Issues for Global Modeling and New Experiments

Siegfried SchubertGlobal Modeling and Assimilation Office

NASA/Goddard

Fifth Meeting of the NAME Science Working GroupPuerto Vallarta, Mexico

6-7 November 2003

Outline

• Overview of NAME Modeling and Data Assimilation Strategic Plan

• Review of NAMAP1

• What next? NAMAP2, CPTs…

• Are we addressing global modeling issues?– How/why do we expect NAME to improve

predictions?

Overview of NAME Modeling and Data Assimilation Strategic Plan

Multi-scale Model Development

Multi-tier Synthesis and Data Assimilation

Prediction and Global-scale Linkages

provide constraints at the process level

assess the veracity of phenomena and the linkages to regional and larger-scales

provide initial and boundary conditions and verification data for predictions

Role of Observations in Model Development and Assessment

I. Multi-scale Model Development

The underlying premise of the NAME modeling strategy is that deficiencies in our ability to model "local" processes are among

the leading factors limiting forecast skill in the NAME region.

Specifically:

•moist convection in the presence of complex terrain and land/sea contrasts;

• land/atmosphere interactions in the presence of complex terrain and land/sea contrasts;

• ocean/atmosphere interactions in coastal regions with complex terrain.

will require both improvements to the physical parameterizations and improvements to how we model the interactions between the local processes and regional and larger scale variability

“Bottom-up” and “top-down” approaches:

1. Multi-scale modeling1. Multi-scale modeling

Cloud-system-resolving models having computational domain(s) large enough to represent interaction/feedback with large scales

Multiscale models explicitly represent convective cloud systems

2. Global/regional models2. Global/regional models

Examine impact of resolution, diagnose behavior of parameterizations in the presence of complex terrain, and larger-scale organization

Understand behavior and limitations of current parameterizations at higher resolutions, pursue improved parameterizations

II. Multi-tier Synthesis and Data Assimilation

Data assimilation is critical to enhancing the value and extending the impact of the Tier I observations

The specific objectives are:

To better understand and simulate the various components of the NAM and their interactions

To quantify the impact of the NAME observations

To identify model errors and attribute them to the underlying model deficiencies

III. Prediction and Global-Scale Linkages

One of the measures of success of the NAME program will be the extent to which predictions of the NAMS are improved

The key issue to be addressed is to determine the extent to which model improvements (and improved boundary and initial conditions) translate into improved dynamical predictions.

“Regional” improvements => improved regional/global scale interactions => improved predictions

Basic idea is that:

Review of NAMAP1

NAMAPModel Assessment for the North American Monsoon

Experiment

D.S. Gutzler H.-K. Kim University of New Mexico NOAA/NCEP/CPC

gutzler@unm.edu hyun-kyung.kim@noaa.gov

NAMAP analysis goalsa) Motivate a set of baseline control simulations for

more focused research by each group

b) Identify and describe inter-model consistencies and differences; tentatively suggest physical explanations for differences

c) Provide measurement targets for NAME 2004 field campaign

d) Examine effects of core monsoon (Tier I) convection differences on larger-scale (Tier II) circulation

NAMAP participating models/groups

Model

Institution / Group ResolutionMoist Convection

RSM NCEP / Juang et al. 20 km / 28LArakawa-Schubert

RSM SIO ECPC / Kanamitsu 20 km / 28LArakawa-Schubert

MM5 UNM / Ritchie 15 km / 23L Kain-Fritsch

Eta NCEP / Mitchell & Yang 32 km / 45LBetts-Miller-Janjic

SFM NCEP / Schemm2.52.5°/ 28L

Arakawa-Schubert

NSIPPNASA / Schubert & Pegion

11°/ 34L Relaxed A-S

Reg

ion

al

Glo

bal

Lateral boundary conditions: Reanalysis SST: NOAA OIv2 11° weekly analysis

Land surface treatments vary

Summer 1990simulations

No obs here! What is the “true” diurnal cycle? All models show convective max between 21Z-04Z How much nocturnal rain should be falling?

Moisture transport & the Gulf of Calif LLJ

Eta: Berbery (2001) RAMS: Fawcett et al (2002)

qv x-sec at 31°N qv map at 925 hPa{Centered on Gulf} {mostly on slopes}

NAMAP low-level jets I (925 hPa, July 12Z avg)

MM5 results “look like” Berbery’s Eta jet in the northern GofC, with a slope jet farther south

NSIPP just generates a slope jet

MM5 NSIPP-1 [regional] [global]

NAMAP: What have we learned so far?

• All models simulate a summer precip maximum; the two global models exhibit delayed monsoon onset (Aug instead of Jul)

• Precipitation diurnal cycle issues: magnitude of late-day convection, amount of nocturnal rainfall?

• Surface quantities (T, LH, SH fluxes) seem very poorly constrained; huge model differences (no validation data)

• Great Plains LLJ weakens after monsoon onset

• Low-level (slope?) jets occur -- but only weakly tied to NAME precipitation? Needs additional analysis, and close observation in 2004 field season

NAMAP2

Greater Focus (compared with NAMAP1)

• Precipitation (emphasizing diurnal cycle) in key NAME regions

• Surface energy budget (land surface interactions) • Comparative analysis of LLJs in Gulf of California

and Gulf of Mexico• Integrate with field campaign• Prediction component

Challenges

Strengthening linkages between modeling, data assimilation and observational activities/programs

•relevancy - timing is everything --> path to operations

•doesn’t happened naturally - requires programmatic nudging/support

Developing “CPT-like” effort -> focus on diurnal cycle

Are we addressing global modeling issues?– How/why do we expect NAME to improve

predictions?

Global modeling issues• Basic “universal” problems relevant to NAMS

– Poor simulation of warm season continental climates– Poor simulation of diurnal cycle (related to above)– Poor predictions of warm season precipitation

• Resolution issues– Need to resolve key phenomena– Application specific (e.g. regional impacts, extreme events)– Computational issues: need for long runs, large ensembles

• Physics issues– Limitations of convection parameterizations, but intimately linked to surface

interactions, atmospheric boundary layer, clouds, etc.– Schemes largely untested at high resolution

• Prediction issues– Role of SSTs (especially other than ENSO)– Role of land surface feedbacks (strength, time scales)– Role of intraseasonal variability (e.g. MJO)– Seasonal differences in predictability (e.g. impact of ENSO)

“Snapshot” of water vapor (white) and precipitation (orange) from a simulation with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) AGCM run at 1/2 degree lat/lon resolution.

Winter (DJF) Summer (JJA)Signal/Total (Z200)

Full

Eddy

Prediction Issues• Winter

– Strong wave response to SST: impacts storm tracks– Models do reasonable job in getting above, and show

some skill in precipitation prediction

• Summer– Stronger zonally-symmetric response to SST: more

subtle interactions with orography, land, etc– Models do poorly in such warm season global/regional

interactions– Getting “local/regional” processes right and their

interactions with global scale is critical to improving predictions