Understanding the MJO through the MERRA

data assimilating model system

Brian Mapes

RSMAS, Univ. of Miami

and

Julio Bacmeister

NASA GSFC

and

Outline1. What is the MJO?

2. Why does it require assimilation-based science?

3. Robust MJO features from 2 active seasons, 2 longitudes (IO vs. WP), 2 MERRA versions

4. Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings

5. Testing the hypotheses & improving the model

The MJO• Madden and Julian 1972

Eastward moving, 40-50 day period

MJO in OLR data

Wheeler and Kiladis 1999

Distinct from c-c Kelvin wave

Outline

Models have trouble with this stuffconvection & cloud problems

Obs

Dominant modes: MJO, Kelvin, ER, WIG

Dispersion curves correspond to equivalent depth 8, 12, 25, 50, 90m. Larger depth –faster phase speed.

All modes: 25 m.

Lin et al. 2005

Outline

Choosing MJO cases

Filtered OLR variance

Meanwhile (when I started project)

Choosing a case in MERRA streams

bestavail

Next(COARE)

Satellite OLR 15N-15S, & filtered

MERRA data used

• Scout runs (~2 degree) – for convenience– so actually, all other cases are available.– trying not to make ‘scout’ an object of research

though

• Real MERRA (1/2 x 2/3 degree) – will the parameterized-resolved rain partition differ?– will heating profiles differ in a corresponding way?

• “convective vs. stratiform”

Outline1. What is the MJO?

2. What is assimilation-based science?

3. Robust features from 2 active seasons, 2 longitudes (IO vs. WP), 2 MERRA versions

4. Analysis tendency derived hypotheses about MJO mechanisms and model shortcomings

5. Testing the hypotheses & improving the model

Incremental Analysis Update (IAU)

i cannot understand this diagram

time

analyzed variable

Z at discrete

times

free model solution: Żana= 0 (biased, unsynchronized, may lack oscillation altogether)

initialized free model

ΔZ/Δt = Żmodel + Żana

ΔZ/Δt = (Żdyn + Żphys) + Żana

use piecewise constant Żana(t) to make above equations exactly true in each time interval*

Modeling system integrates:

*through clever predictor-corrector time integrations

is nudging a bad word (or boring)?

• not if we STUDY the analysis tendencies

• (ΔZ/Δt)obs = (Żdyn + Żphys) + Żana

• If state is accurate (flow & gradients), then Żdyn will be accurate

and thus

Żana ≅ -(error in Żphys)

Outline

Satellite observed OLR 1990 Jan-Apr

15NS 10NS

MERRA analysis model’s OLR

15NS u850 NCEP 10NS

15NS u850 MERRA 10NS

MJO phase definition

0

9

05

1990 MJO phase in time-lon space

0 95

IO WP

1992-3 MJO phase in time-lon space

0 95

IO WP

Line checks: 1990 OLR vs. satellite

MERRA biased high 10-20W in

active phase

misses ~10W IO-WP

difference

IO

WP

Rainrate compared to SSMI (SSMI is over water only)

MERRA

SSMI

0

x 10-4 mm/s

too rainy here

PW: MERRA has humid bias, too little IO-WP difference

1990 MERRA

IO

1990 SSMI

WP

IO too humid especially here

LWP: MERRA too low by half

Total rain:

convective:

anvil:

large-scale cloud:

1992-3

1990 1992-3 COARE

-50 -50-5-5

1990 T 1992-3 COARE

850

250

1990 RH 1992-3 COARE

60<40

60<40

60<40

60<40

1990 1992-3 COARE

0.450.5

1992-3 COAREperiod in MERRA

COARE OSA qv lag regression (Mapes et. al. 2006 DAO)

?

1990 qcond 1992-3

MERRA “Cloud fraction”

25%

+7% -6%

50%

+15% -15%

Cloudsat echo coverage

from Emily Riley MS thesis

MERRA “Cloud fraction”

25%

+7% -6%

50%

+15% -15%

Cloudsat echo coverage

from Emily Riley MS thesis

Outline1. What is the MJO?

2. Why does it require assimilation-based science?

3. Robust features from two active seasons, two longitude belts, two MERRA versions

4. Analysis tendency based hypotheses about MJO mechanisms, and model shortcomings

5. Testing the hypotheses & improving the model

MERRA has a Dry bias at 850, humid bias at 600

[qv] DJF 1990 minus JRA – typical of MERRA vs. all others

Analysis tendencies oppose humidity bias(with a little MJO dependence too)

Żana ≅ -(error in Żphys) zonal mean qv bias

1990 JFMA MJOs DJFM 1992-3 COARE

Bias stripes correspond to Moist Phys tend.

Żana ≅ -(error in Żphys) +

-

+ -

+ -

1990 1992-3 COARE

analysis Qv tend.

Benedict and Randall schematic

deep Mc

• Hypothesis: model convection scheme acts too deep too soon in the early stages of the MJO.

• (Hypothesis for improving it is another seminar)

• Hypothesis: model convection scheme acts too deep too soon in the early stages of the MJO.

• (Hypothesis for improving it is another seminar)

• Might be entangled with the mean state biases.

• “Improving” the model must consider both

MERRA Temperature biases (DJF)• 2 different years, 3 different reference reanalyses

-NCEP2 -ERA -JRA

1990 1992-3

Again: analysis tendencies fight the bias

T budget: DYN-PHYS balance

mostly MST

sharp ‘shelf’ in moist heating profile may be bias source. Again the shallow to deep convection transition issue?

Outline1. What is the MJO?

2. Why does it require assimilation-based science?

3. Robust features from two active seasons, two longitude belts, two MERRA versions

4. Analysis tendency based hypotheses about MJO mechanisms, and model shortcomings

5. Testing hypotheses / improving the model

closing the loop1. Adjust model based on hypotheses

– convection scheme formulations» after learning them (what i’m here for)

2. Re-run in assimilation mode – or replay

» ? advice ?

3. Remake diagrams and evaluate– mean AND variability

» will interplay make results inscrutable?

4. Focus on improved aspects, declare victory.

5. Refine hyp., go to 1. Progress, if not victory...