![Page 1: Toward understanding the MJO through the MERRA data-assimilating model Brian Mapes, U. Miami Stefan Tulich, CIRES Julio Bacmeister, GSFC and](https://reader035.vdocuments.mx/reader035/viewer/2022062322/56649ea05503460f94ba2ab0/html5/thumbnails/1.jpg)
Toward understanding
the MJO through the MERRA
data-assimilating model
Brian Mapes, U. Miami
Stefan Tulich, CIRES
Julio Bacmeister, GSFC
and
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37 years of studying the MJO: Progress in description, but still no widely accepted theory
Madden and Julian 1972 Benedict and Randall 2007
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37 years of studying the MJO: Progress in description, but still no widely accepted theory
Madden and Julian 1972 Benedict and Randall 2007
![Page 4: Toward understanding the MJO through the MERRA data-assimilating model Brian Mapes, U. Miami Stefan Tulich, CIRES Julio Bacmeister, GSFC and](https://reader035.vdocuments.mx/reader035/viewer/2022062322/56649ea05503460f94ba2ab0/html5/thumbnails/4.jpg)
Outline1. Previous GCM studies of moisture
preconditioning & the MJO
2. Using novel MERRA data-assimilating model to study this and other MJO science issues
3. Structure of the MJO in MERRA Not new, but shows model biases
“Analysis tendencies” provide a new aspect to the problem
4. Future work: Model improvement as a path towards understanding
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One of the first GCM moisture preconditioning experiments
• Tokioka et al. (1988): The equatorial 30-60 oscillation and the Arakawa-Schubert cumulus parameterization (J. Meteor. Soc. Japan)
Control No non-entraining plumes
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One of the first GCM moisture preconditioning experiments
• Tokioka et al. (1988): The equatorial 30-60 oscillation and the Arakawa-Schubert cumulus parameterization (J. Meteor. Soc. Japan)
Control No non-entraining plumes
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This modification also improves the MJO in the CAM 3.1
Maloney (2009)
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This modification also improves the MJO in the CAM 3.1
Maloney (2009)
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Still the model is not perfect
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Even worse when looking at rainfall variance
Maloney (2009)
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Improvements are also model dependent
Lee et al. (2009; in press)
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How do we proceed further?
• Standard approach: Tinker with the model physics, run long time integration, diagnose model performance/feedbacks, repeat – Drawback: Time-consuming, tedious, feedbacks may
impact other aspects of the simulation in unintended ways
• Our alternative: Assimilation-based science to study the MJO in global models (illustration of concept here)
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MERRA
• Modern Era Reanalysis for Research and Applications (GEOS-5 based)
• NASA’s new atm. reanalysis, 1979-present
• Still running (3 streams), ~90% available
• Attractive features:
- nowOpenDAP access (you needn’t download)
- many budget terms, not just state variables
- “analysis tendencies” available
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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 integration
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Learning from analysis tendencies
(ΔZ/Δt)obs = (Żdyn + Żphys) + Żana
• If state is accurate (including flow & gradients), then (ΔZ/Δt)obs and advective terms Żdyn will be accurate
• and thus
Żana ≅ -(error in Żphys)
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Choosing MJO cases
good(COARE)
MJO amplitude index
MERRA data available when I started
MERRA stream 2
bestavail
MERRA stream 3
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Satellite OLR 15N-15S& MJO-filtered (contours) – used as reference lines below
Filtered OLR courtesy G. Kiladis eastward wavenumbers 0-9, 30-96 days
I averaged this over 15N-15S
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15N-15S
GIBBS image archive
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MJO phase definition
05
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excluded
IO WP
Objective MJO phase categories
PHASE
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10 phases relative to Benedict and Randall (2007)
9 8 7 6 5 4 3 2 1 0 ‘back (W)’ ‘front (E)’
5 = filtered OLR min.
Benedict & Randall 2007
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MERRA rainrate compared to SSMI (SSMI over water only)
MERRA
SSMI
0
x 10-4 mm/s
too rainy phase 1-2
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MERRA’s rain:
convective:
anvil:
large-scale cloud:
premature rain in phase 2 is mainly convective
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deep Mc
Phase dependent mass flux
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9 8 7 6 5 4 3 2 1 0 ‘back’ (W) ‘front (E)’
5 = filtered OLR min.
Model seems to be choking on the shallow-to-deep transition (even
with Tokioka modification)
Impact? Look at analysis tendencies
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Phase dependent part of qv analysis tendency
1990 1992-3
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Blame the convection scheme!
• seems to act too deep too soon in the early stages of the MJO.
• Analysis qv tendency has to compensate with moistening
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Future work: Improving the model as path towards understanding
• Convection parameterization seems to be too insensitive to low- and mid-level moisture (even with Tokioka modification)
• Question: can we somehow further tighten/adjust the Tokioka limiter to reduce model errors?
Strategy: perform short assimilation runs; does Żana get smaller?
If so, something scientific learned from this technical activity.
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Future work: Use analysis tendencies to develop a better forecast tool?
Consider MJO index of Wheeler and Hendon (2004):
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Future work: Use analysis tendencies to develop a better forecast tool?
Idea: First, composite model analysis tendencies in this phase space
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Future work: Use analysis tendencies to develop a better forecast tool?
Idea: First, composite model analysis tendencies in this phase space
Next, perform multi-day forecasts with these composite tendencies added during runtime.
Forecast improved?