24 January 2013, University of Washington, Seattle, Washington

24 January 2013, University of Washington, Seattle, Washington
The Precipitating Cloud Population of the Madden – Julian Oscillation over the Indian and West Pacific Oceans Hannah C. Barnes Dynamics Seminar 24 January 2013, University of Washington, Seattle, Washington

What is the Madden-Julian Oscillation?
Episodic convective burst Along equator Indian Ocean -> dateline 30-90 day period Boreal Winter Deep baroclinic circulation Madden and Julian 1972

MJO in Indian and West Pacific Oceans
1 2 3 4 5 6 7 8 Wheeler and Hendon Index (2004) EOF analysis of 200 and 850 hPa zonal winds and OLR Two PC time series 8 phases Significant MJO One of most identifiable characteristics is a region of enhanced convection that propagates from the central Indian Ocean into the West Pacific. Associated with this convection is a baroclinic circulation that extends through the troposphere These sailent features used to determine if an MJO exists and its location Wheeler and Hendon (2004) most common method EOF analysis used to create two variables (Real-Time Multivariate Variables 1 & 2) Phase relationship defines location and amplitude defines magnitude of MJO Look one step deeper into the MJO OLR and 850 hPa Winds

Importance of the MJO Lin et al. 2006
Phenomenon whose surface anomalies and convection propagate from Indian and West Pacific Ocean but long range effects (monsoons and tropical cyclone genesis) Particularly important for here is the Rossby Wave trains that result from it creates Lin et al. 2006

MJO and Pacific Northwest
More precipitation, floods when convection near dateline Bond and Vecchi 2003 Oct, Nov, Dec When convection associated with MJO near dateline Western Washington has tendency to experience more precipitation and floods See that has far reaching effects but what exactly in the MJO Floods in Western Washington (Bond and Vecchi 2003) Precipitation rate anomalies

Convectively coupled Kelvin and Rossby waves
MJO Structure Convectively coupled Kelvin and Rossby waves Eastward ~ 5 ms-1 Air-sea interaction Convection and baroclinc structure take the form of a convectively couple K-R wave Complex propagates from Central Indian Ocean eastward through the Meritime Contient and West Pacific to the dateline. At the dateline convection and surface anomalies dissipate but upper-level anomalies circumnavigate the globe People state day peroid, which implies that it takes days for convection to reappear in a given location. However, this is not a continual cycle the MJO is not always present. Rather it is more appropriate to think of it as a eposidic region of enhanced convective activity that is more common in boreal winter. Figure clear shows the Rossby and Kelvin wave anomalies but the convection is shown as one deep cloud. This masks the fact the tropical convection is associated with an entire population of clouds. Rossby Wave Kelvin Wave Rui and Wang 1990

Tropical Cloud Population
MESOSCALE CONVECTIVE SYSTEMS (MCSs) Can see from this figure that there is a broad range, from these shallow, non precipiating clouds to deep and broad mesoscale convective systems. Each of these cloud types are present during the MJO and the previous figures masks the fact that the relative frequency of each type varies during an MJO event. The types are important for a number of reasons, including their impact on the heating profile and vertical mass transport. However, how exactly this population varies is not well understood. That is where my work comes in. We will be focusing on four aspects of the population and evaluate how the cloud population changes during the MJO in the Central Indian Ocean and how it changes by the time is reaches the Western Pacific. Number of studies have emphasized the important role of the cloud population during the MJO. Houze et al. 1980

Importance of Cloud Population
Satellite obs. Models unrealistic without including shallow convection (Zhang and Song 2009) MJO sensitive to deep and shallow heating (Haertel et al. 2008) Entire population This figure from Zhang and Song shows lag regressions of OLR generated from reanalysis data and two model runs. The first contains the complete cloud population while the second omits the shallow clouds. While the model is not perfect, it is much more realistic when shallow convection is included. Similar conclusions were generated in Haettel et al 2008, who found that the MJO is sensitive to deep heating from deep convective and stratiform in addition to shallow heating and upper-level cooling from shallow and congustus clouds. Thus, it is important for us evaluate how the spectrum of clouds varies throughout the MJO. No shallow Zhang and Song 2009

