of ETCs is also required. Although both the cold front and warm sector are relevant for cloud processes, this study focuses specifically on warm fronts. This is because of their substantial impact at high latitudes, affecting precipitation over sea ice and land. Additionally, their connection with the warm conveyor belt makes them an ideal starting point for investigating poleward moisture transport. To this end, the unique perspective of cloud vertical profiles provided by the joint observations of the CloudSat radar (Stephens et al. 2008) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO; Winker et al. 2010) can help to illuminate the ice content in warm-frontal regions.

Ice processes in ETCs may differ between the Southern and Northern Hemisphere. For one, aerosol types and concentrations and their relationship with cloud microphysics are very different between the two hemispheres, which has led some to consider using the Southern Hemisphere (SH) as a proxy for preindustrial times (McCoy et al. 2020). Landmasses have large influence on the large-scale circulation (Brayshaw et al. 2009) in the Northern Hemisphere (NH), and result in hemispheric disparities in the storm track characteristics (Hoskins and Hodges 2002, 2005). The fundamental geographic differences in the two hemispheres may affect the characteristics of ETCs and their associated frontal clouds (Berry et al. 2011; Naud et al. 2012). Indeed, it is reported that the large discrepancies across the CMIP6 models are due to the differences in the representation of SH cloud radiative effect and feedback (Zelinka et al. 2020). These considerations motivate us to explore and contrast the frontal ice cloud contents in the two hemispheres.

A large number of satellite-based studies have been conducted to understand precipitation and cloud distribution in ETCs, but these have traditionally focused more on the NH (Lau and Crane 1995, 1997; Klein and Jakob 1999; Bauer and Del Genio 2006; Naud et al. 2006), with more recent studies featuring analysis of the SH (e.g., Bodas-Salcedo et al. 2012, 2014; Naud et al. 2014) or both hemispheres (Field and Wood 2007; Gordon and Norris 2010). Naud et al. (2012) have documented the dynamical and thermodynamical differences of ETCs between the NH and SH, and how the differences affect the characteristics of warm-frontal clouds in the context of the cyclones life cycle. They also have suggested that frontal cloudiness associated with ETCs may change more drastically over the SH than the NH in a warmer climate since cyclone cloud occurrence over the SH is more sensitive to humidity changes than that over the NH. However, their focus was on cloud fraction and precipitation in warm-frontal zones, and, to our knowledge, a detailed analysis of ice water contents across warm fronts in either hemisphere has not yet been performed.

Therefore, here a compositing method is applied to ice retrievals based on CloudSat and CALIPSO across all warm fronts where ice and precipitation dominate in ETCs. The goal is to explore 1) how ice is distributed across warm-frontal regions in both hemispheres, 2) how this differs in NH (polluted) and SH (pristine) environments, and 3) how ice water path (IWP) relates to the full history of the cyclones. The observational NH–SH ETCs differences in IWP can serve as a set of new model diagnostic tools when applied to model simulations for comparison with observations. Following Naud et al. (2012), we focus on the warm-frontal portion of the storm (excluding occluded cyclones). Also, to make the NH–SH IWP comparison consistent, we only consider oceanic ETCs. The rest of the paper is organized as follows. Section 2 describes the analysis methods and data used. Results and interpretations are presented in section 3. Section 4 summarizes and discusses our findings.

2. Analysis methods and data

In this section, we describe the cyclone and warm-front database, how the IWP information is obtained, and the method employed to combine warm fronts and CloudSat-CALIPSO observations and create composites.

