Wet deposition in shallow convection over the Southern Ocean

Alinejadtabrizi, T.; Lang, F.; Huang, Y.; Ackermann, L.; Keywood, M.; Ayers, G.; Krummel, P.; Humphries, R.; Williams, A. G.; Siems, S. T.; Manton, M.

doi:10.1038/s41612-024-00625-1

Wet deposition in shallow convection over the Southern Ocean

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Published: 22 March 2024

Volume 7, article number 76, (2024)
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Wet deposition in shallow convection over the Southern Ocean

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Abstract

Southern Ocean (SO) air is amongst the most pristine on Earth, particularly during winter. Historically, there has been a focus on biogenic sources as an explanation for the seasonal cycle in cloud condensation nuclei concentrations (N_CCN). N_CCN is also sensitive to the strength of sink terms, although the magnitude of this term varies considerably. Wet deposition, a process encompassing coalescence scavenging (drizzle formation), is one such process that may be especially relevant over the SO. Using a boundary layer cloud climatology, N_CCN and precipitation observations from Kennaook/Cape Grim Observatory (CGO), we find a statistically significant difference in N_CCN between when the upwind meteorology is dominated by open mesoscale cellular convection (MCC) and closed MCC. When open MCC is dominant, a lower median N_CCN (69 cm⁻³) is found compared to when closed MCC (89 cm⁻³) is dominant. Open MCC is found to precipitate more heavily (1.72 mm day⁻¹) and more frequently (16.7% of the time) than closed MCC (0.29 mm day⁻¹, 4.5%). These relationships are observed to hold across the seasonal cycle with maximum N_CCN and minimum precipitation observed during Austral summer (DJF). Furthermore, the observed MCC morphology strongly depends on meteorological conditions. The relationship between N_CCN and precipitation can be further examined across a diurnal cycle during the summer season. Although there was again a negative relationship between precipitation and N_CCN, the precipitation cycle was out of phase with the N_CCN cycle, leading it by ~3 hours, suggesting other factors, specifically the meteorology play a primary role in influencing precipitation.

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Introduction

The atmosphere over the Southern Ocean (SO) is renowned for being the most pristine on Earth^{1,Full size image}

To further investigate the meteorological differences under open and closed MCC, we also examined their composite soundings using ERA5. As shown in Fig. 2, closed MCC (Fig. 2a) exhibits a stronger inversion consistent with the higher EIS in Fig. 1d. On the other hand, open MCC (Fig. 2b) displays a higher inversion altitude, which is typically associated with conditions favorable for enhanced precipitation⁵⁷.

Seasonal variations in the CCN-precipitation relationship

Although we have less than six complete years of all observations, it is of interest to examine the seasonal cycle of these records (Table 3), in an effort to investigate the role of precipitation in this cycle. Turning first to the median baseline N_CCN, we observe a strong seasonal cycle with an Austral summer (DJF) maximum of 157 cm⁻³ and a winter (JJA) minimum of 54 cm⁻³, which is consistent with the long-term CGO records^{20,21,28,29,34,37,69,70}. The average baseline precipitation rate and frequency exhibit a pattern opposite to that of median N_CCN, with a maximum in winter (2.69 mm day⁻¹ occurring 20.3% of the time) and a minimum in summer (0.68 mm day⁻¹ occurring 6.4% of time). We note that the average baseline precipitation rate (1.69 mm day⁻¹) substantially contributes to the overall precipitation rate across this latitude band (2.5–3.2 mm day⁻¹)⁴⁸, which is found in various precipitation products⁴⁸. Statistical independent sample tests, including two-tailed Student’s t test and Whitney U test, were conducted to assess the significance of the observed differences in N_CCN and precipitation. These tests aimed to test the null hypotheses that there is no difference in N_CCN and precipitation across the seasons. The results confirmed that these differences are statistically significant (p < 0.05), reinforcing the validity of our findings.

Table 3 Seasonal variations

Full size table

After segregating the records into open and closed MCC cases, we observe an inverse tendency between N_CCN and precipitation when we analyze the data across different seasons (except for the closed MCC during the winter, which can be attributed to the limited number of cases with precipitation from closed MCC during this season). Further we find that for all seasons, open MCC has a greater average precipitation rate where it is more frequent as well and lower median N_CCN compared to closed MCC (Table 3). The relative difference in median N_CCN is least in summer (12%) and greatest in winter (27%). The most pristine air is observed at CGO when open MCC is present upwind during the winter season. Using the statistical tests (including two-tailed Student’s t test and Whitney U test), we tested the null hypotheses that there is no difference in N_CCN and precipitation between open and closed MCC across different seasons, which confirm the significance of these differences (p < 0.05).

