1 Introduction

Coronal plumes (or rays) are bright ray-like features in the corona that could extend to tens of solar radii (see e.g. Deforest et al., 1997; DeForest, Plunkett, and Andrews, 2001). They were first found in polar coronal holes during the eclipse more than 100 years ago (see descriptions of the observations in van de Hulst, 1950). Coronal plumes have also been identified in UV, EUV and X-ray, such observations having become available in the space era. With Skylab observations at Mg ix, Wang and Sheeley (1995) first identified plume-like features in the low-latitude coronal holes, and they proposed that coronal plumes are not unique in the polar region but may occur in an open field region at any latitudes on the Sun. This has been confirmed by observations from SOHO, TRACE, Hinode, STEREO and SDO (e.g. Del Zanna and Bromage, 1999; Del Zanna, Bromage, and Mason, 2003; Wang and Muglach, 2008; Feng et al., 2009; Tian et al., 2011a; Yang et al., 2011; DeForest et al., 2018). The electron density of coronal plumes is 3 – 8 times larger than that in the inter-plume region (see Wilhelm et al., 2011, and the references therein), and their lifetimes range from \({\sim}\,20~\mbox{hours}\) (Lamy et al., 1997) to days (Withbroe, Feldman, and Ahluwalia, 1991; Young, Klimchuk, and Mason, 1999; DeForest, Lamy, and Llebaria, 2001). Since coronal plumes are always present in coronal holes which are the major source of fast solar wind (e.g. Xia, Marsch, and Curdt, 2003; Xia, Marsch, and Wilhelm, 2004; Cranmer, 2009; Madjarska et al., 2012; Fu et al., 2015), the connection between coronal plumes and the solar wind has been studied intensively. Some have indicated that they might feed sufficient plasma and energy into the fast solar wind (see e.g. Velli, Habbal, and Esser, 1994; Gabriel, Bely-Dubau, and Lemaire, 2003; Gabriel et al., 2005; Tian et al., 2010, 2011a; Fu et al., 2014; Liu et al., 2015), although they might be not the major source (see e.g. Wang, 1994; Habbal et al., 1995; Hassler et al., 1999; Patsourakos and Vial, 2000; Giordano et al., 2000; Wilhelm et al., 2000; Teriaca et al., 2003). More details about the history of the observations of coronal plumes could be found in recent reviews (Wilhelm et al., 2011; Poletto, 2015).

Coronal plumes are classified into two types, beam plumes and network plumes (Gabriel et al., 2009). Beam plumes have (quasi-)cylindrical shape with a base diameter of \(20\,\mbox{--}\,30~\mbox{Mm}\) at about 30 Mm off the surface of the Sun (see e.g. Deforest et al., 1997), and their footpoints normally correspond to coronal bright points (e.g. Wang, 1998; Wang and Muglach, 2008; Pucci et al., 2014). Network plumes (also named “curtain plumes”) are made up of many faint structures that are aligned in a curtain shape (Gabriel, Bely-Dubau, and Lemaire, 2003). Network plumes are rooted in regions along the edge of supergranular boundaries that correspond to network lanes in the chromosphere (Gabriel et al., 2009; Rincon and Rieutord, 2018).

