1. Introduction
The diurnal variability of mesoscale convective systems (MCSs) is well documented (e.g., Gray and Jacobson 1977; McGarry and Reed 1978; Albright et al. 1985; Chen and Houze 1997; Sui et al. 1997; Yang and Slingo 2001; Bowman et al. 2005; Worku et al. 2019). Nevertheless, there is a gap in understanding of convective processes and their variability within the diurnal range. A better understanding of this diurnal behavior of cloud properties is essential in creating more physically coherent models, particularly of organized convection and MCSs. The diurnal variation of tropical precipitation over the open oceans has been found to be primarily characterized by an early morning peak (Nesbitt and Zipser 2003; Kikuchi and Wang 2008). However, an afternoon maxima has also been observed in precipitation over the eastern tropical Atlantic (Gray and Jacobson 1977; Reed and Jaffe 1981; Albright et al. 1985), the South Pacific Convergence Zone (SPCZ; Albright et al. 1985) and the central/eastern tropical Pacific (Augustine 1984). A variety of factors ranging from radiative heating including short wave radiation, SST variation, and land–ocean circulations have been hypothesized to be linked to the diurnal cycle in oceanic precipitation (Nesbitt and Zipser 2003; Kikuchi and Wang 2008). The interaction among surface fluxes, radiation, and convection is critical to comprehend the convective life cycle over the tropics (Raymond 1994).
Over tropical oceans, mesoscale processes resulting in upward motion play a major role in perturbing ocean–atmosphere exchanges (Ogura et al. 1979; Soong and Tao 1980). Cold pools over tropical oceans are one of such essential cloud dynamic processes which provide positive feedback to new convective cell generation and mesoscale organization (Tao et al. 2007; Nasuno et al. 2009). Cold pools are envelopes of convective and mesoscale downdrafts. As these downdrafts reach the surface, air rises on their boundary to reach its level of free convection (LFC; Simpson 1969; Simpson et al. 1977; Charba 1974; Wakimoto 1982; Kingsmill 1995; Knupp 2006; Schlemmer and Hohenegger 2014). Accurate characterization of cold pool properties (e.g., extent, intensity, depth) is necessary in correcting erroneous convective diurnal cycles in the global and regional models (Rio et al. 2009; Schlemmer and Hohenegger 2014). Tropical oceanic cold pool–convection–precipitation processes have been well established in previous studies (Tompkins 2001; Mapes et al. 2006; Holloway and Neelin 2009; Feng et al. 2015; Rowe and Houze 2015; Zuidema et al. 2017; de Szoeke et al. 2017; Garg et al. 2020; Cheng et al. 2020). However, a process-based diurnal perspective of the relationship between cold pools and convection across the global tropics is an open question. Cold pools can persist longer than parent convection, and their role in modifying surface fluxes and radiative balance in the diurnal cycle lacks observational characterization.
Recent global cloud-resolving model (CRM) simulations without cumulus parameterizations have shown promising results in simulating global cloud and precipitation processes (Sato et al. 2009; Noda et al. 2012; Stevens et al. 2019; Hohenegger et al. 2020). Using a high-resolution, convection-permitting global cloud-resolving model, Sato et al. (2009) found that the number of cold pools was greater in the region of heavier precipitation (ITCZ and SPCZ). The authors also observed that the frequency of cold pools over central Pacific attained its peak between 0000 and 0600 LT, in accordance with the precipitation maxima in the region. To further improve the representation of convective processes in these storm-resolving models, extensive observational analysis of global cloud processes is essential (Bowman et al. 2005). Therefore, this study examines the global tropical oceanic mesoscale cold pool diurnal variability which could be used as a reference to improve diurnal cloud processes in these CRMs.
Advanced Scatterometer (ASCAT) wind vectors have been used to calculate the horizontal wind gradient as a tensor to identify gradient features (GFs; Garg et al. 2020). These GFs are bounded concave hulls of wind gradient associated with cold pool boundaries. ASCAT is in a sun-synchronous orbit and hence only crosses a location at a fixed time in a day. RapidSCAT, on the other hand, was on board the International Space Station (ISS) for almost 2 years and was in a non-sun-synchronous orbit (Madsen and Long 2016; Paget et al. 2016; Lin et al. 2017; Wang et al. 2017). Therefore, RapidSCAT was able to observe locations throughout the diurnal range. Previous studies have used RapidSCAT to identify the diurnal cycle of wind vectors over oceans (Lang 2017). QuikSCAT (similar to RapidSCAT in measurement principle) data have also been used to determine the diurnal properties of horizontal wind divergence to characterize regions of mesoscale downdrafts (Wood et al. 2009). Hence, analysis of GFs using RapidSCAT, along with diurnally resolved buoy measurements is performed in this study to determine whether spaceborne scatterometers can resolve the diurnal cycle of GFs associated with cold pools over tropical oceans.
In an earlier study by Yang and Zhang (2018), it was observed that RapidSCAT wind speed and direction were consistent with buoy-observed wind data. Oceanic buoy data has also been used to examine and validate the diurnal variation in precipitation observed by Tropical Rainfall Measurement Mission (TRMM; Serra and McPhaden 2004). In addition, air temperature anomaly from the buoy data has been used to identify cold pools (Kilpatrick and Xie 2015; Zuidema et al. 2017; Garg et al. 2020). Buoys are point-based measurements and observe the atmospheric flow from an Eulerian perspective. These point measurements can characterize wind, precipitation, and thermodynamic changes during a cold pool passage. Comparing RapidSCAT-identified GFs with buoy-identified cold pool (BICP) properties thus would lead to a more robust analysis of diurnal cycle of cold pools and its attributed convection over tropical oceans. Obtaining a conceptual understanding of diurnal rhythm of GFs and BICPs is essential to efficiently characterize cold pool–convection relationships in models.
Theoretically, cold pool properties should be closely linked with their parent convective systems and thus should exhibit diurnal signatures in accordance with parent convection type (Tompkins 2001; Schlemmer and Hohenegger 2014; Grant et al. 2018). Cloud-top infrared brightness temperature (TB) has been used to study convective intensity and organization (Futyan and Del Genio 2007; Di Paola et al. 2014; Rafati and Karimi 2017). In addition, robust relationships between daily precipitation and total column water vapor (TCWV) has been established in the past (Bretherton et al. 2004). Holloway and Neelin (2009) observed that deep convection with high precipitation rates at high TCWV values has a strong relationship with the buoyancy of lifted parcels. Schiro and Neelin (2019) used observations and model to study the probability of MCS occurrence as a function of TCWV and buoyancy. The authors observed that buoyancy increases as a function of TCWV, thus leading to enhancement in deep convection and mesoscale organization. Since cold pools impact buoyancy of air parcels, exploring the relationship between TCWV and GFs would prove to be useful in comprehending effect of TCWV present in the atmosphere on GF properties within the diurnal range.
In this study, the first objective applies the GF method (Garg et al. 2020) to RapidSCAT data to identify tropical oceanic cold pools and the diurnal cycle of GFs is validated against BICP’s diurnal cycle. In the second objective, environmental conditions corresponding to a range of GF types and their association with tropical convection are analyzed on the global scale, which has not been done in the previous studies. A third objective of this study is to observe the changes in surface properties from cold pool activity throughout the diurnal cycle. This study is organized as follows. Section 2 covers the datasets used in the study. Section 3 focuses on comparing ASCAT and RapidSCAT gradient features’ properties. Section 3 also depicts the TB and TCWV climatology associated with tropical oceanic GF distribution. Section 4 discusses the results of diurnal cycle of cold pools and their associated characteristics from RapidSCAT and buoys. Section 5 describes the diurnal relationship between TB, TCWV, and GF properties. Section 6 discusses the physical mechanisms behind the observed diurnal properties of cold pools and concludes the study.
