1. Introduction
Extratropical cyclones are the main providers of precipitation in the midlatitudes (e.g., Catto et al. 2012; Hawcroft et al. 2012) and also the cause of high impact events such as windstorms, blizzards, or extreme precipitation (e.g., Kunkel et al. 2012). Ocean surface latent heat flux (LHF) and sensible heat flux (SHF) have been hypothesized to play a role in rapid cyclone strengthening that can produce these extreme weather events (e.g., Hirata et al. 2019). Moreover, the role of the surface heat fluxes on cyclogenesis (e.g., Mak 1998), the maintenance of the storm tracks (e.g., Hotta and Nakamura 2011), or the moisture supply to the precipitation at the warm front (e.g., Booth et al. 2012) all point to the importance of exploring surface heat fluxes in cyclones and understand the energy flux exchange between the ocean and atmosphere. To achieve this, an examination of a climatology of surface heat fluxes in extratropical cyclones through their evolution and as a function of their main characteristics is needed. Previous work has provided such insights using a reanalysis (Rudeva and Gulev 2011) and focused on winter cyclones that originate in the Gulf Stream region. They found significant variations in surface heat fluxes within the cyclone area as a function of cyclone intensity and through the life time of the cyclones. Similar work should be repeated to establish that these results are similar in other regions, and whether other cyclone characteristics matter for surface heat fluxes. Therefore, there is a strong motivation to use observed surface heat fluxes to explore the relationships between the fluxes and the properties of extratropical cyclones over multiple years and all ocean basins.
Launched in 2016, the Cyclone Global Navigation Satellite System (CYGNSS) mission is a constellation of eight spacecraft with a low orbital inclination that targets near-surface wind measurements in the latitude band between ∼38° north and south (Ruf et al. 2019). Making use of the wind retrievals, surface latent and sensible heat fluxes are also produced (Crespo et al. 2019). While the primary objective is to characterize wind fields and surface heat fluxes in hurricanes and other tropical systems, measurements can also be used to study oceanic extratropical cyclones, at least those confined to the subtropical regions or in their early development stages (Crespo et al. 2017, 2021). In particular, given that cyclones often originate at low latitudes, an examination of surface heat fluxes in cyclones developing in the low latitude oceans can be performed.
Therefore, and also because of their important role for extratropical cyclone formation and intensification, we examine here CYGNSS surface heat fluxes in low-latitude oceanic extratropical cyclones, as a function of cyclone characteristics and age. While for such work it would be simpler to use a gridded reanalysis product, because of the sparsity of assimilated observations in the subtropical oceans, their accuracy can suffer in these regions (e.g., Peng et al. 2013). Despite their irregular sampling, CYGNSS winds agree well within 2 m s−1 with buoys in all weather conditions, and in contrast with other satellite based wind products CYGNSS observations are not impended by heavy precipitation (Asharaf et al. 2021). As shown in Crespo et al. (2021), CYGNSS observations offer frequent revisit times of the cyclones, thereby providing information in all conditions that occur within an extratropical cyclone during its early stages of evolution.
Using cyclone- and frontal-centered composites of CYGNSS surface latent and sensible heat fluxes, this study details how surface heat fluxes are distributed in low-latitude extratropical cyclones, including both hemispheres and all ocean basins within ±40° latitude. Then, using information on the cyclone tracks and age and exploiting CYGNSS’ ability to observe the same cyclone multiple times through its life cycle, we catalogue the changes in the surface heat fluxes as cyclones evolve from their early developing to intensifying to dissipating stage. Given that cyclones’ strength and their environmental moisture amount change through their development as they travel poleward, which both impact cloud and precipitation and ultimately air–sea energy exchanges, we also examine how surface heat fluxes change with both cyclone strength and mean precipitable water.
2. Datasets and method
Before we present the analysis, we describe here the CYGNSS dataset, the extratropical cyclone database, and the method we use to match the two. Then we discuss how we define the cyclones’ development phases.
a. CYGNSS ocean surface heat fluxes
The CYGNSS mission’s eight spacecrafts consist of an onboard radar receiver that measures global positioning system (GPS) signals scattered from the ocean and land surface in the specular direction (Ruf et al. 2019). The strength of the scattered GPS signal over water is a function of surface roughness such that near-surface wind speeds can be retrieved (Clarizia and Ruf 2016). Since rainfall does not appreciably attenuate the 19-cm wavelength (1575 MHz) GPS signals, the overwater wind retrievals can be performed in any weather condition, and as such offer an advantage relative to microwave radiometers, which are affected by heavy rainfall (e.g., Weissman et al. 2012). Furthermore, the multiple spacecraft and orbits allow for a frequent revisit time, higher than other polar orbiters. While the mission was intended to provide wind information in tropical cyclones, because its orbit and field of view allow consistent measurements to be obtained up to ±38° latitude (with intermittent observations up to ∼±40°), CYGNSS wind retrievals are also available for extratropical cyclones that occupy the lower latitudes of the extratropics (Crespo et al. 2017, 2021). About one-third of all extratropical cyclones that travel within ±40° latitude have CYGNSS observations (specular points) within 1500 km of their center (Crespo et al. 2021).
