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EARLY ONLINE RELEASE This is a PDF of a manuscript that has been peer-reviewed and accepted for publication. As the article has not yet been formatted, copy edited or proofread, the final published version may be different from the early online release. This pre-publication manuscript may be downloaded, distributed and used under the provisions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. It may be cited using the DOI below. The DOI for this manuscript is DOI: 10.2151/sola. 2020-036. J-STAGE Advance published date: Sep. 23, 2020 The final manuscript after publication will replace the preliminary version at the above DOI once it is available.

SOLA, 2020, Vol. 16, 201-204(TBA), doi:10.2151/sola.2020-036 1

Seasonal Variations of Atmospheric Aerosol Particles Focused 1

on Cloud Condensation Nuclei and Ice Nucleating Particles 2

from Ground-Based Observations in Tsukuba, Japan 3

Narihiro Orikasa1, Atsushi Saito1,3, Katsuya Yamashita1,4, Takuya Tajiri1, Yuji Zaizen1, 4

Tzu-Hsien Kuo1,5, Wei-Chen Kuo1, and Masataka Murakami2,1 5

1Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan 6

2Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan 7

8

Corresponding author: Narihiro Orikasa, Meteorological Research Institute, Japan 9

Meteorological Agency, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. Email: 10

[email protected] 11

3 Current affiliation: Atmospheric Environment Division, Japan Meteorological Agency, 12

Tokyo, Japan. 13

4 Current affiliation: Snow and Ice Research Center, National Research Institute for 14

Earth Science and Disaster Resilience, Nagaoka, Japan. 15

5 Current affiliation: Department of Natural Sciences and Sustainable Development, 16

Ministry of Science and Technology, Taipei, Taiwan. 17

18

19

Orikasa et al., Seasonal variations of aerosols focused on CCN and INP in Tsukuba 2

Abstract 1

Since March 2012, multi-year ground-based observation of atmospheric aerosol 2

particles has been carried out in Tsukuba, Japan to characterize the aerosol particle 3

number concentrations (NCs), air mass origin relevance, and specifically, their cloud 4

condensation nuclei (CCN) and ice nucleating particle (INP) characteristics. The CCN 5

NCs at any water supersaturation (SS) exhibit strong seasonality, being higher in winter 6

and lower in summer; this pattern is similar in the polluted urban environment in East 7

Asia and contrary to that in the Pacific coastal region. The hygroscopicity (κ) is generally 8

high in early autumn and low in early summer, likely due to the seasonal difference of 9

synoptic-scale systems. In contrast, the INP NCs and ice nucleation active surface site 10

density (ns) at defined temperature (–15 to –35°C) and SS (0%–5%) lack clear seasonal 11

influence. The average INP NCs and ns in this study were comparable at warmer 12

temperatures and approximately one order of magnitude lower at colder temperatures, 13

compared with those in other urban locations under limited dust impact. Moreover, the ns 14

values were one to four orders of magnitude lower and exhibited weaker temperature 15

dependence than previous parameterizations on mineral dust particles. 16

17

18


1. Introduction 1

Atmospheric aerosol particles (APs) can act as cloud condensation nuclei (CCN) 2

and/or ice nucleating particles (INPs) under suitable conditions. These particles 3

significantly impact climate change and weather forecasting by modulating cloud albedo 4

(Twomey 1974) and by modulating precipitation efficiency, spatial distribution and 5

lifetime of cloud systems (Albrecht 1989), causing regional or global energy and 6

hydrological cycles alterations. To assess the influence of APs on cloud and precipitation, 7

it is necessary to comprehend the temporal and regional behavior of the APs with CCN 8

and INP characteristics. As air in the convective boundary layer flows into the low-level 9

or deep convective clouds, the APs involved are vital to cloud formation. Thus, 10

understanding the behavior of APs, especially in the upper convective boundary layer, is 11

crucial. 12

In general, continuous in-situ observations of APs and their physicochemical properties 13