We further note a strong seasonal cycle in the frequency of coverage of open MCC with a wintertime peak of 30.7% of all baseline records and a summertime minimum of 6.2% (Table 3). Such a seasonal cycle is consistent with numerous climatological studies (e.g.^60,68). Conversely no substantial seasonal cycle is evident in the frequency of occurrence of closed MCC (Table 3).

Using HYSPLIT back trajectories we sought to establish whether differences in the air mass origin could be linked to differences in N_CCN, and potentially cloud morphology. The 72-h back trajectories (Fig. 3), however, are not conclusive with only weak differences between seasons and between the open and closed MCC. Immediately upwind of Tasmania, back trajectories of closed MCC predominantly come from the west, while open MCC trajectories have mostly a south-westerly origin. During winter, open MCC back trajectories tend to originate at higher latitudes than closed MCC. Also, for closed MCC during winter, there is an interesting air mass origin region in the Australian Bight, near the coast. The average N_CCN for these scenes was similar to overall average. In general, the baseline air masses originate from higher latitudes, as previously established. Only rarely do these back trajectories cross over Antarctica. The vertical component of these back trajectories (Fig. 4) suggests that large-scale subsidence prevails for both open and closed MCC, somewhat stronger for closed MCC. Back trajectories of open MCC in winter show considerably more spread through the boundary layer, suggesting a well-mixed boundary layer due possibly to active shallow convection. There is less spread in the vertical history of closed MCC back trajectories, consistent with widespread subsidence.

An alternate hypothesis for the cause of the observed seasonal cycle in open MCC and precipitation at CGO pertains to the annual migration in the subtropical ridge, which reaches its highest latitude (38° S) along Australia during February with an average intensity of 1016 hPa⁷¹. During winter the subtropical ridge strengthens in intensity but retreats to lower latitudes (28° S) over the Australian continent. Manton et al.⁴⁸ found a strong correlation coefficient (~−0.6) between precipitation and MSLP across the SO, highlighting the importance of the general circulation in precipitation processes.

The seasonal cycles of atmospheric stability parameters were analyzed (Table 4) to further assess their relationship to the baseline conditions and cloud morphology. These parameters are important for determining the depth of the MABL and evolution of low clouds⁶¹, which are a major source of precipitation in many regions. Lang et al.⁷² also found a connection between EIS and the occurrence of precipitation over Macquarie Island. Both M and EIS show a seasonal cycle with stronger stability (lower M and higher EIS) during summer (DJF), consistent with Lang et al.⁷² and McCoy et al.⁶¹. Also, there is a stronger inversion (61% greater EIS) under closed MCC coverage, consistent with the composite sounding (Fig. 2a). Thus, these meteorological controls may be of further importance in setting the seasonal cycle of precipitation, which may also contribute to the seasonal cycle of N_CCN.

Table 4 Seasonal variations for meteorological parameters

Full size table

In order to evaluate the importance of wet deposition in the evolution of the CCN budget from closed to open MCC across the SO, we sought to employ a quantitative approach. Previous studies have noted the importance of precipitation in the transition from closed to open MCC^64,66,73 and the hypothesis is that wet deposition will clean out the MABL by removing particles. Kang et al.⁴³ utilized the recent summertime Clouds Radiation Aerosol Transport Experimental Study (SOCRATES) campaign over the SO to drive the CCN budget model developed by Wood et al.⁴². This model considers various source and sink terms, including entrainment of CCN from the free troposphere (N_FT), primary production at the sea surface from sea-spray (N_s) and precipitation that is induced by coalescence scavenging (N_p)^7,42,43. Kang et al.⁴³ assumed the system was at steady state and found that the CCN budget was most sensitive to the N_CCN of the overlying free troposphere compared to other sources. Coalescence scavenging was also found to be an important sink.

