Clouds in Darwin and Their Relation to Large-scale Conditions

Transcription

1 MASTER S THESIS 2005:169 CIV Clouds in Darwin and Their Relation to Large-scale Conditions SOFIA HÖGLUND MASTER OF SCIENCE PROGRAMME Space Sciences Luleå University of Technology Department of Applied Physics and Mechanical Engineering Division of Physics 2005:169 CIV ISSN: ISRN: LTU - EX / SE

2 MASTER S THESIS Clouds in Darwin and their relation to large-scale conditions by Sofia Höglund May 2005 Master of Science Programme in Space Engineering Luleå University of Technology, Sweden Department of Applied Physics and Mechanical Engineering

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4 Abstract This thesis provides a climatological overview of the cloudiness in the Darwin region in the very north of Australia. Information on cloud top pressure and optical thickness from the ISCCP Stage D1 data set over the time period 1985 to 2000 has been used to examine how the cloud cover changes over the course of a year, and also how it is affected by the seasonal changes in the region. The most remarkable changes can be seen during the wet summer season, when wet westerly winds sweep in over Darwin and dramatically change the weather conditions. Using cluster analysis to divide the cloud cover into cloud regimes, an interesting link between rainfall trends and cloud cover trends can be detected - a link that cannot as easily be seen when just looking at the average cloud cover. Furthermore, these regimes are also used to examine how the Darwin area is affected by large-scale atmospheric phenomena such as the intra-seasonal Madden Julian Oscillation (MJO) and the El Niño/Southern Oscillation (ENSO). The research has been carried out at the Australian Bureau of Meteorology Research Centre and at the School of Mathematical Sciences at Monash University, both in Melbourne, Australia. i

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6 Preface After having spent three years in Luleå and one year in Kiruna studying at the Master of Science Programme in Space Engineering at Luleå University of Technology (LTU) in Sweden, I wanted to do something different. In February 2004 I came in contact with Steven Siems at Monash University and Christian Jakob at the Australian Bureau of Meteorology Research Centre (BMRC). After exchanging more than a couple of s back and forth, we decided on a subject that we all felt would be very interesting to focus my Master s Thesis on. On an early August morning that same year I left Sweden only to arrive in Melbourne, Australia almost two days later. The coming six months were spent working really hard, but also to experience a wonderful country that is so different from the place I grew up in. When the time came for me to go back home again, I did so with a luggage full of new impressions, new knowledge, and with memories that will last a lifetime. I would first of all like to thank Christian Jakob, my supervisor at BMRC, for all his help and encouragement during the course of my work. Thanks also to Timothy Hume at BMRC for helping me with all the technical difficulties I had along the way, and for always taking the time to answer my questions. I am also very grateful to all the other wonderful people I met at BMRC who made my stay abroad so very enjoyable. I also appreciate all the input and encouragement I received from Steven Siems, my supervisor at Monash University. I would like to thank Sven Molin at LTU for his assistance with the arrangements before I left for Australia. And of course, thanks to Sverker Fredriksson, my supervisor at LTU. Not only did he help me arrange this whole journey, he was also a great support and always eager to help both before, during and after my stay abroad. Last but not least, I would like to thank my family, without whom none of this would have been possible. Where would I be without you? The content of this thesis has been presented at a talk held at BMRC on February 14, A poster was also displayed at the 15th Atmospheric Radiation Measurement Science Team Meeting held in Daytona Beach, Florida, USA, March 14-18, Sofia Höglund Kalix, May 2005 iii

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8 Contents 1 Introduction 1 2 ISCCP Data 5 3 A climatological overview Annual cycle Diurnal cycle Interannual variations A cloud regime oriented overview Clustering technique Darwin cloud regimes Comparison with tropical cloud regimes Relationship to circulation features El Niño / Southern Oscillation Madden Julian Oscillation Trends 41 7 Summary and Conclusions 45 Acknowledgements 49 References 51 v

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10 Chapter 1 Introduction Clouds are fascinating. They are never inactive, they are constantly moving in one direction or the other, changing shapes and always making their presence known to us living below them. Without them we would only have a clear blue sky to look at day after day. There would be no lightning or thunder, no rain or snow, and no hail. Not only do they affect the daily weather here on earth, they also affect the climate seen over long periods of time. This study will focus on clouds in the Darwin region in the very north of Australia; how they change diurnally, seasonally and annually, and how they are related to large-scale conditions in the atmosphere. Let us first introduce the setting of this study - Darwin. Located on the coast to the Timor Sea, as seen in Figure 1.1, the city acts as a gateway for trade and travel between Australia and Asia. The city, named after the famous scientist Charles Darwin, has a tragic history. Being attacked by Japanese warplanes in 1942, it was the only Australian city to suffer from repeated bombing during the World War II. As if that was not enough, in 1974 large parts of the city were again destroyed when a severe storm, Tropical Cyclone Tracy, passed through and killed 49 people. The city was then rebuilt on the same location and now has a population of 68, Due to its location, Darwin enjoys warm temperatures all year round. As a matter of fact, the temperature exceeds 30 o C almost everyday! There is, however, a very distinct annual cycle with two evident seasons; the dry season and the wet season. The dry winter season lasts from May to October and offers very dry conditions with almost no rainfall. The wet summer season, on the other hand, has a far more diverse weather with changing temperatures, high estimation, according to Australian Bureau of Statistics, 1

11 humidity and heavy rainfall. The wet season usually lasts from November through April. Darwin Equator Figure 1.1: Darwin is situated in the very north of Australia, less than 13 o south of the equator. So why was Darwin chosen for this study? The most obvious reason is that both the Australian Bureau of Meteorology and the US Department of Energy s Atmospheric Radiation Measurement Program have research instruments installed in Darwin, making it one of the best instrumented sites in the world. These instruments include a wise variety of active and passive remote sensors, such as precipitation and cloud radars, lidars and a number of radiometers. Together these instruments provide a detailed description of the cloud and precipitation systems encountered in the Darwin area. Another important reason for focusing on the Darwin region is that it is located in the Tropical Warm Pool (TWP), an interesting region because of its varying weather and never-ending changes in atmospheric conditions. Darwin experiences a strong annual monsoon cycle, with onset during the wet season and break periods during the dry season. The region is also known to 2

