Joint variability of cloud fraction with vertical motion and EIS

After examining the spatial distribution of environmental and cloud properties for the dominant synoptic states in January and June, an exploration of potential relation-ships between cloud fraction and synoptic properties may further the understanding of cloud cover at the Azores. Past studies have examined the relationship between low-cloud fraction and stability on seasonal timescales. KH93 examined low-clouds with LTS through seasonal averages of cloud fraction and stability at various locations around the globe, and established a positive relationship, with an r² value of 0.884 and a slope of 5.7. Wood and Bretherton (2006), WB06 hereafter, established EIS as an estimate for the inversion strength, and explored the relationship between low-level cloud fraction and EIS. They also found an even stronger positive relationship between cloud fraction and EIS with an r² value of 0.92. Both studies established a simple linear function to describe the relationship between the stability values and cloud fraction, based on values representative of seasonal means. Here, we explore the possible correlations between instantaneous cloud fraction and synoptic properties for January and June, and then composite the relationships by synoptic regime.

The joint probability distribution functions (PDF) of ERA–Interim cloud fraction and EIS for January and June are seen in the far left column in Figure 4.10. Note that the correlation is calculated for the total cloud fraction, not low-level cloud

4.3 Joint variability of cloud fraction with vertical motion and EIS 43

fraction as it was in KH93 and WB06. The WB06 linear relationship is overlaid with a dashed line on the June cloud fraction and EIS plot. A strong relationship is not easily distinguishable between cloud fraction and EIS, if at all, between these two fields on the daily timescale. The correlations from the composited data for synoptic state for January and June are seen in the right four columns in Figure 4.10. Still, a correlation is not easily distinguished, suggesting that a robust relationship is not present between CF and EIS, at least not on short timescales.

JanuaryJune

All Pre-trough Trough Post-trough Ridge

Figure 4.10 Joint PDFs of cloud fraction and EIS for the entire monthly data sets (All) and for each archetypal synoptic pattern for both June and January. The dashed line shows the EIS versus low-cloud amount found in Wood and Bretherton 2006 A correlation is not clear for the entire monthly data sets or when broken down into synoptic state.

The synoptic projections for January and June suggest that sustained vertical velocity modulate the baseline EIS values and and the vertical motion field has a more striking resemblance to cloud structures. For this reason we calculate the joint PDF for cloud fraction and vertical velocity, shown in Figure 4.11. The entire monthly data sets for January and June cloud fraction and vertical velocity does not show a robust correlation. Calculating the correlation from the composited data for each pattern does not reveal much of a relationship either. A subtle negative slant is

distinguishable in the trough and post-trough patterns, where higher cloud fraction is associated with stronger values of ascent. This subtle tilt is consistent with the expected synoptic behavior for such trough and post-trough states. Although the projected vertical velocity and cloud fraction properties for the SOMs bear a striking resemblance, these PDFs suggest that a robust relationship between the variables cannot be established on daily timescales, even when composited by synoptic state.

JanuaryJune

All Pre-trough Trough Post-trough Ridge

Figure 4.11 Joint PDFs of cloud fraction and vertical velocity for the entire monthly data sets (All) and calculated for the composited synoptic patterns for both June and January. No correlation is found when calculated over the entire data set for each month.

45

Chapter 5 Conclusion

The technique of self-organizing maps (SOMs) was employed to develop a climatol-ogy of synoptic and cloud patterns centered on the Azores islands in the Northeast Atlantic. The SOM technique proved successful in identifying these dominant syn-optic states for June and January, establishing an understanding of not only summer and winter months individually, but providing broad insights into the annual cycle of synoptic and cloud properties. The second and more primary goal of understanding the projected cloud and environmental properties for the dominant synoptic states was also explored. The Azores consistently lie in an area of substantial variability in both synoptic configuration and environmental and cloud properties.

A surprising amount of synoptic intrusions are experienced at the Azores in June (20.5% of the time). Also, a substantial frequency of ridge like conditions were expe-rienced in January (25.4% of the time), which provide conditions supportive to the formation of stratocumulus. Much greater variability in synoptic patterns within each month is experienced when the SOMs are calculated for each individual month, as op-posed to calculating the SOM over the annual cycle. This substantial within month variability supported a closer examination of individual months, in which January

and June are highlighted.

The dominant synoptic states were established for January and June, providing a spatial understanding of the behavior of environmental and cloud properties in the region. Ridges, troughs, and transitional patterns are present for both months, but they must be understood individually to grasp the magnitude and orientation of summer versus winter ridge patterns, for example. Since the analysis is based on anomalies, frequencies cannot be compared across months. The use of anomalies essentially performed a high-pass filter, which filters out the low-frequency (seasonal) variability, leaving the short-term synoptic variability. We interpret higher-frequency nodes as archetype synoptic patterns. Performing the analysis on a month-by-month basis accentuates the synoptic variability relevant for that particular month.

The Bermuda High is present in all synoptic states in June but is most dominant in the ridge and post-trough states. For synoptic patterns in which the Bermuda High is weaker, synoptic intrusions at higher latitudes are permitted into the domain, as evinced by the weak trough in the trough synoptic state. This trough pattern is associated with a weaker, spread out Bermuda High at lower levels. When an upper level ridge over the Bermuda High is present with the ridge axis centered over the Azores, associated subsidence conditions east and southeastward of the Azores create a prime region for the persistence of stratocumulus cloud decks. The baseline tongue structure of EIS is modulated by sustained vertical velocity fields, where subsidence is associated with increased stability. The stratocumulus decks are therefore dominant in regions with sustained subsidence and higher stability values, located downstream of a ridge axis. The western most border of this cloud deck reaches the Azores. The Azores at times exhibits both the northern, cold, thick cloud regime as well as the warmer stratocumulus deck. This highly variable nature of the clouds at the Azores are attributed to these overlapping cloud regimes.

