Tropical Convection: A Product
of Convergence
But What Drives Convergence?
 ONE THEORY: CISK
 Conditional Instability of the Second Kind
 A Positive Feedback Mechanism . . .
CISK
Evaporation
LHR
Cum. Towers:
Upper Level DIV
Surface Low:
Low Level CONV
Increased Winds
Increased Evaporation
CISK: Convergence Driven by LH Release Aloft
Is this the Whole Story?
Other Process….
 Barotropic Instability
 Sea Surface Temperature Gradients
(Lindzen and Nigam)
*All processes play a role to some extent*
But how do they compare?
General Circulation:
Conv driven by upper-level Div
Local Circulation:
Conv driven by SST gradient
Qui ckTim e™ a nd a
TIFF (Uncompressed ) deco mpre ssor
are need ed to s ee th is picture.
Basic Hypothesis:
-Momentum Balance of Hadley circulation aloft does
not account for total low-level moisture Convergence
-SST directly influence Convection apart from
thermodynamic properties
-Variation or Gradient in SST pattern important for
Convection
In Tropics
Small Changes
Large Influence
Environment of Tropical Ocean
Basic Approach/Methodology
 ATS capped at 700mb (height of inversion)
 Inversion decouples upper ATS from below
 No influence from LHR in cumulus towers (CISK)
 Convergence in lower layer driven by SST Gradient
Pressure Gradient
 Well Mixed BL
SST and gradients correlated in vertical
 Model Eddy (anomalous) surface flow
 Zonally averaged flow well represented by Hadley Circulation
 Compare model with observational data (FGGE) in order to
determine relative importance of low-level forcing in eddy
convergence
Model Development
Vertical Temp structure of BL linear function of SST:
Flow in Boundary Layer Incompressible:
Given Temp & Density
Pressure via Hydrostatic Eq
Momentum Equations: Balance of
PGF, Coriolis, Friction
Zonal Component:
Coriolis
Meridional Component
PGF
Turbulent
Stress (friction)
Compute Eddy SLP from Observed temperature using:
Initial Results :
Major Approximation/Error:
-Lindzen & Nigam assume top of Boundary Layer
(taken to be 700mb or 3km) is flat and does not vary
in time
-Convection occurs instantaneously
-These simplifications are later revised in order to
Get realistic flow pattern in the model
(back-pressure effect)
Back-Pressure adjustment
-In original model, BL (700mb sfc) is a rigid sfc that can’t be
modified
-In reality, vertical motion above SFC LOW raises the top of the BL
(700mb sfc) and this adiabatic expansion acts to cool the lower
tropopause
raises pressure
Negative feedback
-This cooling is eventually dampened by ample LHR ;
But it takes time for convective clouds to develop (~30mins)
2 Major New Variables Introduced:
= Deviation of 700mb layer from flat 3km sfc
Proportional to uptake of mass via convergence
Proportional to cooling of tropopause
* If large
cooling offsets warm SST
Convergence
suppressed
= Time Scale ~ Cloud development time
Represents adjustment time of ATS to reach steady state
*If small, LHR quickly compensates cooling from h’
Convergence excessive
Revised Equations in Model
- Allows for modulation of 700mb sfc with upward
vertical motion
variation in top of BL
Note new variables directly proportional to each other:
time scale
conv/div
New Solutions
 If tau=30s
looks like old model (excessive convergence)
 If tau=3hrs
Weak to no convergence (Big back-pressure)
 If tau=30mins
resembles flow from real data
Solution with tau=30mins :
Both Gradients Important
Forcing from Meridional
-Represents ITCZ better
Forcing from Zonal
-Represents SPCZ better
Criticisms/Notes
Questionable parameterizations
-3km can be considered too high for mean Boundary Layer
-Time Adjustment of 30 mins chosen b/c it looks the ‘nicest’
(No theoretical Justification)
Poor Results for NH Winter
-Boundary Layer is shallower
-Greater influence from motions aloft
Are Results repeatable
-How does model compare against other reanalysis
and data sets (future work)
*Conceptual Problem*
Inherent Ambiguity: What drives what?
Low level vs. Upper Level
SST gradient
(Lindzen Nigam)
Deep Convection/LHR
(Gill & others)
Pressure gradient
Pressure gradient
Low-level flow
Low-level flow
*Different Forcing can yield similar results
*Each Mechanism only valid given assumptions made