Both types of coronal plumes are associated with bright network lanes, where the associated magnetic features are dominated by unipolarity (see e.g. Newkirk and Harvey, 1968; Wang and Sheeley, 1995; Wang et al., 1997; Deforest et al., 1997; Wang and Muglach, 2008; Wang, Warren, and Muglach, 2016; Avallone et al., 2018). Recently, by investigating the evolution of the magnetic features associated with tens of coronal plumes as observed by HMI, Wang, Warren, and Muglach (2016) found that coronal plumes form where unipolar network elements inside coronal holes converge to form dense clumps and fade as the clumps disperse again. Similar results have been confirmed by Avallone et al. (2018), who also made a quantitative analysis and found that coronal plumes appear when convergence of the associated magnetic flux surpasses a flux density of \({\sim}\,200\,\mbox{--}\,600~\mbox{Mx}\,\mbox{cm}^{-2}\). Along with the dominant polarity, small bipolar features also frequently appear nearby and might be canceling with the dominant unipolarity (e.g. Wang and Sheeley, 1995; Wang et al., 1997; Wang and Muglach, 2008). Based on the behaviors of magnetic features in the footpoints of the coronal plumes, Wang and Sheeley (1995) proposed a magnetic reconnection scenario, in which reconnection occurs between open fields (corresponding to the dominant unipolarity) and nearby small-scale loops (corresponding to the small bipolar features). Energy dissipation due to such magnetic reconnection process should take place at the base of the corona (Wang, 1994). The released energy in the magnetic reconnection might conduct downward to the chromosphere and result in plasma evaporation, which maintains the high density in the coronal plumes (Wang, 1998).

Many studies have focused on the connection between coronal plumes and other dynamic phenomena in the solar atmosphere. For coronal plumes with coronal bright points persisting at their bases, studies found that they appear several hours after the coronal bright points first appeared and fade away hours after the coronal bright points disappeared (Wang, 1998; Wang and Muglach, 2008; Pucci et al., 2014). While coronal jets are also closely linked to bipolar regions with one dominant polarity (see e.g. Doschek et al., 2010; Huang et al., 2012; Sterling et al., 2015; Panesar et al., 2016; Panesar, Sterling, and Moore, 2018; Panesar et al., 2018), the connection between coronal jets and coronal plumes has also been investigated. Using white light observations during the eclipse data and EIT 195 Å data from SOHO, Lites et al. (1999) reported that a polar coronal plume was disturbed by a jet with a speed of \({\sim}\,200~\mbox{km}\,\mbox{s}^{-1}\) embedded therein. Raouafi et al. (2008) showed that over 90% of the 28 studied jets were associated with plumes, and they also found that 70% of those plume-related jets were followed by plume haze occurring minutes to hours later while the rest of the jets resulted in brightness enhancement in the pre-existed plumes. In more recent studies, the close relationship between coronal plumes and coronal jets has been confirmed (Raouafi and Stenborg, 2014; Panesar et al., 2018). They further found that a large number of small jets and transient bright points frequently occurring in the bases of coronal plumes could be the main energy source for coronal plumes. A more comprehensive summary on the relation between jets and coronal plumes could be found in a state-of-the-art review article by Raouafi et al. (2016).

Recently, using the high resolution observations of the transition region from the Interface Region Imaging Spectrograph (IRIS), Tian et al. (2014) discovered that networks in the upper chromosphere and transition region of coronal holes are occupied by numerous small-scale jets (hereafter, network jets). Network jets having lifetimes of 20 – 80 s and speeds of \(80\,\mbox{--}\,250~\mbox{km}\,\mbox{s}^{-1}\) are prevalent in the network regions. They have been suggested to be powered by magnetic reconnection and many of them were found to be heated to at least \(10^{5}~\mbox{K}\). Network jets have also been found in quiet-sun regions to be slower and shorter than those in coronal holes (Narang et al., 2016; Kayshap et al., 2018).

Since both coronal plumes and network jets are rooted in the network and both of them might be powered by magnetic reconnection, one would naturally ask the question: what is the relationship between the two phenomena? To investigate this point, we analyze a set of IRIS and SDO coordinated observations of network regions to see whether or not there are evident differences in the dynamics of coronal plumes and network jets. We develop an automatic method to identify and track jets in the network regions. With this method, we obtain birth rates, lifetimes, lengths and speeds of the network jets. We then compare these parameters obtained for different parts of the network regions.

The paper is organized as follows: the observations and the data analysis techniques are described in Section 2 (see also the Appendix for the description of the algorithm); the results of the analysis are shown in Sections 3 and 4; the discussion is given in Section 5 and the conclusions are presented in Section 6.