2. Observational datasets and methodology
Table 1 lists the datasets used in this study with their geophysical variables, spatiotemporal resolutions, and maximum temporal difference from RapidSCAT. Due to effective field of view (FOV) of RapidSCAT sensor, the satellite was not able to provide information about small cold pools and thus puts a lower limit on detectable cold pool size. Since we are aiming to observe mesoscale cold pools over tropical oceans, RapidSCAT provided ample information about oceanic convection throughout the diurnal range. From Table 1, it can be seen that the maximum temporal difference of the datasets used in this study with respect to RapidSCAT is ~1 h for TRMM 3B42. Therefore, there might be cases where a GF is present, there may not be precipitation in the vicinity as the system may have moved. However, overall this would not significantly affect the process interpretation carried out in this analysis as we are broadly focusing on climatological aspect of GFs and their associated properties, i.e., even with this temporal difference, a significant sample of GFs collocated with other datasets is available, which should provide a robust characterization of GF properties.
Summary of datasets used in this study.
a. Satellite scatterometers
Spaceborne scatterometers have provided ocean vector winds routinely for many decades (Mapes et al. 2009; Kilpatrick and Xie 2015; Hilburn et al. 2016). Both Ku- (QuikSCAT, RapidSCAT) and C-band (ASCAT) scatterometers have been used to observe oceanic convection (Perry et al. 2001; Mapes et al. 2009; Elsaesser and Kummerow 2013; Portabella et al. 2012; Kilpatrick and Xie 2015; Garg et al. 2020). This study uses both C-band (ASCAT-A; hereafter referred to as ASCAT) and Ku-band (RapidSCAT) scatterometers for the overlapping period of almost two years (3 October 2014–19 August 2016). NASA’s RapidSCAT was a conically scanning two-beam instrument which operated at 13.4-GHz frequency on a prograde 51.6° inclined non-sun-synchronous orbit on the International Space Station (ISS). Two beams swept out a circle around the instrument in the direction of the nadir vector. This study only uses inner swath data as the scanning geometry allowed the instrument to observe each geolocation four times within the inner beam. The instrument’s incidence angle was 49° for the inner beam and 56° for the outer beam. The slant range of the instrument was 600 km (inner) and 678 km (outer), while the ground swath was 900 km for inner and 1100 km for outer swaths. RapidSCAT operated at 92.5% uptime, where a majority of instrument outages were due to vehicular docking at ISS (Lang 2017). Moreover, about 80% of the data were considered to be of a usable quality; most of the reduction in observational quality was due to the change in altitude and attitude of ISS. This study uses level 2B 12.5-km Instantaneous Field of View (IFOV) version 1.0 climate quality ocean surface wind vectors provided by NASA–Physical Oceanography Distributed Active Archive Center (NASA PODAAC; SeaPAC 2015).
ASCAT on board European Organization for the Exploration of Meteorological Satellites (EUMETSAT) Meteorological Operational (MetOp-A) satellite, on the other hand, operates at 5.2 GHz and has three vertically polarized antennas (45°, 60°, and 135°) with an IFOV of 12.5 km. Unlike RapidSCAT, ASCAT has two swaths of 500 km, with a gap of 360 km in-between. RapidSCAT winds used a sea surface temperature (SST) dependent Ku-band geophysical model function (GMF; Wang et al. 2017) while ASCAT data uses a C-band GMF known as C-2015 (Ricciardulli and Wentz 2015). RapidSCAT data has wind vector quality flags, including the Impact based MultiDimensional Histogram (IMUDH) rain flag algorithm. This rain flag algorithm used rain rate from Special Sensor Microwave Imager (SSM/I) to flag rain-contaminated winds. It is different from the ASCAT rain flag used in Garg et al. (2020), where Remote Sensing Systems (RSS) provided collocated rain rates from a range of radiometers. Since ASCAT is in sun-synchronous orbit, the orbital overpass time over a location is 0900 and 2100 local time (LT). On the other hand, RapidSCAT was in a non-sun-synchronous orbit and thus provided observations throughout the day within the diurnal range. Yang and Zhang (2018) observed that ASCAT and RapidSCAT wind vectors were relatively consistent over the RapidSCAT’s observational period with a minor overestimation of wind speeds (RMSE of 1.15 m s−1) by RapidSCAT when the wind speeds were larger than 20 m s−1. This means that RapidSCAT-observed cold pool size may be overestimated for strong convective systems with wind speeds greater than 20 m s−1. Overall, Yang and Zhang (2018) observed that RapidSCAT matched well with the ASCAT dataset with an overall average bias of 0.27 m s−1 and 0.42° for wind speed and direction, respectively, and thus is useful for climatological applications.
b. Tropical Rainfall Measuring Mission (TRMM)
Convective processes can be better understood by analyzing cold pools and precipitation together. As in Garg et al. (2020), in addition to scatterometers, TRMM 3B42 0.25°, 3-hourly gridded data (TRMM 2011) is regridded to RapidSCAT FOV to analyze precipitation within the GFs. The nearest time step from TRMM is used to assign precipitation to the RapidSCAT GFs. The combination of these two datasets not only provides spatial overview of GFs in the vicinity of heavy precipitation, but also helps in understanding covariability of GF–precipitation diurnal cycle.
c. Global buoy network data
Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON; McPhaden et al. 2010) over the Pacific, Prediction and Research Moored Array in the Tropical Atlantic (PIRATA; McPhaden et al. 2010) over the Atlantic, and Researched Moored Array for African–Asian–Australian Monsoon Analysis (RAMA; McPhaden et al. 2010) over the Indian Ocean have a long-term record of atmospheric observations of 2-m temperature, wind speed and precipitation rate. Figure 1a shows the location of buoys used in this study over the three basins. Note that buoys with red symbols did not have a rain gauge, and hence they are excluded from precipitation analysis. Thermal cold pools over the buoys are identified using air temperature anomaly, i.e., if the temperature drop exceeds 1.5°C in 1 h or 2°C in 2 consecutive hours (Kilpatrick and Xie 2015; Garg et al. 2020). High-resolution 10-min data are used from each buoy in this study. In addition to air temperature anomaly, wind speed (m s−1), and precipitation rate (mm h−1 converted to mm day−1) data are used to understand the cold pool diurnal cycle. Since some buoys had significant periods of missing data, only buoys with greater than 85% of available observations are used. Buoy data are used from 2005 to 2019 to analyze a longer continuous record of observations. Ocean basins are classified as Indian (IO), east Pacific (EPAC), west Pacific (WPAC), and Atlantic (AO), as shown in Fig. 1b. The latitudes and longitudes of the box edges are provided in Table 2. All diurnal analysis is performed over these boxes for both RapidSCAT and buoy datasets.
(a) Location of all buoys used over the Pacific, Atlantic, and Indian Oceans. Blue-marked buoys had temperature, wind speed, and precipitation data while red-marked buoys did not have precipitation data. (b) Regional classification for the study into Indian (IO), eastern Pacific (EPAC), western Pacific (WPAC), and Atlantic (AO) Oceans.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
(a) Location of all buoys used over the Pacific, Atlantic, and Indian Oceans. Blue-marked buoys had temperature, wind speed, and precipitation data while red-marked buoys did not have precipitation data. (b) Regional classification for the study into Indian (IO), eastern Pacific (EPAC), western Pacific (WPAC), and Atlantic (AO) Oceans.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
(a) Location of all buoys used over the Pacific, Atlantic, and Indian Oceans. Blue-marked buoys had temperature, wind speed, and precipitation data while red-marked buoys did not have precipitation data. (b) Regional classification for the study into Indian (IO), eastern Pacific (EPAC), western Pacific (WPAC), and Atlantic (AO) Oceans.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Minimum and maximum latitude (°) and longitude (°) for each region box.