Following Kang et al.⁴³, we also employed the CCN budget model along with available datasets to perform a CCN budget analysis. Considering the relatively constant N_FT and N_s during the summer when moving between open and closed MCC (as suggested by Kang et al.⁴³), we can assume that

$$\frac{{N}_{{closed}}-{N}_{{open}}}{\Delta t}=\dot{{N}_{{pclosed}}}-\dot{{N}_{{popen}}}$$

(1)

This equation suggests that the reduction in N_CCN from closed to open MCC can be attributed to precipitation along the trajectory. The precipitation sink term depends upon the following terms⁷⁴:

$$\dot{{N}_{P}}=K\,N\,{P}_{{CB}}h/{z}_{i}$$

(2)

where P_CB represents the precipitation rate at cloud base, K = 2.25 m² kg⁻¹ is a constant that depends on the collection efficiency of cloud droplets by drizzle drops, h is the cloud thickness and z_i is the depth of the MABL⁷⁴. Due to the lack of cloud base precipitation measurements in our datasets, we use surface precipitation data as a proxy for P_CB. Cloud thickness and boundary layer height are estimated using the composite ERA5 soundings.

Our calculations indicate that it would take approximately 3 h for precipitation during the transition from closed to open MCC to remove the 12% median CCN differences between closed (165 cm⁻³) and open MCC (145 cm⁻³) during summer. It should be noted that this estimate is defined by constraints in our calculation. Extending such quantitative analysis to other seasons is limited by a lack of enough observational information (e.g., the N_FT and N_s). Nonetheless, our expanded budget analysis for other seasons indicates transition time within the range of 2–6 h. Another limitation of this analysis pertains to the estimation of precipitation that is induced by coalescence scavenging (N_p), which relies on the precipitation rate at the cloud base level⁷⁴. Our measurements, however, are only available at the surface. Recent research by Kang et al.⁷⁵, found substantial differences between precipitation rates at the cloud base and the surface for MABL clouds over the SO using in situ and airborne cloud radar observations. K, which has been used in estimating N_p, is also subject to uncertainty. Nevertheless, these limitations highlight the significance of future coordinated efforts, such as multi-platform measurement campaigns in this region to supply critical datasets needed for cloud-aerosol-precipitation research, including a more comprehensive CCN budget analysis.

Meteorological influences on diurnal precipitation

Moving beyond the seasonal cycle, we can also explore potential relationships between the median N_CCN and precipitation at the diurnal time scale, aiming to understand whether CCN acts as the driver of the precipitation, or vice versa. We limit this analysis to the summer season, when solar forcing is most intense and can readily decouple, thin and even completely burn off MABL clouds^76,77. The Lang et al.⁶⁸ cloud climatology readily found such a diurnal cycle in closed MCC over the latitude band between 40° to 50° south. For this analysis, however, we employ all baseline samples (open, closed and other) to increase the sample size and ensure a sufficient amount of data for analysis and to clearly illustrate the diurnal cycle. Furthermore, in order to adequately assess the diurnal cycle, data for precipitation and N_CCN from 2011 to 2015 has been included in this analysis (both data were not available before 2011). Looking first at the precipitation (Fig. 5b, c), a strong diurnal cycle is evident in both mean precipitation rate and frequency with a peak in the early morning (5 AM local time) and a minimum in the afternoon. It should be mentioned that the 25th and 75th percentile of precipitation were not presented as both values were zero during the summer. Lang et al.⁷² and Tansey et al.⁵⁸ found a similar diurnal cycle in precipitation over Macquarie Island. The median N_CCN at CGO (Fig. 5a) also displays a diurnal cycle, contrasting with the precipitation pattern with a peak concentration observed at 8 PM. Ayers and Gillet²⁰ and Ayers et al.⁶⁹ also found a diurnal cycle for N_CCN over the CGO. Wider research suggests that the thinning of the marine boundary layer clouds through solar forcing/burn off is likely to be driving the diurnal cycle of the precipitation rate^72,78. Additionally, we observe a diurnal cycle in MSLP (Fig. 5d) and the two stability parameters (M and EIS) (Fig. 5e, f). These meteorological conditions, particularly the stability of the atmosphere and related dynamics, are likely other drivers of the observed diurnal cycles in precipitation. It is especially intriguing to note that the minimum of MSLP coincided with the peaks of precipitation frequency and intensity. These findings further emphasize the crucial role of meteorological controls on ACPI.