a. Extratropical cyclones and warm fronts

To analyze ETCs and warm fronts, we use the Extratropical Cyclones and Fronts Database (ECFD; Naud et al. 2010, 2016). The database contains the location of ETC centers tracked with the NASA Modeling, Analysis, and Prediction Climatology of Midlatitude Storminess algorithm (MCMS; Bauer and Del Genio 2006; Bauer et al. 2016). The algorithm identifies and tracks cyclones using a series of thresholds applied to local minima in ERA-Interim (Dee et al. 2011) 6-hourly sea level pressure fields. For each 6-hourly cyclone occurrence identified with this algorithm, the ECFD also provides the mean precipitable water (PW) and mean 500-hPa ascending vertical velocity (ω) obtained from the second version of the Modern-Era Reanalysis for Research Applications (MERRA-2; Gelaro et al. 2017) in a circular area of 1500-km radius from the cyclone center. In addition, the ECFD includes the location of the attendant cold and warm fronts. More specifically for the analysis presented here, the warm-frontal boundaries are obtained using the Hewson (1998) temperature gradient method applied to potential temperature fields from MERRA-2, at 1 km above the surface. An overview and detailed description of the ECFD is provided in Naud et al. (2010, 2015, 2016).

b. Ice water path

CloudSat is a sun-synchronous, polar-orbiting satellite that has an approximately 0130 and 1330 equator crossing time, and has horizontal resolutions of 1.7 km along track and 1.3 km across track, and vertical resolution of 480 m (oversampled to 240 m). CloudSat carries a 94-GHz Cloud Profiling Radar (CPR: Stephens et al. 2008), which is sensitive to cloud- and precipitation-size particles with a minimum detectable signal of about −30 dBZ. The primary instrument carried on CALIPSO is the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), which is a nadir viewing two-wavelength (532 and 1064 nm) polarization-sensitive lidar that is most sensitive to optically thin clouds with optical thickness of 0.01 or less (Winker et al. 2010) and ice crystals. As CloudSat and CALIPSO are both part of the A-Train constellation (Stephens et al. 2002; L’Ecuyer and Jiang 2010), the two satellites fly in close formation. Since CloudSat is more sensitive to thicker clouds and CALIPSO is more sensitive to thin clouds, the two measurements complement each other in characterizing the vertical structure of clouds. The CloudSat 2C-ICE product (Deng et al. 2010, 2013) is derived from a synergetic ice cloud retrieval algorithm from a combination of CloudSat radar reflectivity together with CALIPSO lidar attenuated backscatter profiles. The 2C-ICE IWC profile retrievals are based on an optimal estimation algorithm that Incorporates microphysical scattering models of needles, plates, and cloud-ice aggregates smaller than 1 mm. When CALIPSO lidar measurements are available, typically near cloud top or through cirrus clouds, 2C-ICE can provide superior retrievals of water content compared to radar-only retrievals. These IWC profiles are used to calculate the ice water path (IWP). A comprehensive evaluation of the CloudSat 2C-ICE product with collocated aircraft in situ measurements and other cloud products is documented in Deng et al. (2013). This study reported that the uncertainty of 2C-ICE IWC is around 30%, and the CloudSat 2C-ICE and DARDAR retrieval products tend to have a good agreement. However, the 2C-ICE has slightly better agreement with in situ data because it incorporates parameterized radar signals for ice cloud volumes located below the CloudSat radar’s detection threshold, adding an extra constraint. As a result, this product has been used in multiple climate model evaluation studies (e.g., Li et al. 2012, 2023a,b).

c. Associating CloudSat profiles with ETCs and warm fronts

Based on four years (2007–10) of ECFD storms collocated with CloudSat datasets, a total of 1092 and 4005 oceanic ETCs (between 30° and 75° latitudes) are observed over the NH and the SH, respectively. The cyclones with a center over the ocean are selected for the analysis if CloudSat profiles are available within ±3 h of the cyclone identification and within 1000 km of any point along the warm front, as further explained below. For these 4 years, the cyclones with a near coincident CloudSat orbit sampling the warm-frontal region (Fig. 1a) populate the storm track regions of both hemisphere without any regional bias (cf. Hoskins and Hodges 2002, their Fig. 5; Hoskins and Hodges 2005, their Fig. 4) and similarly CloudSat profiles sample the region evenly (Fig. 1b). Additionally, the two hemispheres display similar latitude distributions of both cyclones and CloudSat profiles. Although not shown, we also found very similar distribution across seasons, with winter being the most populous season.

Fig. 1.
Fig. 1.

Number of occurrences of (a) ETC centers with a warm front and CloudSat overpass and (b) collocated near-coincident CloudSat profiles for the period 2007–10. Zonal means of fractional occurrences for (a) and (b) are presented next to each map.