2 Observations and Data Analysis

2.1 Details of Observations

The data were taken on 2015 December 4 from 01:21 UT to 02:19 UT. They were collected by IRIS (De Pontieu et al., 2014) and the Atmospheric Imaging Assembly (AIA, Lemen et al., 2012) and the Helioseismic and Magnetic Imager (HMI, Schou et al., 2012) aboard the Solar Dynamics Observatory (SDO, Pesnell, Thompson, and Chamberlin, 2012).

IRIS was operated in a very large sit-and-stare mode and the IRIS slit-jaw (SJ) imager was observing only at 1330 Å passband with a cadence of 9 s. The spatial scale of the IRIS SJ images is \(0.167^{\prime \prime }\times 0.167^{\prime \prime }\). In order to reduce the telemetry load, the data had been binned by \(2\times 2~\mbox{pixels}\) and thus the spatial scale of each grid of the data array is \(0.334^{\prime \prime }\times 0.334^{\prime \prime }\). The level 2 IRIS data are used and no further calibration is required.

The AIA and HMI data were downloaded from JSOC. The analyzed AIA data are taken in 1600 Å and 171 Å passbands. The cadences of the AIA 1600 Å and 171 Å data are 24 s and 12 s, respectively. The pixel size of the AIA data is \(0.6^{\prime \prime }\times 0.6^{\prime \prime }\). The HMI line-of-sight magnetograms with a cadence of 45 s and a pixel size of \(0.6^{\prime \prime }\times 0.6^{\prime \prime }\) are used. The AIA and HMI data are prepared with standard procedures provided by the instrument teams, and the level 1.5 data are analyzed.

The images taken from different passbands are aligned using as reference several features (such as obvious bright points/dots) in the images. We first align IRIS 1330 Å images to AIA 1600 Å, and then HMI magnetograms to AIA 1600 Å. Although images from different passbands of AIA have been aligned with each other by the data processing pipeline, we also checked the alignment between 1600 Å and 171 Å using referent features and found an offset of \(1^{\prime \prime }\) at both Solar_X and Solar_Y directions.

In Figure 1, we show the context of the region-of-interest taken with AIA, HMI and IRIS around 01:21 UT. The region includes the boundary of an on-disk coronal hole (see Figure 1(a)). We can see that the region consists of a cluster of positive magnetic features (Figure 1(b)), which are aligned in a typical magnetic structure of network regions (see e.g. Xia, Marsch, and Curdt, 2003; Xia, Marsch, and Wilhelm, 2004). The dominant polarities are in line with the coronal hole seen in the AIA data. The network regions could be clearly seen in AIA 1600 Å and IRIS SJ 1330 Å images (see the bright lanes in Figures 1(c) and (d)). Many jets rooted in the network lanes are found, and they can be clearly seen in the IRIS SJ 1330 Å images. In AIA 171 Å images (Figures 1(a) and (e)), we can see that a set of plume-like features are rooted in some of the regions.

Figure 1
figure 1

Context images for regions studied in the present work taken on 2015 December 4. (a) The AIA 171 Å image giving an overview of the coronal structures in and around the studied regions. The contours (white lines) outline the boundaries of the coronal hole determined in the AIA 193 Å image. The region enclosed by the rectangle (green lines) is zoomed-in in panels (b – e), where shown are HMI magnetic features (b), AIA 1600 Å image (c), IRIS SJ 1330 Å image (d), and AIA 171 Å image (e). The rectangles (white lines) in panels (b) – (e) give the field-of-view used for jet identification and tracking. The regions outlined by blue lines with the markings of R1 and R2 are the network regions where coronal plumes are clearly present. The regions outlined by yellow lines with the markings of R3 and R4 are the network regions where coronal plumes are hardly seen.