Objectives Variability of precipitating clouds in MJO using TRMM Precipitation Radar Associated humidity and wind shear Will do this by using the TRMM Precipitation Radar to investigate the precipitating cloud population of the MJO in the CIO and WP. Not enough simply to know what types of clouds are present we want to have an idea of which environments favor certain populations. In order to deduce investigate this we will look at the relative humidity and shear fields in the ERA-interim reanalysis

TRMM Satellite Instrumentation
λ= 2 cm Important! PR measures 3D structure of radar echoes It has a resolution of 17 dBz, which means that it can sense most precipitation and provide the 3D structure of echoes. This is particularly important for my work since I use this 3D structure of the echoes to define four aspects of the precipitating cloud population. Bob Slide Radar is 2 cm -> sense rain Provides the 3D structure of precipitating regions of storm, which use to categorize convection over the tropics Wavelength—short for radar. Obtains data at high resolution in a 3D volume. Allows us to study vertical structure of echoes. Not trying to determine precip amounts. 92.5 minute orbit; approximately 16 orbits per day. Revisits specific locations times per 30 day period (dependent on latitude). Our data set consists of 1648 TRMM 2A23 & 2A25 rain containing orbit files from the monsoon season (June – September) in 2002 & 2003. The TRMM 2A25 data contains corrected TRMM reflectivity data in 250 m height bins. The TRMM 2A23 algorithm detects the presence of a bright band and categorizes precipitation as stratiform, convective or other (unclassified). Kummerow et al, 1998

Data and Methodology TRMM PR 2A23 (rain type classification)
2A25 (attenuated corrected reflectivity) ERA-interim reanalysis 1999 – 2011, October – February, Wheeler and Hendon Index > 1 Bootstrapping In order to complete the TRMM PR analysis we use two products that provide us which classify each echo and its attuation corrected reflectivity. As I mentioned earlier we want to relate these changes in the cloud population to the large-scale so we will be using the ERA-interim reanalysis data set. In both aspects of the analysis we will be making composites for 14 years of data by phase of the HW index during the months of Oct-February when the MJO is most common and when the WH >1 1, thereby ensuring that a significant MJO is present. In order to gain some sense of the significance of the variability we use a bootstrapping method to generate twenty samples of 100 days for each phase. This was conducted in four regions, but for purposes of this talk I am going to focus on the central indian ocean and southeast West Pacific. TRMM orbit

Geographic Regions Central Indian Ocean Southeast West Pacific
In order to complete the TRMM PR analysis we use two products that provide us which classify each echo and its attuation corrected reflectivity. As I mentioned earlier we want to relate these changes in the cloud population to the large-scale so we will be using the ERA-interim reanalysis data set. In both aspects of the analysis we will be making composites for 14 years of data by phase of the HW index during the months of Oct-February when the MJO is most common and when the WH >1 1, thereby ensuring that a significant MJO is present. In order to gain some sense of the significance of the variability we use a bootstrapping method to generate twenty samples of 100 days for each phase. This was conducted in four regions, but for purposes of this talk I am going to focus on the central indian ocean and southeast West Pacific. Central Indian Ocean Southeast West Pacific

TRMM PR Identification
Identify each contiguous 3D echo object seen by TRMM PR Convective component Stratiform component Extreme characteristic Contiguous 3D volume of convective echo > 30 dBZ Top height > 8 km “Deep convective core (DCC)” Horizontal area > 800 km2 “Wide convective core (WCC)” Contiguous stratiform echo with horizontal area > km2 “Broad stratiform region (BSR)” Continue to use a methodology developed by the Houze group in It is unique since we start by isolating contiguous 3D echoes. This means that we are actually running our statistics on physical storms, not just pixels. Once storms identified when then separate each storm into ist convective and stratiform components and apply our thresholds. These have been used to study convection in the South Asian and South America. We have changed the thresholds slightly to be appropraite for oceanic regions. For convective regions we first find the 30 dBZ echo. If that echo exceeds 8km in altitude it is a DCC, if it covers more than 800 km2 it is a WCC. Then turn to stratiform regions, if these cover km2 it s a broad stratiofrm regions. So these categories capture the large end of the cloud population. To investiage the small end of the population we use the echoes tha TRMM classifies as isolated shallow. These have echo tops more than 1km below the freezing level and are separate from deeper convection. Thus, we obtain an idea of the full cloud population and how it varies with the MJO. UNIQUE: 3D STORMS USED PREVIOUSLY FOR INDIAN MONSOON AND SOUTH AMERICAN CONVECTION Bob Slide Done previously with indian Monsoon and in South America Emphasize contiguous 3D echoes - Not just looking at single pixels in the radar data. Actually analyzing coherent storms Storms with deep or wide convective cores Storms containing broad stratiform regions Not mutuall exclusive deep and broad or deep and wide in same storm Analyzing components of a precipitating region “Isolated shallow echo (ISE)” Echo top > 1 km below freezing level and separate from deeper convection Houze et al, 2007, Romatschke et al. 2010, Rasmussen and Houze 2011, Zuluaga and Houze 2013