Retrievals of ocean surface latent and sensible heat fluxes are performed using combined information from the CYGNSS winds and estimates of surface and near-surface temperature and specific humidity from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017). The fluxes are calculated using the bulk formula with version 3.5 of the Coupled Ocean–Atmosphere Response Experiment (COARE 3.5) algorithm (Edson et al. 2013). Here we use wind and flux retrievals of the Climate Data Record (CDR), version 1.0, dataset that was released in May 2020. A complete description of the retrieval process along with performance assessment are available in Crespo et al. (2019). They found some disagreements with buoy surface heat flux estimates, and more specifically in high wind conditions for which CYGNSS fluxes might be underestimated. However, their work also indicates that uncertainties in both sensible and latent heat CYGNSS fluxes were more dependent on uncertainties in MERRA-2 derived temperature and humidity products than the actual wind measurements. This issue with the reanalysis somewhat adds motivation to use observed winds rather than winds provided by a reanalysis in the data-sparse subtropical ocean regions.
b. Extratropical cyclones and fronts identification
Many techniques have been proposed to identify and track extratropical cyclones (e.g., Neu et al. 2013 and references therein), here we use a method developed by Bauer and Del Genio (2006) that utilizes local minima in sea level pressure. The algorithm, further revised in Bauer et al. (2016), has been applied to ERA-Interim gridded sea level pressures, and more recently to ERA5, and provides a database of 6-hourly cyclone locations (cyclone “occurrences”) as well as the full track these occurrences belong to (time series). Availability of storm-track information allows the analysis of storm evolution and the identification of storm life cycle phase. Hereinafter, the full temporal history of each system is referred to as a “storm” or a “track,” but the individual 6-hourly systems that constitute each track will be referred to as “cyclones” or “snapshots.”
To supplement information on cyclone centers, we applied published methods of detection for cold and warm fronts (Naud et al. 2010, 2016). Warm fronts are located using the temperature gradient method of Hewson (1998) applied to MERRA-2 potential temperature at 1 km above the surface. For cold fronts, the same method is used, but, because it sometimes cannot provide a reliable detection in conditions of low baroclinicity (Schemm et al. 2015), we also use the Simmonds et al. (2012) method, which identifies cold-frontal zones by examining a change in wind direction and strength in time, at the surface and at 850 hPa. We also use MERRA-2 to obtain the wind information. We developed a method that combines Hewson’s and Simmonds et al.’s detections to optimize cold-frontal identifications (Naud et al. 2016). The fronts identification algorithm also includes an attribution component where the warm and cold fronts are paired with a 6-hourly cyclone center.
Therefore, we use a database of 6-hourly extratropical cyclone locations that also includes, in addition to the location of the cyclone center, the location of the cold and warm fronts. Furthermore, for each of the 6-hourly cyclone center and front identifications, we have information on the actual trajectory of the cyclone over time, from first to last identification. The locations of the cyclones and their associated fronts are available for public access in a database that covers 2006 to the present day.
c. Matching cyclones and CYGNSS observations
Here we use the identifications of cyclones and associated fronts from March 2017 (the start of the CYGNSS science mission) to December 2020 (see the data availability statement section for a description of access to the database). As hinted above, of all of the 6-hourly extratropical cyclones identified in 2017–20 between about 20°–70°N/S, we only consider those that have CYGNSS observations within 1500 km of the cyclone center and in a ±3-h time window. This CYGNSS subset includes about ⅓ of all cyclones found within ±40° latitude (Crespo et al. 2021). For the entire 2017–20 period, this translates into a total of 22 746 six-hourly cyclones (and associated cold and warm front) with CYGNSS observations (referred to as “CYGNSS cyclones”) that contribute to a total of 4434 individual storm tracks.
d. Definition of development phase
As mentioned above, the cyclones are tracked in time and the tracks are used to determine each 6-hourly cyclone’s age. Recall that here a cyclone is an individual 6-hourly identification, while a track is the full history of a collection of related cyclones. Each of these tracks is constituted by a first detection of a minimum in sea level pressure that obeys a number of conditions to be considered a candidate extratropical cyclone as detailed in Bauer et al. (2016). As the storm evolves in time, it eventually reaches a maximum in intensity before decaying until it is no longer detected. For each 6-hourly cyclone observed with CYGNSS, we pull out its history and examine all of the other snapshots that belong to the same track such that we can determine its age.