12 be affected by the intra-seasonal Madden-Julian oscillation (MJO) and the El Niño/Southern Oscillation (ENSO), which makes it even more interesting. In addition to the reasons already mentioned, a big field experiment, the Tropical Western Pacific - International Cloud Experiment (TWP-ICE), will be held in the region in January and February 2006, with scientists from around the world coming together to study cloud structure and their impact on the environment. The results presented in this thesis will hopefully give these scientists some useful information that can be further used during the experiment. There is one acronym that will appear frequently throughout this thesis; ISCCP. It stands for the International Satellite Cloud Climatology Project and is, with a few exceptions, the data set from which most of the data used in this study is taken. But why are satellite data used in this study? First of all, ISCCP offers global coverage of 3-hourly data for more than 18 years. This kind of coverage is not possible to get by using ground based cloud observations. Since global information is available for such a long time, it is also possible to use it to compare and contrast with other data. Recent studies by Jakob and Tselioudis (2003) and Jakob et al. (2005) used cluster analysis to objectively divide clouds into frequently recurring cloud regimes and found that the Tropical Western Pacific is populated by four such regimes, each with radiatively and thermodynamically separable characteristics. The purpose of this study is to see if it is possible to use this same technique to identify regimes in the Darwin region and also to see how well they compare to the rest of the tropical region. The influence of large-scale atmospheric features, such as the previously mentioned monsoon, the MJO and the ENSO, on Darwin has already been examined in previous studies (e.g., Drosdowsky (1996) and Wheeler and Hendon (2004)). These studies will provide the background to this study, where the cloud regime approach will be used to assess the influence of these large-scale features on the cloud properties. The Earth s climate system is complex and not easy to fully understand. To try to design a model describing this whole system is even harder. Climate models must be capable of reproducing the Earth s seasonal cycles, the variability in the climate system, and phenomena like the ENSO. The models must also be capable of reproducing the changes in the radiative energy balance with changing amounts of aerosols, water vapor, clouds, and also changing surface properties. It is known that clouds have a big effect on 3

13 climate, and vice versa, but exactly how big is not yet known. Neither is it known exactly how the interaction between clouds and their environment works. The intention of dividing clouds into cloud regimes is to contribute to the improvement of the representation of clouds in climate models. This thesis begins with a description of the ISCCP data used in the study. A climatological overview of the cloud characteristics related to the weather and the climate in the Darwin region then follows. Going into the core part of this study, the cluster analysis and also the cloud regimes found in the cluster analysis are described. The scale is then broadened as the cloud regimes in Darwin are compared to regimes representing the whole tropical region. This is followed by a closer look at how the Darwin cloud cover is influenced by large-scale conditions such as the MJO and ENSO. Finally, an interesting link between the cloud cover and the rainfall in the region is discussed. 4

14 Chapter 2 ISCCP Data Most of the data used in this study come from the Stage D1 product of the International Satellite Cloud Climatology Project (ISCCP), Rossow and Schiffer (1999). The intention of this project is to provide information that can be used to improve our understanding of the affect clouds have on climate, and how they can be modelled in General Circulation Models (GCMs). ISCCP uses visible and infrared images from a network of weather satellites, up to five geostationary and two polar orbiting, to create data sets and analysis products. These satellites are equipped with sensors that measure radiances at different wavelengths. Retrievals from two of these wavelengths, 0.6 µm for the visible (VIS) spectrum and 11 µm for the infrared (IR) spectrum, are used to derive information on cloud top pressure and optical thickness every 3 hours. These two variables are both derived during daytime, but VIS data are not available during the night and no optical thickness retrievals can therefore be made during that time. In this study, ISCCP data between the years 1985 and 2000 are used. The ISCCP D1 data set has data for 6596 equal-area grids covering the globe, each grid cell having a size of 280x280 km 2. Darwin is located close to the intersection between four grid cells, and hence, these are used in this study. As can be seen in the map in Figure 2.1, two of the grids are located mostly over water, while the remaining two are located mostly over land. This creates some interesting differences between the cloud cover over the different grids, as will be shown later in this thesis. An algorithm is used to determine the cloud top pressure and the optical thickness of each pixel (of size 4-7 km) in each grid cell. A pixel is considered to be cloudy if either the VIS or the IR radiance differs from the corresponding clear sky value. The cloud top pressure is determined from the cloud 5

15 3 4 Darwin 1 2 Figure 2.1: Map over northern Australia showing the four ISCCP grid cells used in this study. top temperature and represents the radiating top of the clouds. It can be considered equivalent to the cloud top height above mean sea level. Since the satellite sensors see only the topmost cloud layer, they are not able to see what is beneath that. For example, if there are a lot of high-top thick clouds covering the sky, the sensors will not see whether there are any clouds below them. The cloud optical thickness is retrieved from the satellite-measured visible solar reflectivity for a certain pixel. ISCCP determines the optical thickness by assuming that the pixel is uniformly covered by clouds and also that clouds warmer than 260 K are composed of water droplets with an effective radius of 10 µm, while clouds colder than 260 K consist of crystals with an effective radius of 30 µm. Figure 2.2 is an example of a CTP-τ histogram. These histograms will be used throughout this thesis to represent the cloudiness. They are made by counting all the pixels in the four grid cells that have a certain combination of cloud top pressure and optical thickness and then dividing by the total number of pixels in these grid cells. Doing this for all the 42 combinations creates the histogram. The shading in each histogram box gives the cover in percent for each of the 42 combinations. Adding them gives the total cloud 6

16 <180 TCC= CTP (hpa) > >60 Tau Figure 2.2: CTP-τ histogram illustrating the average cloudiness in Darwin calculated between 1985 and 2000, with respect to cloud top pressure and optical thickness. The shading gives the cover in percent for each box. TCC is the total cloud cover. cover (TCC). As these histograms contain information on optical thickness, they only represent the daytime cloudiness. In this case, that is 0-6 UTC, or rather, 9.30 AM PM local Darwin time. The retrievals at 9 UTC (6.30 PM local time) were originally included in this data set, but since the sun sets around this time, and thus makes the VIS channel measurements somewhat questionable, they were removed from the data set in this study. Figure 2.3 illustrates the ISCCP cloud classification, as re-created from Rossow and Schiffer (1999), and shows how the VIS data can be used to divide the cloud cover into the nine more well-known cloud types. This classification should be taken with a grain of salt though, the boundaries between the cloud types are a lot more blurry in reality. It is nonetheless good to have this picture in mind when looking at the CTP-τ histograms that will be shown throughout this thesis. Figure 2.4 is another example of a plot that will be used to represent the cloudiness throughout this thesis. It is a cloud cover contour plot and is made by counting all the pixels in the four grid cells that have a certain cloud top 7