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

Similar to Naud et al. (2010, 2012), CloudSat IWP values are composited along a line perpendicular to the warm front. For this, as illustrated in Fig. 2, we collect all CloudSat profiles with IWP > 0 gm−2 in a rectangular region bounded to the west and east by the lowest and largest longitudes of the warm front, respectively, and extending from south to north of the warm front by up to 1000 km both ways. Then, each selected CloudSat profile with IWP > 0 gm−2 is assigned its Euclidian distance to the warm front. For this, 1) we apply a linear regression to the warm-front longitude–latitude vectors, 2) find the perpendicular to the regressed line that crosses the location of CloudSat profiles, and 3) compute the distance along the perpendicular line between the CloudSat profile and the intersect at the regressed line. Once all CloudSat profiles are assigned a distance, we accumulate IWPs in a common reference grid with distance to the warm front on the horizonal axis, of 100-km horizonal resolution, and calculate median and standard deviation of IWPs per grid cell for NH and SH separately. These constitute the composites of IWPs that we analyze in the next section.

Fig. 2.
Fig. 2.

An example of a SH cyclone with center located at latitude −55.91° and longitude 31.91° (green dot) observed at 0000 UTC 12 Apr 2007 with a CloudSat intersect (solid line with colors changing from blue in the warm sector to red in the cold sector) of the warm front (pink dots) to illustrate the CloudSat and warm-front (WF) pairing method. The regressed line along the warm front is in solid black, as well as are the two perpendiculars to this regression line at each extremity of the warm front. CloudSat profiles within the region marked by the two perpendiculars are kept and colored here.

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

d. Cloud hydrometeor occurrence and environment

Because CloudSat reflectivity profiles are sensitive to both suspended and precipitating condensate, we use the term “hydrometeors” to refer to both cloud and precipitation in each profile. To find the location of hydrometeor layers in the warm-frontal regions, we collected near coincident 2B-GEOPROF-lidar profiles (Mace and Zhang 2014). This product provides the cloud base and top heights of up to five hydrometeor layers in the vertical. These layers are flagged as containing hydrometeors either according to the CALIPSO cloud mask product or based on the CloudSat derived cloud mask available in GEOPROF (Marchand et al. 2008), or both. Here it is transformed into a vertical hydrometeor mask of 250-m vertical resolution, where each 250-m layer is marked as either containing hydrometeors where it overlaps with one of the five GEOPROF-lidar hydrometeor layers, or clear sky where no hydrometeor is reported. The CloudSat data are supplemented with ECMWF profiles along each orbit and available in a product called ECMWF-AUX. These files contain temperature profiles that will be used in the analysis.

3. Results and interpretations

Before we discuss and compare IWPs in both hemispheres, we first present the differences in the ETC characteristics in the two hemispheres.

a. ETC characteristics and internal structure of warm-frontal clouds over the NH and SH

Consistent with earlier work by Naud et al. (2012), notable differences exist in the characteristics of ETCs between the NH and SH: precipitable water (PW) is on average higher in the NH cyclones (Fig. 3a), and there are more of the most vigorous cyclones, using the mean 500-hPa vertical velocity (ω500) to measure strength, in the NH than the SH oceans (Fig. 3b). ETCs with higher PW and ω500 tend to have more vertically and horizontally extensive frontal clouds (Field and Wood 2007; Naud et al. 2017, their Fig. 11), so we expect warm-frontal clouds in the NH cyclones to be more extensive than those in the SH. Therefore, we first investigate how hydrometeor frequency of occurrence and the internal structure of warm-frontal clouds generally differ between the NH and SH. The vertical profiles of hydrometeor locations are constructed as described in section 2d. Then we apply the same technique as detailed in section 2c to assign the distance to front to each vertical cloud mask profile, then accumulate all available profiles in a transect grid of 100-km horizontal resolution for the distance-to-front horizonal axis and 250-m resolution altitude y axis. The composites report the total number of cloud occurrences normalized by the total number of available CloudSat profiles.

Fig. 3.
Fig. 3.

