ISCCP cloud clusters, TRMM rain clusters, and tropical water and

energy budgets

David DuncanChris Kummerow

ISCCP at 30

Outline of talk

Goal:

Follow deep convective raining systems in a Lagrangian framework to see their environmental impacts and subsequent feedbacks on rainfall

This is similar in scope to Stephens et al. (2004) that centered on the MJO, but generalized to all basins

and deep cloud systems

Scene classification System tracking Environmental effects of tracked systems

Why system speed matters Clouds, SST, water vapor, radiative fluxes

Feedbacks Conclusions

Data Sources

TRMM Microwave Imager (TMI) using GPROF retrieval

TRMM Precipitation Radar (PR)

ISCCP cloud regimes

CMORPH rainfall at 3-hrly, 0.25° resolution

Ancillary fields from SeaFlux, ERA-Interim, and SRB

Global Tropical Moored Buoy Array (GTMBA) for in-situ SST

Scene Classification

Classifying by precipitation (Elsaesser et al. 2010)

Clustering by precipitating cloud top height, rain rate, stratiform/convective fraction of rain

Uses TRMM PR 2A25 data exclusively

Similarity across all basins

Classifying by clouds (Rossow et al. 2005) Clustering by cloud top pressure and optical

thickness into 6 regimes

Uses visible and IR data from ISCCP D1 product

Only available during daytime

Precipitation characteristics of these weather states are explored in Lee et al. (2013)

Scene Classification Using TMI data and a K-means clustering

algorithm, 1°x1° oceanic patches are clustered into three regimes

Scenes need to be 100% ocean, lie within 30°N-30°S, and have at least one pixel with a rain rate of >0.5mm/hr

Rainwater path (RWP), surface rain rate, convective fraction of rain rate are the clustering parameters

Advantages to using passive microwave data for classification purposes

Classification Results

The organized convective class exhibits the greatest fraction of stratiform rainfall, largest surface rain rate, and highest rainwater path

Will focus on organized convective class since it should have the strongest effects on water and energy budgets, despite producing the least total rain

18% of scenes produce 64% of all rainfall; in line with 70-80% of rainfall coming from 10-20% of cloud clusters (Mohr et al. 1999)

How do these match up with ISCCP classes?

ShallowUnorg.

Convective

Org. Convectiv

eFraction of

scenes82% 17% 1%

Mean scene RR

0.18 mm/hr

1.6 mm/hr 7.0 mm/hr

Fraction of all rainfall

36% 48% 16%

Classification Results

Matching TMI-derived precipitation classes with ISCCP weather states:

Organized convective class matches with WS1 in most cases

Unorganized convective class is primarily WS1 and the remainder is mostly the other ‘convectively active’ regimes– WS2 and WS3

Shallow class is closer to the background distribution of weather states, shown in dashed lines on plot

Shallow Unorg. Conv

Org. Conv

WS1 Vigorous deep convection 13.7 56.6 83.6

WS2 Thick cirrus, less vigorous convection

12.5 9.1 3.4

WS3 Isolated, smaller-scale convection

26.4 23.6 9.7

WS4 Thin cirrus 15.4 4.2 0.8

WS5 Scattered cumulus 23.8 3.7 1.8

WS6 Marine stratus 8.0 2.8 0.6

Indian

West Pacific

Central Pacific

East Pacific

Atlantic

CFADs of reflectivity from PR,

shown for the Organized

Convective class

Separated by ocean basin (shown

below)

Left column is stratiform pixels

Right column is convective pixels

Demonstrates the similarity and

consistency of vertical structure for

TMI-derived precipitation clusters,

regardless of location Ind W. Pac C. Pac E. Pac Atl

Tracking Method

Other studies have used outgoing longwave radiation (OLR) or brightness temperatures to track deep convection in the Tropics– why not use rain itself?

Catalogue groups of raining pixels that are contiguous in time and space and exceed a rain rate threshold

Latitudinal range of 15°N-15°S

7mm/hr is the rain rate threshold used, the same as the mean scene rain rate from the organized convective regime

Rain rates Tracked groups OLR

Tracking Results

Great similarities are found in the propagation characteristics of systems in each basin

West-moving systems are more likely in every basin

Atlantic basin in a slight outlier, with many more west-movers and more systems that move quickly

In the remaining analysis, systems are separated by speed into fast (>6m/s) and slow (<2m/s)

All tracked systems, 2003-2009, separated by basin

IndWPa

cCpacEPac

Atl

IndWPa

cCpacEPac

Atl

IndWPa

cCpacEPac

Atl

Tracking Results

Putting together the classification and tracking methods—what are we tracking?

Co-locate tracked systems with TMI-derived precipitation classes

55% of tracked systems are co-located with the TMI-derived organized convective class—why not higher?