17 50 Cloud Top Pressure (hpa) Cirrus Cirrostratus Deep Convection Altocumulus Altostratus Nimbostratus 800 Cumulus Stratocumulus Stratus Cloud Optical Thickness Figure 2.3: ISCCP cloud classification with respect to cloud top pressure and optical thickness. [Re-created from Rossow and Schiffer (1999)] < CTP (mb) > Year Figure 2.4: Contour plot illustrating the average annual IR-only cloud cover between 1985 and 2000 as a function of cloud top pressure. The shading gives the cover in percent. pressure and then dividing by the total number of pixels in these grid cells. Doing this for all the seven cloud top pressures, and then using shading to outline the contours, creates the contour plots. The shading gives the cover in percent. Adding the cover for all the seven cloud top pressures gives the 8

18 total cloud cover. Since these contour plots contain IR-information only, they represent both the nighttime and daytime cloudiness. One drawback of this is that very thin clouds, e.g., cirrus clouds, cannot be detected by the IR channel alone. Since they are so thin, the radiation seen by the satellite sensors in the IR spectrum looks almost like radiation when there are no clouds at all. To detect these clouds the VIS channel needs to be added. 9

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20 Chapter 3 A climatological overview Instead of plunging head-first into the more in-depth part of this study, it is important to first have a general understanding of the weather situation in the Darwin region. The weather is a direct effect to its interesting location, a subject that was touched upon in the introduction. Lying in the Tropical Warm Pool, Darwin exhibits a tropical climate. This chapter is meant to give an overview of how, and why, the clouds change diurnally, seasonally and annually, and also how other aspects, such as the wind direction, affect the clouds. 3.1 Annual cycle As the Earth revolves around the Sun, the amount of energy it receives changes over the course of a year. This produces the different seasons. Even though the differences between the seasons are not as palpable near the equator as they are closer to the poles, Darwin still experiences a very distinct annual cycle. This becomes evident when looking at Figure 3.1 and 3.2, both showing the annual cycle averaged over the 16 years from 1985 to Figure 3.1 is created using the data from the IR channel, and shows how the total cloud cover (TCC) changes from month to month. The full black line shows the average over all four grid cells used here (see Figure 2.1). It is comparatively high (70-80%) during December, January and February, while it is much lower during May, June, July, August and September (15-30%). The dotted and dashed lines in the figure show the total cloud cover for each of the four individual grid cells. Apparently, there are more clouds over water (grid 3 and 4) than over land (grid 1 and 2), especially from March through August. During the dry winter season, the soil in the region gets 11

21 Grid 1 Grid 2 Grid 3 Grid 4 TCC (%) Month Figure 3.1: Annual cycle of the total cloud cover averaged between 1985 and very dry and cannot provide the air with as much moisture as during the wet summer season. The result is that less clouds form over land during the dry season. Over water, however, this phenomenon does not occur, and the differences between the seasons are not as big. This explains why grid 3 and 4 have a higher total cloud cover than grid 1 and 2, especially during the aforementioned months. Figure 3.2 shows the IR-only cloud cover as a function of cloud top pressure. The figure provides a simple overview over how the average cloud top height changes from month to month. Blue colours indicate low coverage of clouds, while green, yellow and red indicate a fairly high coverage of clouds. Months with high total cloud cover, as seen in Figure 3.1, are dominated by high-top clouds, while months with low total cloud cover have a higher coverage of low-top clouds. The main reason for this annual cycle lies in the change in large-scale circulation. During the dry season there is mostly large-scale subsidence (sinking air) in the region. This generates a subsidence inversion. Usually the temperature decreases with height in the troposphere, but during an inversion it increases instead. Because inversions represent a very stable atmosphere, i.e., an atmosphere that resists upward displacement, they act as a lid on vertical air motion (convection). Since convection cannot break 12

22 < CTP (mb) > Month Figure 3.2: Contour plot of the annual cycle of the cloud cover averaged between 1985 and 2000 as a function of cloud top pressure. The shading gives the cover in percent. through the inversion most of the clouds that form during the dry season are shallow. Also, as already mentioned, the moisture supply from the soil to the atmosphere is weak during this season, contributing to even lower cloud cover over land. During the wet season, the monsoon is connected with large-scale ascending (rising) motion. This removes the inversion and creates better conditions for convection to become deep. Hence, high-top clouds form. When these clouds reach the tropopause (the boundary between the troposphere and the stratosphere), they spread out and thereby form a high coverage of cirrus and anvil clouds. Much like its more well-known Indian counterpart, the Australian monsoon is believed to exist because of the thermal contrast between land and sea. This creates a wind system over the northern parts of Australia that reverses direction between winter and summer. Usually the wind blows from land to sea in winter and from sea to land in summer. For the Australian monsoon, this means that the prevailing winds go from being southeasterly in the winter to becoming northwesterly in the summer, bringing deep convection and heavy rainfall. This not only affects the agriculture, but also the trades between northern Australia and the rest of the world. There is more than 13

23 one way to define the onset date of the monsoon. Drosdowsky (1996) used zonal (west-east) and meridional (north-south) wind data and defined the monsoon onset as the first so-called active period of deep low-level westerly winds and strong upper-level easterly winds. Retreat date was consequently defined as the last active period. By studying 35 complete monsoon seasons Drosdowsky found that the average onset date is 28/29 December and the average retreat date is 13 March. In this study, November through April is defined as the wet (summer) season. Hence, the monsoon onset/retreat dates fit into this definition with a big margin. Consequently, May through October is defined as the dry (winter) season. This is all consistent with the results in Figures 3.1 and 3.2. The influence of the monsoon on the cloud cover is not only visible when looking at the annual cycle, it is also apparent when looking at the diurnal cycle, which will be shown in the next section. Another interesting detail that can be seen in Figure 3.2 is the high percentage of low-top clouds in September, October and November, as well as in April and May. These are all transition months between the seasons. It seems as if the wet season starts to build up in September, only to gradually develop during the coming months and then peak in December, January and February. It then starts to diminish again in March, when the dry season begins to develop instead. Looking deeper into the differences between the two seasons, it is now time to take a look also at the optical thickness of the clouds. TCC=0.31 TCC=0.65 <180 < CTP (hpa) CTP (hpa) >800 > >60 Tau >60 Tau Figure 3.3: Seasonal CTP-τ histograms averaged between 1985 and 2000 showing the average cloudiness during the dry season (left) and the wet season (right) with respect to cloud top pressure and optical thickness. The shading gives the cover in percent. Figure 3.3 shows the CTP-τ histograms of the seasonal average cloud cover 14