d. Global precipitation measurement merged infrared (IR) brightness temperature
This study uses the merged 4-km, 30-min IR TB dataset provided by NASA MERGIR (Janowiak et al. 2017) for the RapidSCAT period. Global infrared satellite datasets are merged on a global grid after performing zenith- and parallax-correction, thus reducing discontinuities in overlapping swath edges. The closest timestamp with RapidSCAT GF record is used to extract IR data. To observe the parent convective system properties in the vicinity of RapidSCAT-observed GFs, equivalent diameter is calculated using the GF polygon’s major axis to create a circular buffer area around the GF. Minimum TB pixel within the buffer region is geolocated and attributed to a particular GF. In this way, a global minimum TB GF climatology is created in this study. Also, diurnal variation in minimum TB associated with GFs is analyzed.
e. Total column water vapor
ERA5 data at 0.28°, 1-h spatiotemporal resolution is used to observe TCWV within the vicinity of RapidSCAT GF buffer (C3S). Similar to TB, the circular buffer around each GF is used to locate the maximum TCWV pixel associated with each GF. This exercise resulted in a global diurnal GF–maximum TCWV climatology for the RapidSCAT observational period.
f. Gradient feature algorithm
Garg et al. (2020) devised a tensor-based, storm-centric gradient feature algorithm to identify cold pools using ASCAT vector winds. Following Garg et al. (2020), this study uses 6.8 × 10−5 s−1 (primary) and 1.2 × 10−4 s−1 (secondary) wind gradient thresholds to locate GFs in RapidSCAT data as the field of view and underlying scatterometry principles are similar for Ku-band scatterometer. Fig. 2 shows an example of GF (peach colored polygon) at 0316 UTC 11 January 2015. RapidSCAT wind vectors are shown as the peach-colored arrows with TRMM 3B42 precipitation (Fig. 2a), IR TB (Fig. 2b), and TCWV (Fig. 2c). Note that heavy precipitation [>25mm (3 h)−1], cold TB (<190 K) and high TCWV (>70 kg m−2) is associated with the GF case shown here, which suggests deep convection in the vicinity. For ASCAT, the Garg et al. (2020) GF dataset is used for the overlapping time period of 2 years. This comparative analysis between RapidSCAT and ASCAT-identified GFs is presented to explore the biases due to differences in wavelength of the instruments.
RapidSCAT-observed GF (peach polygon) and vector winds (peach-colored arrows) with (a) TRMM 3B42 precipitation [mm (3 h)−1], (b) IR TB (K), and (c) ERA5 TCWV (kg m−2) at 0316 UTC 11 Jan 2015.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
RapidSCAT-observed GF (peach polygon) and vector winds (peach-colored arrows) with (a) TRMM 3B42 precipitation [mm (3 h)−1], (b) IR TB (K), and (c) ERA5 TCWV (kg m−2) at 0316 UTC 11 Jan 2015.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
RapidSCAT-observed GF (peach polygon) and vector winds (peach-colored arrows) with (a) TRMM 3B42 precipitation [mm (3 h)−1], (b) IR TB (K), and (c) ERA5 TCWV (kg m−2) at 0316 UTC 11 Jan 2015.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
3. Global cold pool properties
a. RapidSCAT-observed GF and BICP spatial distribution
ASCAT-identified tropical oceanic GF properties were studied in detail in Garg et al. (2020). The authors observed that the number distribution of GFs matched well with mesoscale organization identified by TRMM (Nesbitt et al. 2006; Houze et al. 2015), implying that precipitation, convective organization, and cold pool activity is tightly linked. In addition, the ASCAT-observed GF climatology had spatial similarity with MCS lifetime and frequency observed in Huang et al. (2018). For RapidSCAT-identified GFs, the spatial distribution in a 5° × 5° grid box of number density of GFs (Fig. 3a), GF size (km2, Fig. 3b) and TRMM 3B42 precipitation within GFs [mm (3 h)−1, Fig. 3c] is shown here. In addition to GF climatology, BICP number climatology is shown for each buoy on top of GF number climatology in Fig. 3a. In Fig. 3a, highest density of GFs is concentrated within the ITCZ and SPCZ. WPAC ITCZ depicts the highest number of GFs (maximum: 3500), followed by SPCZ (maximum: 3000) and east-central Pacific (maximum: 3000). The Indian Ocean ITCZ trails behind the Pacific Ocean in the number of GFs with maxima over the northeast-central Indian Ocean (maximum: 2500), which is also the location of the frequent occurrence of deep convection associated with the Madden–Julian oscillation (MJO; Kerns and Chen 2020). Southwest monsoon convection over South Asia is also captured in the number of GFs. Atlantic ITCZ has the least number of GFs (maximum: 1500) with a peak over central Atlantic. Trade wind regions are traditionally defined as the regions within the tropics where the air currents blow from east to west near the equator, i.e., from the northeast in the Northern Hemisphere and from the southeast in the Southern Hemisphere. The region north and south of the ITCZ is considered in this study as the trade wind regime. Comparing trade wind regions with ITCZ, the number of cold pools is significantly less for the Pacific (maximum: 800), Atlantic (maximum: 600), and the southern Indian Ocean trade wind region (maximum: 500). Comparing BICP and GF number climatology, both exhibit very similar number density within all four boxes, thus validating the hypothesis of using GFs as proxy to identify tropical oceanic cold pools. For instance, within the WPAC box, both GF and BICP number maxima (>3000) is near the date line (180°).
RapidSCAT-observed (a) number of GFs (shading) and buoy-observed number of cold pools (ovals), (b) area of GFs (km2), and (c) GF-attributed TRMM 3B42 precipitation [mm (3 h)−1] from 2014 to 2016. The boxes on each panel are as defined in Fig. 1 and Table 2.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
RapidSCAT-observed (a) number of GFs (shading) and buoy-observed number of cold pools (ovals), (b) area of GFs (km2), and (c) GF-attributed TRMM 3B42 precipitation [mm (3 h)−1] from 2014 to 2016. The boxes on each panel are as defined in Fig. 1 and Table 2.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
RapidSCAT-observed (a) number of GFs (shading) and buoy-observed number of cold pools (ovals), (b) area of GFs (km2), and (c) GF-attributed TRMM 3B42 precipitation [mm (3 h)−1] from 2014 to 2016. The boxes on each panel are as defined in Fig. 1 and Table 2.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The area of GF polygons (km2) is calculated from RapidSCAT wind vectors, similar to Garg et al. (2020), as shown in Fig. 3b. The mean largest GFs are observed within ITCZ, SPCZ, and tropical cyclone regions. Over the Pacific ocean, the east-central Pacific ITCZ has larger GFs (maximum: 1750 km2) compared to WPAC ITCZ (maximum: 1400 km2). This observation matches with the GF area maxima over the EPAC in ASCAT-observed GFs (Garg et al. 2020). Indian Ocean ITCZ has larger GFs (maximum: 1800 km2) on average. In the case of the Atlantic ITCZ, northeast-central Atlantic has relatively larger GFs (maximum: 1500 km2) than northwestern Atlantic (maximum: 1000 km2). Compared to ITCZ, smaller GFs (maximum: 1000 km2) are observed over the trade wind regions for the Pacific and Atlantic ocean, but the Indian ocean has a similar GF size within and outside the ITCZ. Note that regions with a high number of GFs do not necessarily coincide with larger GFs. This could be due to the fact that cold pools can continue to grow in size by merging and intersecting with other cold pools in the vicinity even if there is no precipitation in the vicinity as observed in previous studies (e.g., Rowe and Houze 2015; Feng et al. 2015).