First, we calculate the intensity of each of the individual cyclone snapshots belonging to the same track. For this, we use two separate measures: 1) the minimum in sea level pressure at the storm center and 2) the cyclone “depth,” which is the difference in sea level pressure between the center and the outermost closed sea level pressure contour. Then, we identify the time at which the storm reaches peak intensity along its track. It is the 6-hourly cyclone that either has the lowest SLP at the center or is the deepest along the full track. Whichever condition happens first determines the time of maximum intensity along the track. The reason for using both measures is because 1) the pressure at the storm center is dependent on the overall regional conditions, some storms see a decline in pressure caused by their moving into an area of relatively low pressure conditions, not because of a change in their intensity; 2) the cyclone depth calculation becomes complicated in situations where the outermost pressure contour is ill defined, which often occurs in the last stages of the storm life, for example near coastlines or if another system is in close proximity. This causes artificial jumps in cyclone depth that are not related to cyclone intensity. Use of both measures helps to alleviate these issues.
Once the 6-hourly cyclone at peak intensity is identified along the full track, the age of each of the remaining 6-hourly cyclones that constitute the track is defined by the number of 6-hourly increments with respect to the time of peak intensity: a developing cyclone’s age will be a negative number while a decaying cyclone’s age will be a positive number. Examining the subset of 6-hourly cyclones with CYGNSS measurements, we find that ⅔ of these cyclones are prepeak (i.e., they precede the time of peak intensity along their track) and a third in postpeak (i.e., they occur after the time of peak intensity). We arbitrarily define three main evolution phases by sorting the CYGNSS cyclones’ age in increasing order, then partitioning the population into three subsets, each with an equal number of cases. The first half of the prepeak cyclones (⅓ of total) occur at least 36 h prior to the peak intensity identification, this is defined as the “early developing” or “developing” phase. The other half is considered to be “intensifying” or “late developing” phase and includes the ages from −36 h to peak intensity (0 h). The postpeak remainder constitutes the “dissipating” phase.
One characteristic of the cyclone database is that it includes extratropical cyclones that are the result of a tropical cyclone transition. This is problematic when examining the early developing phase, because some storm tracks may include tropical systems that range from a tropical depression to a major hurricane and that are not baroclinic systems. To confine our analysis to extratropical cyclones, we remove tracks that at some point follow a westward trajectory over at least 24 h within ±30° latitude (even if they eventually undergo an extratropical transition). With these systems removed (70 tracks in all), of the original 4434 storm tracks that at some point in time were observed with CYGNSS, 500 distinct tracks were repeatedly observed by CYGNSS during each of the three main development phases (i.e., were observed at least three times). These tracks tend to populate the typical storm-track regions of both hemispheres (cf. Hoskins and Hodges 2002, 2005), as shown in Fig. 1. Note that Fig. 1 shows the entirety of the storm tracks, including time steps that are not observed with CYGNSS (black lines), and also marks cyclone centers that are outside CYGNSS observational latitude range as long as at least one specular point is available within up to 1500 km of the cyclone center.
Map of extratropical cyclone tracks (solid lines) that are observed during developing, intensifying, and dissipating phases with CYGNSS. The 6-hourly storm centers that actually have observations within 1500 km and 3 h are marked with red plus signs. The blue lines indicate the 38°N/S parallels that mark the CYGNSS polarmost latitudes for data availability. Extratropical cyclones that have undergone a tropical–extratropical transition are not included. The entirety of the storm tracks is shown, even when they have exited the regions observable with CYGNSS, and some cyclone centers are also found outside this region when CYGNSS observations are found within 1500 km equatorward of the cyclone center.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
3. Composites of CYGNSS surface heat fluxes
Before we explore how the fluxes change with cyclones evolution, we first examine the spatial distribution of the surface heat fluxes in the cyclones, starting with the full database of CYGNSS cyclones and then for subsets defined by the cyclones’ characteristics.
a. Composites of all observed cyclones
Because of the nonuniform and partial CYGNSS sampling of the cyclone area, we use a compositing approach to fill in the gaps and therefore average together multiple cyclone regions to obtain an overall mean picture of the surface heat fluxes in cyclones. As detailed in Crespo et al. (2021), to build these composites, we construct a polar grid of 14° angular and 100-km radial increments around and from the storm center respectively [see also Naud et al. (2012) for more details and other fields]. The CYGNSS specular points are then assigned to one of the 14°/100-km grid cells based on their position with respect to the cyclone center. Cyclone-centered composites are obtained by superimposing all 6-hourly cyclones using the center as the anchor. To facilitate analysis of storms in both hemispheres, Southern Ocean cyclones are flipped along the north–south direction to position the polar side of the cyclone at the top of the figures in a Northern Hemisphere–centric viewpoint.