24 calculated over the time period 1985 to All CTP-τ histograms are created using VIS data, and thus show information only for the hours with daylight, i.e., 9.30 AM to 3.30 PM local Darwin time. As the legend shows, light colours (yellow-orange) indicate a low percentage of clouds at that specific combination of cloud top pressure and optical thickness, and dark colours (red-brown) indicate a high percentage. It is evident that not only do the average cloud top heights differ between the seasons, but also the optical thickness of the clouds. Optically thin, high-top cirrus clouds and some low-top clouds with medium optical thickness dominate the dry season cloud cover. Almost no optically thick clouds exist and the total cloud cover is also low, only The wet season has a high coverage of high-top cirrus clouds of low optical thickness as well. But it also has a total cloud cover of 0.65, which is more than twice as high as during the dry season. This can be explained by the high amount of deep convective clouds that cover the summer sky in this region. Deep convective clouds are clouds with a lot of vertical movement within them, and thus often produce precipitation. The average cloud cover in the lower troposphere (CTP > 560 hpa) looks essentially the same for the two seasons. The differences can be found higher up in the troposphere. Apart from the already mentioned deep convective clouds, the wet season also has a high coverage of high-top clouds with medium optical thickness, i.e., cirrostratus clouds. The reason behind these differences has to do with the already mentioned change in large-scale circulation. The explanations provided to the contour plot in Figure 3.2 applies also to the histograms in Figure 3.3. It might seem strange that the dry season histogram shows a high coverage of high-top cirrus clouds, while the contour plot in Figure 3.2 showed a very low cloud cover during this season. This has to do with the way the ISCCP data are generated. As explained in Chapter 2, very thin clouds can be detected only by the VIS channel. Hence, these very thin clouds can only be seen in CTP-τ histograms and not in contour plots. 3.2 Diurnal cycle As the Earth revolves around the Sun, it also spins around its own axis. Completing one spin in 24 hours, each day can be seen as a small season with periods of sunlight and periods of no sunlight. As is to be expected, also the cloud cover changes during the course of the day. Figure 3.4 shows the average diurnal cycle of the total cloud cover, calculated between the years 1985 and The full black line represents the total 15

25 Grid 1 Grid 2 Grid 3 Grid 4 TCC (%) Local Time Figure 3.4: Diurnal cycle of the total cloud cover averaged over all months from January 1985 to December cloud cover averaged over all four grid cells. A small peak can be seen during the afternoon, but apart from that the average total cloud cover is fairly constant around 45%. The dotted and dashed lines show the total cloud cover for the individual grid cells. This plot shows yet again the differences between the grids over land and those over water. Grids 1 and 2 (over land) have far more varying curves than grid 3 and 4 (over water). Water has a much higher heat capacity than land. Also, in water the solar radiation can penetrate deeper, while in soil it is absorbed in the top few millimetres. These two effects taken together mean that for a given amount of energy, the soil heats up much faster than water. It also cools down much faster once the sun sets. This is why the diurnal cycle in temperature can be a lot smaller by the sea than inland. Since the turbulent exchange between the surface and the air depends partially on the temperature difference between them, it becomes very active over land in the afternoon (the warmest part of the day), often leading to the formation of at least some low clouds, and sometimes even deep convective clouds. As a result of this, the cloud cover over land is more affected by whether or not the ground receives sunlight, and this also explains why the curves for grids 1 and 2 look different from those for grids 3 and 4. 16

26 < CTP (mb) > Local Time Figure 3.5: Contour plot of the diurnal cycle of the cloud cover averaged between 1985 and 2000 as a function of cloud top pressure. The shading gives the cover in percent. Figure 3.5 illustrates how the cloud cover changes during the day as a function of cloud top pressure. There is a high coverage of very low clouds during the afternoon, at the same time the total cloud cover peaks (see Figure 3.4). No specific cloud type dominates during the rest of the day, but the coverage of high-top clouds gets higher, although still fairly small, during the evening. High-top clouds appear later than low-top ones because it takes some time for them to grow. Often the turbulent exchange between the surface and the air is sufficient to make small low-top clouds, but to make deep convection often requires some mesoscale 2 circulations. The most prominent one in the Darwin region is the sea breeze, i.e., a wind that blows from the sea onto the land. Even once convection gets going, it still takes some time for the large cirrus clouds to form as an outflow from the convection. They constitute the high cloud cover, and they need time to form. Once they exist they can take on their own dynamics and survive for quite some time. All this taken together explains the delay in the appearance of the high clouds. The diurnal cycle shown in Figure 3.5 is the average over the whole 16-year period, and no consideration is taken to the different seasons or the different 2 The scale of meteorological phenomena ranging from a few km to about 100 km. 17

27 grids. Since this does not reveal much, it is yet again time to divide the year into the wet and the dry season. The results can be seen in Figure 3.6. < CTP (mb) > Local Time < CTP (mb) > Local Time Figure 3.6: Contour plots of the seasonal diurnal cycles of the cloud cover averaged between 1985 and 2000 as a function of cloud top pressure. Dry season on top and wet season at the bottom. The shading gives the cover in percent. The dry season (top) is dominated by low-top clouds with a peak in cloud cover in the afternoon. The coverage of high-top clouds during the dry season is less than 3%. However, these contour plots are based on IR data only, and since the IR channel cannot detect very thin clouds, there is still a possibility that very thin high-top cirrus clouds do exist during this season. 18