The global climatology of RapidSCAT GF-attributed unconditional precipitation rate is shown in Fig. 3c. Following the trend of GF number and size, the 3-hourly mean precipitation is higher in the ITCZ and SPCZ. This observation also matches well with the mean conditional rain rate within MCSs, as observed in Nesbitt et al. (2006). Pacific ITCZ has a mean precipitation maximum of 1.25 mm (3 h)−1 over WPAC, collocated with GF number density maxima. Similar mean precipitation of 1 mm (3 h)−1 is observed over the Maritime Continent, although the GF number density is relatively less than the western Pacific. SPCZ has a distinct peak in precipitation [>1.25 mm (3 h)−1], where the GF number density is maximum. Over the Indian Ocean, the Bay of Bengal and Arabian Sea have relatively higher mean precipitation [>1.25 mm (3 h)−1] than the rest of the Indian Ocean basin. This observation could be attributed to the tropical cyclone activity in the Bay of Bengal and the Arabian sea as well as the monsoonal circulation over the basin. In the case of the Atlantic basin, east-central and west Atlantic and the Caribbean Sea have similar mean precipitation [maximum: 1.25 mm (3 h)−1]. Trade wind regions of the Pacific, Atlantic, and Indian Ocean have significantly lower GF-precipitation [maximum: 0.4 mm (3 h)−1], which relates well to the GF number observations.
These global RapidSCAT-observed GF and BICP number density, mean area, and precipitation climatologies provide an idea about the locations where strong convection leads to numerous, larger, and GFs with heavy precipitation. Therefore, these statistics are essential to understand the convection–precipitation dynamics over the global tropical oceans and could serve as a reference to diagnose representation of convective processes in current and future generation models.
b. Differences between ASCAT and RapidSCAT GFs
Figure 4 shows the ratio of RapidSCAT and ASCAT GF number (Fig. 4a), size (Fig. 4b), and TRMM 3B42 precipitation (Fig. 4c). In Fig. 4a, WPAC, EPAC, IO and AO have a relatively similar number (ratio = 0.8–1.1) between ASCAT and RapidSCAT. The ratio is higher north of the equator outside the four boxes, thus signifying a higher number of GFs observed by RapidSCAT. Considering the WPAC and Maritime Continent between 10° and 20°N, RapidSCAT has more GFs than ASCAT (ratio > 1.75). SPCZ, on the other hand, shows a ratio of <1, suggesting that ASCAT observed more GFs in the region. Southern Hemisphere trade wind regions over all three basins also depict fewer GFs from RapidSCAT than ASCAT (ratio < 0.8). These differences can be due to different orbital inclination, beamwidth, and orbital overpass time of RapidSCAT and ASCAT, thus leading to different GF frequency between the two scatterometers.
Ratio of RapidSCAT and ASCAT observed (a) number of GFs, (b) area of GFs, and (c) GF-attributed TRMM 3B42 precipitation from 2014 to 2016.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Ratio of RapidSCAT and ASCAT observed (a) number of GFs, (b) area of GFs, and (c) GF-attributed TRMM 3B42 precipitation from 2014 to 2016.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Ratio of RapidSCAT and ASCAT observed (a) number of GFs, (b) area of GFs, and (c) GF-attributed TRMM 3B42 precipitation from 2014 to 2016.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
GF size differences in Fig. 4b shows larger values from RapidSCAT as compared to ASCAT over equatorial regions within WPAC, IO and AO, but shows similar values between ASCAT and RapidSCAT within EPAC. Within the ITCZ, the Indian Ocean and Maritime Continent observed the maximum difference in GF size (>1.8), followed by eastern Atlantic (1.5–1.8), western Pacific (1.25–1.6), and eastern Pacific (1.1–1.5). Over trade wind regions, RapidSCAT has a relative decrease in GF size (0.5–0.75) over the Pacific and the Atlantic basins. An enhancement in GF size and number on the coastal boundaries is prominent over the Maritime Continent, the Indian Ocean, and the Atlantic Ocean. To identify the potential sources of size differences between the two scatterometers, collocated GFs are identified between the two scatterometers and the shape and structure of the GFs is analyzed. It is found that since ASCAT has a dual swath with a gap in between, it observes relatively smaller GFs. On the other hand, RapidSCAT had a full continuous swath, thus allowing a broader view of the ocean surface, and hence larger GFs are observed. Also, RapidSCAT was able to sample throughout the diurnal range while ASCAT only samples twice a day, which is mostly at the time of convective activity minima (0900 and 2100 LT). Therefore, RapidSCAT was likely able to observe larger GFs as compared to ASCAT due to both the swath geometry and GF diurnal variability.
TRMM-observed GF precipitation provides a spatial distribution of precipitating and nonprecipitating GFs as shown in Fig. 4c. Analyzing the differences between ASCAT and RapidSCAT GF-precipitation can help understand the impact of rain on GF identification from the two instruments. In Fig. 4c, RapidSCAT-observed GF precipitation shows a nominal decrease over all the basins as compared to ASCAT (ratio = 0.7–0.9). RapidSCAT observed an increase in GF-precipitation over trade-wind regions of the Pacific (1.25–1.75), Atlantic (1.25–1.6), and Indian (1.25–1.5) Ocean. Coastal enhancement in GF-precipitation is also observed for all the basins. ASCAT data in Garg et al. (2020) was derived from Remote Sensing Systems (REMSS), where a collocated radiometer–scatterometer rain flag was available. This additional radiometer information was helpful in diagnosing vector winds with heavier precipitation bias. RapidSCAT data, on the other hand, is obtained from PODAAC, where they have scatterometer-derived rain quality flags. This difference in the rain flag algorithm could be one of the reasons behind the precipitation differences between RapidSCAT and ASCAT-observed GFs. Other possible reasons could be scatterometer operating frequency, beam geometry and the sampling differences in the two instruments. To summarize, difference between RapidSCAT and ASCAT number, size, and precipitation can be attributed to swath width, orbital differences (polar versus inclined), length of record, diurnal sampling, and the climate variability between the two satellite records.
c. Global GF-attributed minimum TB and maximum TCWV
IR TB is used here to examine the vertical extent of convection associated with GFs. Figure 5a shows the variation of minimum TB within the vicinity of GFs. The coldest TB is over the WPAC ITCZ and SPCZ (<220 K), followed by the Maritime Continent and Indian Ocean ITCZ (220–230 K), EPAC and east Atlantic ITCZ (230–235 K). These regions are associated with high number of larger, heavily precipitating GFs (Fig. 3) and thus these GFs can be linked to more vigorous deep convective activity. Over trade wind regions, much warmer TB (>255 K) is observed which can be related to shallow convection. Over marine stratocumulus regions of the southeast Pacific (30°S–0°, 120°–60°W) and Atlantic (30°S–0°, 40°W–0°) basins, the mean TB is greater than 275 K within GFs.
Global tropical climatology of GF-attributed (a) minimum brightness temperature TB (K) and (b) maximum total column water vapor (TCWV; kg m−2) for RapidSCAT time period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Global tropical climatology of GF-attributed (a) minimum brightness temperature TB (K) and (b) maximum total column water vapor (TCWV; kg m−2) for RapidSCAT time period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Global tropical climatology of GF-attributed (a) minimum brightness temperature TB (K) and (b) maximum total column water vapor (TCWV; kg m−2) for RapidSCAT time period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
TCWV has been identified as a key factor in regulating tropical precipitation (Bretherton et al. 2004; Peters et al. 2009). To analyze TCWV–GF relationships, a global climatology of maximum TCWV within GF vicinity is created as shown in Fig. 5b. Highest maximum TCWV is observed within the ITCZ over WPAC and SPCZ (>55 kg m−2). The Indian Ocean, EPAC, Maritime Continent and Atlantic ITCZ (50–55 kg m−2) have lower TCWV values than WPAC and SPCZ. Trade wind regions over the Pacific, Indian and Atlantic Ocean have much smaller GF-associated TCWV values (25–40 kg m−2). These observations match well with minimum TB (Fig. 5a) and GF properties (Fig. 3) and thus establish linkages between the amount of water vapor present in the atmospheric column, precipitation, and cold pool number and size. In this way, Fig. 5 helps attain a broader understanding of how cloud and convective system environments differ over global tropical oceans, as well as potential air–sea interactions in these different precipitation climates.