Histograms of mean cyclone-wide (a) PW and (b) ascending ω500 in absolute value for cyclones with CloudSat observations in the vicinity of the warm front over the NH (solid black) and SH (dashed black).

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

Figures 4a and 4b show the resulting CloudSat–CALIPSO cloud frequency of occurrence for the NH and SH composited across warm fronts. We use the same convention for both hemispheres: along the x axis, negative values indicate the location of the warm sector and positive values indicate the cold/poleward region. The location of the warm front at 1 km above the surface is defined to be the origin (x = 0). Typically, the strong ascent of the (moist) warm conveyor belt at the location of the surface front causes clouds to form and vertically expand noticeably there, occur more often and up to higher altitudes relatively abruptly and expand into the poleward side of the warm front, consistent with the canonical model introduced by Bjerknes and Solberg (1922). This behavior is found for both hemispheres for the subset of cyclones considered here. Despite limitations in observing hydrometeors within the first 1 km above the surface due to surface reflectance issues (Marchand et al. 2008), both hemispheres display a maximum in frequency of hydrometeors at low levels (below ∼4 km) just poleward of the front (within ∼500 km). The vertical extent of the cloud deck at the front is larger for the NH than SH, as seen when considering the region of over 50% frequency of occurrence. Overall NH warm-frontal clouds tend to reach higher altitudes. These differences with clouds over the NH being higher, deeper, and more extensive than those over the SH, are likely due to higher PW and ω500 values in the NH compared to the SH. This might be better diagnosed by exploring further the internal structure of the cloud distributions using CloudSat CPR reflectivity (Ze), given they are directly related to particle size, although attenuation by heavy precipitation should be treated with caution.

Fig. 4.
Fig. 4.

Composite transects across warm fronts in (left) the NH and (right) the SH of (a),(b) CloudSat-CALIPSO hydrometeor frequency of occurrence; (c),(d) the median values of the CloudSat radar reflectivity; (e),(f) the median values of 2C-ICE IWC; and (g),(h) the median values of temperature across warm fronts, as a function of the distance between the CloudSat profile footprint and the warm front (x axis) and altitude (y axis). Solid contours highlight the 25%, 50%, and 75% frequency levels in (a) and (b), the −3-, 0-, and +3-dBZ levels in (c) and (d), the 5, 25, and 50 mg m−3 levels in (e) and (f), and the 273- and 248-K levels in (g) and (h).

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

Figures 4c and 4d show the distributions of the median values of CloudSat CPR reflectivities for the NH and SH. As mentioned in section 2b, CloudSat senses precipitating clouds with Ze > 0 and nonprecipitating Ze < 0, which includes small ice or drizzle-size particles. Thus, Ze tends to decrease as altitude increases since ice particles dominate at higher levels of clouds, and larger raindrops concentrate at lower levels of clouds. Overall, Ze is higher over the NH than SH, suggesting that clouds across NH warm fronts are more often precipitating compared to those in the SH. This is to be expected given the higher values of PW in NH ETCs.

Not surprisingly, the maximum in IWC is found poleward of the surface front location, within about 500 km, at an altitude of approximately 3–4 km in both hemispheres (Figs. 4e,f). This maximum coincides with an area of relatively larger reflectivities. However, the maximum is larger and slightly more poleward and upward in NH than SH. One might conclude that temperature profiles are systematically colder in the NH than the SH to explain the higher IWC. However, temperatures are actually warmer in the NH compared to the SH (Figs. 4g,h). Therefore, the higher IWC in the NH compared to the SH is not caused by temperature differences between the two hemispheres.