28 In contrast to the dry season, the wet season (bottom) has a more interesting cloud cover. Apart from the expected high coverage of low-top clouds during the afternoon, it also has a fairly high coverage of high-top clouds during the late afternoon and night. Because the air is more humid during the wet season, these high-top clouds are able to not only form, but then also build as the day and night progresses. As explained earlier, the afternoon heating combined with the convection associated with mesoscale circulations such as the sea breeze explain why these high-top clouds often appear later in the day compared to the low-top clouds. It was mentioned earlier that changing wind directions have a big influence on the weather in the Darwin region, especially during the monsoon season. Consequently, it also has a big influence on the cloud cover. Darwin exhibits two types of convection depending on active monsoon (westerly winds) or not (easterly winds). During the wet season the northern parts of Australia are known to experience what is called the wet westerlies. These are associated with the monsoon circulation and dramatically change the weather conditions, as can be seen in Figure 3.7. The wind data used to create this plot was measured at 700 hpa at the Darwin Airport. Westerly winds usually advect moist air of maritime origin into the Darwin region. The first time this happens each season is often considered to be the onset of the monsoon season, as has already been discussed. These active bursts are often related to a large-scale intra-seasonal oscillation called the Madden Julian Oscillation. This phenomenon will be further investigated in Chapter 5. Easterly winds usually bring dry air originating over the continent into the Darwin region. The resulting cloud cover can be seen in the top plot in Figure 3.7, having a high coverage of low-top clouds during the afternoon. As soon as the wind direction changes to westerlies (bottom), a high coverage of high-top clouds appears during the night and early morning, and the coverage of low-top clouds during the afternoon decrease considerably. Note that this decrease could possibly be caused by blocking of the satellite view onto the low clouds by high clouds, as described in Chapter 2. 19

29 < CTP (mb) > Local Time < CTP (mb) > Local Time Figure 3.7: Contour plots of the wet season diurnal cycle of the cloud cover averaged between 1985 and 2000 as a function of cloud top pressure. Here shown with respect to wind direction. Easterly winds on top and westerly winds at the bottom. 3.3 Interannual variations To complete the climatological overview, the interannual variations of clouds in the Darwin region are now investigated. From a climatological point of view, 16 years is not a very long period of time, but the results presented in this section will still prove to be interesting later on in this thesis. 20

30 Wet season Annual mean Dry season TCC (%) Year Figure 3.8: Interannual variations of the total cloud cover between 1985 and Figure 3.8 illustrates the average annual total cloud cover between the years 1985 and The full black line is the average over the whole year, while the dashed and dotted lines represent the average for the wet and the dry season. Looking at the average over the whole year, no clear trends can be seen except perhaps a small increase after The 16-year time period is too small to allow any definite conclusions, but the dips in 1987, 1991, 1994 and 1997 are, nonetheless, interesting. These dips happen to coincide with El Niño events. In general, it can be said that Australia experiences drier periods during El Niño, which would explain the decrease in total cloud cover during these years. El Niño and its influence on the Darwin region will be further discussed in Chapter 5. The average total cloud cover for the wet season shows a slightly larger trend, this time starting around 1990, but not really becoming distinguished until after

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32 Chapter 4 A cloud regime oriented overview Up until now, the cloud cover has been treated as a single entity. Even though a lot of information can be gained this way, there is also a lot of information lost. There is of course more than one way to approach this dilemma, but in this study, cluster analysis is used to find repeatedly recurring cloud regimes. These cloud regimes represent the most common cloud situations when averaging over the whole 16-year time span. This chapter will begin with a more thorough description of the clustering technique used, and then the results of the analysis will be presented and discussed. 4.1 Clustering technique The algorithm used in this clustering analysis is called KMEANS and was developed by Anderberg (1973). In short, KMEANS is a statistical clustering method that iteratively searches for a predefined number of clusters (k) and then assigns each data vector in the input data set (of size N) to the nearest cluster centroid. In this case, the input data set is the VIS data set (and therefore only contains daytime data). The aforementioned data vector is here made up of the 42 variables included in that data set, i.e., the cover in percent for certain combinations of cloud top pressure and optical thickness. More specifically, the KMEANS algorithm can be described in the following four steps: 1) k data vectors from the data set are randomly chosen as initial cluster centroids of one member each; 2) the Euclidian distances from each of the remaining N-k data vectors to the cluster centroids are calculated, and each data vector is assigned to the nearest centroid; 3) after each assignment the centroid of the gaining cluster is recalculated; 4) after all data vectors 23

33 have been assigned, the centroids found in step 3) are used as new seed points and the procedure is iterated. Eventually, the centroids will stop moving and the loop is terminated. One drawback of this method is that it is somewhat dependent on the randomly chosen initial clusters in the first step. To give this cluster analysis some more reliability, also a second version of the KMEANS algorithm is used. The first step is still the same in this method, but instead of recalculating the gaining cluster after each assignment, this method first assigns all of the remaining N-k elements and then recalculates the clusters. This is then repeated until the centroids stop moving and the loop is terminated. In this analysis, it is found that 200 iterations are sufficient for both KMEANS methods, while a varying number of clusters are used in search of the optimal result. Both methods end up giving the same result, lending some credibility to this analysis. Almost 70,000 (68,538 to be exact) data points can be found in the Darwin region for the period 1985 to Since the KMEANS algorithm has been designed so that it assigns each and every data vector to a cluster, all ISCCP data points contribute to the result of this cluster analysis. To begin with, two clusters are used in the first analysis. Since this is obviously insufficient to describe all the different cloud situations, the number of clusters has to be increased until a threshold is reached. When increasing the number of clusters to three, four, five and six, each new calculation results in the appearance of a new cluster with unique characteristics. Not until seven clusters are used can a similarity be seen between the new cluster and one of the old ones. The same thing happens when using eight, nine and ten clusters. It is therefore, somewhat subjectively, decided that six clusters are the ultimate number of clusters to use in this study. 4.2 Darwin cloud regimes It is probably hard to envision real cloud situations by just reading the technical description of a mathematical method. This section is therefore meant to visualize the results found when using the clustering technique described in the previous section. Each cluster represents a cloud regime, meaning that the six clusters found in the cluster analysis are the six most frequently recurring cloud situations in the Darwin region. These regimes are illustrated in the CTP-τ histograms in Figure 4.1, here sorted by increasing total cloud cover. 24