In our effort to examine the meteorological impacts on ACPI behaviors, we employed multilinear regression to examine the relationship between meteorological factors, precipitation and aerosol. Subsequently, we removed the meteorological influences on precipitation and N_CCN and examined the residuals. Notably, our findings indicate that, after controlling for the meteorology, the correlation between precipitation and N_CCN decreased from −0.045 to −0.034 (see details in the attached supplementary materials), suggesting that a substantial portion of the relationship between precipitation and CCN is explained by meteorological factors. Additionally, we conducted a comprehensive analysis comparing multilinear regression models of precipitation with and without considering CCN, revealing minimal differences in R² values and correlation coefficients (see details in the attached supplementary materials). This underscores the limited influence of CCN on precipitation, emphasizing the substantial role played by meteorological factors in sha** their relationship. It is important to note that our findings do not imply that the role of CCN is negligible, especially in a changing climate. Even minor influences from the CCN could have important effects on climate given the complexity of the climate system.

Importantly we note that the diurnal cycle in precipitation leads the small diurnal cycle in CCN by roughly 3 h (Fig. 5). To test the significance of this time lag, we applied the Bootstrap** method by which 10000 samples were randomly drawn with a sample size of 8000 (from a total of 8200-time steps). The results indicate that in over 98% of the instances, the minimum N_CCN occurred after the maximum precipitation (in just 2% of the instances, the minimum N_CCN preceded the maximum precipitation). This suggests that the diurnal cycle in precipitation is unlikely to be led by CCN (with a confidence level of 98%). These analyses emphasize that multiple factors, particularly the meteorology, are likely at play in sha** the diurnal patterns of precipitation, and the influence of CCN on precipitation is not a dominant mechanism over our study area. Again, further investigation is needed to explore how precipitation may be influencing the diurnal cycle of CCN through processes like wet deposition, but it is clear that the relationship between CCN and precipitation is complex.

Overall, this analysis once again suggests that a greater understanding of the sinks of aerosols is required to more accurately close the CCN budget across the SO. Specifically, this analysis highlights the potential importance of precipitation in this regard, and the role of the large-scale circulation in driving precipitation across this region.

Methods

Data

The Baseline Air Pollution Monitoring (BAPMon) Station at Kennaook/Cape Grim (CGO) began operations in 1976 as part of the World Meteorological Organization BAPMon program of global atmospheric composition measurements relevant to climate. The observatory is situated at 40° 40ʹ 56ʺ S, 144° 41ʹ 18ʺ E, at the north-west tip of Tasmania, to ensure observations of SO air with minimal anthropogenic influences. The observatory building is located 94 m above sea level, roughly 100 m inland from the coastline break^20,37,70. Figure 6 depicts the location of Cape Grim.

**Fig. 6: Overview of the study area and methodology.**

The N_CCN for particles active at several supersaturations, but predominantly at 0.5% supersaturation (other supersaturations are not available hourly), was determined using a continuous-flow, streamwise thermal gradient CCN counter (CCNC, model CCN-100, Droplet Measurement Technologies, Longmont, CO, USA)⁷⁰. The CCN counter supersaturation was calibrated annually using monodisperse ammonium sulfate particles³⁷. The hourly N_CCN over CGO for 11 years (2011–2021) was used to examine the role of precipitation and cloud morphology on SO N_CCN. The data are available in the World Data Centre for Aerosols (http://www.gaw-wdca.org/). However, it should be noted that 5 months of CCN records were lost during a COVID lockdown (from the end of September 2020 to March 2021) when the instrument was non-operational. The hourly precipitation data, expressed as mm day⁻¹, were from the Australian Bureau of Meteorology rain gauge at CGO (Station ID: 091331) for the same period of time.

The measurement of radon is also carried out hourly with the dual-flow-loop two-filter atmospheric radon detectors over the CGO station^79,80. With its predominantly terrestrial source, unreactive nature, and 3.82-day radioactive half-life, radon is an unambiguous tracer of terrestrial influences on sampled air masses^81,82,83. The fifth generation of European ReAnalysis (ERA5) wind data, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF)⁸⁴, which is available through the Copernicus Climate Change Service Climate Data Store (https://cds.climate.copernicus.eu), were used along with the radon to determine baseline conditions for the same time period.

Finally, the hourly climatology of open and closed MCC is calculated from Himawari-8 imagery (13 µm channel) using a hybrid convolutional neural network (for more details, see Lang et al.⁶⁸) for the six years (2016–2021). Himawari-8, a geostationary meteorological satellite, was launched by the Japanese Meteorological Agency in July 2015 and covers a large part of the SO⁸⁵. The classification of MCC into open and closed categories was not based on cloud fraction or liquid water content, but rather relied on a convolutional neural network’s pattern recognition capabilities, as demonstrated by Lang et al.⁶⁸. They constructed and trained it to identify highly ideal open and closed MCC imagery. All other scenes (e.g., clear sky, frontal clouds, cirrus clouds, stratus, disorganized MCC) are simply grouped together as “other” which restricts the ability to examine these circumstances. It should be noted that our analysis is limited by the availability of Himawari-8 records, which became operational in July 2015⁸⁵, for classifying the cloud morphology at the time of writing.