d. Correlation between GF number, size, TB, and TCWV
To statistically identify the relationships between the GF number, size (km2), minimum TB (K), and maximum TCWV (kg m−2); scatterplots are shown in Fig. 6, shaded by GF precipitation (mm day−1). Mean values of the respective parameters are in a 5° × 5° grid box as shown in Figs. 3–5. These points represent all tropical locations and not just the four boxes in Fig. 1 and Table 2. This dataset is filtered on 10th percentile of GF number in order to remove the data points with few GFs. Spearman’s rank correlation coefficient (r) is also shown; all r values are significant at the 95% level. In Fig. 6a, the nonlinear distribution (r = 0.291) of GF size and number corroborates well with the spatial climatology in Fig. 3b where bigger GFs do not necessarily coincide with high number of GFs. Heavier precipitation (>7 mm day−1) is concentrated between 1000 and 1800 km2 for a wide range of number of GFs (100–3000). In Fig. 6b, there is a moderately strong negative relationship (r = −0.667) between minimum TB and number of GFs with heavily precipitating GFs corresponding to colder TB (<245 K) for a wide range of number of GFs. Figure 6c depicts a strong positive (r = 0.712) relationship between maximum TCWV and number of GFs with heavy precipitation related to moister (>45 kg m−2) environments. GF size portrays a relatively weaker correspondence to TB (r = −0.307) and TCWV (r = 0.173) in Figs. 6d–e with heavy precipitation related to larger (1000–1800 km2), colder TB (<245 K), and moister vertical column GFs. A strong negative relationship exists between GF-attributed minimum TB and maximum TCWV (r = −0.895) with heavy precipitation associated with colder TB (220–240 K) and moister vertical columns (40–60 kg m−2). These scatterplots provide two major findings. First, GF number and size are nonlinearly related with heavier precipitation associated with larger GFs. Second, number of GFs exhibit more linear and direct relationship with TB and TCWV as compared to GF size. These observations signify that convective system type and ambient moisture play a major role in producing cold pools over the ocean surface while the size depends on a range of factors in addition to these two environmental properties (e.g., intersecting cold pools and kinematic background). These results are important to constrain cold pool parameterizations in current and future models.
Scatterplots shaded according to GF precipitation (mm day−1) and r values for (a) GF size (km2) and number of GFs, (b) minimum brightness temperature TB (K) and number of GFs, (c) maximum total column water vapor (TCWV; kg m−2) and number of GFs, (d) minimum brightness temperature TB (K) and GF size (km2), (e) maximum total column water vapor (TCWV; kg m−2) and GF size (km2), and (f) minimum brightness temperature TB (K) and maximum total column water vapor (TCWV; kg m−2) for RapidSCAT time period. These values are within 5° × 5° grid boxes similar to spatial climatologies in Figs. 3–5.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Scatterplots shaded according to GF precipitation (mm day−1) and r values for (a) GF size (km2) and number of GFs, (b) minimum brightness temperature TB (K) and number of GFs, (c) maximum total column water vapor (TCWV; kg m−2) and number of GFs, (d) minimum brightness temperature TB (K) and GF size (km2), (e) maximum total column water vapor (TCWV; kg m−2) and GF size (km2), and (f) minimum brightness temperature TB (K) and maximum total column water vapor (TCWV; kg m−2) for RapidSCAT time period. These values are within 5° × 5° grid boxes similar to spatial climatologies in Figs. 3–5.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Scatterplots shaded according to GF precipitation (mm day−1) and r values for (a) GF size (km2) and number of GFs, (b) minimum brightness temperature TB (K) and number of GFs, (c) maximum total column water vapor (TCWV; kg m−2) and number of GFs, (d) minimum brightness temperature TB (K) and GF size (km2), (e) maximum total column water vapor (TCWV; kg m−2) and GF size (km2), and (f) minimum brightness temperature TB (K) and maximum total column water vapor (TCWV; kg m−2) for RapidSCAT time period. These values are within 5° × 5° grid boxes similar to spatial climatologies in Figs. 3–5.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
e. Buoy-identified cold pool duration distribution
Before discussing the diurnal range of BICP duration, it is important to examine its distribution. Figure 7 shows the BICP duration observed during the buoy observational period for the four boxes in consideration. Cold pool duration in Fig. 7 is a crucial parameter to understand the role of cold pools on convective life cycle and surface fluxes as it can be directly related to the parent convective system life cycle. Note that for all the four basins, the distribution is peaking between 50 and 70 min with a few cold pools of longer longevity (>100 min). Also, WPAC and EPAC have slightly higher peak than AO and IO, suggesting relatively vigorous convective activity in these basins which is consistent with the previous figures.
Distribution of buoy-observed cold pool duration (min) from 2005 to 2019.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Distribution of buoy-observed cold pool duration (min) from 2005 to 2019.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Distribution of buoy-observed cold pool duration (min) from 2005 to 2019.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Overall, the RapidSCAT GF climatology matches well with the ASCAT-observed GFs in Garg et al. (2020) and observed tropical convective systems properties (Nesbitt et al. 2006; Huang et al. 2018). In addition, BICP number matches well with RapidSCAT GF number density. In this way it can be said that the BICP dataset not only validates the GF analysis but also can act as a reliable source for investigating long-term trends. Now, a diurnal climatology of these GFs and BICPs enabled by RapidSCAT and buoys is presented.
4. Diurnal properties of GFs and BICPs
Surface properties and cloud–radiation interactions primarily govern the intrinsic character of the diurnal cycle of storm environmental parameters and the time scales of storm-internal processes (Johnson 2011; Ruppert and Klocke 2019). Here we examine the diurnal cycle of GFs with the goal of examining the interactions between radiative heating/cooling, clouds, and precipitation on diurnal time scale. To better characterize RapidSCAT GF and BICP diurnal properties, GFs and BICPs are divided into three groups based on first (0%–33.3%), second (33.3%–66.7%), and third (greater than 66.7%) tercile of their size and duration, respectively. The primary reasoning behind dividing the data into three terciles is to look at smaller- (first), medium- (second) and larger- (third) cold pools in terms of duration and size. The hypothesis behind comparing diurnal properties based on GF size and BICP duration is that these properties should be directly related with each other similar in principle to the relationship between MCS size and duration established previously (Chen and Houze 1997; Zuidema 2003; Roca et al. 2017). Figure 8 shows the three tercile values of GF size (Fig. 8a) and BICP duration (Fig. 8b). Overall, the values are similar between WPAC, EPAC, AO, and IO for all the terciles. For instance, WPAC shows maximum BICP duration and the largest GF size in the third tercile. This observation shows that there is a strong regional relationship between BICP duration and GF size, but elaborating on whether regionally larger cold pools exist in regions where they last longer is left to future study.
