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Title: DYNAMO


1
DYNAMO (Dynamics of the MJO) The US
Participation in CINDY2011 (Cooperative Indian
Ocean Experiment on Intraseasonal Variability in
Year 2011) US CLIVAR Summit 2009
2
Outline
  • Importance and problems of the MJO
  • perspectives from operational forecast
  • perspectives from research
  • 2. Rationale, objectives, and hypotheses
  • 3. Program structure
  • (a) Field Campaign
  • (b) Modeling
  • 4. Remaining Issues
  • 5. Experimental Design (breakout session)

3
Operational MJO Prediction -- Perspective
  • Our lack of understanding of the MJO initiation
    process has made operational prediction
    challenging
  • Statistical forecast techniques and MJO
    composites perform well during established MJO
    events but not during MJO transitions
  • Dynamical model predictions are improving (and
    are the way forward) but currently offer limited
    skill
  • Daily monitoring remains our most important and
    reliable method for anticipation of MJO
    development/demise
  • Operational MJO prediction is often forced to be
    reactive to the development or demise of the MJO
    after the fact

4
How can DYNAMO help Operational MJO prediction?
  • Current MJO monitoring / prediction is mainly
    based on the Wheeler-Hendon (2004) MJO index,
    which is formed using zonal winds and OLR. It is
    an excellent index to track the propagation of
    the MJO, but does not provide sufficient
    information about MJO initiation. 
  • Targeted DYNAMO field obs may contribute to
    improved documentation of the MJO initiation
    process.
  • Thus DYNAMO can have an immediate impact on
    operational MJO prediction as important
    precursors for the development of the large-scale
    MJO signal will be identified more clearly than
    in the past
  • (see example)

5
Operational MJO Prediction -- Example
(b)
(a)
GEFS MJO Index Fcst ? Developing MJO
Official CPC MJO Forecast
(d)
(c)
East Asian Cold Surge
Obs
Information included in the February monthly
outlook
6
Operational MJO Prediction Forecast Models
NCEP
CMC
UK Met
(d)
  • Dynamical models becoming an important component
    of the MJO forecast process.
  • Model prediction framework is in place and can
    benefit from model improvements.
  • Operational model prediction likely to benefit
    from DYNAMO CPT (i.e., better model physics)

ABOM
ECMWF
7
Operational MJO Prediction -- Applications
8
Limited intraseasonal prediction skill (lt 15
days) particularly low during the initiation of
the MJO in the Indian Ocean and during the
passage of the MJO over the Maritime Continent.
Correlation between predicted (by CFS) and
observed MJO indices (Courtesy of Jon Gottschalck
and Qin Zhang)
9
CPC Operational Support for DYNAMO Field Campaign
1. Setup DYNAMO briefing web page(s) ? Build
upon existing CPC briefing pages, NAME experience
2. Participate in tropical weather briefings
and provide expert assessment on MJO status and
forecast evolution 3. Realtime experience using
Gotowebinar briefings 4. Maintain, provide
assessment of CLIVAR MJO index forecast tools
Example Briefing Page --Multiple Categories
Observational Data Forecast Tools
10
Background II Importance of the MJO Science
Perspective
  • monsoons, ENSO
  • extreme events (flood, tropical storm/cyclones)
  • Indian Ocean Dipole and Indonesian Throughflow
  • teleconnections, extratropical circulation/weather
  • North Atlantic Oscillation, Arctic Oscillation,
    Antarctic Oscillation
  • atmospheric and oceanic chemistry and biosystem
    (ozone, CO2, aerosols, chlorophyll)
  • global angular momentum, Earths rotation rate,
    length of the day

Maloney and Hartmann 2000
11
  • Challenges presented by the MJO
  • inability to consistently/knowingly reproduce
    the MJO in global weather and climate models

Lin et al 2006
12
Weaker MJO signals in the Indian Ocean than the
western Pacific in GCMs that reproduce the MJO to
a certain extent. MJO variance in 850 hPa
zonal wind (contours)
(Zhang et al 2006)
13
U850
precipitation
2001
2000
14
Scenarios of MJO Initiation
  • A Internal Initiation The MJO is initialized
    over the tropical Indian Ocean through local
    interaction between the large-scale circulation
    and convective activity that self-organizes into
    large-scale patterns through atmospheric energy
    buildup, multi-scale interaction, air-sea
    interaction, or other processes.
  • B External Initiation Perturbations from either
    the extratropics or upstream (west) lead to
    changes in the large-scale circulation and/or
    thermodynamics over the tropical Indian Ocean.
    Deep convection subsequently organizes into
    large-scale patterns that feed back to the
    large-scale circulation, giving rise to the MJO.