As shown in Crespo et al. (2021) for a shorter time period, and here in Fig. 2, CYGNSS sensible and latent heat fluxes in extratropical cyclones show a clear contrast between the region to the west of the storm center where fluxes are relatively the largest, and the region to the east where fluxes are relatively the weakest (Figs. 2a,d). This spatial distribution is consistent with similar composites of winter North Atlantic cyclones that originated in the Gulf Stream region (Rudeva and Gulev 2011). Our results confirm that the overall spatial distribution of the surface heat fluxes in cyclones does not depend on the region where the cyclones originate or travel. The winds in contrast are relatively isotropic in this reference grid, albeit suggest a maximum to the west and poleward of the storms center. To get a more detailed view of the spatial distribution of fluxes and winds, we also construct cold- and warm-front-centered composites.
(left) Cyclone-centered, (center) cold-front-centered, and (right) warm-front-centered composites of CYGNSS (a)–(c) SHF, (d)–(f) LHF, and (g)–(i) wind speed for all cyclones observed with CYGNSS in 2017–20.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
Cold-front-centered composites are obtained by using a linear regression of the cold-front general direction and by rotating (to align the cold fronts along the vertical) and translating (to ensure the fronts superimpose with their poleward most coordinates aligned) the CYGNSS information in each cyclone polar grid before performing the averaging (see Naud et al. 2016 for more details). To align the cold fronts, the cyclone centers are no longer superimposed, and the upper half of the composites averages together sectors of the cyclones that have little in common because cold fronts tend to move about the storm center as the cyclones age (e.g., Neiman and Shapiro 1993; Martin 1998). Therefore, we only show the composites from the point of the cold front closest to the storm center’s latitude equatorward. These composites clearly show the sharp contrast in sensible (Fig. 2b) and latent (Fig. 2e) heat fluxes across the cold-frontal region, with a maximum in the post-cold-frontal region (west of the cold fronts), a strong gradient at the cold front and a minimum close to the front in the warm sector (east of the cold fronts). The winds tend to be stronger close to the polar side of the cold fronts (i.e., near either the storm center or the peak of the warm sector, defined as the intersect of cold and warm fronts), with a slight shift of the maximum toward the post-cold-frontal region.
Warm-front-centered composites are easier to construct given the tendency for warm fronts to be aligned with the storm center, in this case a simple rotation of the polar grid for each cyclone is performed such that the warm fronts are superimposed along the horizontal axis eastward from the storm center, before performing the cyclone-centered compositing (see Naud et al. 2012 for more details). While the sensible heat flux warm-front-centered composite (Fig. 2c) resembles greatly the cyclone-centered composites (Fig. 2a), thereby suggesting that not much more information can be gained from this transformation, the latent heat flux warm-front-centered composite (Fig. 2f) reveals that the minimum in latent heat flux aligns very well with the warm-front locations, indicating that the moisture contribution of the ocean to the atmosphere is at a minimum in this area where precipitation is maximum. As for the warm-front-centered composite of the winds (Fig. 2i), they tend to be maximum in a c-shaped area around the center that resembles the early composite of Field and Wood (2007; their Fig. 3). Despite the added value of using different anchors to examine the flux and wind distributions, we are still averaging together systems that have little in common, so next we explore the fluxes in cyclones using conditional subsetting.
Cyclone-centered composites of the number of CYGNSS specular points for 2017–20, as a function of cyclone mean PW (increasing from top to bottom), and mean ascent strength (increasing from left to right), categories based on 2006–16 full cyclone database (Naud et al. 2017). The numbers at the top of each panel refer to the number of cyclones with CYGNSS observations that fall in each PW/ascent category.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
b. Conditional subsetting
While compositing is a very useful technique to compensate for nonuniform sampling and to provide an overall picture with the most salient features highlighted, it involves mixing together systems that are inherently very different. In fact, a composite does not necessarily resemble any of the individual cases that are included in its construction. To some extent, this issue can be alleviated by sorting the candidate cyclones into well-defined categories. Here we use a classification inspired by the work of Field and Wood (2007). The cyclones are classified based on the mean amount of precipitable water (PW) available in their environment, and on their ascent strength. These two cyclone characteristics play a major role in the production of clouds and precipitation in the cyclones and together they help characterize the strength of the moisture flux in the warm conveyor belt (e.g., Eckhardt et al. 2004). The ascent strength is chosen for this reason as a measure of the cyclone’s strength, instead of more common measures such as surface wind speed, sea level pressure minimum, or cyclone depth (depression between the center and the outer pressure contours). Another motivation for using these two quantities for the cyclone snapshots classification is that it does not directly relate to the surface conditions and the heat fluxes.