34 CTP (hpa) CTP (hpa) CTP (hpa) < >800 < >800 < >800 Regime No. 1 - RFO=40% - TCC= >60 Tau Regime No. 3 - RFO=19% - TCC= >60 Tau Regime No. 5 - RFO=10% - TCC= >60 Tau CTP (hpa) CTP (hpa) CTP (hpa) < >800 < >800 < >800 Regime No. 2 - RFO=18% - TCC= >60 Tau Regime No. 4 - RFO=7% - TCC= >60 Tau Regime No. 6 - RFO=5% - TCC= >60 Tau Figure 4.1: Cluster mean histograms illustrating the six most frequently recurring cloud regimes in Darwin with respect to cloud top pressure and optical thickness. The shading gives the cover in percent. RFO states the relative frequency of occurrence of each regime and TCC is the total cloud cover. The first cluster is likely to be a dry season regime, i.e., a regime that occurs most frequently during the dry winter season. The dry season regime is very suppressed in the deep convective sense. It is dominated by lowtop clouds with cloud top pressures less than 560 hpa and of low to medium optical thickness. This regime has the lowest total cloud cover (0.13) and it is the most frequently occurring regime with a relative frequency of occurrence of 40%. Cluster number 2 is another suppressed cloud regime, but with a significantly higher total cloud cover (0.54). It has a high coverage of low-top (CTP > 680 hpa) clouds of low to medium optical thickness, and also a slightly smaller coverage of optically thin, high-top cirrus clouds. The relative frequency of occurrence of this regime is 18%. Clusters number 3 and 4 look almost exactly the same at a first glance. 25

35 They are both dominated by high-top (CTP < 440 hpa) cirrus clouds with low optical thickness. They also have a small coverage of low-top (CTP > 560 hpa) clouds of low to medium optical thickness. They do, however, differ when it comes to total cloud cover. Regime number 4 has a total cloud cover of 0.82, compared to 0.64 for regime number 3. The reason behind this can be found in the upper left corner of the CTP-τ histograms. As the legend shows, a coverage of over 15% is represented by dark brown, no matter how much higher than 15% the coverage actually is. In this case, regime number 3 has a coverage of almost 50% in this box, while regime number 4 has a coverage of only 23%. Another difference between these two regimes is that regime number 3 occurs almost three times as often as regime number 4, with a relative frequency of occurrence of 19% compared to 7%. The two remaining regimes are both convectively active, meaning that they can both be accounted for most of the rainfall in the region. Cluster number 5 is a regime dominated by high-top (CTP < 440 hpa) clouds of medium optical thickness, but also with a small coverage of deep convective high-top clouds of high optical thickness and with a high total cloud cover (0.94). Cluster number 6 has the highest total cloud cover (0.99) of all regimes, and it is also the most convectively active regime with a high coverage of high-top (CTP<310 hpa) deep convective clouds of high optical thickness. These two regimes have a frequency of occurrence of, respectively, 5% and 10%. It is safe to assume that almost all of these occurrences are during the wet season. 4.3 Comparison with tropical cloud regimes The cloud regimes presented so far in this study were derived using ISCCP data for the Darwin region only. This is, however, a rather small region. It would therefore be interesting to know how the Darwin regimes relate to the more general regimes found when using data for the whole Tropics. To do this, the cluster centroids found by Tselioudis (2005) are used. These centroids were found using the same KMEANS algorithm as in this study, but the ISCCP data set stretched from July 1983 to September 2001 and over the whole tropical region (15 o N - 15 o S). Since the centroids for the tropical regimes are already known, all that is left to do is to calculate the distances between the ISCCP data points for the Darwin region and the new centroids, and then assign each data point to the nearest centroid. This is the same technique as describe previously in this chapter, except this time the centroids are already known, and all that is left to do is calculate the distances and assign each data points to a cluster. The relative frequency of occurrence for each tropical regime in the Darwin region is then calculated by dividing 26

36 the number of times each regime occurs by the total amount of occurrences (68,538). Figure 4.2 shows CTP-τ histograms of the aforementioned tropical regimes and the calculated relative frequency of occurrence. It is evident that some of the tropical regimes are very similar to the Darwin regimes found in the previous section. A more in-depth discussion of the tropical regimes, and the assignment of Darwin cloud occurrences to them, will now follow. CTP (hpa) CTP (hpa) CTP (hpa) < >800 < >800 < >800 SSCL - RFO=53% - TCC= >60 Tau STC - RFO=19% - TCC= >60 Tau CC - RFO=5% - TCC= >60 Tau CTP (hpa) CTP (hpa) CTP (hpa) < >800 < >800 < >800 SSCH - RFO=2% - TCC= >60 Tau MIX - RFO=15% - TCC= >60 Tau CD - RFO=6% - TCC= >60 Tau Figure 4.2: Cluster mean histograms illustrating the six most frequently recurring cloud regimes in the whole tropical region (15 o N - 15 o S) with respect to cloud top pressure and optical thickness. The shading gives the cover in percent. RFO states the relative frequency of occurrence of each regime and TCC is the total cloud cover. The first regime is, by far, the most frequently occurring one. It occurs over half of the time (53%) and has a total cloud cover of It is dominated by shallow, low-top (CTP > 680 hpa) cumulus and stratocumulus clouds with low to medium optical thickness. It is considered to be a mix between Darwin regimes number 1 and 2 (see Figure 4.1), and will hereafter be referred to as the suppressed shallow clouds with low total cloud cover (SSCL) regime. The second regime has no counterpart among the Darwin regimes, and there- 27

37 fore occurs rarely, only 2% of the time. Much like the SSCL regime, it is dominated by shallow, low-top (CTP > 680 hpa) clouds. The optical thickness though is higher than for the SSCL regime and the stratocumulus clouds are thus more dominant in this regime. To continue the comparison to the SSCL regime, this regime has a much higher total cloud cover (0.77), which gives it its name; suppressed shallow clouds with high total cloud cover (SSCH). The third regime has a high coverage of high-top (CTP < 440 hpa) cirrus clouds with low optical thickness. This is the second most recurring regime with a relative frequency of occurrence of 19%, and it can be seen as a mix between Darwin regimes number 3 and 4 (see Figure 4.1). The total cloud cover (0.76) is slightly smaller than for the SSCH regime. This regime will hereafter be referred to as the suppressed thin cirrus (STC) regime, although it also has a small coverage of convectively active clouds. The fourth regime is the most diverse one, containing contributions from most cloud groups, including deep convective clouds. Because of this, it will simply be referred to as the MIX regime. It has a fairly high relative frequency of occurrence (15%), which is believed to be because it steals cloud occurrences that lie close to (i.e., are similar to) other regimes. The MIX regime has a total cloud cover of The two remaining regimes are both convectively active. Regime number 5 is dominated by high-top (CTP < 440 hpa) cirrus and cirrostratus clouds of medium optical thickness, but it also has a small coverage of deep convective high-top clouds of high optical thickness. It has a very high total cloud cover (0.96), but occurs only 5% of the time. This regime shows many similarities to Darwin regime number 5, but it has a smaller relative frequency of occurrence (5% compared to 10% for the Darwin regime), which probably means that some of the cloud occurrences that were assigned to Darwin regime number 5 have now instead been assigned to the MIX regime. This regime will hereafter be referred to as the convectively active cirrus (CC) regime. The sixth regime has the highest total cloud cover (0.98), but occurs only 6% of the time. It is the most convectively active regime, with a high coverage of high-top (CTP < 310 hpa) deep convective clouds of high optical thickness, and will therefore be referred to as the convectively active deep cloud (CD) regime. Comparing it to Darwin regime number 6, it is obvious that the two regimes are similar. Figures 4.1 and 4.2 show that some of the regimes in the two regime sets are very similar. Figure 4.3 takes a closer look at the relationship between the two regime sets. Each plot shows, for each tropical regime, how the regime composition would have been if the Darwin regimes had been used instead. Figure 4.3 confirms what was discussed earlier in this section. The CC and 28