b. IWP distributions over the NH and SH

Given the differences between the NH and SH discussed above, the median IWP is overall higher in the NH than SH warm fronts (Fig. 5a). In both hemispheres, the distribution of IWP is closely related to the location of the front, although the peak values differ in their front-relative position. In the SH, the maximum in IWP occurs at the location of the front at the surface, while in the NH, the IWP maximum occurs 100 km poleward of the surface front. Recall that the warm front is identified at 1 km above the surface, therefore the mean location of the actual surface front will be slightly shifted into the warm side (x < 0) of the composites. The two hemispheres give similar IWP in the warm sector, where ice clouds tend to be generated by convection along or ahead of the cold front, or connected to the warm conveyor belt ascent (Wernli et al. 2016). The two distributions differ where clouds are typically found in warm-frontal regions, with IWP reaching close to 390 g m−2 at 100–200 km north of the warm front in the NH, when the SH peak reaches 260 g m−2 at the central point. Greater median values of IWP are found in the NH than SH in the entire cold sector cross section. To ensure that the differences are not influenced by sampling error, Fig. 5b also shows the frequency of occurrence of CloudSat profiles with IWP > 0 g m−2 as a function of their distance to the warm front. The NH distribution is more uniform than its SH counterpart.

Fig. 5.
Fig. 5.

Distributions of (a) IWP median values and (b) occurrence frequency of IWP > 0 g m−2 profiles over the NH (solid black) and the SH (dashed black) as a function of the distance between the CloudSat footprint and the warm front (WF). The thin vertical line indicates the location of the warm front at 1 km above the surface.

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

Since ETC characteristics have a significant influence on the IWP distribution (Field and Wood 2007), we next examine how the NH–SH differences in IWP change when analyzing warm fronts occurring in each hemisphere but with similar ETC characteristics. Would the NH–SH differences in IWP become negligible if the characteristics of ETCs are constrained to be similar in both hemispheres? To answer this question, we select ETCs that are characterized by similar distributions of PW and ω500 between the NH and SH (Fig. 6), and compare the NH–SH differences in IWP (Fig. 7). To ensure comparable distributions of PW and ω500 between the NH and the SH, we initially restricted the PW range to 0–23 mm and the ω500 range to 0–0.3 Pa s−1. Given the smaller sample size of ETCs in the NH compared to the SH, we implemented an anchoring approach. Basically, each NH ETC (759 cases) was paired with an SH ETC that shared similar PW and ω500 values. As demonstrated in Fig. 6, this approach successfully aligned the PW distributions of NH and SH cyclone subsets, while some slight differences persisted for ω500, such that the NH cyclones are still slightly more vigorous.

Fig. 6.
Fig. 6.

As in Fig. 3, but for a subset of ETCs with comparable distributions of (a) PW and (b) ω500 between the NH (solid black) and the SH (dashed black).

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

Fig. 7.
Fig. 7.

Distributions of (a) median IWP and (b) cloud depth (cloud-top height minus cloud-base height) for ETC subsets with comparable distribution of PW and ω500 between the NH (solid black) and the SH (dashed black) as represented in Fig. 6. The thin vertical line indicates the location of the warm front at 1 km above the surface.

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

Although the differences in IWP peaks between the NH and SH become smaller as the NH maximum decreases slightly from 390 to 360 g m−2 and the SH increases from 260 to 310 g m−2 (Fig. 5a vs Fig. 7a), the NH warm front still exhibits larger IWP in the region poleward of the surface front compared to SH IWP, with the largest differences observed in the 0- to +400-km area. It is worth noting that the shape of the NH–SH differences in IWP resembles the shape of the NH–SH differences in cloud depth (depth = cloud-top height − cloud-base height; Fig. 7b). However, the magnitude of the NH–SH differences in IWP is not comparable to the NH–SH differences in cloud depth, which is primary controlled by the cloud-base height. In fact, for the PW-strength constrained subsets of ETCs, the median cloud-top height is rather similar between the NH and SH (not shown). Differences in cloud-base heights could be related to differences in the slope of the warm fronts, implying weaker NH warm-front slopes. Given the relationship between slope and latent heating reported by Igel and van den Heever (2014), in turn weaker slopes in the NH imply greater latent heating in NH warm-frontal regions.