(including CCN and INP characteristics) in the upper convective boundary layer is 14

challenging. Although aircraft observations produce vertical profiles, the data are 15

commonly discontinuous. However, a previous study suggested that AP data, including 16

the CCN, from aircraft measurements in the boundary layer were comparable to ground-17

based measurements with insignificant nearby aerosol sources (Yamashita et al. 2015). 18

To estimate the impact of APs on climate change and weather forecasting using 19

numerical models, including aerosol physicochemical properties as prognostic variables, 20

determining the CCN and INP characteristics of aerosols (e.g., dust, sea salt, sulfate, black 21

carbon, organic carbon, etc.) is imperative. Moreover, obtaining the long-term 22

observation data for APs physicochemical properties, including the CCN and INP 23

characteristics, is relevant for validating and improving the numerical modeling of 24


aerosol-cloud-precipitation interactions. 1

Many studies are available on long-term observations of the physicochemical 2

properties of APs and CCN activation spectra. However, as far as we know, no study 3

focuses on the seasonal and interannual variabilities of APs including the CCN and INP 4

properties. A possible reason for this absence of studies is that online measurement 5

techniques (e.g., the continuous-flow diffusion-chamber type; CFDC-type) and offline 6

approaches (such as filter sampling) for deriving INP properties are not fully automated. 7

Based on the design by Rogers et al. (1988), MRI built the CFDC-type ice nucleus counter 8

(INC) (Saito et al. 2011), that introduced the automation of ice coating of cylinder walls 9

and measuring the INP temperature and supersaturation spectra. This device is suitable 10

for long-term automatic INP characteristics measurements. 11

In this study, we analyze multi-year APs, CCN, and INP datasets measured in Tsukuba, 12

Japan. The aims of this study are: to characterize seasonal and interannual variations in 13

the APs, CCN, and INP properties data; to investigate the relationships between these 14

properties; to assess their dependence on the origin of air masses in the area; and to 15

establish the similarities and differences between previous studies and our results. 16

17

2. Methods 18

2.1 Site and instrumentation 19

Multi-year ground-based continuous observation has been conducted since March 20

2012 on the MRI campus in Tsukuba (hereafter TKB, 36.056°N, 140.125°E, 24 m above 21

mean sea level) which is located 50–60 km north-east of Tokyo. The location, schematic 22

diagram, and configuration of the monitoring system are shown in Fig. S1. The APs in 23


ambient outside air were sampled using a PM10 inlet (11 m above ground level) at a flow 1

rate of 120 L min–1 via a stainless-steel line with an internal diameter of 7.0 cm. The 2

branching method, which transfers inlet sample air to each instrument through an L-3

shaped stainless steel tube, was applied to satisfy the isokinetic sampling condition. The 4

air was dried using a diffusion dryer filled with silica gel (Yamashita et al. 2014). 5

The monitoring system comprises an array of aerosol instruments including a scanning 6

mobility particle sizer (SMPS) (model 3936L75, TSI Inc.), a standard optical particle 7

counter (OPC) (model KC-01E, Rion Inc.), a polarization OPC (POPC) (Kobayashi et al. 8

2014), a dual-column continuous-flow streamwise thermal-gradient CCN counter 9

(CCNC) (CCN-200, DMT LLC; Roberts and Nenes 2005), and the MRI's CFDC-type 10

INC (Saito et al. 2011). The size range and finest time resolution of measurements by 11

each instrument are presented in Table S1. 12

The CCNC was operated at a constant water supersaturation (SS) of 0.5% for one 13

column, and cyclically at 0.1, 0.2, 0.5, 0.8, and 1.0% SS over 30 min for the other. The 14

operation procedure for automatic INC measurements are described in Saito et al. (2011). 15

Ice nucleation spectra were obtained at five temperatures from –15 to –35°C (5°C 16

intervals) and in the humidity range of an ice saturation to SS of ~15%. Here, we utilized 17

