Issues for Global Modeling and New Experiments Siegfried Schubert Global Modeling and Assimilation Office NASA/Goddard Fifth Meeting of the NAME Science Working Group Puerto 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 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 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

NAMAP Model Assessment for the North American Monsoon Experiment D.S. GutzlerH.-K. Kim University of New MexicoNOAA/NCEP/CPC

NAMAP analysis goals a)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 ModelInstitution / GroupResolutionMoist Convection RSMNCEP / Juang et al.20 km / 28LArakawa-Schubert RSMSIO ECPC / Kanamitsu20 km / 28LArakawa-Schubert MM5UNM / Ritchie15 km / 23LKain-Fritsch EtaNCEP / Mitchell & Yang32 km / 45LBetts-Miller-Janjic SFMNCEP / Schemm 2.5  2.5°/ 28L Arakawa-Schubert NSIPPNASA / Schubert & Pegion 1  1°/ 34L Relaxed A-S Regional Global Lateral boundary conditions: Reanalysis SST: NOAA OIv2 1  1° weekly analysis Land surface treatments vary Summer 1990 simulations

 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