15
  • Recent Advancement in the MJO Study
  • Moisture pre-conditioning for deep convection
    by shallow convective moistening (Kemball-Cook
    and Weare 2001 Bretherton et al, 2004
    Derbyshire et al. 2004 Holloway and Neelin 2009)
  • Shallow diabatic heating sensitivity of
    low-level moisture convergence and surface wind
    (Wu 2000 Zhang and Hagos 2009)
  • Multiscale interaction upscale momentum
    transport (Biello et al. 2007 Maloney 2009)
  • Air-sea coupling (Fu et al. 2003)
  • All are sore points of cumulus
    parameterization.

16
Current Thinking on Key Processes of MJO
Initiation
Lower Troposphere
Moisture Profile
Deep
Shallow
Convective Types
17
  • DYNAMO Hypotheses Criteria and Guidance
  • Testable using DYNAMO field observations and
    models
  • Testing leading to specific information helping
    model improvement
  • Two MJO initiation scenarios internal vs.
    external initiation
  • Different stages of MJO initiation convective
    suppression, transition, persistence and
    termination
  • Based on recent modeling, theoretical, and
    observational results
  • role of moisture
  • role of diabatic heating profile
  • role of multi-scale interaction
  • role of the upper ocean

18
DYNAMO Hypotheses (abridged, under
development) Scenario A  internal
initiation Hypothesis I Limited moisture supply
inefficient moistening by shallow convection gt
prolonged convective suppression prior to MJO
initiation Hypothesis II Two-stage
transition shallow convection with low
precipitation efficiency gt lower-tropospheric
moistening slow shallow convection with high
precipitation efficiency gt surface and low-level
moisture convergence fast Hypothesis III A
balance between shallow, deep, and stratiform
precipitation gt convection sustained on the MJO
scales dominance of stratiform precipitation gt
convective termination Hypothesis IV Through
large variability in mixing and associated
entrainment cooling, the SCTR and Wyrtki Jets
provide interactive feedback and independent
background for MJO initiation
19
DYNAMO Program Structure
Science Steering Committee
Program Supporting Office Data M/A Field Operation
20
DYNAMO Modeling Activities General Strategy
  • A CPT will be proposed (LOI submitted, Eric
    Maloney, PI), including five modeling centers
    NCAR, NCEP/EMC, NASA/GMAO, NASA/GISS (MAP), GFDL
    (CPPA) NRL/MTR pending
  • DYNAMO will contribute to the field observation
    component of the CPT
  • Connections between DYNAMO and the modeling
    centers will be strengthened through the CPT
  • A DYNAMO Modeling Working Group (members overlap
    with the CPT) will conduct additional activities
    to support DYNAMO (experimental design,
    hypothesis testing, etc.)
  • Leverage with existing modeling activities
    relevant to DYNAMO CAPT, WCRP/TFSP, etc.

21
DYNAMO Modeling Activities Preliminary Ideas
  • CPT
  • (i) diagnostic metrics for processes of
    convection-circulation coupling (e.g., MJO) and
    its applications to selected AR4 and AR5 models
  • (ii) common parameterization sensitivity
    experiments
  • (iii) general procedures for identifying
    misrepresentations of processes connecting
    convection (MJO) and the mean state in models
  • DYNAMO modeling working group
  • experimental design
  • hypothesis testing
  • YOTC -gt YOTC2
  • reforecast (CFS)

22
DYNAMO/CINDY2011 Field Campaign
  • sounding-radar array
  • ship-based measurement of air-sea flux, aerosol,
    and upper-ocean mixing
  • addition mooring of surface meteorology and
    upper ocean measurement
  • enhanced soundings at operational sites