To establish whether differences in median IWP are caused by either an overall greater IWP in NH or instead a larger occurrence of warm fronts with anomalously high IWP, we investigate the variability of IWP across warm fronts in each hemisphere. The method is described as follows. For this test, we use for each hemisphere the subsets of cyclones selected such that distributions of PW and ascent strength are similar between the two hemispheres. Then in each hemisphere dataset separately, we do the following: first, we create two subgroups, each with 400 cases randomly drawn from the total number of cases. Second, for each subgroup, we composite IWP (as in Fig. 7). Third, the absolute IWP difference composite between the two subgroups is estimated. Finally, we repeat steps 1 to 3 for 100 times. We then create a new composite figure of IWP difference with distance to the warm front on the x axis and each of the 100 realizations along the y axis (Fig. 8). We follow the same steps for SH and compare the results with NH (Fig. 8b vs Fig. 8a). Each plot in Fig. 8a or 8b indicates that differences as large as 100 g m−2 (in absolute value) can occur at any location along the cross section given the relatively small sample size used for each individual composite (400 warm fronts). The second observation is that, along the y axis, there is also significant variation in the pair-to-pair IWP difference. However, IWP is noticeably more variable for NH (Fig. 8a) than SH cross sections (Fig. 8b), suggesting that in the NH there is more variability in IWP between warm fronts than in the SH. This leads to another question: in a database that is constrained to have a very similar distribution of water vapor and storm strength, what causes the larger IWP and IWP variability in the NH warm fronts? This could be related to the cyclones’ history and where the moisture is sourced. To help answer the question, we extend our investigation of IWP and its variability by analyzing the locations where ETCs originate.

Fig. 8.
Fig. 8.

Absolute difference in IWP composite transects between two 400 members randomly selected subset, i.e., the variability of IWP based on the bootstrapping method for 100 pairs, over the (a) SH and (b) NH as a function of the distance between CloudSat and the warm front (WF) when the ETC characteristics between the NH and SH are similar.

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

c. Origin of ETCs

So far, we considered each cyclone and attendant warm front as independent events, but the ECFD provides the full track history of each cyclone. Therefore, for each ETC snapshot in time and space for which there is a CloudSat view of the warm-frontal region, the ECFD indicates the location and time of identifications of the same cyclone in all previous 6-hourly time steps back to the time of first identification. While land-falling or land-originating storms are a rare occurrence in the Southern Hemisphere, a significant number of cyclones originate on or travel through land areas in the Northern Hemisphere. However, removing cyclones on or near land did not impact the differences in IWP or IWP variability. Forcing the storm genesis latitudes to be similar for both hemispheres, matching the overall storm life time (to avoid comparing short against long-lived systems that might not intensify or source moisture in the same way), equalizing the time from onset to time of observation (to avoid comparing mature against intensifying cyclones), or controlling for the age of the cyclone (in terms of time to peak intensity) had no impact either.

Since the ITCZ is shifted north, ETCs over the NH tend to have access to larger moisture content than their SH counterparts at the same absolute latitude (cf. also Naud et al. 2012), especially for air originating in the tropics. This means that ETCs that originate close to the northern tropics develop in regions with greater initial environmental PW than those originating close to the southern tropics. Figure 9 summarizes how the IWP variability changes when we eliminate the cases of ETCs that are either currently (i.e., at the time CloudSat observations are available; Fig. 9a) or originally (i.e., the observations occur later in the cyclone life; Fig. 9b) located close to the tropics (at latitudes equatorward of 40°), as well as the transect of the standard deviation divided by median IWP for the 100 random pairs (Fig. 9c) for Fig. 8 (blue), Fig. 9a (green), and Fig. 9b (red).

Fig. 9.
Fig. 9.

As in Fig. 8, but without ETCs that are (a) currently and (b) originally located at latitudes less than 40°N/S. (c) Transect of the standard deviation divided by median IWP for the 100 random pairs in (left) the NH and (right) the SH for Fig. 8 (blue), (a) (green), and (b) (red).