To classify the cyclones, we calculate for each 6-hourly cyclone snapshot the mean MERRA-2 PW within 1500 km of the storm center; for the strength we use the mean MERRA-2 500-hPa vertical velocity in the same 1500-km radius but only for those grid cells for which there is ascending motion (i.e., 500-hPa vertical velocities are negative in pressure coordinates). The MERRA-2 data are from GMAO (2015). Note that here the spatial scale of the vertical velocities is much larger than the typical scale of convection, and as such characterizes the cyclone’s dynamics overall. The choice of 1500 km is arbitrarily chosen as a compromise between the inclusion of an area large enough to fully characterize the cyclone environment (including the sometimes very long cold-frontal boundary), while excluding as much as possible neighboring systems, both cyclones and anticyclones. We use the PW/strength categories defined in Naud et al. (2017), which were obtained by dividing the entire 6-hourly cyclone population for 2006–16 into three equal number of cases for PW and similarly for strength. The categories thus obtained for PW are as follows: PW < 11 mm for dry cyclones, PW between 11 and 19 mm for medium PW cyclones, and PW > 19 mm for wet cyclones; for strength, we use a mean vertical velocity greater than −4.7 hPa h−1 for weak cyclones, between −6.5 and −4.7 hPa h−1 for medium strength cyclones, and less than −6.5 hPa h−1 for strong cyclones. Because of CYGNSS’ restricted latitude band, very few of the low PW 6-hourly cyclones have CYGNSS observations within 1500 km as they predominantly occupy the high latitude regions; even for the medium PW categories, the polar side of the cyclones exhibits far fewer specular points than the equator side (Fig. 3). Consequently, for the remainder of the analysis, we will only consider the medium and large PW cyclone categories and utilize the cold front-centered composites that focus on a region of the cyclones much less affected by the sparsity of specular points. For these categories, we next explore how latent and sensible heat fluxes change with cyclone characteristics.
As demonstrated in Fig. 4, the change in sensible heat fluxes with ascent strength is an increase in the region of maximum flux (post-cold-frontal region) and a decrease in the region of minimum flux (warm sector). This is observed for both categories of mean PW. This causes an increase in the gradient of sensible heat flux at the cold front. For each ascent strength category, the change in flux with an increase in PW is a decrease in the entire cold-frontal region, with a tendency for sensible heat fluxes to become negative in the warm sector for an increase in PW for the medium and strong cyclones categories. We note that, while the decrease in the warm sector as a function of increasing cyclone strength is equivalent in each PW category, the associated increase in the post-cold-frontal region tends to be much stronger for the medium PW category (∼+45 W m−2 for the maximum between weak and strong ascent) than the large PW category (∼+30 W m−2). This suggests that air–sea heat exchange is somewhat more sensitive to changes in dynamics in dry than moist regions.
Cold-front-centered composites of CYGNSS SHF as a function of cyclone mean PW (increasing from top to bottom) for medium and large PW categories only, and of cyclone ascent strength (increasing from left to right) for 2017–20. The numbers at the top of each panel refer to the number of cyclones with CYGNSS observations that fall in each PW/ascent category from Fig. 3.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
Qualitatively, we observe similar changes in latent heat flux as a function of mean PW and ascent strength (Fig. 5). An overall decrease in latent heat fluxes is observed as PW increases, along with an increase in the post-cold-frontal region (∼+40 W m−2 for moderate PW) and a slight decrease in the warm sector (less than 20 W m−2 for both PW categories) as ascent strength increases. Overall, not surprisingly, the increase in ascent strength causes an increase in fluxes where the winds are maximum (Fig. 2h).
As in Fig. 4, but for LHF.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
4. Changes in surface heat fluxes as extratropical cyclones evolve
As defined earlier, we consider three main phases for the cyclone development: early development, intensification, and dissipation phases. Among all cyclone occurrences and tracks, we first consider the 500-tracks subset for compositing by development phase, so we can ensure that the same storm tracks populate each phase.
a. Mean fluxes per development phase group
In the post-cold-frontal region of the cyclones, both sensible and latent heat fluxes increase between the early developing and the intensifying phases by +4 and +20 W m−2 for the maximum in flux, respectively, but decrease from intensifying to dissipating by −8 and −30 W m−2, respectively (Fig. 6). However, in the warm sector, the relatively weaker sensible and latent heat fluxes continually decrease from the developing through to dissipating stages. This evolution is consistent with previous analysis by Rudeva and Gulev (2011), although they had used a different method to identify the cyclone’s age that did not take into account the time of peak intensity. As a result, the evolution of the sensible heat fluxes differs here from these previous results: the decrease in the warm sector continues into dissipation, while they had found an increase during the second half of the cyclones life. To better understand what influences the surface heat flux changes, we also explore changes in wind speed, temperature contrast and humidity contrast (Fig. 7).