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2.2 Network Jet Identification and Tracking

Because network jets are extremely abundant in network regions, in order to identify and track them, we have developed an automatic method that is briefly described in this section. If a feature is brighter than 2.5 times of the background and extended for more than \(4^{\prime \prime }\) from the base near a network lane, it is considered as a jet-like feature. An identified jet-like feature is then traced back and forth in time to obtain its evolution. The height of a jet-like feature is determined by the distance between the base and the point along its propagation direction where its brightness drops below 2.5 times of that of the background. If a jet-like feature can be recognized in more than one frame and its height is changing with time, it is considered as a network jet. By determining the heights of a jet changing with time, its speed can be obtained. More details of the algorithm can be found in the Appendix.

3 Dynamics in the Network Regions

The region observed by IRIS SJ 1330 Å and by AIA at 171 Å that has been analyzed in this paper is shown in Figure 1 and the time evolution of the features is shown in the associated online animation . In the IRIS SJ 1330 Å images, we can see that jets are abundant in the network lanes. The network jets are fine and dynamic, which are the typical characteristics of these phenomena (Tian et al., 2014). In regions R1–R4, the network jets ejected in directions almost parallel to each other, which allows our automatic algorithm to be used (see the Appendix for details). In AIA 171 Å images, it is clear that plume-like features are rooted in the regions of R1 and R2, while they are hardly seen (or too weak to be seen) in the regions of R3 and R4. The selection of these regions is based on the fact that their connections (if any) to the coronal plumes could be confirmed with no doubt for a few days. By tracing these regions in AIA observations, the coronal plumes rooted in R1 and R2 could be clearly seen from December 1 to 6 and there was no clear coronal plume associated with R3 and R4 from December 3 to 6. Meanwhile, the other network regions are located in more complex regions such as regions too close to active region loops or in the back of coronal plumes, which do not allow one to draw any solid conclusion on their connections to coronal plumes. On IRIS SJ 1330 Å and AIA 1600 Å images, we can see that the network regions of R1 and R2 are much brighter than R3 and R4. In particular, the average irradiance of R1 and R2 seen in IRIS SJ 1330 Å is about 2 – 4 times of that in R3 and R4 throughout the entire observing time period.

As shown in Figure 1 and the animations , network jets rooted in the studied regions are very dynamic. In AIA 171 Å images, we also observed many disturbances in the coronal plumes. We speculate that a number of network jets were resulting in disturbances in the coronal plumes. Because of the complex background in both transition region and coronal images, in most of the cases we cannot identify a one-to-one relation between transition region activities and the coronal plume disturbances.

The HMI magnetograms show that the regions are dominated by positive polarities (Figure 1(b)). In agreement with Wang, Warren, and Muglach (2016) and Avallone et al. (2018), the regions with clear coronal plumes (R1 and R2) have magnetic features more compact than those in R3 and R4. In most of the regions during the observing period, very few negative polarities were seen in the regions. This indicates that magnetic features with opposite polarity to the major one, if present, should have sizes/lifetimes under the resolution or field strengths under the resolving power (i.e. \({\sim }\,10~ \hbox{Mx}\, \hbox{cm}^{-2}\) in the present case, Liu et al., 2012). To investigate this issue, one will need higher resolution data, which might be provided by the Goode Solar Telescope (GST, Goode et al., 2010; Cao et al., 2010), the forthcoming Daniel K. Inouye Solar Telescope (DKIST) and the planned Chinese Advanced Solar Observatories – Ground-based (ASO-G). Occasionally, we can also observe that small negative polarities appear near the dominant positive ones and immediately disappear within around 1 minute (i.e. close to the HMI temporal resolution), thus we cannot link such magnetic activities to any particular network jets.

We examine the magnetic topology of the region using full-disk potential field extrapolations provided by the PFSS package (Schrijver and De Rosa, 2003) in solarsoft. In Figure 2, we display the potential field extrapolations of the region based on observations taken on the days from December 1 to December 4, which are tracking the region from the east hemisphere to the west hemisphere. It is clear that the region of interest is dominated by open field lines which could be seen at least three days before our observations. The open topology of the magnetic field is in agreement with coronal holes observed in the AIA coronal passbands, and it provides a condition for the birth of coronal plumes and network jets. As described above, however, some network lanes in this open field region are rich in coronal plumes but some others are not, while both are rich in network jets. In the following, we will compare the dynamics of the network jets in R1–R4, and investigate the possible relation between coronal plumes and network jets.

