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Evaluation of GCM convection schemes via data assimilation: e.g. to study the Madden-Julian Oscillation in a model that doesn’t have one . Brian Mapes RSMAS, University of Miami with Julio Bacmeister (then NASA, now NCAR). Why assimilation-based science? . I. MJO is low frequency

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slide1

Evaluation of GCM convection schemes via data assimilation:e.g. to study the Madden-Julian Oscillation in a model that doesn’t have one

Brian Mapes

RSMAS, University of Miami

with

Julio Bacmeister

(then NASA, now NCAR)

why assimilation based science
Why assimilation-based science?

I. MJO is low frequency

= small Eulerian (local) rate of change

  • many small processes (tendency terms), or small imbalances among bigger terms, ‘could’ cause the observed changes
  • many simple/toy single-effect demonstration models exist, but

Physically comprehensive modeling is needed at this stage

why assimilation based science3
Why assimilation-based science?

II. Slow speed of motion

even wrt weak tropical flows

  • resting/uniform basic states questionable

III. MJO large scale, yet confined...

zonally, seasonally

Geographically realistic modeling is needed at this stage

why assimilation based science4
Why assimilation-based science?

IV. But GCMs don’t simulate it well...

  • or would be solved long ago

MJO’s well-observed, well-resolved large scale structure needs to be brought into a model’s quantitative framework empirically by assimilation

new merra reanalysis
New! MERRA reanalysis

Modern Era (from 1979) Reanalysis

for Research and Applications

Budget datasets incl. analysis tendencies

Uses GEOS-5 GCM (formerly NSIPP)

OBS precip, u850 GEOS5

  • no MJO -- Good news!

Kim et al. 2009

slide6

MERRA’s variables

Z [T,u,v,qv]

satisfy:

ΔZ/Δt = Żmodel + Żana

ΔZ/Δt = (Żdyn + Żphys)+ Żana

free model solution: Żana= 0

(biased, weather unsynchronized, lacks MJO)

use piecewise constant Żana(t) to make above equations exactly true

in each 6h time interval

while visiting analyzed states exactly

“Replay” analyzed wx

initialized free model

some analyzed state variable

Z

at some point

time

slide7

ΔZ/Δt = Żmodel + Żana

ΔZ/Δt = (Żdyn + Żphys)+ Żana

Poor man’s version

(& interpretive aid):

Żana= (Ztarget– Z) /trelax

any analyzed variable

Z

at 6h intervals

model drift balanced by

nudge

nudged trajectory

Interpolate analyses to GCM grid & time steps: ‘target’ state

time

slide8

Misses analysis (in direction toward model attractor) by a skinch, but analysis is already biased that way

miss analysis by a skinch (a 1/trelax)

(analyzed MJO a bit weak)

analyzed

vs.

Observed

Z

time

slide9

Poor man’s data assimilation: nudge to analyses

ΔZ/Δt = Żmodel + Żana

ΔZ/Δt = (Żdyn + Żphys)+ Żana

  • Żana= (Ztarget– Z) /trelax
  • Need to choose trelax
  • Any small value will converge to same results
  • Strong forcing (incl. q & div) forces rainfall (M. Suarez), but can blow up model (B. Kirtman)
  • Dodge trouble, and do science: discriminate mechanisms, by using different trelax values for different variables (e.g. winds; div vs. rot; T, q)
learning from analysis tendencies
Learning from analysis tendencies

(ΔZ/Δt)obs = (Żdyn + Żphys)+ Żana

  • If state is kept accurate (LS flow & gradients), then (ΔZ/Δt)obs and advective terms Żdyn will be accurate
  • and thus

Żana ≅ -(error in Żphys)

example 1 mean heating rate errors dt dt moist dt dt ana
Example 1: mean heating rate errorsdT/dtmoist dT/dtana

100

500

mb

1000

15-30 December, 1992 (COARE)

(magnitudes much smaller)

High wavenumber in model T(p) profile disagrees w/obs. & so is fought by data assim = WRONG

Strange “stripe” of moist-physics cooling at 700mb (melting at 10C, & re-evap)

example 2 mjo related physics errors just do more sophisticated ana averaging mjo phase composites
Example 2: MJO-related physics errorsjust do more sophisticated Żana averaging (MJO phase composites)
  • Case studies (JFMA90, DJFM92)
    • of 3D (height-dependent) fields (dT/dtana , dq/dtana , etc)
    • averaging Indian-Pacific sector longitudes together
  • 27-year composite
    • of various 2D (single level or vertical integral) datasets
    • as a function of longitude
slide13

Error lesson: model convection scheme acts too deep (drying instead of moistening) in the leading edge of the MJO.

slide14
When MJO rain is over Indian Ocean, W. Pac. atmosphere is observed to be moistening, but GCM doesn’t, so analysis tendency has to do it
slide16

SUMMARY: GEOS-5 moist physics errors

produce -- in addition to sizable MEAN biases --

too little moistening & too much conv. rain

here

9 8 7 6 5 4 3 2 1 0

‘back’ (W) ‘front’ (E)

Objective, unbiased-sample MJO mosaic of CloudSat

radar echo objects

Riley and Mapes, in prep.

physics lack of convective organization a whole nuther talk
Physics: lack of convective ”organization” ? (a whole nuther talk)

org = 0.1

org =0.5

New plume ensemble

approach

(in prep)

ok a better scheme candidates
OK, a “better” scheme (candidates)
  • For schemes as mission-central as convection, evaluation has to be comprehensive
  • Żana is a powerful guide to errors!
    • Mean, MJO... but also diurnal, seasonal, ENSO,...
    • simply save d()dt_ana, as well as state vars ()
    • send into existing diagnostic plotting codes
    • similar to (obs-model) analyses, but automatic
      • (all data on same grid, etc.)
how to get ana datasets nudge gcms to world s great analyses
How to get Żana datasets? Nudge GCMs to world’s great analyses
  • Full blown raw-data assimilation is expen$$ive, & really...are we gonna beat EC, JMA, NCEP?
  • Multiple GCMs nudged to multiple reanalyses
    • Bracket/ estimate/ remove 2-model (anal. model + eval. GCM) error interactions
  • Commonalities teach us about nature, since all exercises share global obs. & intensive assim.
  • Differences play valuable secondary role of informing individual model improvement efforts
  • (Shameless: CPT proposal in community’s hands now...)