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

It is clear that removing either group slightly reduces the variability in the NH and brings the NH and SH variabilities closer to one another. However, it is interesting to point out that eliminating ETCs originating at low latitudes in the NH has a greater impact on reducing the variability in IWP compared to those currently located near the tropics. The fact that the standard deviation of IWP normalized by the median IWP is rather flat (Fig. 9c) for both hemispheres and all ETC subsets suggests that the variability is not caused by the method. Instead, it confirms that the variability is largely influenced by the larger IWP in the NH than SH, especially where it is maximum near the intersect with the surface front in Figs. 8 and 9. Finally, we examine the corresponding NH–SH differences in IWP when eliminating ETCs currently (Fig. 10a) or originally (Fig. 10b) located close to the tropics. Results clearly show that removing ETCs currently or originally within the 40° parallels bring the maximum IWP in each hemisphere close to one another, the IWP peaks become basically the same at ∼300 g m−2, but eliminating ETCs originating at low latitudes brings both peaks closer together at the warm-front intersect point (Fig. 10b). This underscores the importance of the origin of ETCs for the characteristics of warm-frontal clouds and their associated IWP at a later time. However, there are still differences in the shape of the NH and SH IWP distributions.

Fig. 10.
Fig. 10.

As in Fig. 7a, but without ETCs that are (a) currently and (b) originally located at latitudes less than 40°N/S.

Citation: Journal of Climate 37, 8; 10.1175/JCLI-D-23-0391.1

This study solely investigates the point of origin of ETCs, specifically focusing on the first time a detection of the ETCs is achieved, but does not examine the full historical trajectory of these storms. This approach leads to a noteworthy observation that although the NH and SH may initially have similar ETCs, their subsequent paths can potentially influence the IWP differently. Therefore, the remaining differences in IWP between the NH and SH regions could be attributed to the distinct historical paths taken by individual ETCs. We also recognize that the remaining differences in IWP could be at least in part explained by the effect of aerosols and latent heating, as discussed in the next section.

4. Limitations and uncertainties

Although there are some advantages of using CloudSat and CALIPSO to study IWP, there are inherent limitations and uncertainties in our results, which are worth discussing.

First, in the high latitudes of the Southern Ocean, the atmospheric freezing level typically resides at altitudes below 1 km since sea surface temperatures tend to remain below 6°C year-round (McFarquhar et al. 2021; Montoya Duque et al. 2022; Tansey et al. 2022). This lower freezing level over the SH than NH can itself result in IWP differences between the two hemispheres. This is because the CloudSat CPR may not be reliable below ∼750 m above the surface (Lamer et al. 2020), leading to potential low biases in IWP over the SH. Figures 4g, 4h, 4e, and 4f show that the maximum in IWC is well above the melting level. This suggests that even if in the SH surface clutter masks out ice presence close to the surface, and therefore causes an underestimate in IWP more so for the SH, Fig. 4 suggests the missing levels would have IWC < 6 g m−3, much less than in the area of maximum IWC (Figs. 4e,f), and not enough to fully explain the NH–SH differences of Fig. 7. We also apply additional analysis by excluding high-latitude cyclones from our database, repeating a similar procedure as in Fig. 10. The results indicate that removing ETCs currently and originally located at latitudes greater than 50°N/S had small impact compared to excluding those located at latitudes less than 40°N/S.

Second, supercooled water at cloud top has been shown to occur a lot more often over the SH than the NH (Hu et al. 2010), down to temperatures close to −30°C (Huang et al. 2015). While warm-frontal clouds tend to reach much higher levels and lower temperatures, it cannot be excluded that pockets of supercooled liquid might exist within the lower reaches of warm-frontal clouds in the SH. This would occur at altitudes where the lidar is fully attenuated because of either liquid presence or precipitating ice, a distinction that cannot be made with the radar reflectivities. Therefore, the residual difference in IWP between the two hemispheres might be related to more frequent embedded supercooled liquid in SH warm-frontal clouds.

Third, reflectivity in precipitating clouds can be subject to attenuation. As our results show, clouds in the NH warm fronts are deeper and more extensive compared to those in the SH. Therefore, one may infer that attenuation due to heavy rain is more prevalent in the NH than in the SH, and thus, the differences in the frequency of attenuated retrievals between the NH and the SH can introduce some biases in our results. To address this, we explored and compared rain occurrence, rain rate, and the occurrence of attenuation between the two hemispheres. No significant differences were found, suggesting that radar attenuation does not noticeably impact the hemispheric differences in the IWP, although we cannot rule out its impact on the residual differences in Fig. 10.