INC data limited to an SS range of 0%–5%, representing exclusively ice nucleation via 18

the condensation/immersion freezing mode. The entire procedures required 19

approximately 4 h and was typically performed two times from 9 AM to 5 PM on 20

weekdays. Double impactors with 50% cut-off diameter of 1 μm (~1 L min–1 flow rate) 21

were installed upstream of the INC inlet for APs in the supermicron size range removal. 22

In this study, data from March 2012 to December 2018 were utilized, although the INC 23

and POPC data start from April 2013 and April 2015, respectively. The total sampling 24


periods of SMPS, standard OPC, POPC, CCNC, and INC are 48,720 (2,091), 50,235 1

(2,130), 26,548 (1,120), 36,842 (1,607), and 3,087 (983) hours (the number of days), 2

respectively. 3

2.2 Data analyses 4

For the data processing of SMPS, standard OPC, POPC, and CCNC, the number 5

concentration (NC) and size distribution (SD) from each instrument were analyzed using 6

the finest time resolution. The CCNC data for the stepwise SS setting column were 7

processed for the duration at each SS. We processed INC data for each supersaturation 8

scanning at –15, –20, –25, –30, and –35°C, and INP NCs and ice nucleation active surface 9

site (INAS) (e.g., Connolly et al. 2009) density (ns) measured in SS range of 0%–5% are 10

used in this study. After estimating the critical dry diameter from the measured SD of the 11

APs and the CCN NC at a defined SS using Eq. 5 in Schmale et al. (2018), the "apparent" 12

hygroscopicity value (κ) can be alternatively derived from the "κ–Köhler" equation (Eq. 13

6; Petters and Kreidenweis 2007), based on the methods reported in Sullivan et al. (2009). 14

The ns from the INC measurements were derived as described in Kuo et al. (2019). To 15

identify mineral dust particles from the POPC measurements, the algorithm developed by 16

Takatori et al. (2015) was applied. 17

To investigate the origin of air masses, three-day backward trajectories starting at 500 18

m above the observation site were employed. The data were obtained using the hybrid 19

single-particle Lagrangian integrated trajectory (HYSPLIT4) model (Draxler et al. 2014). 20

21

3. Results 22

3.1 Overall statistics on the NCs of the APs, CCN, and INP, and origin of air masses 23


The time-series of monthly-averaged NCs of the APs for several size ranges are shown 1

in Fig. 1, with the overall statistics of the NCs presented in Table S2. The CCN NCs at 2

0.5% SS are within the NCs range between those for APs > 0.1 and > 0.3 μm, while the 3

INP NCs at –25°C are comparable to those for APs > 5 μm. The latter does not indicate 4

that APs > 5 μm were mostly INPs. 5

Figure 2a shows the monthly-averaged relative distribution of the origin of air masses 6

from the three-day backward trajectories, and the 11 regions produced are displayed in 7

Fig. 2b. Evidently, air masses are dominant in the JS and NW regions between June-8

August and October-March, respectively. Overall, the mean distribution is ~40% in the 9

JN and JS regions surrounding Japan, ~30% in the NW, ~10% in the CN, and ~10% in 10

the Pacific Ocean regions, ME and SE. 11

3.2 CCN NCs and κ seasonal variations 12

Monthly variations of CCN NCs and κ at different SSs are displayed in Fig. 3. 13

Considering the median values for all years, the CCN NCs are generally high in winter 14

(November-February) and low in summer (June-September excluding July) at all SSs 15

compared to other seasons. Their interannual variations are minor, whereas monthly 16

fluctuations are significant, as depicted by the error bars in Fig. 3a–e. In contrast, the κ is 17

characterized by sizable interannual variability and high monthly fluctuations, as shown 18

in Fig. 3f–j. The κ values were generally high in August-September and low in May-June 19

and in December. Other local maxima emerge at SSs of 0.5% and 0.8% in January. 20

The frequency distribution of the κ manifests the difference between June and 21

September, displayed in Fig. 4. The distribution shifts to more hygroscopic particles in 22

