Upload
ganit
View
34
Download
0
Embed Size (px)
DESCRIPTION
and. Understanding the MJO through the MERRA data assimilating model system. Brian Mapes RSMAS, Univ. of Miami and Julio Bacmeister NASA GSFC. Outline. What is the MJO? Why does it require assimilation-based science? - PowerPoint PPT Presentation
Citation preview
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...