The first-, second-, and third-tercile values of (a) GF size (km2) and (b) cold pool duration (min) for WPAC, EPAC, IO, and AO.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The first-, second-, and third-tercile values of (a) GF size (km2) and (b) cold pool duration (min) for WPAC, EPAC, IO, and AO.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The first-, second-, and third-tercile values of (a) GF size (km2) and (b) cold pool duration (min) for WPAC, EPAC, IO, and AO.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
a. RapidSCAT-observed GFs
This subsection first examines the diurnal behavior of RapidSCAT-observed GF number, precipitation, and size. Based on the regional classification shown in Fig. 1b, GF and BICP diurnal probability density is shown in Fig. 9. Overall, GFs and BICPs depict a bimodal distribution in all three terciles. For WPAC in Figs. 9a–c, the morning and afternoon peak in GFs and BICPs is between 0000–0700 LT and 1300–1800 LT for all three terciles. EPAC (Figs. 9d–f) has similar peaks in GF and BICPs between 0000–0600 and 1300–1900 LT for second and third terciles, but a dominant peak in afternoon between 1200 and 1800 LT for first tercile of BICP. The diurnal variability probability density of GFs and BICPs over the AO in Figs. 9g–i exhibits similar morning and afternoon occurrence peaks as WPAC and EPAC between 0000–0600 and 1300–1700 LT for first (Fig. 9g), second (Fig. 9h), and third (Fig. 9i) terciles. IO (Figs. 9j–l) follows a similar number distribution for GFs and BICPs as the other basins. Figure 9m shows the BICP and GF frequency averaged across all basins and terciles and exhibits a bimodal distribution concordant with the rest of the basins. Although both BICP and GF number densities exhibit bimodality across all basins and terciles, there are differences in peak timings and amplitudes between the two datasets. For instance, the BICP number peak timing is earlier as compared to RapidSCAT GFs for almost all the basins. Also, buoys exhibit a relatively stronger peak in BICP number in the afternoon as compared to RapidSCAT. These differences can be primarily attributed to sensor, sampling frequency, and coverage differences. Buoys being point measurements do provide us valuable information about the in situ properties of cold pools with a range of intensities and their environments, while RapidSCAT on the other hand can only observe cold pools larger than the effective resolution of the scatterometer and hence likely miss many smaller cold pools. Therefore, on average, buoy-observed smaller cold pools may be earlier in the convective life cycle, whereas RapidSCAT may observe cold pools later in their life cycle. Combining the GF and BICP dataset thus provided us a broader overview of cold pools with varying intensity and lifetimes and although there are differences between the two datasets, they both match well in the broader diurnal signature, corroborating the hypothesis that RapidSCAT can reliably observe mesoscale cold pools over tropical oceans.
The first-, second-, and third-tercile probability density distribution of GF number (red) and BICP frequency (blue) for (a)–(c) WPAC, (d)–(f) EPAC, (g)–(i) AO, and (j)–(l) IO and (m) averaged across all basins and terciles across the tropical oceans over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The first-, second-, and third-tercile probability density distribution of GF number (red) and BICP frequency (blue) for (a)–(c) WPAC, (d)–(f) EPAC, (g)–(i) AO, and (j)–(l) IO and (m) averaged across all basins and terciles across the tropical oceans over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The first-, second-, and third-tercile probability density distribution of GF number (red) and BICP frequency (blue) for (a)–(c) WPAC, (d)–(f) EPAC, (g)–(i) AO, and (j)–(l) IO and (m) averaged across all basins and terciles across the tropical oceans over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The diurnal cycle of precipitation rate from TRMM and in situ observations has been studied previously (Nesbitt and Zipser 2003; Bowman et al. 2005; Kikuchi and Wang 2008). Nevertheless, precipitation within the context of cold pool number and size in the diurnal range has not been studied. As shown in Fig. 3, GF number, size, and precipitation are not always in sync with each other over the global tropics. Temporal delays between these three GF properties can signify causality, thus it is important to look at their diurnal cycle together. Figure 10 shows the normalized sum of GF number (solid black), size (dashed red) and precipitation (dashed blue) for all four boxes and terciles and global average. For WPAC morning peaks (Figs. 10a–c), GF-attributed precipitation is at 0200 LT followed by GF number and size at 0400 LT. WPAC’s afternoon maxima is at 1300 LT for precipitation followed by 1600 LT for GF number and size. Similar lead-lags were observed for the EPAC (Figs. 10d–f), IO (Figs. 10j–l), and mean across all basins and terciles (Fig. 10m). Note that AO (Figs. 10g–i) exhibits more than two peaks in all the terciles, which could be due to the frequent tropical easterly wave activity resulting in occurrence of convection at different times of the day as observed in previous studies (e.g., Diedhiou et al. 1999; Ventrice et al. 2012; Janiga and Thorncroft 2016). The uniqueness in AO’s diurnal cycle needs to be examined carefully as it will be interesting and valuable to identify the possible reasons behind the multimodal peaks and is left for future work. The important observation here is that GF precipitation peaks first during the day, which can lead to more cold pools due to precipitating downdrafts. Once these cold pools propagate over the ocean, they can grow in size as they continue to merge with other cold pools, as well as formed due to secondary convection initiation. This observation is in agreement with theoretical and model-based study of Feng et al. (2015), where individual cold pools intersected during MJO to produce larger cold pools in models and observations.
The first-, second-, and third-tercile normalized sum values of GF number, size (km2), and precipitation (mm day−1) for (a)–(c) WPAC, (d)–(f) EPAC, (g)–(i) AO, (j)–(l) IO, and (m) averaged across all basins and terciles across the tropical oceans over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The first-, second-, and third-tercile normalized sum values of GF number, size (km2), and precipitation (mm day−1) for (a)–(c) WPAC, (d)–(f) EPAC, (g)–(i) AO, (j)–(l) IO, and (m) averaged across all basins and terciles across the tropical oceans over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The first-, second-, and third-tercile normalized sum values of GF number, size (km2), and precipitation (mm day−1) for (a)–(c) WPAC, (d)–(f) EPAC, (g)–(i) AO, (j)–(l) IO, and (m) averaged across all basins and terciles across the tropical oceans over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
b. Buoy identified cold pools
Atmospheric variables such as wind speed and direction, precipitation, air temperature, and surface fluxes are perturbed during a cold pool passage (Young et al. 1992, 1995; Esbensen and McPhaden 1996; Wu and Guimond 2006; Schlemmer and Hohenegger 2014; Zuidema et al. 2017). Kerns and Chen (2018) performed validation of satellite-based surface winds using a range of observational data such as buoys and ground-based weather stations during the Dynamics of the Madden–Julian oscillation (DYNAMO) field experiment. One objective of this study is to quantify the changes in surface parameters during cold pool temporal evolution. This section presents the diurnal cycle of BICP duration, temperature drop, wind speed change, and precipitation rate over the cold pool life cycle. These analyses are performed based on first, second, and third tercile values of cold pool duration (Fig. 8b). Figure 11 shows the box plots of these parameters for the WPAC to look at the interquartile (IQR) range of each tercile. Other regions have similar distribution, and are not shown for brevity.
The first-, second-, and third- tercile box plots over WPAC for buoy-observed (a)–(c) cold pool duration (minutes), (d)–(f) maximum temperature change (°C), (g)–(i) maximum wind speed change (m s−1), and (j)–(l) maximum precipitation change (mm day−1) during cold pool passage over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The first-, second-, and third- tercile box plots over WPAC for buoy-observed (a)–(c) cold pool duration (minutes), (d)–(f) maximum temperature change (°C), (g)–(i) maximum wind speed change (m s−1), and (j)–(l) maximum precipitation change (mm day−1) during cold pool passage over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The first-, second-, and third- tercile box plots over WPAC for buoy-observed (a)–(c) cold pool duration (minutes), (d)–(f) maximum temperature change (°C), (g)–(i) maximum wind speed change (m s−1), and (j)–(l) maximum precipitation change (mm day−1) during cold pool passage over the diurnal period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
The WPAC in Fig. 3a shows a high number of cold pools with a peak duration of 50–60 min (Fig. 7). Boxes with IQR of cold pool duration (Figs. 11a–c), maximum temperature drop (Figs. 11d–f), wind speed change (Figs. 11g–i), and precipitation rate change (Figs. 11j–l) for all three terciles do depict a moderate diurnal signature. The mean cold pool durations for the first, second, and third terciles are 18.72, 54.02, and 92.25 min, respectively. Similar increasing order is observed for mean maximum temperature drop with values of −0.2°, −0.35°, and −0.69°C, respectively, for the three terciles. Mean maximum wind speed changes are 1.32, 1.42, and 2.15 m s−1. Similarly, the mean maximum precipitation changes are 4.04, 6.56, and 13.15 mm day−1 for the three terciles. Precipitation depicts the most prominent diurnal signature among the four parameters with peaks at 0300 and 1800–2100 LT for the three terciles. Maximum wind speed change and temperature change also show similar but less prominent morning and afternoon peaks. BICP duration remains almost constant throughout the day, thus suggesting no major change in duration. These diurnal variations, having a bimodal distribution, agree largely with the RapidSCAT diurnal cold pool variations presented in the previous section.