September, especially at lower SSs, with a significant contribution from κ > 0.5. 23

3.3 INP NCs and ns seasonal variations 24


The monthly INP NCs and ns variations are shown in Fig. 5, with both parameters 1

characterized by considerable interannual variability and large monthly fluctuations. In 2

fact, the months showing maximum and minimum values differ as the temperature varies. 3

According to the arithmetic mean for all years, the INP NCs are highest in June and lowest 4

in February, although local maxima are present in September (T = –15°C), April (T = –5

25°C), and December (T = –35°C), while local minima emerge in March and November 6

(T = –15°C) and in October (T = –25°C). Similarly, the ns reaches its peak value in 7

September and least value in February, with local maximum in June (T = –30°C) and 8

December (T = –35°C). 9

10

4. Discussion 11

4.1 Comparison with seasonal variations of CCN NCs and hygroscopicity in other regions 12

Seasonal variations of CCN NCs and κ parameters with APs physicochemical 13

properties have been reported at regionally representative locations such as high alpine 14

site, Amazon rain forest, boreal forest, coastal, rural, or urban areas (e.g., Schmale et al. 15

2018). In Schmale et al. (2018), the Noto Peninsula site (NOT; Iwamoto et al. 2016) in 16

the Pacific coastal area and the Seoul site (SEL; Kim et al. 2014) in urban environment 17

are included from the East Asia region. For TKB, whose location is categorized by a rural 18

background, the APs are controlled by two major sources: long-range transport by 19

continental or maritime air masses and local pollution, although the latter influence may 20

be transient. The CCN NCs at an SS of 0.2% for TKB are comparable to those for NOT 21

and about half those for SEL. For NOT, the CCN NCs are highest in spring and lowest in 22

winter, a trend similar to that of another coastal site near Taipei in Taiwan (TPE; Cheung 23


et al. 2020). For TKB and at SEL, the seasonal variations exhibit similar trends, with the 1

highest values in winter and lowest in autumn at all SSs. This behavior is attributed 2

primarily to the closeness of the inversion layer to the ground in winter and secondarily 3

to local pollution. For SEL, the CCN NCs for all SSs display strong negative correlations 4

with the planetary boundary layer height, without seasonal dependence (Kim et al. 2014). 5

A coastal site such as NOT is considered not directly influenced by the inversion layer 6

height in winter, but likely by a convectively mixed boundary layer depth. 7

According to Andreae and Rosenfeld (2008), a κ value of 0.3 reflects polluted 8

continental environments. The median κ values in this study ranged from 0.08 (1.0% SS) 9

to 0.38 (0.1% SS), and these are significantly lower than those for corresponding SSs for 10

the coastal TPE (from 0.18 at 0.8% to 0.56 at 0.12%). The mean κ value in this study of 11

0.17 at 0.5% SS falls between the categories for rural (0.48) and urban (0.1) areas 12

(Schmale et al. 2018). The lower κ values for TKB relative to the average continental 13

environment value suggest a comparable or higher contribution of organics compared to 14

other sites in East Asia (e.g., Meng et al. 2014; Iwamoto et al. 2016; Cheung et al. 2020). 15

As shown in Fig. 2, the air masses display no clear difference in origin in June and 16

September. However, in early summer, TKB is typically located north of the Baiu front, 17

whereas in early autumn, the autumnal rain front exerts a major influence over the main 18

island of Japan, with tropical cyclones coming from the south. Therefore, continentally 19

polluted air masses may contribute to the lower κ values in June, while maritime polluted 20

air masses from the Pacific become dominant and contribute to the higher κ values in 21

September. The air masses are characterized by the monthly-average SDs of the APs 22

measured by the SMPS for both months displayed in Fig. S2, with fewer particles in 23

September than in June. 24


4.2 Comparison of INP NCs and ns seasonal variations with other regions 1

There are a limited number of studies on the seasonal variations of measured INP. 2