Finally, we acknowledge that the effect of aerosols is not explicitly examined in this study but remains important for cloud microphysics and processes. While our focus is on fronts occurring over the oceans, the atmosphere over the NH oceans contains a significantly higher concentration of aerosols, which also differ in type, from those in the SH (e.g., McCoy et al. 2020). These aerosols may play a crucial role in cloud formation and ice processes since the availability of greater aerosol concentrations can enhance the efficiency of ice cloud formation in warm fronts (e.g., Naud and Kahn 2015). In fact, Naud et al. (2017) found a tendency for cyclones in high aerosol optical depth environments to have greater cloud fraction on the poleward side of the warm fronts compared to low aerosol environments. Our results would suggest that, in addition, high aerosol environments (i.e., NH warm fronts) also have larger IWP than more pristine environment (i.e., SH warm fronts), all else being equal. This could be one possible explanation for the remaining IWP differences between the NH and SH after removing ETCs originating equatorward of 40°N/S. To verify this, a future study employing high resolution (at a minimum mesoscale-resolving) simulations is needed to evaluate the respective contributions of aerosol concentrations and ice cloud formation in warm fronts.

5. Summary

Using four years (2007–10) of storms in the Extratropical Cyclone and Fronts Database collocated with CloudSat datasets, this study constructs and examines a global 4-yr climatology of ice water content in warm-frontal clouds in both hemispheres. Both IWP distributions and its variability across warm-frontal regions are examined and compared between the Northern and Southern Hemispheres. Overall, the study highlights the importance of ETCs’ environment through their life cycle for ice water content in warm-frontal regions. The key findings are summarized below.

  • ETCs in the NH have higher PW and ω500 than those over the SH, resulting in more extensive and icy frontal clouds over the NH.

  • NH–SH differences in IWP become smaller when forcing ETC characteristics to be similar (comparable distribution of PW and ω500), yet the NH still has higher IWP values and variability than the SH in warm-frontal zones.

  • IWP values and variability between the NH and SH are relatively close to one another when eliminating ETCs originating at low latitudes, more so than when just eliminating those currently at low latitudes.

Our research highlights the importance of the environment in which ETCs form for ice production across the warm-frontal regions through the ETCs’ life time. Since the entire historical paths taken by individual ETCs have the potential to explain the NH–SH differences in IWP, it is of great importance to further investigate how climate models establish the relationship between PW at the point of origin and its subsequent impact on IWP, as well as the relationship between IWP and liquid water path. This will be the subject of future work.

As discussed in the previous section, the residual difference in IWP between the two hemispheres might be related to more frequent embedded supercooled liquid in SH frontal clouds compared to their NH counterparts. Nevertheless, it is worth noting that aerosol effect could be another major reason to explain the remaining IWP differences between the two hemispheres. It is also known that aerosol effects on cloud nucleation and growth are a dominant source of latent heating in frontal zones. Based on a high-resolution simulation, previous work has shown for a single warm front over the continental United States that differing aerosol concentrations affect the mechanisms for ice and liquid production but not the overall precipitation (Igel et al. 2013). Furthermore, latent heating primarily impacts the thermal structure of the front by diminishing its slope (Igel and van den Heever 2014), which would imply that condensation and cloud nucleation are more active in NH than SH warm fronts, according to our results. The interplay between aerosols, ice production, latent heating and hemispheric contrasts will be further explored with numerical simulations similar to Igel et al. (2013), that encompass diverse continental and maritime thermodynamic and aerosol conditions.

Acknowledgments.

The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). This study was supported by NASA MAP, Grant 80NSSC21K1728.

Data availability statement.

Detailed information about the ECFD, and access to the data, can be found at https://data.giss.nasa.gov/storms/obs-etc/. Also, the data of A-Train observations can be found from the CloudSat Data Processing Center at www.cloudsat.cira.colostate.edu. More specifically, detailed information on 2B-GEOPROF and 2C-ICE data can be found from the CloudSat Data Processing Center at https://www.cloudsat.cira.colostate.edu/data-products/2b-geoprof and https://www.cloudsat.cira.colostate.edu/data-products/2c-ice, respectively.

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