Cold-front-centered composites of (a)–(c) SHF and (d)–(f) LHF as a function of cyclone development phase, from (left) early developing, through (center) intensifying, to (right) dissipating for the 500 tracks that are observed by CYGNSS during each phase.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
Cold-front-centered composites of (a)–(c) wind speed, (d)–(f) surface temperature contrast, and (g)–(i) surface humidity contrast as a function of cyclone development phase, from (left) developing, through (center) intensifying, to (right) dissipating for the 500 tracks that are observed by CYGNSS during each phase.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
In the post-cold-frontal region, the wind speed increases between developing and intensifying phase, then decreases during dissipation. While qualitatively similar changes are seen in the temperature contrast, the humidity contrast shows a very minor increase during intensification. The increase in sensible heat flux is clearly impacted by both changes in wind and temperature contrast, but the wind increase dominates the latent heat flux magnitude increase, with the location of the maximum modulated by the changes in humidity contrast. In the warm sector, both temperature and humidity contrasts decrease from one phase to the next, while the winds peak during intensification. This suggests a reduced influence of the winds on the flux changes in the warm sector.
A more detailed perspective is gained by examining differences in composites (Fig. 8). Between developing and intensifying phases, while there is a clear contrast in the sign of the sensible heat flux change between west and east of the cold fronts, the latent heat fluxes display a clear increase at and slightly in advance of the cold fronts. Meanwhile, between dissipating and intensifying phases, most of the cold-frontal region shows a decrease in both sensible and latent heat fluxes, while there is an increase in both fluxes in a 500-km-wide band to the east of the cold fronts. Because the cold-front identification is typically done at about 1 km above the surface, it is possible that this band corresponds to the actual location of the cold front at the surface (due to the sloping of the front, at 1 km the cold-front surface would be to the west of the surface front). Because of the variability across all cases that populate these composites, we cannot exclude that sometimes this band might also be influenced by the warm conveyor belt (e.g., Madonna et al. 2014) and/or atmospheric rivers (e.g., Zhang et al. 2019) that typically run along and to the east of the cold fronts.
Difference in cold-front-centered composites of CYGNSS (a),(b) SHF and (c),(d) LHF (left) between intensifying and developing phases and (right) between dissipating and intensifying phases of cyclone evolution. The vertical dashed lines mark the location of the cold fronts.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
To get a better understanding of which component of the fluxes causes the changes, we also examine changes in CYGNSS winds and MERRA-2 sea–air temperature and humidity contrasts as a function of cyclone development phase (Fig. 9). In the post-cold-frontal region, there seems to be a strong connection between changes in fluxes and changes in surface winds (consistent with Alexander and Scott 1997): the winds increase in strength between developing and intensifying phases, with the greatest change occurring along the cold front, and then decrease into the dissipating phase, with the greatest change occurring closer to the peak of the warm sector (i.e., intersect between cold and warm front). This increase in the wind speed tends to dominate the changes in latent heat fluxes, at least for the evolution from developing to intensifying phases, which shows little difference in surface humidity contrast. Sensible heat flux changes seem to be related to changes in both sea–air temperature contrast and winds.
Difference in cold-front-centered composites of (a),(b) CYGNSS wind and MERRA-2 sea–air (c),(d) temperature and (e),(f) humidity contrast (left) between intensifying and developing phases and (right) between dissipating and intensifying phases of cyclone evolution. The vertical dashed lines mark the location of the cold fronts.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
In the warm sector, winds appear to have a lesser impact than both temperature and humidity contrasts, for both transitions from developing to intensifying and then to dissipating. More specifically between intensification and dissipation, the increase in sensible and latent heat fluxes near the warm conveyor belt region is explained by the change in sea–air temperature and humidity contrasts, as both increase in this area. Since both sea surface temperature (SST) and air temperature decrease in the warm sector as cyclones reach the dissipating stage, a positive difference in temperature contrast (defined as SST minus air temperature) indicates that the air temperature experiences a decrease of larger magnitude than the decrease in SST. The same can be said of the changes in surface and air specific humidity changes, they both diminish but at different rates. Therefore, it seems that while the cyclones are dissipating, the region along and east of the cold front tends to experience a more efficient decrease in air temperature than the accompanying decrease in SST, thereby causing a loss of energy from the ocean into the atmosphere through the dissipating phase even though the strength of the cyclones is decaying and the associated contribution from latent heat release in the atmosphere presumably as well. This might be because of an eastward shift in the mean of the region of maximum poleward moisture transport as cyclones decay.
These changes in fluxes throughout the cyclone evolution are, however, strongly dependent on both changes in the cyclone strength, as well as in the environmental PW, as cyclones tend to travel poleward as they evolve. Indeed, cyclone strength peaks during the intensifying phase (Fig. 10b), while the mean cyclone PW decreases from developing to intensifying (Fig. 10a). So next we explore the change in fluxes through the cyclone development when constraining both changes in strength and PW.