38 Frequency (%) SSCL - RFO=53% Darwin Regime No. Frequency (%) SSCH - RFO=2% Darwin Regime No. Frequency (%) STC - RFO=19% Darwin Regime No. Frequency (%) MIX - RFO=15% Darwin Regime No. Frequency (%) CC - RFO=5% Darwin Regime No. Frequency (%) CD - RFO=6% Darwin Regime No. Figure 4.3: Bar charts showing which Darwin regimes would have appeared, had that regime set been used instead of the tropical regime set. CD regimes are much alike Darwin regimes number 5 and 6. The few times the SSCH regime occurs would have been assigned mostly to Darwin regime number 2. The occurrences that get assigned to the SSCL regime in the tropical regime set would have been assigned mostly to regimes number 1 and 2, but also to regime number 3, in the Darwin regime set. The assumption that the STC regime is a mix between Darwin regimes number 3 and 4 is also proved to be correct. And finally, it is also confirmed that the MIX regime is as mixed as its name hints. As the relationship between the two regime sets is now known, and it has been confirmed that the Darwin regimes and the tropical regimes are very similar, the next step is to decide which regime set to use for the rest of this study. On one hand, the Darwin regime set represents the Darwin cloud cover specifically. On the other hand, the tropical regime set is more general and will most likely be used in a wider range of research in the future. Using 29

39 the tropical regime set here would therefore make the results of this study more comparable with other possible studies in the future. With this in mind, it is decided to use the tropical regime set. It should be mentioned that even though only the results for the tropical regime set will be presented here, both regime sets were used during the research, showing practically the same results. 30

40 Chapter 5 Relationship to circulation features Now that the cloud regimes are known, the next step is to show some examples of how they can be used. This chapter will focus on large-scale circulation features and how they affect the Darwin cloud cover. First off is the El Niño / Southern Oscillation, and then the Madden Julian Oscillation will be addressed. 5.1 El Niño / Southern Oscillation El Niño is a major weather phenomenon in the tropical Pacific Ocean caused by the reversion of surface pressure. Usually, under non-el Niño conditions, the winds blow westward across the tropical Pacific. The pressure at the west coast of South America is usually higher than the pressure at the other side of the Pacific Ocean. Since the wind blows from a region of higher pressure toward a region of lower pressure, this causes the trade winds in the tropical Pacific to blow westward. As a result of this, the surface water in the east Pacific is forced to move westward, causing the cooler water from below to rise. Hence, the Pacific Ocean is cooler in the east than in the west. This is the usual condition during non-el Niño events. During El Niño events, however, the pressure over the western Pacific rises, causing the trades to weaken. If it is a major El Niño event, the trades will reverse and blow eastward instead. These changed conditions will persist for about a year, only to quickly collapse and go back to normal. Every now and then, the westward trades are so strong that the ocean temperatures in the east Pacific get exceptionally cold. This is referred to as a La Niña event. The atmospheric variations associated with El Niño and La Niña events are called 31

41 the Southern Oscillation. This phenomenon is usually referred to as ENSO, short for El Niño/Southern Oscillation. The worst ENSO affected region is the South American west coast, especially Peru. For example, when the ocean warms up during major El Niño events it causes fish and marine plants to die. As Peru relies on the export of fish, this has a major effect on the country s economy. However, this is not the only area affected by ENSO. As a matter of fact, changed weather conditions can be seen throughout the globe as a result of ENSO. The northern and eastern parts of Australia often experience drier periods during El Niño events, and wetter periods during La Niña events 3. In the past, the onset of El Niño events was almost impossible to detect beforehand, and often the event was not detected until it was fully developed. In recent years, however, significant improvement has been made in this area and there are now several ways of monitoring ENSO. One of the most commonly ENSO-associated conditions is that of changing sea surface temperatures (SST). By measuring SST in the regions affected by ENSO and comparing these to records from previous years, it is possible to detect an ENSO event before its actual onset. Another way of monitoring ENSO is with the Southern Oscillation Index (SOI), and this is also the index that will be used in this study. SOI is the standardized Mean Sea Level Pressure (MSLP) difference between Darwin and Tahiti and is calculated as SOI = 10 P diff P diffav. (5.1) SD(P diff ) Here, P diff is the average MSLP difference between Tahiti and Darwin, P diffav is the long-term average P diff for the month in question, and SD(P diff ) is the long-term standard deviation of P diff for that same month. A negative SOI indicates weak trade winds across the Pacific, and hence an El Niño episode. A positive SOI reveals stronger trade winds - a La Niña episode. SOI values for the years examined in this study can be seen in Table 5.1. The SOI index ranges from about -35 to +35, and in this case the region is considered to be in an El Niño episode when SOI values are equal to or lower than -10. These values are bold-faced in Table 5.1. Normal conditions are defined as SOI values between -10 and +10, while SOI values equal to or higher than +10 are considered La Niña episodes and are marked bold grey in Table 5.1. As was mentioned in the climatological overview, 1987, 1991, 1994 and 1997 are all considered to be El Niño years. This is also 3 Up-to-date information on ENSO and its affect on Australia can be found on 32