TMI-derived regime

Tracked system frequency

Shallow 2%

Unorg. Convective 43%

Org. Convective 55%

Contoured CMORPH rain rate of a sample tracked system

Environment Analysis Mean latitude and longitude are

computed at every time step, then co-located with closest grid box from each dataset

A time series is extracted for each tracked point for analysis of ancillary fields before and after passage of the system

Time series are composited together to show the mean evolution of each field

All of the following analysis is separated into fast and slow systems, and by ocean basin

t = 1

t = 2

t = 3

SST

Rain rate

Environment– Clouds

OLR provides a good check on the method of co-location

Inter-basin variability is significant, with slow systems in the W. Pac and Indian ocean basins definite outliers

Dashed = fast Solid = slow

IndWPa

cCpa

cEPa

cAtl

Environment– Water Vapor

Total precipitable water (TPW) peaks at system passage (lag=0), with fairly symmetrical increase and decrease before and after system passage

Essentially no net effect found in TPW, so deep convection neither dries nor moistens the atmospheric column at a 72hr lag, common for all speeds and basins

Magnitude of change largely agrees with results from a separate method (Masunaga 2012)

Most moistening, ~4mm of the 6mm total, occurs between 500-850mb

IndWPacCpacEPac

Atl

Dashed = fast

Solid = slow

Environment– Water Vapor Water vapor convergence is calculated from

ERA-Int wind vectors and specific humidity

A sharp decrease in water vapor convergence coincides with sharply decreasing rain rates after system passage

Due to a conservation of water, every grid box should obey the equation below

Reanalysis water vapor convergence is not strong enough to balance the water budget, though rain rates in reanalysis are underestimated

IndWPa

cCpacEPac

Atl

Environment– Evaporation

Evaporation is directly proportional to surface latent heat flux (LHF), which is taken from SeaFlux

LHF is a function of SST, near surface humidity, and surface winds

Speed and basin both play significant roles

The generally larger size of slow systems aids in creating a larger circulation, causing stronger surface winds and higher LHF

IndWPa

cCpacEPac

Atl

Environment– SST

Diurnal nature of heavy rainfall in the Tropics, peaking in early morning, is visible and similar for all basins

Decreases of 0.1-0.3°C witnessed, dependent upon basin and system speed

Total difference (-72hr to +72hr SST) shows that slow systems have a bigger impact on SST in every basin

SST recovery rates are quite basin dependent, likely due to differences in ocean mixing and mixed layer depth Ind

WPacCpacEPacAtl

Dashed = fast

Solid = slow

Environment- Radiation

What is the main driver of the observed drop in SST?

Deep clouds cause ~200W/m2 drop in net surface radiative flux

Changes in SW Down accounts for almost all variation in the net surface flux

Diurnal signal is again quite noticeable

Are radiative fluxes the most important driver of observed SST variability?

IndWPa

cCpacEPac

Atl

IndWPa

cCpacEPac

AtlDashed = fast

Solid = slow

Radiation and SST

Gridded SST products use interpolation for raining grid cells. Does SeaFlux SST match the environmental evolution seen by in-situ buoy measurements?

To ascertain the degree to which radiative changes cause the observed drop in SST, we need to use buoys because SeaFlux shows a fundamentally different evolution

Radiation and SST Shown below, in-situ measurements of SST match very well with radiative

changes

In a simple scale analysis, changes in surface radiative flux are integrated to give an equivalent ocean mixed layer depth (MLD):

Integrating from -18hrs to +6hrs lag, the period of greatest SST decrease, yields a calculated MLD of 15m; integrating from -18hrs to +36hrs gives a MLD of 45m, very close to the climatological mean MLD of ~40m

Feedbacks

Following a slow-moving system, the environment is suppressed—nearly 0.1°C lower SSTs for multiple days. Does this have a noticeable impact on subsequent development of deep convection?

Use rain rate information from CMORPH to see if there’s a signal present

Feedbacks Need to look at rain rates a few days after the system’s passage to

escape the ‘persistence’ of slow-moving systems’ high rain rates in the PDF

The signal is quite noisy due to the low frequency of the highest rain rates

Environments affected by fast systems are more likely to exhibit high rain rates 4 or 5 days after system passage

PDF difference = (PDF[slow] – PDF[fast])/PDF[slow]

Areas on the PDF dominated by slow systems are warm colors and fast systems are cold colors

Conclusions

TPW WV Conv. SST Evap Net Sfc. Rad.

Fast

Slow

Conclusions

Deep convective systems in the Tropics exhibit great similarity and consistency in vertical structure and propagation characteristics in all ocean basins

Differences in cloud fields affect various elements of local water and energy budgets, with system propagation speed of key importance

For tracked systems, SST drops 0.1-0.3°C and TPW increases symmetrically ~5-7kg/m2 in the mean evolution

Depressed SSTs persist in environments affected by slow-moving systems, impacting the likelihood of heavy rainfall days later