Central Indian Ocean (CIO) Analysis
So start in the CIO

The MJO in the CIO 1 2 Active 3 Transition to Suppressed 4 5
Transition to Active 1 2 Active 3 Transition to Suppressed 4 5 Suppressed For purposes of this study we will be defining the active stage of the MJO to occur when the BSR peak, which occurs in phase 3 in the CIO. We will be considering all phases as we look at the variabilty of the cloud population but as we investigate the large-scale atmosphere we will be focusing on the five phases that surround the active phase of the MJO. Which are phases 1-5 in the CIO and are highlighted here. 6 7 8

Isolated Shallow Echoes
10N MJO Phase % Pixels 10S 60E 90E We will start on the small end and work our way up. I want to spend some time going through this figure with you since it will be reappearing frequently. We first broke the entire domain into cells that are 0.5 by 0.5. Then we counted the number of times an ISE occurred in that box and normalized by the total number of times TRMM passed over, regardless of the presence of an echo. Thus, this is showing how frequently an ISE occurs over a given location in the CIO. This frequency is reported as a precentage and gives an idea of the areal coverage of these echoes. Visually does not seem to have a lot of variability. Most striking element is concentration near 10S, this is assoicated with the ITCZ as was shown in Schmaucher and Houze Though do see an enhancement in the ITCZ in phase 5 and decrease in phase 3. Even though visually small, if you calculate the same freuqnecy but this time consider the entire domain at once and plot it by phase. The blue lines are the twenty samples, the black is the average, and red is 99% confidence. You can see that the freuqneyc of these echoes signficantly varies with minimum during active stages and maximum during suppressed. % 20 samples (blue), average (black), and 99% confidence interval (red) Frequency (%)

20 samples (blue), average (black), and 99% confidence interval (red)
Deep Convective Cores MJO Phase % Pixels 0.025 Now look at large end of the spectrum. Notice that the color bar has changed and while it is evident that deep convective cores are much less freuqnet than ISE. We continue to see visually small variability. Perhaps we see a peak in phase 3 and minimum in phase 5, which is confirmed in the statistics of the region. DCC are significantly more common in phases 1-3, leading up and inclduing the active stage than the suppressed phase 5. % Frequency (%) 20 samples (blue), average (black), and 99% confidence interval (red)

Broad Stratiform Regions
MJO Phase % Pixels Images are drastically different as look at BSR. The variability in these echoes is readily apparent in the maps. You can even get a sense of the eastward propagation of these echoes. And the statistics show that this peak is highly significant. % 20 samples (blue), average (black), and 99% confidence interval (red) Frequency (%)

MJO Precipitating Cloud Population
All cloud types vary significantly ISE suppressed, 2 phases after active DCC, WCC, and BSR simultaneous active Areal variability - BSR dominate Number variability ISE dominate WCC > DCC > BSR ISE DCC WCC BSR % MJO Phase x104 Look at precipitating cloud population as a whole in the MJO we see that all the clouds analyzed vary signficantly with phase of the MJO. Isolated shallow echoes peak during the suppressed stage and just two phases after the active stage. However, DCC, WCC, and BSR all peak in phase 3. This bottom figure shows the mean and 99% confidence intervals for the ISE in rad, DCC and dark blue, WCC in cyan and BSR in green. It is readily apparent that in terms of the variabilty in the areal coverage of each echo type broad stratiform regions dominant, which is consistent with other studies. However, the areal coverage of the population highlights the variabilty in the heating profile. This is important but not the only aspect of the population that is important. If you look in terms of number you can get a sense of the mass transport. If we look in terms of number we see a slightly different picture. ISE dominate by far. But even in terms of deep convective elements. We see that the WCC are the most frequent, then DCC, and BSR are last. The shapes are the same, but but emphasizes that can’t only think in terms of area. So have an idea of the cloud population, how does the large scale atmosphere fit into this. x10^4 250