Regarding the free troposphere (FT) measurements, Conen et al. (2015) reported INP 3

NCs at –8°C using an offline technique throughout the year, and the seasonal trends for 4

the high alpine station, Jungfraujoch (JFJ), in Switzerland, were attributed to boundary 5

layer influence. In contrast, Lacher et al. (2018) presented background FT INP NCs at –6

31 to –32°C from an online technique involving nine field campaigns at JFJ, with the 7

influences of the local boundary layer, dust and marine events excluded. Despite higher 8

background FT concentrations in some seasons due to increased dust and marine events, 9

no seasonal trend is apparent. The median and mean INP NCs measured at –30°C in this 10

study are comparable to those for the background FT. 11

Notably, most INP data involve studies in North America and Europe without locations 12

strongly affected by air pollution (Petters and Wright 2015; Mason et al. 2016). Chen et 13

al. (2018) investigated the INP NCs down to –25°C for urban air pollution in Beijing, 14

China, in winter, concluding no direct air pollution impact on these for the temperature 15

range investigated. The average INP NCs in this study are comparable to the values of 16

10–3–10 L–1 for temperatures ranging from –10 to –25°C reported in Chen et al. (2018). 17

Comparison of the INP NCs to the AP NCs is displayed in Fig. 6, along with the 18

parameterization of “global” average INPs proposed by DeMott et al. (2010). No 19

correlations between the INP NCs and the AP NCs were found in any month, and neither 20

were the NCs nor surface areas of AP larger than 0.5 μm classified as aspherical particles 21

(including mineral dust) by POPC. Despite the significant scatter in Fig. 6a–c, the DeMott 22

parameterization sometimes overpredicts the measured INP by an order of magnitude in 23

winter and spring whereas the average prediction is satisfactory or involves 24


underprediction by an order of magnitude in autumn. 1

The ns calculated from the five INC operating temperatures for all data in this study are 2

shown in Fig. 7, along with the parameterizations of dust and/or surrogate particles by 3

Niemand et al. (2012), Atkinson et al. (2013), and Broadly et al. (2012). The ns values 4

range from one to four orders of magnitude lower, with weaker temperature dependence 5

than those from the previous parameterizations. One of the main reasons for the relatively 6

low ns values is the difference in particle populations; the AP in this study should be 7

largely composed of various types of aerosols with lower ice-nucleating abilities than 8

those of dust particles. For instance, sea spray aerosols may partly contribute to the low 9

ns values in this study, as indicated in DeMott et al. (2016). 10

We also compared of the ns values in this study to those of several field campaigns at 11

other locations from previous studies (not shown). The ns values at –30°C are 12

approximately 1.5 and 2.5 orders of magnitude lower than those from JFJ under the 13

background FT and Saharan dust events conditions, respectively (Lacher et al. 2018). 14

Gong et al. (2019) reported the ns values between –15 and –20°C from a spring field 15

campaign in Cyprus, in the Mediterranean region, were mainly influenced by the long-16

range transport of anthropogenic emissions from Europe and the Middle East. The 17

reported ns values are comparable to those in this study, likely because of the insignificant 18

dust-impact on the sampling data. 19

20

5. Conclusions 21

To characterize the CCN and INP characteristics of atmospheric APs, we conducted 22

the ground-based observations in Tsukuba, Japan, representing a rural polluted 23


environment in East Asia. From our multi-year datasets, the CCN NCs exhibit strong 1

seasonality, with high values in winter and low values in summer, consistent with the 2

results for urban polluted sites such as SEL, and in contrast to those of Pacific coastal 3

sites such as NOT and TPE. Although the κ displayed no clear seasonal trend, high values 4

were common in early autumn and low values in early summer. This pattern is attributed 5

to the seasonal difference of the synoptic-scale systems around the study site. The mean 6

κ of 0.17 at 0.5% SS was lower than the representative value of 0.3 for polluted 7

continental environments, suggesting a higher contribution of weakly hygroscopic 8

materials such as organics. The INP NCs and ns data also lack clear seasonality, although 9

high values were frequent in June and September, with low values common in February. 10