Figure 2
figure 2

The magnetic field lines in the regions derived from the potential field extrapolations based on HMI data taken on December 1 (a), December 2 (b), December 3 (c) and December 4 (d). The green and purple lines are representative of open field lines and the white ones are closed field lines. The yellow rectangles indicate the studied region as shown in Figure 1(b) – (e).

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4 Statistical Analysis of the Network Jets

In R1–R4, we identify 1293 network jets, of which 619 are located in regions R1 and R2 and the rest 674 located in regions R3 and R4. Based on their coronal plume activities, in the statistics we group network jets from R1 and R2 as one category, and R3 and R4 as another category. The birthrates of network jets in these two types of regions are \(7.8\times 10^{-16}~\mbox{m}^{-2}\,\mbox{s}^{-1}\) (R1 and R2) and \(4.7\times 10^{-16}~\mbox{m}^{-2}\,\mbox{s}^{-1}\) (R3 and R4), respectively. Please note that these birthrates are the lower limits, because we only take into account the network jets that allow for speed calculations (see the Appendix for details). In Figure 3, we give the statistical analysis of three parameters (lifetime, height and speed) of the network jets in the two categories of regions.

Figure 3
figure 3

Statistical histograms of the lifetimes (a), heights (b) and speeds (c) of the network jets identified and tracked in the regions of R1 and R2 (red lines) and R3 and R4 (black lines). The values following “mean” and “stddev” are the mean values and standard deviations of the corresponding parameters, respectively.

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In the regions rich in coronal plumes (i.e. R1 and R2), the average lifetimes, heights and speeds of all 619 network jets are 45.6 s with a standard deviation (\(1\sigma \)) of 35.0 s, \(8.1^{\prime\prime }\) with \(1\sigma \) of \(1.6^{\prime \prime}\) and \(131~\mbox{km}\,\mbox{s}^{-1}\) with \(1\sigma \) of \(64~\mbox{km}\,\mbox{s}^{-1}\), respectively. In the regions poor in coronal plumes (i.e. R3 and R4), the average lifetimes, heights and speeds of all 674 network jets are 50.2 s with \(1\sigma \) of 35.4 s, \(5.5^{\prime \prime }\) with \(1\sigma \) of \(1.9^{\prime\prime}\) and \(89~\mbox{km}\,\mbox{s}^{-1}\) with \(1\sigma \) of \(45~\mbox{km}\,\mbox{s}^{-1}\), respectively. On average, the network jets from regions rich in coronal plumes are higher and faster than that from the regions poor in coronal plumes. However, we also see that the distributions of each parameter from the two kinds of regions largely overlap. The obtained values of heights and speeds are spreading in the ranges consistent with the measurements in the previous studies (Tian et al., 2014; Narang et al., 2016; Kayshap et al., 2018), but the mean and most probable values measured here are smaller. This could result from the line-of-sight effect since the region studied here is closer to the disk center than theirs. Although the obtained lifetimes are generally in agreement with the previous studies (Tian et al., 2014; Narang et al., 2016; Kayshap et al., 2018), there is a large portion showing a lifetime of 18 s (i.e. two frames in the time series). This kind of short-lifetime jets were not included in the studies of Tian et al. (2014) and Narang et al. (2016) due to the temporal resolutions of their data, but they appear to be common (see e.g. De Pontieu, Martínez-Sykora, and Chintzoglou, 2017; Martínez-Sykora et al., 2017).