23
Current Thinking on Key Processes of MJO
Initiation
Lower Troposphere
Moisture Profile
Deep
Shallow
Convective Types
24
DYNAMO/CINDY2011 Observation Strategy
25
Program Synergy
CINDY2011/DYNAMO (September 2011 January 2012)
atmospheric heating and moistening profiles,
cloud and precipitation, upper-ocean mixing and
turbulence, aerosol AMIE (late 2011 early
2012) radiation, cloud, atmospheric profiles
(pairing with DYNAMO SMART-RAMF2) HARIMAU (2004
- ) cloud, atmospheric boundary
layer PAC3E-SA/7SEAS (2011) aerosol,
convection ONR Air-Sea (late 2011) meso-scale
air-sea-wave interaction
26
Composite of TRMM Precipitation Anomalies Based
on MJO Phases
Phase 1
Phase 2
Phase 3
27
MJO Probability in the Indian Ocean (1980-2008)
Season and ENSO
(Courtesy of Kunio Yoneyama)
28
After TOGA COARE, Why DYNAMO?
  • Unique prediction challenge (low skill vs. very
    low skill)
  • Unique climate modeling challenge (in some GCMs
    moderate MJO vs. weak or no MJO)
  • Unique MJO life stage (mature, propagation vs.
    initiation)
  • Unique large-scale background (warm pool vs.
    South Asian monsoon, Wyrtki Jets,
    Seychelles-Chagos thermocline ridge)
  • New observing technology (RAMA, Radar)

29
Expected Outcome of DYNAMO
  • a unique in situ data set available to the
    broader research and operations communities,
    whose utility will match GATE and TOGA COARE
    data
  • advancement in understanding of the MJO dynamics
    and initiation processes
  • identification of misrepresentations of processes
    key to MJO initiation that are common in models
    and must be corrected to improve MJO simulations
    and predictions
  • provision of baseline information to develop new
    physical parameterizations and quantify MJO
    prediction model improvements, and
  • enhanced MJO monitoring and prediction capacities
    that deliver climate prediction and assessment
    products on intraseasonal timescales for risk
    management and decision making.

30
DYNAMO Climate Implications
  • Field data to be collected will be available to
    all climate modeling centers
  • Research results from DYNAMO and the CPT will
    provide targeted information for model
    improvement (entrainment/detrainment rates,
    precipitation efficiencies, heating profiles,
    etc.)
  • DYNAMO will improve climate prediction and
    assessment products on intraseasonal timescales
    for risk management and decision making
  • Improved MJO capability in climate models may
    help dynamical ENSO prediction
  • Improved MJO capability in climate models will
    increase our confidence in their credibility in
    climate simulations and projection.
  • First comprehensive air-sea interaction field
    campaign in the equatorial Indian Ocean
    landmark for future climate process studies

31
Major Issues to be Resolved
  • Ship time Ron Brown vs. Revelle (need to install
    TOGA radar)
  • Sounding operation at Diego Garcia
  • DYNAMO CPT connection
  • DYNAMO ONR IO Exp coordination
  • DYNAMO IAG coordination

32
Thank you!
  • Comments, Questions and Suggestions
  • are Welcome!

DYNAMO website http//www.eol.ucar.edu/projects/dy
namo/
33
Initial Field Observation Cost Estimates(excludin
g ship time)
  • Soundings (one ship, two island) 2M (NSF
    deployment)
  • S-PolKa 2M (NSF deployment)
  • SMART-R 0.3M (NSF ATM, JAMSTEC)
  • TOGA Radar 0.4M (NSF ATM)
  • AMF2 0 (DOE/ARM)
  • Surface flux, aerosol, drifters, moorings 1M
    (NOAA)
  • Ocean 4M (NSF OCE, ONR)
  • Total 10M

34
DYNAMO Science Steering Committee
  • Simon Chang (NRL/MRY)
  • Chris Fairall (NOAA/ESRL)
  • Wayne Higgins (NOAA/NCEP/CPC)
  • Richard Johnson (CSU)
  • Chuck Long (PNNL)
  • Steve Lord (NOAA/NCEP/EMC)
  • Mike MaPhaden (NOAA/PMEL)
  • Eric Maloney (CSU)
  • Mitch Moncrieff (NCAR)
  • Jim Moum (OSU)
  • Steve Rutledge (CSU)
  • Augustin Vintzileos (NOAA/NCEP/EMC)
  • Duane Waliser (CalTech/JPL)
  • Chidong Zhang (UM)

35
DYNAMO Data Policy and Management
  • Follow the standard policy of recent field
    campaign (NAME, VOCAL)
  • real-time data stream to operations center
    through GTS
  • fixed time frame for data release
  • International data exchange within CINDY2011
  • The CINDY2011 data center will be established
    at JAMSTEC
  • DYNAMO data center will be at EOL
  • Two data centers will be mirrored at each site
    with links, so data access to both data will be
    transparent to users
  • Similar approach will be pursued between
    DYNAMO/CINDY2011 and other programs (HARIMAU,
    AMIE, ONR Air-Sea, 7SEAS).
  • If there will be YOTC 2 for 2011-12, then its
    data infrastructure will be linked to the
    DYNAMO/CINDY2011 data center.

