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Understanding the MJO through the MERRA
data assimilating model system
Brian Mapes
RSMAS, Univ. of Miami
and
Julio Bacmeister
NASA GSFC
and
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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
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The MJO• Madden and Julian 1972
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Eastward moving, 40-50 day period
MJO in OLR data
Wheeler and Kiladis 1999
Distinct from c-c Kelvin wave
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Outline
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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
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Outline
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Choosing MJO cases
Filtered OLR variance
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Meanwhile (when I started project)
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Choosing a case in MERRA streams
bestavail
Next(COARE)
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Satellite OLR 15N-15S, & filtered
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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”
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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
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Incremental Analysis Update (IAU)
i cannot understand this diagram
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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
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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)
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Outline
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Satellite observed OLR 1990 Jan-Apr
15NS 10NS
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MERRA analysis model’s OLR
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15NS u850 NCEP 10NS
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15NS u850 MERRA 10NS
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MJO phase definition
0
9
05
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1990 MJO phase in time-lon space
0 95
IO WP
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1992-3 MJO phase in time-lon space
0 95
IO WP
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Line checks: 1990 OLR vs. satellite
MERRA biased high 10-20W in
active phase
misses ~10W IO-WP
difference
IO
WP
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Rainrate compared to SSMI (SSMI is over water only)
MERRA
SSMI
0
x 10-4 mm/s
too rainy here
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PW: MERRA has humid bias, too little IO-WP difference
1990 MERRA
IO
1990 SSMI
WP
IO too humid especially here
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LWP: MERRA too low by half
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Total rain:
convective:
anvil:
large-scale cloud:
1992-3
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1990 1992-3 COARE
-50 -50-5-5
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1990 T 1992-3 COARE
850
250
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1990 RH 1992-3 COARE
60<40
60<40
60<40
60<40
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1990 1992-3 COARE
0.450.5
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1992-3 COAREperiod in MERRA
COARE OSA qv lag regression (Mapes et. al. 2006 DAO)
?
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1990 qcond 1992-3
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MERRA “Cloud fraction”
25%
+7% -6%
50%
+15% -15%
Cloudsat echo coverage
from Emily Riley MS thesis
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MERRA “Cloud fraction”
25%
+7% -6%
50%
+15% -15%
Cloudsat echo coverage
from Emily Riley MS thesis
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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
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MERRA has a Dry bias at 850, humid bias at 600
[qv] DJF 1990 minus JRA – typical of MERRA vs. all others
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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
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Bias stripes correspond to Moist Phys tend.
Żana ≅ -(error in Żphys) +
-
+ -
+ -
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1990 1992-3 COARE
analysis Qv tend.
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Benedict and Randall schematic
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deep Mc
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• Hypothesis: model convection scheme acts too deep too soon in the early stages of the MJO.
• (Hypothesis for improving it is another seminar)
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• 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
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MERRA Temperature biases (DJF)• 2 different years, 3 different reference reanalyses
-NCEP2 -ERA -JRA
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1990 1992-3
Again: analysis tendencies fight the bias
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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?
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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
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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...