In the speed regime, the histograms show two peaks at \({\sim}\,50~\mbox{km}\,\mbox{s}^{-1}\) and \({\sim}\,110~\mbox{km}\,\mbox{s}^{-1}\) with a clear division at \({\sim}\,90~\mbox{km}\,\mbox{s}^{-1}\). Although with less samples, such a speed distribution has also been observed in Tian et al. (2014), but with different peak speeds at \({\sim}\,110~\mbox{km}\,\mbox{s}^{-1}\) and \({\sim}\,160~\mbox{km}\,\mbox{s}^{-1}\) and a division at \({\sim}\,140~\mbox{km}\,\mbox{s}^{-1}\). Such a difference between their results and ours can be understood if we take into account the line-of-sight effect. The questions are whether such a two-peak distribution is always present and whether this two-peak distribution indicates different species of network jets. These could be interesting topics for future studies using higher cadence data and a larger sample.

The distribution histograms of the lifetimes, heights and speeds of the network jets overlap with those of spicules that also originated from network regions (see e.g. Sterling, 2000; Xia et al., 2005; De Pontieu et al., 2007a; Zhang et al., 2012; Pereira, Pontieu, and Carlsson, 2012; Pereira et al., 2014). We speculate that a part of the network jets identified here correspond to spicules in the IRIS SJ 1330 Å passband. Comparing with the speed histogram of spicules (De Pontieu et al., 2007a; Pereira, Pontieu, and Carlsson, 2012), we found that the ratio of the number of the network jets with speeds higher than \(100~\mbox{km}\,\mbox{s}^{-1}\) to those higher than \(50~\mbox{km}\,\mbox{s}^{-1}\) is more than 50%, which is much larger than that found in type II spicules.

5 Discussion

Since both network jets and coronal network plumes originate in network regions, the connection between them should be investigated. In the present study, we made a statistical analysis of a few parameters of network jets in four network regions (R1–R4), in which coronal plumes are clearly seen in R1 and R2 but almost invisible in R3 and R4. We found that the network jets in R1 and R2 are statistically higher and faster than those in R3 and R4. These observational results indicate more dynamic and energetic nature of network jets in the regions where coronal plumes are clearly seen. It suggests that the network regions of R1 and R2 could provide a condition in favor of both more energetic network jets and stronger coronal plumes. The coronal plumes belong to the possible sources of the fast solar wind (see Introduction for details). In the present work, we found that the network jets associated with coronal plumes are more energetic and dynamic. The more energetic jets might reach and feed plasma to the corona that could contribute to the mass loss by the solar wind in coronal plumes.

While the unipolar features in R1 and R2 are more compact (implying a higher degree of convergence), it provides stronger compression in the regions. The compression in the footpoints of the magnetic flux tubes might produce shocks that could feed mass and energy to the higher solar atmosphere. The shocks have to be strong and dense enough to power plasma to produce coronal plumes in the region. In this scenario, one would expect that the network jets are also powered by shocks and therefore many network jets should directly feed mass and energy to coronal plumes. This will require a further study using data targeting regions with simpler background (e.g. polar regions), which allows one to identify a one-to-one relation between network jets and coronal plumes. The higher degree of compression might also produce more complicated shearing motions in the footpoints of magnetic flux tubes rooted in the region. The complex shearing motions could generate a complexity of magnetic topology above the photosphere. Such a complexity of the magnetic topology includes magnetic braids (Parker, 1983b,a). As a result of magnetic braiding, magnetic reconnections occur and could heat the plasma to coronal temperatures and accelerate the plasma to more than \(100~\mbox{km}\,\mbox{s}^{-1}\) (Cirtain et al., 2013; Huang et al., 2018).

If there were mixed polarities in the region, the higher degree of convergence might directly drive more magnetic reconnection to occur and thus feed more mass and energy to the higher solar atmosphere (He et al., 2010). In the present cases, although there are opposite polarities found near the dominant polarity, they are too rare to agree with the occurrence of the network jets. However, it is possible that many opposite polarities cannot be resolved with the current data, and higher resolution data provided by GST and DKIST might shed light on this problem.