36
Moisture Profiles Observations
(Kiladis et al. 2005)
37
NCAR CAM3
(Zhang and Song 2009)
38
Circulation Sensitivity to Heating Profiles
Stratiform heating
Deep heating
Shallow heating
39
Seychelles-Chagos Thermocline Ridge (SCTR)
  • shallow thermocline driven by wind pattern S of
    equator (Ekman divergence)
  • heating intensified in thin ML cool water
    available in close proximity to sea surface
  • role of sub-surface mixing?
  • changes in surface fluxes, SST linked to MJO
    (Vialiard et al, 2008)
  • potential feedback?

40
Wyrtki Jets
  • vigorous eastward surface currents in boreal
    spring/fall
  • dominant currents / dominate transport
  • current structure is unique from equatorial
    Pacific/Atlantic
  • to date, current structure is under-resolved,
    mixing not measured
  • anticipate high vertical shear, strong mixing
    when jets present

41
Indian Ocean Dipole (IOD)
http//www.jamstec.go.jp/frsgc/research/d1/iod/
42
Stratiform Rain Fraction
(19982000 TRMM PR rain 0.6 m yr-1. Schumacher
and Houze 2003)
43
NCAR S-PolKa Radar
  • S-band (10 cm) convection spectrum,
    organization, and evolution are needed to
    determine MJO phase and convective/stratiform
    partition
  • Single Doppler convective storm circulations
    (updrafts)
  • Ka-band (8 mm) 3D structure of shallow cumulus
    clouds
  • Dual wavelength (Ka- and S-band) vertical
    profile of boundary layer specific humidity
  • Dual polarimetric microphysics of oceanic
    tropical convection
  • Need upgrade

44
Proposed Sounding-Radar Array
Hanimaadhoo
Gan
Gan
Diego Garcia
45
DYNAMO Field Observations
  • Sounding array vertical profiles of diabatic
    heat and moisture budgets (Q1, Q2), divergence,
    wind shear
  • Radar array cloud population statistics, cloud
    and precipitation structure and evolution, cloud
    microphysics, boundary-layer humidity
  • Ship-based surface fluxes and rain rate
    profiles of temperature, salinity, current, and
    turbulence aerosol
  • Mooring-based surface fluxes and rain rate
    subsurface temperature, salinity, current, flux,
    mixing, and wave propagation

46
  • DYNAMO/CINDY2011 EOP
  • SMART C-band radar AMF2
  • surface met and upper ocean moorings
  • drifters

AMIE
47
Long-Term Monitoring in the Indian Ocean
48
RAMA
49
HARIMAU
Yamanaka et al. 2008
50
PAC3E-SA
51
  • ?pod
  • moored subsurface flux measurement
  • analogous to a subsurface flux tower