The mean ns values were comparable to or approximately an order of magnitude lower 11

than data for urban locations when dust-impact periods were excluded. 12

In this study we presented the seasonal and interannual variations of the CCN and INP 13

under complex influences of rural and urban polluted environments associated with 14

continental and maritime air masses. The chemical composition and mixing state of APs 15

from concurrent measurements, however, were not involved in this study. These will be 16

addressed in future studies to clarify parameters of APs that are linked with their CCN 17

and INP characteristics. 18

19

Acknowledgments 20

This study was partially supported by JSPS KAKENHI grant numbers JP23244095, 21

JP16K05558, and JP17H00787. The authors thank two anonymous reviewers for their 22

valuable comments and advice. 23


1

Supplements 2

The supplementary information includes the size ranges and finest time resolutions of 3

measurements by each instrument (Table S1), the summarized overall statistics on the 4

monthly average values of APs, CCN and INP (Table. S2), the location and configuration 5

of the MRI's aerosol monitoring system (Fig. S1), and the seasonal variations of averaged 6

SDs of APs measured by the SMPS (Fig. S2). 7

8

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Yamashita, K., M. Murakami, T. Tajiri, and A. Saito, 2014: Ground-based monitoring 5

of cloud condensation nuclei and its quality control at Meteorological Research 6

Institute. Earozoru Kenkyu, 29, 174–182 (in Japanese with English abstract), 7

doi:10.11203/jar.29.174. 8

Yamashita, K., M. Murakami, T. Tajiri, A. Saito, H. Ohtake, and N. Orikasa, 2015: 9

CCN ability of background aerosols and the feasibility of rain enhancement by 10

hygroscopic seeding. Kishou Kenkyu Note, 231, 279–289 (in Japanese). 11

12

13

List of Figure Captions 14

Fig. 1. Time series of the monthly average NCs of APs > 0.01, 0.3, 0.5, 1, 2, and 5 μm, 15

as well as the CCN NCs at 0.5% SS and INP NCs at –25°C. The points represent the 16

monthly median value for different parameters, excluding the arithmetic mean for the 17

INP. The error bars and shading indicate the monthly 10th and 90th percentiles, except 18

the 10th percentile and maximum for the INP. 19

Fig. 2. Monthly relative distributions of (a) the origin of air masses from the three-day 20

HYSPLIT4 backward trajectory analysis and (b) the 11 regions from the regional 21

classification. The AVE in (a) represents the averaged frequency for all months. 22

Fig. 3. Monthly variations of (a–e) CCN NCs and (f–j) κ at SS values of: 0.1% (a, f), 23


0.2% (b, g), 0.5% (c, h), 0.8% (d, i), and 1.0% (e, j). The filled squares and red open 1

circles denote the monthly median values for each year and for all years, respectively. 2

The error bars cover the 10th and 90th percentiles for each year. 3

Fig. 4. Frequency distributions of κ for June (upper) and September (lower) at 0.5% (left) 4

and 0.1% (right) SS. The lines represent the cumulative relative frequencies. 5

Fig. 5. Monthly variations of (a–e) INP NCs and (f–j) ns at the following temperatures: –6

15°C (a, f), –20°C (b, g), –25°C (c, h), –30°C (d, i), and –35°C (e, j). The INP 7

parameters herein only cover values for SS of 0%–5%. Monthly median values are 8

plotted as in Fig. 3. Note that certain median values are not shown as they were below 9

the limit of quantification. The blue asterisks and open triangles represent the monthly 10

arithmetic mean and maximum values for all years, respectively. 11

Fig. 6. Relationship between the INP NCs and the parameters of APs > 0.5 μm by the 12

POPC in February (a, d), May (b, e), and September (c, f) at –25°C for the following: 13