Large-Scale Relative Humidity
Frequency (%) Number 4 Pressure (hPa) 8 2-3 1 Starting with the large-scale relative humidity. One the left I have the areal frequency of each cloud types as we just saw. On the right I am showing the average relative humidity profile for each phase of the MJO. The solid lines are the phases that are convectively active and the dashed lines are suppressed. I want to focus on the mid and upper level RH. Starting in phase 8 the atmosphere is relatively dry. However, you can see that it is moistening, especially if you compar it to phase 1, this black line. Then the RH peaks in phases 2 and 3 and proceeeds to decline in phase 4. Looking back to the figure on the left we see that the BSR regions significantly increase from phase 1-2 and peak in 3. We see a similar situation if we consider the WCC, as can be seen here by the number of WCC. Thus, this seems to suggest that the RH begins to increase prior to signifcant increases in deep and wide convection and then acts a positive feedback comes into play with the cloud population and RH increasing together. Both the deep convection and RH decline together. Lot of attention to moisture and the MJO, but one thing that has gotten less attention but is extremely interesting is the variabilty of the shear. We consider two shear layers. First, lets look at the low level shear which we define as MJO Phase ISE DCC WCC BSR Relative Humidity (%) Solid lines = active, dashed = suppressed

Large-Scale 1000-750 hPa Shear
Shading = shear magnitude ms-1 I want to focus on the time leading up, including and following the active stage so I have phases 1 through five shown here. The colors is the magnitude and vectors are direction. We notice that the low level shear starts to increase in phase 2 and maximizes in phase 4. Looking at the variability of the cloud population we see that the shear increases with the mesoscale features since BSR and WCC increase from phases 1-3. Why does this matter. Frequency (%) Number ISE DCC WCC BSR MJO Phase

Strong Low-Level Shear Favors Development with Locally Stronger Surface Convergence
Stratiform Region Convective Core WCC and BSR are often associated with MCSs. Within an MCS it has this rear-inflow jet that comes from the mid-troposphere and flows towards the surface. If we have a shear profile as shown this jet will transport higher winds to the surface, create locally stronger surface convergence that may foster stronger convergence. Seeing that low-level shear may influence these storms what about the upper level shear. Houze et al. 1989

Strong Low-Level Shear Favors Development with Locally Stronger Surface Convergence
WCC and BSR are often associated with MCSs. Within an MCS it has this rear-inflow jet that comes from the mid-troposphere and flows towards the surface. If we have a shear profile as shown this jet will transport higher winds to the surface, create locally stronger surface convergence that may foster stronger convergence. Seeing that low-level shear may influence these storms what about the upper level shear. Houze et al. 1989

Shading = shear magnitude ms-1 We are now considering shear between the hpa levels. We notice immedately and relatively rapid increase in shear from phases 1-3 and a strong maximum in phase 4. Notice that the BSR maximize in phase 3, one phase prior to the shear peak. I find this extremely interesting since Frequency (%) ISE DCC WCC BSR MJO Phase

Very Strong Upper-Level Shear Separates Stratiform from Convective Moisture
Within an MCS the stratiform region is partially sustained my moisture being transported by an ascending front to rear flow from the convective region to the stratiform region. If we have very strong shear, as shown in the blue arrows here. The convective and stratiform regions of the storm may move in opposite directions. This isolates the stratiform region from its convective moisture source and causes it to dissipate. Houze et al. 1989

Very Strong Upper-Level Shear Separates Stratiform from Convective Moisture
Bob Slide When lots of convection can organize upscale into a mesoscale system that has a region of intense cells Emphasize stratiform is the stratiform component and convective component. Not marine stratiform common here Analyzing components of precipitating systems Using trmm to isolate individual storms and then analyzing the convective and stratiform component of those storms Houze et al. 1989