Table 3 summarizes the first-, second-, and third-tercile values of mean BICP duration and change in wind speed and temperature, and precipitation rate for EPAC, AO, and IO. Comparing WPAC, EPAC, AO, and IO basin, WPAC has the highest mean values and ranges for the buoy-observed parameters, followed by Indian, Atlantic, and EPAC Ocean basins. The morning (0000–0600 LT) and afternoon peak (1500–1800 LT) for all three basins is similar to WPAC in all four variables. These observations are consistent with previous results showing the highest degree of convective system organization in the WPAC (Albright et al. 1985; Nesbitt and Zipser 2003; Bowman et al. 2005; Burleyson and Yuter 2015). A thorough discussion of these observations is provided in the discussion and conclusions section.
BICP duration (min), temperature change (°C), wind speed change (m s−1), and precipitation change (mm day−1) for the east Pacific (EPAC), Atlantic Ocean (AO), and Indian Ocean (IO) basins.
5. Diurnal covariation of GFs with minimum
RapidSCAT-observed GFs and BICPs show a bimodal diurnal distribution in their frequency with a dominant late night/early morning peak and a secondary afternoon peak. To conceptually understand the cloud systems and moisture environments of convective systems associated with these two peaks, minimum TB and maximum TCWV within GFs is analyzed here. Figure 12 shows the distribution of departures from the diurnal mean frequencies of TB (Fig. 12a) and TCWV (Fig. 12b) probability density for the global tropical oceans. Comparing both histograms, there is a prominent early morning peak between 0000 and 0600 LT across all values of TB and a secondary afternoon peak between 1500 and 1800 LT in TB values > 220 K, with lower frequencies in most TB values during midday. Note the shift in the GF-attributed TB frequency toward very high clouds with suppressed lower clouds between 0900 and 1500 LT and vice versa between 1500 and 2100 LT. Also, the bimodality weakens for the colder temperatures (≤200 K), thus suggesting that for GFs associated with intense deep convection, the early morning/late night peak is dominant.
Diurnal distribution of departures from the diurnal mean frequency in each bin of GF-attributed (a) minimum TB (K) and (b) maximum TCWV (kg m−2).
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Diurnal distribution of departures from the diurnal mean frequency in each bin of GF-attributed (a) minimum TB (K) and (b) maximum TCWV (kg m−2).
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Diurnal distribution of departures from the diurnal mean frequency in each bin of GF-attributed (a) minimum TB (K) and (b) maximum TCWV (kg m−2).
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
A very similar pattern exists in values of TCWV (Fig. 12b) with elevated values of TCWV present in the early morning, and an afternoon departure peak present, although of lower magnitude. The similarity between 2D diurnal distributions of these two variables with GF and BICP distributions suggest that the depth of convection and environmental water vapor influences the formation and maintenance of near-surface cold pools throughout the day.
To further establish the relationship between GF properties (size and precipitation) and TB–TCWV, Gaussian kernel density estimation (KDE) plot is shown in Fig. 13. The method to calculate KDE uses Scott’s factor (Scott 2015) of 0.13, which is used to calculate the bandwidth of the given dataset. Note in Fig. 13a, two distinct peaks are present corresponding to colder TB (200–230 K)—larger GFs (500–1500 km2) and warmer TB (275–285 K)—smaller GFs (<800 km2). This bimodality corresponds to deep convection leading to colder TB and bigger GFs in the first mode and shallow convection with smaller GFs and warmer TB in the second mode. Minimum TB shows a bimodal distribution with GF precipitation (Fig. 13c) with the first peak between colder TB (200–240 K)—moderate-to-heavy precipitation (2.5–12.5 mm day−1)—and the second peak for warmer TB (275–285 K)—lighter precipitation (<5 mm day−1). In the case of maximum TCWV in Fig. 13b, the joint distribution shows a peak between 55 and 65 kg m−2 for most of the GF size range. This suggests that GFs of varied size range are concentrated within a small range of TCWV values over tropical oceans. GF precipitation exhibits a similar trend with TCWV showing a peak at 55–65 kg m−2. The peak TCWV value of 65 kg m−2 is in agreement with Schiro et al. (2016), where the authors observed precipitation peaking at 67 kg m−2 and decreasing toward higher values. Note that the density in Fig. 13 corresponds well with Fig. 6 in terms of relationship between the variables presented here.
Kernel density estimated distribution (KDE) of (a) minimum TB (K)–GF size (km2), (b) maximum TCWV (kg m−2)–GF size (km2), (c) minimum TB (K)–GF precipitation (mm day−1), and (d) maximum TCWV(kg m−2)–GF precipitation (mm day−1).
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Kernel density estimated distribution (KDE) of (a) minimum TB (K)–GF size (km2), (b) maximum TCWV (kg m−2)–GF size (km2), (c) minimum TB (K)–GF precipitation (mm day−1), and (d) maximum TCWV(kg m−2)–GF precipitation (mm day−1).
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Kernel density estimated distribution (KDE) of (a) minimum TB (K)–GF size (km2), (b) maximum TCWV (kg m−2)–GF size (km2), (c) minimum TB (K)–GF precipitation (mm day−1), and (d) maximum TCWV(kg m−2)–GF precipitation (mm day−1).
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Diurnal covariability of minimum TB and maximum TCWV with GF size and precipitation is examined to further explore the linkages between these variables over the diurnal cycle. For this analysis, daytime is defined as time period between 0900 and 2100 LT and nighttime as between 2100 and 0900 LT. Figure 14 shows the normalized frequency difference of GFs, computed as (Day − Night)/(Day + Night) frequency in each bin of the four variables shown. The numbers in each grid box is the total number of GFs in that bin. Bootstrap sampling with 1000 iterations is used to identify statistically significant bins at the 95% confidence level, and values shown meet that threshold. In Fig. 14a, larger GFs (>2500 km2) with colder TB (175–200 K) have a higher frequency during the night and thus stronger covariability (ratio < −0.7). A similar trend is observed in Fig. 14c between TB and GF-precipitation with colder cloud tops associated with heavier precipitation. For warmer TB (250–300 K), there is a higher frequency of moderately large nocturnal GFs (5000–15 000 km2) and precipitation (20–60 mm day−1). TB between 200 and 250 K does not have a strong diurnal change with GF size and precipitation but it does show some moderate frequency of daytime GFs (ratio 0.15–0.3).