Methodology

The initial hypothesis was evaluated using these datasets with two different methodologies. The first method defines baseline conditions, as local wind directions between 190 between 280 degrees (e.g.^37,86) and ambient radon concentration less than 150 mBq m⁻³, which includes approximately 80% of baseline sector observations³⁷. To assess the sensitivity of the analysis to radon concentration thresholds, we also tested a lower threshold of 100 mBq m⁻³. Qualitatively, the results are not sensitive to different radon concentration thresholds. Air sampled in the ‘baseline’ sector (190°–280°) frequently traveled thousands of kilometres across the SO since last land contact. Baseline sector and other detailed parameters for this method are depicted in Fig. 6 (green colours) and will be discussed later. The second method was based on back-trajectory calculations made with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model⁸⁷ employing ERA5 meteorological data, providing the transit history of each air parcel (red colour in Fig. 6). It provided information on the impact of MCC type on each air parcel on its journey to CGO. Back trajectories were started at 1000 m elevation; nevertheless, the results are qualitatively similar to an initial elevation of 500 m. Since the results for both methods are consistent, we employed only the first method. This analysis is limited to 2016–2021 due to the availability of open and closed MCC climatology data from Himawari-8 observations. However, for the diurnal cycle analysis of precipitation and N_CCN under baseline conditions, we used the full 11-year dataset to increase the sample size. Cloud morphology is not considered in the diurnal cycle analysis due to the small sample size for the period of summer 2016 to 2021.

Application of the baseline constraints, using ERA5 wind data and the radon constraint, leads to the elimination of 63% of all hourly records. For the remaining 17470 h, the fraction of coverage for both open and closed MCC (FC_MCC) was calculated using the Lang et al. classification⁶⁸ on Himawari-8 imagery over various upwind averaging times (1, 3 and 6 h) in the baseline sector with various upwind domain radii (50, 100, 200, 300, 500 and 1000 km) as shown in Fig. 6 (green colours). The upwind averaging time accounted for the travel time of air parcels in the baseline sector to reach CGO, while various domain radii were considered to assess the potential influence of upstream frontal systems on the results. For each upwind radius and averaging time, the mean domain FC_MCC for both open and closed MCC was computed. The FC_MCC is defined as the percentage of the baseline sector covered by either open or closed MCC within the specified radius. To distinguish whether a sample was primarily open or closed MCC, two FC_MCC thresholds were used: 50% was considered as a basic requirement while 80% was considered as a more certain requirement. These trials assessed the sensitivity of the results to the upwind radius and upwind averaging time. For each configuration, we considered cases where either open or closed MCC covered more than 50% or 80% of the baseline sector (FC_MCC >50% or 80% for open and closed). The median and the 5th and 95th percentiles of N_CCN were determined for the times when each cloud class (open or closed MCC) was dominant (FC_MCC >50% or 80%). Mean precipitation intensity and frequency were also determined for each case.

To briefly investigate potential meteorological factors influencing the observed cycles in precipitation from open and closed MCCs, we examined the stability parameters including the estimated inversion strength (EIS) and the marine cold air outbreak parameter (M). Each is calculated using ERA5 reanalysis. EIS estimates the strength of the planetary boundary layer inversion and is defined as⁸⁸

$${EIS}={LTS}-{\Gamma }_{m}^{850}({z}_{700}-{LCL})$$

(3)

where the LTS is the lower tropospheric stability defined as the difference in potential temperature between 700 hPa and the surface (LTS = θ₇₀₀−θ_surf)⁸⁹, ${\Gamma }_{m}^{850}$ is the moist-adiabatic potential temperature gradient at 850 hPa, Z₇₀₀ is the altitude of the 700 hPa level and LCL is the lifting condensation level. Greater EIS indicates stronger temperature inversions, which can suppress vertical mixing and reduce the potential for cloud development and precipitation^72,88,90,91. M was originally defined by Kolstad and Bracegirdle⁹² and modified by Fletcher et al.⁹³ as the difference between the surface skin potential temperature and the 800 hPa potential temperature⁹³. However, given that our study area is in the Southern Hemisphere, we used the 850 hPa potential temperature for the M calculation. This is consistent with Papritz et al.⁹⁴ over the South Pacific. Moreover, considering the surface skin temperature instead of sea surface temperature will exclude the areas of high sea-ice cover⁹³. Positive M indicates a cold air mass over a relatively warmer surface, leading to an absolutely unstable boundary layer. This condition favours cellular convection, a typical signature of cold air outbreaks, which can result in precipitation^61,93.