Precipitating Cloud Population and Large-Scale Atmospheric Conditions
Pre Active Active Post Active Phase 1 2 3 4 DCC Peak Decrease WCC, BSR Increase Mid-Level Relative Humidity Sharp Increase Slower Increase Low-Level Shear Upper-Level Shear So to summarize what we have found in the central Indian Ocean. Deep convective cores broadly peak in phases 1-3 but WCC and BSR begin to significantly increase in phase 2 and maximize in phase 3. Thus, all deep convective elements peak in phase 3. then all the elements decrease in phase 4. Associated with this variablity in the cloud population we see that the RH begins to increase prior to the wide convective and stratiform elements, then slowly continues to increase as these elements increase and decline with these entities. In terms of shear both the low and upper level shear increase through the active stage and peak one phase later. Remember that I mentioned that stronger low-level shear may favor convective development. Well why would the WCC decline prior to the low-level shear. Well we think it has to do with the fact that the population is defined both by the shear profiles and humidity. So in phase 4 the RH has significantly declined, so even though the shear may be strong conditions are not favorable. The Indian Ocean seems to suggest some relationships, but are these consistent with the West Pacific, can we gain further knowledge by invesigating that region.

Southeast West Pacific (SEWP) Analysis
Now turn to the SEWP region.

MJO in the SEWP 1 Suppressed 2 3 Transition to Active 4 5 Active 6
Roughly a two phase delay between the active stage in the CIO and the active stage in the SEWP. We will define phase 6 as the active stage since we will see that the BSR peak during this time. Our focus for the large-scale will be phases 4-8, as the MJO transitions into and out of its active stage. 6 Transition to Suppressed 7 8

Isolated Shallow Echoes
10N MJO Phase % Pixels 10S 140E 170E Start with cloud populaiton. Once again visually small variability in the ISE. I have the SEWP surrounded by these black boxes so you know where to focus. Notice in these figures that the ISE are concentrated to the north, again climatological position of the ITCZ shown in Schuacher and Houze (2003). Visually seems to be slight enhanced in phase 3. This is confirmed when look at statistics across the entire region by phase. ISE peak during the suppressed and minimze during the active. Notice however that the peak in ISE is a few phase prior ot the active stage, phse 6. In the CIO the peak was shortly after the active stage. % 20 samples (blue), average (black), and 99% confidence interval (red) Frequency (%)

20 samples (blue), average (black), and 99% confidence interval (red)
Deep Convective Cores % Pixels MJO Phase The deep convective once again are much less frequency than ISE but sill have relatively small variabilty. However, this peak in phase 7 is more evident that the peak seen in the CIO. Also, hints as why we divided the WP into three regions. The MJO has a tendency to propagate into the SPCZ rather than follow the equator and that can see seen as a concentration of DCC in the SEWP. Looking at the frequency by phase notice the sharp peak in phase 7. % Frequency 20 samples (blue), average (black), and 99% confidence interval (red)

Broad Stratiform Regions
MJO Phase % Pixels Finally the BSR, Once again the variability is visually striking. Looking by phase it significantly peaks in phase 6 and like the CIO is assymetric around the active stage with the increase slower than the decrease in areal coverage. % Frequency (%) 20 samples (blue), average (black), and 99% confidence interval (red)

MJO Precipitating Cloud Population
All cloud types significantly vary ISE suppressed, 3 phases before active BSR one phase before DCC and WCC Areal variability - BSR dominate Number variability ISE dominate DCC > WCC > BSR % x10^4 Area Number Number To summarize the SEWP is quite similar to the CIO. However there are a few important differences to point out. ISE peak just before the active stage, not just after as was seen in the CIO. Also WCC, DCC, and BSR no long simulateously peak. BSR peak in phase 6 while the DCC and WCC peak in phase 7. In terms of area once again BSR dominate. But looking in terms of number we now see that there are more DCC than WCC. Mainly similar but some details different. It is quite nice that the DCC and WCC peak in a different phase than the BSR since it will enable us to more explicitly consider what conditions have these components of the cloud population. ISE DCC WCC BSR MJO Phase MJO Phase