Normalized ratio of daytime and nighttime difference in frequency for (a) minimum TB (K)–GF size (km2), (b) maximum TCWV (kg m−2)–GF size (km2), (c) minimum TB (K)–GF precipitation (mm day−1), (d) maximum TCWV (kg m−2)–GF precipitation (mm day−1), and (e) minimum TB (K)–maximum TCWV (kg m−2) for RapidSCAT time period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Normalized ratio of daytime and nighttime difference in frequency for (a) minimum TB (K)–GF size (km2), (b) maximum TCWV (kg m−2)–GF size (km2), (c) minimum TB (K)–GF precipitation (mm day−1), (d) maximum TCWV (kg m−2)–GF precipitation (mm day−1), and (e) minimum TB (K)–maximum TCWV (kg m−2) for RapidSCAT time period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Normalized ratio of daytime and nighttime difference in frequency for (a) minimum TB (K)–GF size (km2), (b) maximum TCWV (kg m−2)–GF size (km2), (c) minimum TB (K)–GF precipitation (mm day−1), (d) maximum TCWV (kg m−2)–GF precipitation (mm day−1), and (e) minimum TB (K)–maximum TCWV (kg m−2) for RapidSCAT time period.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Maximum TCWV (Figs. 14b,d) in the range of 10–40 kg m−2 shows a stronger nighttime frequency difference for GF sizes and precipitation between 2500–12 500 km2 and 10–50 mm day−1, respectively. TCWV between 40 and 80 kg m−2 is associated with the biggest GFs and the heaviest precipitation (similar to Fig. 13) but exhibits moderate nocturnal frequency with few daytime occurrences. Figure 14e shows the covariability of minimum TB and maximum TCWV where higher TCWV (>40 kg m−2) is associated with colder cloud-top temperatures (175–200 K) dominant during nighttime. Warmer TB (260–300 K) also depicts a relatively higher nocturnal GF frequency with moderate TCWV (10–50 kg m−2). TB in the range of 200–260 K is associated with 10–80 kg m−2 of TCWV and a lower nocturnal frequency. Daytime occurrence of GFs is primarily associated with moderate TB (200–250 K) but with both low (10–20 kg m−2) and high (>65 kg m−2) TCWV. Overall, Fig. 14 shows that deep convection with moderately high TCWV and cold TB is associated with larger and heavily precipitating nocturnal GFs while daytime convection is generally associated with warmer TB, moderately sized GFs, and lower TCWV than at night.
6. Discussion and conclusions
This study uses NASA’s 12.5-km RapidSCAT ocean vector winds to understand the diurnal cycle of gradient features (Garg et al. 2020) or GFs, which are used to infer cold pool boundaries over tropical oceans. A nearly 2-yr tropical oceanic GF climatology is produced depicting the number, size (km2), and TRMM precipitation [mm (3 h)−1] within the GFs. The GF frequency and precipitation matches well with MCS number observed previously (Nesbitt et al. 2006; Houze et al. 2015), and size agreed with MCS lifetime shown in Huang et al. (2018), thus linking the global GF characteristics against MCS properties observed in previous studies. Larger cold pools are associated with deeper, moister, and more heavily precipitating convective systems while smaller cold pools are associated with shallower and relatively drier convection, thus signifying a strong relationship between cold pool dynamics and convective system properties.
A comparison of RapidSCAT- with ASCAT-identified GF properties is also carried out to identify differences due to the instruments. RapidSCAT observed more and larger GFs within ITCZ, Maritime Continent, and tropical cyclone-experiencing regions. The numerous, bigger, and more precipitating RapidSCAT GFs is likely due to larger swath, slight retrieval differences, and diurnal sampling as compared to twice a day sampling. RapidSCAT unconditional GF-precipitation is less than ASCAT, which could have been due to differences in rain flags used in the wind retrievals.
Two independent datasets, RapidSCAT and buoy observations, are used to observe the diurnal variability of tropical oceanic cold pools. GFs and BICPs are classified according to the first, second, and third terciles of size and duration, respectively. Similar trends between the two parameters for each basin suggest that cold pool duration and GF size are interdependent. This observation may improve the cold pool parameterizations in convection-parameterizing models. Cold pool number diurnal cycle from RapidSCAT and buoy is in good agreement with previous studies on diurnal cycle of precipitation, showing a dominant morning peak (0300–0600 LT) and a secondary afternoon peak (1200–1700 LT) in cold pool properties. Temporal lead–lag between diurnal rhythm of GF precipitation, number and size suggests causality between these cold pool–associated properties. Correspondence between GF and BICP variables signify that cold pools are responsible for modulation of different geophysical parameters and thus play a very important role in surface–atmosphere exchange throughout the diurnal range. A notable diurnal cycle of minimum cloud top infrared brightness temperature and maximum total column water vapor associated with RapidSCAT-observed GFs matches with the GF and BICP diurnal cycle of number, size, and precipitation. In addition, relationships between minimum TB with GF size and precipitation depict a dominant peak associated with colder temperatures and larger, more heavily precipitating GFs. On the other hand, a secondary peak in TB corresponds well with smaller and moderately raining GFs.
The unimodal distribution of maximum TCWV with GF size and precipitation suggests that a narrow range of TCWV is associated with largest and heavily precipitating GFs. The GF–TCWV relationship is in agreement with Schiro et al. (2016), where the authors observed that nighttime precipitation peaks for a narrow range of TCWV and it decreases beyond that threshold. Diurnal covariability of GF size and precipitation with minimum TB and TCWV further provides evidence that colder cloud tops with moister atmospheric column are attributed to bigger and rainier GFs at night while daytime GFs are linked to warmer cloud tops and a relatively drier atmospheric column. These results are generally consistent across all tropical oceans, with regional variability in cold pool size, frequency, and precipitation, which could be due to differences in environmental properties and thus convection type.
The results from this study are summarized in the form of a conceptual illustration shown in Fig. 15, where nocturnal convection is shown on the left-hand side and daytime convection is on the right-hand side within one diurnal cycle. During the daytime, the static stability within the cloudy regions increase while it decreases at night, thus resulting in a nocturnal–early morning peak in tropical oceanic precipitation (Randall and Dazlich 1991; Ruppert and Hohenegger 2018). In the absence of shortwave radiation at nighttime, radiative cooling is dominant at the cloud top. But, due to longwave radiation from the surface, low-level radiative heating leads to increase in conditional instability within the low- to midtroposphere, as has been observed in the previous studies (Randall and Dazlich 1991; Ruppert and Hohenegger 2018). It can be inferred here that this increase in instability enhances upward motion, and reduces entrainment in a moister vertical column (high TCWV), thus resulting in deeper convection (low TB) at nighttime, but proving this relationship requires further research. Deep convective systems (e.g., MCSs), likely with stronger and more numerous updrafts and downdrafts lead to heavier precipitation and numerous, larger cold pools at the surface at nighttime/early morning. Ruppert and Klocke (2019) used a global cloud-resolving model to observe the diurnal modes of tropical upward motion. The nocturnal peak in convective activity and upward motions found therein correspond well with the GF and BICP diurnal properties observed in this study. On the other hand, daytime solar insolation results in stronger radiative heating at cloud top, hence decreasing the static instability in the atmosphere. This results in shallower convection (warmer TB), lower TCWV, and smaller and weaker GFs and BICPs as compared to nighttime convective cold pools observed in this study, although conditional instability may be aided due to diurnal surface warming and lower tropospheric vertical motion due to direct radiative heating (Gray and Jacobson 1977; Ruppert and Johnson 2016; Ruppert and Hohenegger 2018). Further investigation in horizontal and vertical geophysical parameters is required so as to better characterize the diurnal variability in cold pools and is a topic of future work.
Illustration summarizing the diurnal modes of cold pools, their associated convective system types, and their properties.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Illustration summarizing the diurnal modes of cold pools, their associated convective system types, and their properties.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Illustration summarizing the diurnal modes of cold pools, their associated convective system types, and their properties.
Citation: Journal of Climate 34, 23; 10.1175/JCLI-D-20-0909.1
Acknowledgments
The authors thank NASA Ocean Vector Wind Science Team for providing funding through Grant NNH17ZDA001N-OVWST 17-OVWST-17-0019. The authors also would like to thank the NASA PODAAC team for providing RapidSCAT data. The authors also would like to thank the GTMBA Project Office of NOAA/PMEL for providing global mooring buoy network data. The authors also want to thank NASA GES DISC for providing TRMM 3B42 data used in this study. The authors also would like to thank Copernicus Climate Change Service (C3S 2017) as total column water vapor data were generated using the C3S service in October 2020. The authors also would like to thank NOAA/NCEP for infrared brightness temperature data. We also would like to thank Ms. Vidushi Sharma for designing the conceptual illustration (Fig. 15) for this manuscript.
Data availability statement
The data used in this study are provided on https://publish.illinois.edu/scat-coldpools/data-products/.
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