42 Table 5.1: Table showing the SOI, as defined in Eq. (5.1), for all months from January 1985 to December Numbers in black bold-face indicate El Niño events, while numbers in grey bold-face indicate La Niña events. evident in Table 5.1, with these years having many low SOI values. The early months of 1998 stand out with exceptionally low SOI values, and this is also the most recent strong El Niño event. Strong La Niña events are not as common, though 1988/89 had a fair amount of high SOI values. To take a closer look at how the Darwin cloud cover changes with ENSO, the tropical regimes will now be used to reveal how often each of these occur during the different states. Since SOI values are calculated only on a month-to-month basis, every occurrence during a month is assigned to that month s SOI value. Calculating the number of times each regime occurs during a certain ENSO state, and then dividing that with the total number of occurrences of all regimes during that state, creates Figure 5.1. Some scientists claim that ENSO s influence on Australia is most significant from September to December, others favour December to February. To keep the consistency in this study, all the wet season months, i.e., November to April, are used here. As expected, the suppressed SSCL regime is the most frequently occurring regime during all states of ENSO. It is especially dominating during El Niño events, which is consistent with the fact that northern Australia experiences dry conditions during El Niño episodes. The opposite happens to the two most convectively active regimes, CC and CD. 33

43 Frequency (%) El Nino Normal Conditions La Nina Figure 5.1: Stacked bar chart illustrating the frequency of occurrence of each regime during the different ENSO states during the wet season. From bottom to top: SSCL (yellow), SSCH (green), STC (cyan), MIX (blue), CC (red), CD (black). They both have fairly low frequencies of occurrence during El Niño events, but instead occur much more frequently during La Niña events. This is also to be expected since Australia experiences wetter than normal periods during La Niña events. Contrary to the SSCL, CC and CD regimes, the frequencies of occurrence for the SSCH, STC and MIX regimes stay practically the same during all states. Even though these results were expected, it is important to carry out the experiments and have the suspicions confirmed. This also shows that the cloud regime approach is a great tool when studying the ENSO s impact on the clouds. 5.2 Madden Julian Oscillation Another large-scale feature that is believed to have a large affect on the weather in Darwin is the Madden Julian Oscillation (MJO). While analyzing zonal wind anomalies, Madden and Julian (1971) discovered a day oscillation in the tropical Pacific. This oscillation appeared to be a large 34

44 circulation cell centred somewhere in the mid-pacific. Until recently, this oscillation has not received much attention. The last two decades, however, have seen a significant increase in research within the area of intra-seasonal variations, such as the MJO, and their effect on the monsoon. In short, one can say that the MJO is characterized by variations in outgoing long-wave radiation (OLR), sea surface temperatures (SST), wind, cloudiness and rainfall. The intra-seasonal variations timescale ranges between 10 and 90 days, but the MJO usually contributes variance in the range days with a spectral peak around days. As always, the problem when studying variations in the atmosphere is that it is often hard to know exactly how much of a variance can be attributed to a certain process. There are so many complex processes going on at the same time. In this case, because there are several intra-seasonal variabilities within each monsoon season, there are still uncertainties when it comes to the MJO and its effect on the Australian monsoon. Hendon and Liebmann (1990) claimed that the monsoon onset is strongly influenced by the MJO, while Drosdowsky (1996) found no such relation. These two studies used different definitions of both the MJO and the monsoon onset. In this study, however, the MJO definition described in Wheeler and Hendon (2004) will be used to study the relation between the MJO and the cloud regimes in Darwin. Wheeler and Hendon used empirical orthogonal function (EOF) analysis on 200 hpa and 850 hpa zonal winds and OLR to create what they chose to call Real-time Multivariate MJO series 1 (RMM1) and 2 (RMM2) to represent the characteristics of the MJO. These time-series variables were used to create a phase space (as seen in Figure 5.2), dividing the MJO into eight phases when the MJO is relatively strong. Each of these phases lasts for about six days on average. The MJO composites in Figure 5.3 illustrate the wind and OLR conditions during each of the eight MJO phases. The OLR in the Darwin region is very high during phase 1, indicating conditions of low total cloud cover and/or a majority of clouds with low optical thickness. Easterly winds dominate during this phase. The region experiences roughly the same conditions during phase 2, only with stronger easterly winds. Also, segments of very low OLR can now be seen moving in from the west. These segments reach even closer in phase 3, as the strong easterly winds continue to dominate and the OLR gets lower in the region. In phase 4, the OLR in Darwin starts to become very low and the winds have now become northerly. Phase 5 brings the most convectively active conditions to Darwin, with extremely low OLR and strong westerly winds sweeping in over the region. These wet westerly winds 35

45 Figure 5.2: Phase space for January 22 - April 27, 1988, showing the cycle of the Madden Julian Oscillation (MJO). The Real-time Multivariate MJO series 1 (RMM1) and 2 (RMM2) represent the characteristics of the MJO and are the result of an empirical orthogonal function (EOF) analysis on 200 hpa and 850 hpa zonal winds and outgoing long-wave radiation (OLR). [Adapted from Wheeler and Hendon (2004)] continue to dominate throughout phase 6 and 7, as the OLR starts to increase in the region. Finally, in phase 8 the OLR is back at the high values found in the first two phases, and the wind now comes from the south instead. Using the monsoon onset definition by Drosdowsky (1996), described earlier in the climatological overview, Wheeler and Hendon found that more than 80% of the onset dates occur in phases 4-7, i.e., one active half-cycle. Although that is a relation of sorts, the timescale is too big and it is believed that the actual day of monsoon onset is set by some other feature, and not the MJO. The RMM1/RMM2 time-series from Wheeler and Hendon will now be used to try to find a relation between the MJO and the Darwin cloud regimes. RMM1 and RMM2 values are calculated on a day-to-day basis, and hence each occurrence during that day is assigned to those values. Counting the number of occurrences of each regime in each MJO state, and then dividing 36

46 Figure 5.3: MJO composites illustrating the wind and OLR conditions during each of the eight MJO phases in the region ranging from 15 o N - 35 o S, and from 80 o E o E. Darwin is situated close to the centre of each plot (12.4 o S, o E). The shading gives the OLR in W/m 2 and the arrows give the wind direction at 850 hpa. [Adapted from Wheeler and Hendon (2004)] that number with the total amount of occurrences during that state, creates the plot in Figure 5.4. As always, the SSCL regime dominates in all of the MJO phases. As illustrated by Figure 5.3, the northern parts of Australia experience suppressed conditions during phase 1 and 2, with high OLR and easterly winds. This 37