Histograms of (a) mean PW and (b) mean ascent strength per cyclone in the developing (dashed), intensifying (solid), and dissipating (dot–dashed) phases for the 500 tracks that are observed in each phase by CYGNSS. The red vertical dotted lines indicate the thresholds used to define the PW/ascent strength categories (11 and 19 mm for PW; −6.5 and −4.7 hPa h−1 for ascent strength).
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
b. Flux changes per age for fixed PW-ascent strength
Here, we are using the same PW/strength categories as defined for Figs. 4 and 5, but to ensure that sample size does not interfere with the analysis, we revert to the full database of CYGNSS cyclones and consider each 6-hourly occurrence individually. In effect, we are removing the constraint that the same storm be observed by CYGNSS through all three phases. We average together fluxes for cyclones that are in the same ascent strength category, the same PW category and the same development phase category. Then for each ascent strength/PW category we contrast the mean flux composites between different development phase. By averaging together 6-hourly cyclones in a given development phase that are found in a fixed PW/strength category (i.e., adding a third dimension to our cyclone database partitioning), we are comparing cyclones in different development phase but that share similar ascent strength and mean PW.
Since each cyclone phase has a propensity to fall preferably in a given PW/strength category (e.g., the intensifying cases have a propensity to fall in the strong cyclones category), we obtain sample sizes per phase that can be rather different (Table 1). To minimize the sample size impact, we composite latent and sensible heat fluxes by randomly selecting cyclones (regardless of age or PW/strength category) for a hierarchy of number of cyclone groupings: 200, 400, 600, 800, 1000, 1200, 1400, and 1600. We repeat the random selection per number of cyclones category 100 times and then calculate the standard deviation across the 100 composites for each number of cyclone category. Then, when we compare composites between development phases, we only consider differences in fluxes that are greater than the standard deviation obtained above for that location in the cyclone-centered grid, using the number of cyclone category that most closely matches the smallest number of cyclones between the two phases that are compared. The number of cyclones per category is provided in Table 1.
Number of CYGNSS cyclones that fall in each category defined by ascent strength, mean PW, and development phase.
Because the results are very similar (at least qualitatively) whether we consider sensible or latent heat fluxes, we focus here on sensible heat fluxes to simplify the discussion. First, we consider the change between developing and intensifying phase for each PW/strength category. The magnitude of the change in sensible heat flux depends strongly on which PW/strength category is considered (Fig. 11). There is very little change for cyclones with large PW but weak ascent, while the changes are more significant for medium PW and strong ascent. In the post-cold-frontal region, the increase in sensible heat flux that we discussed above for the 500-tracks subset is only present in the medium PW categories for medium and strong ascent. In fact, the figure suggests that in this region, the change in fluxes is dependent on the flux distribution of the developing phase (i.e., large decrease where the flux is maximum). The decrease in the warm sector we observed for the 500-tracks subset is only visible for the two PW/strong ascent categories, as well as high PW/medium ascent category. In all cases the magnitude of the change is much weaker than what was found in section 4a. If we impose a condition that cyclones do not change in strength or PW, only one category resembles the changes found for the 500-tracks subset. Therefore, this suggests the contrast in fluxes in Fig. 8 is simply indicating that as cyclones transition from early development to intensification, the fluxes are impacted by both an increase in storm strength and a drying of the environment.
Difference in cold-front-centered composites of CYGNSS sensible heat fluxes (colored) between intensifying and developing cyclones classified on the basis of mean PW (increasing from top to bottom) and mean ascent strength (increasing from left to right). The solid contours represent the mean sensible heat flux per category for the developing phase. The two numbers at the top of each panel indicate the number of cyclones per category that fall in the intensifying and developing phases, as summarized in Table 1.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
Between the intensifying and dissipating phases (Fig. 12), the change in sensible heat flux is again changing in magnitude across the PW/ascent strength categories. That being said, with the exception of the medium PW/weak ascent category, while the magnitude of the difference in flux changes, the sign of the sensible heat flux change is rather consistent across all categories, with a decrease in both post-cold-frontal region and warm sector, and some degree of increase at the cold front/warm conveyor belt. This consistency in sign of the flux changes across all six cyclone classification categories suggests that as cyclones reach their dissipation stage, the changes in sensible heat flux are only modulated by changes in the cyclone properties but the structure remains regardless of the cyclone properties during the intensifying phase. This also suggests that the increase in flux along and to the east of the cold fronts between the intensifying and dissipating phases is relatively independent of the changes in the cyclones properties as they dissipate (loss of strength, drying). This implies that the sensible and latent (not shown) heat flux changes east of the cold-frontal region are more affected by the frontal-scale than cyclone-scale changes during dissipation.