52
subsurface heat fluxes at 0 140W
53
shipboard profiling flux measurements
multiple high-res modern ADCPs sampled rapidly
Hull 300 kHz 75 kHz Over-the-side 150
kHz
Chameleon turbulence profiler
54
(No Transcript)
55
(No Transcript)
56
DYNAMO Hypotheses Hypothesis I Convective
suppression prior to MJO initiation is prolonged
because, in the absence of external influences,
moisture source through surface evaporation over
warm sea surface with weak to moderate surface
wind is sufficient to support only isolated
precipitating systems. The timescale of the
suppressed phase prior to MJO initiation is
determined by the low efficiency of
lower-troposphere moistening by shallow
convection. Hypothesis II Population of
shallow convection plays a two-stage role in the
transition period of MJO initiation when the
precipitation efficiency is low, the moistening
effect in the lower troposphere dominates, which
slowly creates a favorable condition for deep
convection when the precipitation efficiency is
high, the low-level heating effect dominates,
which induces surface and low-level moisture
convergence as an energy source for deep
convection and accelerates the initiation
process.
57
DYNAMO Hypotheses Hypothesis III A delicate
balance between precipitating shallow convection
and deep, stratiform precipitation is needed to
sustain convective period over the MJO space
scale. In the absence of pre-existing large-scale
influences, the time scale of the active phase of
the MJO is determined by a graduate shift from
this balance to a dominance of stratiform heating
profiles on the large scale. Hypothesis IV
Upper-ocean processes contribute to MJO
initiation through maintaining high SST prior to
the initiation and rapid surface cooling during
the transition and early active periods.
Turbulent mixing plays a critical role in these
because of the shallow mixed layer associated
with the SCTR, the current shear associated with
the Wyrtki jets, and their intraseasonal
variability. Hypothesis V External
(extratropical and upstream) perturbations, with
their large-scale ascents and low-level
convergence, play an activating role to
accelerate MJO initiation primed by internal
processes.
58
Hope Dynamical Models can have better skill than
Statistical models
model
statistical
persistence
  • POAMA hindcasts 10 members from 1st of month for
    25 years.
  • Correlation RMS for RMM1 and RMM2 (combined)

Courtesy M. Wheeler
59
  • Background I Importance of the MJO
  • Societal benefit
  • monsoons, ENSO
  • teleconnections, extratropical
    circulation/weather
  • extreme events (flood, tropical storm/cyclones)
  • seamless weather-climate prediction (2-4 weeks)
  • Current MJO prediction at NCEP/CPC
  • Contributions from operations centers of the US,
    Canada, Brazil, Japan, Australia, UK, ECMWF
    (Taiwan, India in the near future)
  • End users
  •  emergency responding agencies (e.g., American
    Red Cross, International Federation of Red Cross
    and Red Crescent Societies)
  •  US government (e.g., USAID, Forest Service,
    National Marine Fisheries Service, River Forecast
    Centers, NWS Regional HQs)
  •  private industry (e.g., American Electric
    Power, Earth Satellite Corporation, Moore Capital
    Management, and many more)

60
  • DYNAMO Objectives
  • Collect in situ observations from the equatorial
    Indian Ocean that are urgently needed to advance
    our understanding of the processes key to MJO
    initiation and to improve their representations
    in models
  • (b) Identify critical deficiencies in models that
    are responsible for the low prediction skill and
    poor simulations of MJO initiation, and assist
    the broad community effort of improving model
    parameterization
  • (c) Provide guiding information to enhance MJO
    monitoring and prediction capacities that deliver
    climate prediction and assessment products on
    intraseasonal timescales for risk management and
    decision making over the global tropics.

61
MJO Probability in the Indian Ocean (1980-2008)
IOD
62
  • The Need of an MJO Process Study
  • MJO initiation in the Indian Ocean poses a unique
    challenge to prediction, simulation, and
    understanding of the MJO
  • Recent progress has brought us to the dawn of a
    breakthrough in the MJO study
  • Data needed to make the breakthrough are
    available only from field campaigns (not provided
    by previous field campaigns in the Indian Ocean,
    e.g, INDOEX, JASMINE, MISMO, Vasco-Cirene)
  • The modeling community is experienced with using
    field observations to assist model improvement
    and development.

63
DYNAMO Timeline
Daily Planning Process In-Field Data
Management. Operational Data Collection Facility
Coordination Status Operations Center User
Services In-field Catalog
Project Planning Phase
Initial Feasibility Cost Estimates (July 2009)
Facility Science Coordination Mtg (October 2010)
Site setup Initiate Data Collection Comm System
Test
Science Planning Meeting (April 13-14, 2009)
Proj Team selection Prepare Ops Plan Data Mgmt.
Plan Logistics
Routine Archive/ Access
Data Processing Quality Control
Field Phase
Initial Site Survey
Operations Planning Mtg. Finalize Ops
Plan Project Safety Review Finalize Data Mgmt Plan
1 yr
-1 yr
Data Analysis Workshop
Program Assessment
R.V Ron Brown Time Request (April, 2009)
Draft SPO EDO US Clivar Summit (July 16, 2009)
Long-Term Data Management Support Phase
OFAP FacilitiesDecisions (May 2010)
NSF Proposal Submissions (January 2010)
Facilities Request LOI (May 15, 2009)
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