47

Summertime months have the highest amounts of low-level clouds at the Azores, which are predominantly associated regions of subsidence in ridge and post-trough patterns. June does not see the extreme forms of troughs and ridges found on the corner of the SOM as frequently as its less extreme states on the middle-edges of the SOM. The Azores experience an advance and retreat of more northern, high-level clouds, as well as slight eastward and westward shifts of the stratocumulus deck, creating a sloshing back and forth between the two cloud regimes with potential for overlapping cloud properties at the Azores.

January offers a different environment, with greater spatial variability in the region in cloud and environmental properties. Instead of large-scale flow controlled by the strength and persistence of the Bermuda High, the development of the Icelandic low modulates the High in the trough and post-trough states. This Icelandic Low influence causes great differences in cloud regimes between states, particularly in the trough and post-trough states. The post-trough state even exhibits a post-cold-frontal stratocumulus cloud regime found in previous studies (Mechem et al., 2010).

January, like June, also exhibits the subsidence-dominated stratocumulus regime.

That said, the state with the most dominant low-level clouds is still the ridge pattern, persisting in the region of subsidence southeast of the Azores, just like June. The pre-trough pattern exhibits many of the characteristics of zonal flow, but looking at cloud properties reveals a clear frontal cloud structure approaching the Azores.

Correlations between cloud fraction and vertical velocity and EIS on daily timescales for each month offered no relationship of joint variability. Calculating the joint vari-ability by each dominant state, did not reveal a robust relationship either. The rela-tionships established between stability and low-cloud fraction in past studies (KH93 and WB06) were calculated for seasonal averages, and robust relationships were es-tablished. The lack of relationship in the present study is attributed to the fact that

we are examining the correlations on a daily timescale, not a seasonal time scale.

This is because the relationships do not hold for instantaneous time scales, but in-stead require sustained environmental properties over longer time scales to encourage an environment with a strong enough inversion and the build up of low-level moisture that is suitable for persistent low-level cloud coverage.

The findings of this study support the first hypothesis that synoptic intrusions do occur from higher latitudes in summer months. January exhibits the greatest variety of flow patterns, both in extremes of magnitude but also variations spatially.

The locations of stratocumulus decks agree with Norris and Klein (2000) findings of where they should be located, regions of subsidence and (to the east of) a surface ridge. Though it may be of value to look at all of the generated SOM nodes for each month, the nodes shown in this study achieved the goal of depicting the primary conditions summer and winter in the Northern Atlantic region, specifically the Azores.

A permanent campaign in the Azores would benefit from a climatological under-standing of the environmental and cloud properties in this highly variable region. A synoptic classification of meteorological patterns shows that the Azores lie in a strong gradient of atmospheric and cloud properties, with opposing vertical velocities and overlapping cloud regimes commonplace for the Azores. This variability suggests that the Azores should not be taken as a singular representation for the cloud conditions in the NEA without first considering the meteorological features within the region.

The main takeaway points are as follows:

1. The SOM technique can successfully identify the full range of synoptic regimes.

2. Stratocumulus are most commonly found downstream of a ridge axis in areas of sustained subsidence and persistent high stability.

3. The Azores experience regular synoptic intrusions, even during the warm sea-son.

49

4. January exhibits a substantial frequency of stratocumulus occurrence.

5. Vertical velocity modulates EIS so that sustained subsidence is associated with enhanced stability.

6. Patterns of vertical motion and cloud fraction are strikingly similar but exhibit very little robust statistical correlation due to calculations on daily time scales.

7. Most importantly, the Azores lie in a region of overlapping meteorological and cloud regimes.

5.1 Appendix

Figure 5.1 January forward trajectories show the transitions from node to node, with arrows showing where the next successive data sampled is plotted.

The circles indicate how many times a data sample has been mapped in a row, showing how persistent the node is. Corner nodes are the most persistent, implying that data tend to stay in these nodes for lengthy amounts of time.

5.1 Appendix 51

Figure 5.2 Sammon Map shows the relative locations of nodes to each other.

Nodes in the upper right and lower right corners are more similar to each other than the lower left corner. The interior nodes are the furthest apart from each other.

Figure 5.3 Elbow plot used to show how optimal number of nodes are chosen. Quantization error decreases with increasing number of nodes while topographic error increases with increasing number of nodes. The optimal number chosen is 25 nodes, highlighted in the region shaded with yellow.

Ridge Composite (4,5,9,10,15)

Trough Composite (11,12,16,17,21)

500-mb geopotential heights

Cloud Fraction (ERA–Interim)

5100 6000

[m]

0 0.5 1

Cloud Top Temperature (ERA–Interim)

260 310

210

[K]

Figure 5.4 An example of composited 500-mb geopotential height and ERA–Interim total cloud fraction and CTT values for ridge and trough like nodes in June. Nodes used to composite these figures are shown under the pattern names. Though the patterns generally resemble the structures in the corresponding projected environmental and cloud quantities, the cloud structures are much less defined and the geopotential heights are less intense in magnitude.

BIBLIOGRAPHY 53

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