Difference in cold-front-centered composites of CYGNSS sensible heat fluxes (colored) between dissipating and intensifying cyclones classified on the basis of mean PW (increasing from top to bottom) and mean ascent strength (increasing from left to right). The solid contours represent the mean sensible heat flux per category for the intensifying phase. The two numbers at the top of each panel indicate the number of cyclones per category that fall in the dissipating and intensifying phases, as summarized in Table 1.
Citation: Journal of Applied Meteorology and Climatology 60, 11; 10.1175/JAMC-D-21-0074.1
5. Conclusions
Using CYGNSS wind and surface latent and sensible heat fluxes retrievals, we explore the changes in surface heat fluxes in low-latitude extratropical cyclones as they evolve. With cold-front-centered compositing and conditional subsetting, we describe the distribution of latent and sensible heat fluxes within cyclones: the fluxes are maximum to the west of the center and minimum to the east. This is consistent with the work of Rudeva and Gulev (2011) who found a similar maximum in fluxes in the region that marks the transition between an anticyclone to the west and cyclone to the east. However, here we show that 1) using a cold-front-centered composite, the maxima are clearly found in the post-cold-frontal region of the cyclone; and 2) using warm-front-centered composites reveals that the minimum in latent heat flux is along the warm front, while the minimum in sensible heat flux extends into the warm sector, to the east of the cold fronts.
As cyclone’s strength increases, so does the magnitude of the maximum in flux in the post-cold-frontal region, while the minimum in flux in the warm sector diminishes, thereby exacerbating the gradient in flux across the cold fronts. As cyclone’s mean PW increases, the fluxes decrease everywhere within the cold-frontal side of the cyclones, thereby weakening the flux gradient across the cold fronts.
As cyclones transition from an early development into an intensification phase, the fluxes in the post-cold-frontal region increase, and this increase is also found for the latent heat fluxes at the cold front, and in a narrow (∼500 km) band to the east of the cold front. To the east of this band for latent heat flux and in the entire warm sector for the sensible heat flux, the fluxes decrease between the two phases. The post-cold-frontal increase in surface heat fluxes is strongly impacted by a strong increase in surface winds, while the warm-sector decrease is dominated by a decrease in both sea–air temperature and humidity contrasts.
Between the intensifying and dissipating phases in contrast, the fluxes decrease everywhere except in the narrow band to the east of the cold front. The changes in sensible and latent heat fluxes in the post-cold-frontal and warm sector are strongly related to changes in both temperature and humidity sea–air contrasts, the change in wind appears to have a lesser impact as cyclones reach their dissipating phase.
Our analysis suggests that during the intensification period there is a strong connection between the cyclone’s dynamics and the post-cold-frontal surface heat flux maximum, but less so with the warm-sector heat flux minimum. In the dissipating phase though, the cyclone-scale conditions appear to only have a modulating effect on the flux changes magnitude, the actual sign of the changes in fluxes appears to be impacted by the circulation in the cold-front region instead. Our results confirm that, at the warm front, clouds and precipitation both participate in energy being directed from the atmosphere into the ocean, but along the cold front, the increase in surface fluxes implies a continuous transfer from ocean into atmosphere through the cyclone life. While Booth et al.’s (2012) simulations had demonstrated that surface heat fluxes in the warm sector can participate in the cyclone intensification, our results suggest that they might also delay complete dissipation after the cyclone has reached its peak intensity. This would have to be verified using numerical models, but the interplay between the surface fluxes and the storm persistence after peak intensity is intriguing and is an interesting avenue for future research.
Acknowledgments
The work was funded by NASA CYGNSS competed science team grants 17-CYGNSS17-0024 NNH17ZDA001N-CYGNSS, 20-CYGNSS20-0010 NNH20ZDA001N-CYGNSS, and 80NSSC21K1470 and by the CYGNSS mission under NASA Science Mission Directorate Contract NNL13AQ00C. A portion of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship is acknowledged. We thank the editor Dr. A. D. Rapp and three anonymous reviewers for their help in significantly improving the quality of this paper.
Data availability statement.
The CYGNSS wind and flux Climate Data Record, version 1.0, data files are distributed through the NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) (https://podaac.jpl.nasa.gov/CYGNSS?sections=data). The extratropical cyclone and front database is stored at the NASA Center for Climate Simulation data portal (https://portal.nccs.nasa.gov/datashare/Obs-ETC/Fronts-ETC/) and is accessible through the GISS website (https://data.giss.nasa.gov/storms/obs-etc/). The MERRA-2 output used in the analysis, PW and 500-hPa vertical velocities, can be accessed through the GES DISC interface (https://disc.gsfc.nasa.gov/datasets/M2T1NXSLV_5.12.4/summary).
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