Large-Scale Relative Humidity
Frequency (%) Number Pressure (hPa) MJO Phase So turning the large-scale lets once again start in the RH. Once again I have the RH profiles shown here with the solid lines showing the active stages. This is more difficult to see but phases 4 and 5 are the solid blue and black lines. These are significantly drier in the mid-troposphere than phases 6 and 7, which are the red and green sold lines. Looking back at the cloud population variablity we see that BSR increase in phase 5 and peak in phase 6, while DCC and WCC peak in phase 7. This suggests to us that BSR are not the only aspect of the cloud population that may contain a mosit mid-troposphere. WCC are also often associated with MCS, however these are smaller than those that produce BSR. But either way these MCSs can maintain a moisture mid-troposphere. It also suggests that RH is not the only factor in determining the cold population. So lets look at the low-level shear. 6-7 4-5 ISE DCC WCC BSR Relative Humidity (%) Solid lines = active, dashed = suppressed

ISE DCC WCC BSR Number Frequency (%) MJO Phase Shading = shear magnitude Once again focus on the lower right hand box of each figure. We notice that the low level shear starts to increase in phase 5 and peaks in phase 7. Looking back at the cloud population we see that BSR, WCC, and DCC also increase and peak during this time which supports our hypothesis that stronger low-level shear can favor convective initation by fostering locally strong surface convergence. Also want to point out that the scale is the same as was seen in the CIO. So the magnitude of the variability in the large scale is much smaller but the cloud population variabilty it comparable. ms-1

ISE DCC WCC BSR Frequency (%) MJO Phase Shading = shear magnitude Looking at the upper-level shear we see that it is moderately strong in phase 6 and maximizes in phase 7. Once again looking at the BSR variability we see that see that BSR peak in phase 6. Which once again supports the notion the very strong upper level shear may be determinental to large stratiform regions. ms-1

Precipitating Cloud Population and Large-Scale Atmospheric Conditions
Pre Active Active Post Active Phases 5 6 7 8 DCC, WCC Increase Peak Decrease BSR Mid-Level Relative Humidity Low-Level Shear Upper-Level Shear To summarize. The SEWP and CIO are quite similar. However, the greatest difference you see between the regions is lag of one phase in the SEWP. While in the CIO DCC, WCC, and BSR peaked together the DCC and WCC peak one phase after the BSR in the SEWP. Acompanying this trend is the fact that relatively humidity also peak one phase after the BSR. Despite these timing differences the reigons are remakably similar.

Conclusions Precipitating Cloud Population
Precipitating cloud population varies significantly Areal variability – BSR dominate Number variability ISE dominate DCC & WCC > BSR Central Indian Ocean Southeast West Pacific Ocean ISE Suppressed, after active Suppressed, before active BSR Active DCC and WCC Active, with BSR Active, one phase after BSR Not only the large components of MCSs but the small isolated shallow echoes all significantly varied with phase. Broad stratiform regions dominate the areal coverage avariabilty which is important for the heating profile. But ISE dominate the number variability and DCC and WCC are more common than BSR by number. This is important in terms of vertical mass transport. However, the regions do differ. The central indian ocean has ISE peak just after the active stage but they peak just before in the SEWP. Also DCC, WCC, and BSR all peak togher in the CIO bu DCC nad WCC peak on phase after the BSR in the SEWP.

Conclusions: Precipitating Cloud Population and Large-Scale Atmosphere
DCC WCC BSR Mid-Level Relative Humidity Relatively Moist Moist Low-Level Shear Relatively Strong Strong Upper-Level Shear -- Moderate Despite these differences we see that the cloud population seems to be related to the relative humidity and shear profile. Both DCC and WCC favor condiions with a moist mid troposphere and strong low level shear. However, it appears that DCC do not require as most or strong shear as WCC. BSR also favor environments with most RH and low level shear but they are also sensitive to the strength of the upper level shear. If this is too strong it rips the stratifrom from its moisture source and leads to its demise. RH leads then positive feedback with the deep convection Strong low-level shear -> strong surface convergence Very strong upper-level shear -> stratiform torn from convective source

(Kingsmill and Houze 1999a)
Future Work Kinematics and microphysics 11 rain events, Zuluaga and Houze (2013) Compare kinematics to TOGA COARE Expand with microphysical data Relate storm structure to large-scale Modeling??? (Kingsmill and Houze 1999a)

Acknowledgements Bob Houze Committee Rob Wood and Mike Wallace
Beth Tully Houze group 626 Officemates Grads 2010 Family Funding DOE DE-SC / ER and DE-SC DYNAMO – NSF AGS PMM-NASA Grant NNX10AH70.

24 January 2013, University of Washington, Seattle, Washington

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