In coronal plumes, propagating disturbances are usually seen (see e.g. DeForest and Gurman, 1998; Marsh et al., 2003; De Pontieu et al., 2007b; Wang et al., 2009; De Pontieu and McIntosh, 2010; Tian et al., 2011b,a, 2012, etc.). It has been reported that spicules (De Pontieu et al., 2011; Pant et al., 2015; Jiao et al., 2015; Samanta, Pant, and Banerjee, 2015; Jiao et al., 2016) and/or shocks (Hou et al., 2018) could be the possible sources of the propagating disturbances in the coronal plumes. In the present study, the observations show many propagating disturbances along the coronal plumes. We can only speculate from the animation that some of the network jets should be directly linked to the propagating disturbances in the coronal plumes. However, because of the complex background emission, we were not able to identify a one-to-one correspondence between such disturbances and network jets. Whether network jets can directly trigger propagating disturbances in the coronal plumes remains an open question.

The results shown in the present work should be re-examined by more observations, especially those taken during solar minimum. Such data should have a very high cadence, allowing the dynamic network jets to be traced, and the targets should be in or around coronal holes. Furthermore, it is ideal to have two kinds of regions (with and without connection to coronal plumes) in one dataset to avoid uncertainty brought about by line-of-sight and instrumental effects. Unfortunately, searching through the IRIS database, we can say that the dataset analyzed in the present work remains the unique one that is suitable for the purpose. Since many network jets could be spicules that can be observed by ground-based instruments, e.g. the New Vacuum Solar Telescope (NVST, Liu et al., 2014), the Swedish Solar Telescope (SST, Scharmer et al., 2003), GST and DKIST, further studies using high spatial–temporal resolution observations of the chromosphere should shed more light on this topic.

6 Conclusions

In the present study, we studied activities in four regions of network lanes, in which coronal plumes (observed by AIA in the 171 Å passband) are clearly seen being rooted in two (R1 and R2) of the regions but are very faint in the other two (R3 and R4). In all these regions, network jets seen in IRIS SJ 1330 Å passband are abundant. Positive polarities are dominant in these regions and negative polarities could only be seen occasionally. The positive polarities are more compact in R1 and R2, suggesting a higher degree of convergence. The average irradiance of R1 and R2 seen in IRIS SJ 1330 Å is 2 – 4 times of that in R3 and R4.

We developed an automatic method to identify and trace network jets in these regions. With the method, we identified and traced 619 network jets in R1 and R2 and 674 in R3 and R4. With these samples, we carried out statistical analyses of lifetimes, heights and speeds of the network jets in R1 and R2 and R3 and R4, respectively. The lifetimes, heights and speeds of the network jets in R1 and R2 are 45.6 s with a standard deviation (\(1\sigma \)) of 35.0 s, \(8.1^{\prime \prime }\) with \(1\sigma \) of \(1.6^{\prime \prime }\) and \(131~\mbox{km}\,\mbox{s}^{-1}\) with \(1\sigma \) of \(64~\mbox{km}\,\mbox{s}^{-1}\), respectively. In R3 and R4, the average lifetimes, heights and speeds of the network jets are 50.2 s with \(1\sigma \) of 35.4 s, \(5.5^{\prime \prime }\) with \(1\sigma \) of \(1.9^{\prime \prime }\) and \(89~\mbox{km}\,\mbox{s}^{-1}\) with \(1\sigma \) of \(45~\mbox{km}\,\mbox{s}^{-1}\), respectively. These results show that the network jets are on average higher and faster (i.e. more dynamic) in the regions with visible coronal plumes than that without clear coronal plumes. The more energetic network jets that are associated with coronal plumes might reach and feed plasma to the corona and then contribute to the balance of the mass loss by the solar wind. We suggest that the convergence of motions at the base of the network regions could build up energy and the energy could be released to the higher solar atmosphere through shocks and/or small-scale (under the current resolution) magnetic reconnections.