HYSPLIT back-trajectories were checked to examine whether any differences between open and closed MCC were linked to airmass origins. Each back-trajectory was run from ERA5 fields for 72 h initiated at a height of 1000 m at CGO. To run the back-trajectories, instances were classified as either open or closed depending on whether the FC_MCC for a 100 km upwind length and 3 h averaging time was greater than 80%.

Data availability

The CCN concentration measurement, analyzed during the current study are available in the World Data Centre for Aerosols [http://www.gaw-wdca.org/]. The ECMWF-ERA5 reanalysis datasets are available through the Copernicus Climate Change Service Climate Data Store [https://cds.climate.copernicus.eu]. The precipitation data can be obtained by contacting [climatedata@bom.gov.au]. The radon data is available from the World Data Centre for Greenhouse Gases (WDCGG) [https://gaw.kishou.go.jp/] and from Alastair Williams from Australian Nuclear Science and Technology Organisation (ANSTO). The climatology of open and closed MCC available from the F. Lang on reasonable request.

Code availability

All relevant codes used in this work are not publicly available but can be made available to qualified researchers upon reasonable request from the corresponding author.

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Acknowledgements

This research has been supported by the Australian Research Council Discovery Projects (DP190101362). Continued support for the Kennaook Cape Grim Program from the Australian Bureau of Meteorology and Commonwealth Scientific and Industrial Research Organisation (CSIRO) is also gratefully acknowledged.

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Authors and Affiliations

School of Earth, Atmosphere and Environment, Monash University, Melbourne, Victoria, Australia
T. Alinejadtabrizi, F. Lang, S. T. Siems & M. Manton
Australian Research Council Securing Antarctica’s Environmental Future (SAEF), Melbourne, Victoria, Australia
T. Alinejadtabrizi & S. T. Siems
Australian Research Council Centre of Excellence for Climate Extremes (CLEX), Melbourne, Victoria, Australia
T. Alinejadtabrizi & Y. Huang
Department of Geophysics, Universidad de Concepción, Concepción, Chile
F. Lang
School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Melbourne, Victoria, Australia
Y. Huang
Australian Bureau of Meteorology, Melbourne, Victoria, Australia
L. Ackermann
CSIRO Environment, Melbourne, Victoria, Australia
M. Keywood, P. Krummel & R. Humphries
Australian Antarctic Program Partnership, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia
M. Keywood & R. Humphries
Visiting Scientist Emeritus, Bureau of Meteorology, Melbourne, Victoria, Australia
G. Ayers
Environmental Research, ANSTO, Lucas Heights, New South Wales, Australia
A. G. Williams

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T. Alinejadtabrizi
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F. Lang
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Y. Huang
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T. Alinejadtabrizi prepared the original draft of the paper and performed most of the data analysis. F. Lang & L. Ackermann prepared the climatology of open and closed MCC for 2016 to 2021. F. Lang also calculated stability parameters over the study area. All co-authors provided editorial feedback on the paper. All co-authors read and approved the final manuscript.

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Alinejadtabrizi, T., Lang, F., Huang, Y. et al. Wet deposition in shallow convection over the Southern Ocean. npj Clim Atmos Sci 7, 76 (2024). https://doi.org/10.1038/s41612-024-00625-1

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Received: 24 April 2023
Accepted: 14 March 2024
Published: 22 March 2024
DOI: https://doi.org/10.1038/s41612-024-00625-1
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Wet deposition in shallow convection over the Southern Ocean

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Wet deposition in shallow convection over the Southern Ocean

Abstract

Similar content being viewed by others

Oscillations in deep-open-cells during winter Mediterranean cyclones

Extreme Convective Events in the Lofoten Basin

Oceanic eddy-induced modifications to air–sea heat and CO2 fluxes in the Brazil-Malvinas Confluence

Introduction

Seasonal variations in the CCN-precipitation relationship

Meteorological influences on diurnal precipitation

Methods

Data

Methodology

Data availability

Code availability

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding author

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Additional information

Supplementary information

Supplementary Information

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