(upper) NCs of all particles and NCs of aspherical particles, which are compared with 14

the parameterization of DeMott et al (2010); (lower) total surface area of aspherical 15

particles and its ratio to that of all particles. 16

Fig. 7. Plot comparing the ns values from previous studies on dust parameterization (solid 17

lines) and the measured ranges of APs using MRI's CFDC-type INC. The red asterisks 18

and open triangles denote the median and maximum values at five temperatures (–15, 19

–20, –25, –30, and –35°C), respectively, with error bars of 10th and 90th percentiles. 20

21

Fig. 1. Time series of the monthly average NCs of APs > 0.01, 0.3, 0.5, 1, 2, and 5 μm, as well as the CCN NCs at 0.5% SS and INP NCs at ‒25°C. The points represent the monthly median value for different parameters, excluding the arithmetic mean for the INP. The error bars and shading indicate the monthly 10th and 90th percentiles, except the 10th percentile and maximum for the INP.

Fig. 2. Monthly relative distributions of (a) the origin of air masses from the three-day HYSPLIT4 backward trajectory analysis and (b) the 11 regions from the regional classification. The AVE in (a) represents the averaged frequency for all months.

Fig. 3. Monthly variations of (a‒e) CCN NCs and (f‒j) κ at SS values of: 0.1% (a, f), 0.2% (b, g), 0.5% (c, h), 0.8% (d, i), and 1.0% (e, j). The filled squares and red open circles denote the monthly median values for each year and for all years, respectively. The error bars cover the 10th and 90th percentiles for each year.

Fig. 4. Frequency distributions of κ for June (upper) and September (lower) at 0.5% (left) and 0.1% (right) SS. The lines represent the cumulative relative frequencies.

Fig. 5. Monthly variations of (a‒e) INP NCs and (f‒j) ns at the following temperatures: ‒15°C (a, f), ‒20°C (b, g), ‒25°C (c, h), ‒30°C (d, i), and ‒35°C (e, j). The INP parameters herein only cover values for SS of 0%‒5%. Monthly median values are plotted as in Fig. 3. Note that certain median values are not shown as they were below the limit of quantification. The blue asterisks and open triangles represent the monthly arithmetic mean and maximum values for all years, respectively.

Fig. 6. Relationship between the INP NCs and the parameters of APs > 0.5 μm by the POPC in February (a, d), May (b, e), and September (c, f) at ‒25°C for the following: (upper) NCs of all particles and NCs of aspherical particles, which are compared with the parameterization of DeMott et al (2010); (lower) total surface area of aspherical particles and its ratio to that of all particles.

Fig. 7. Plot comparing the ns values from previous studies on dust parameterization (solid lines) and the measured ranges of APs using MRI's CFDC-type INC. The red asterisks and open triangles denote the median and maximum values at five temperatures (‒15, ‒20, ‒25, ‒30, and ‒35°C), respectively, with error bars of 10th and 90th percentiles.

Orikasa et al., Seasonal variations of aerosols focused on CCN and INP in Tsukuba

2

Tabl

e S1

. Ove

rvie

w o

f siz

e ra

nges

and

fine

st ti

me

reso

lutio

ns o

f mea

sure

men

ts b

y ea

ch in

stru

men

t.

SOLA, 2020, Vol. 16, 201-204(TBA), doi:10.2151/sola.2020-036

3

Tabl

e S2

. Sum

mar

y of

ove

rall

stat

istic

s on

mon

thly

ave

rage

val

ues o

f APs

, CC

N, a

nd IN

P.

Orikasa et al., Seasonal variations of aerosols focused on CCN and INP in Tsukuba

4

Fig. S1. Location (upper left), schematic diagram (upper right), and configuration (lower) of

MRI's aerosol monitoring system.

SOLA, 2020, Vol. 16, 201-204(TBA), doi:10.2151/sola.2020-036

5

Fig. S2. Seasonal variations of averaged size distributions (SDs) of APs measured by the SMPS.

Monthly averaged SDs in June (plus) and September (cross) are also plotted.

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