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PACS/GAPP Research Overview: Warm Season Precipitation David S. Gutzler Dept. of Earth & Planetary Sciences University of New Mexico Albuquerque, NM 87131.

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Presentation on theme: "PACS/GAPP Research Overview: Warm Season Precipitation David S. Gutzler Dept. of Earth & Planetary Sciences University of New Mexico Albuquerque, NM 87131."— Presentation transcript:

1 PACS/GAPP Research Overview: Warm Season Precipitation David S. Gutzler Dept. of Earth & Planetary Sciences University of New Mexico Albuquerque, NM David J. Gochis National Center for Atmospheric Research RAP / ASP Boulder, CO

2 What is PACS/GAPP Warm Season Precipitation Initiative ? Determine the sources and limits of predictability of warm season precipitation over N. America with emphasis on the intraseasonal-to-interannual timescales. Explicit emphasis on the role of the land surface in modulating warm season precipitation Conduct field observations, diagnostic analyses and modeling studies to improve the prediction of warm season precipitation

3 PACS/GAPP Warm Season Precipitation Research: Motivating Questions How can PACS/GAPP research drive improvements in simulating warm season precipitation? What is the PACS/GAPP strategy for improvements in operational climate prediction of warm season precipitation anomalies?... empirical vs dynamical prediction?... relative importance of land/ocean boundary conditions?... temporal stability of empirical correlations? What are top priorities for improvements in the observing and information dissemination system?

4 Modeling Studies: Re-affirming a persistent problem

5 Sensitivity of NCEP RSM-simulated precipitation to choice of physics/surface parameterizations Kanamitsu & Mo (J. Climate 2003) USGS physics SiB physics AZNM obs July 1999

6 Sensitivity of MM5-simulated precipitation to choice of convective parameterization (I) Gochis et al. (Mon. Wea. Rev. 2002) Kain-Fritsch PERSIANN (observed) B-M-J Grell

7 Sensitivity of MM5-simulated precipitation to choice of convective parameterization (III) Ritchie & Gutzler (2002) Grell & Kain-Fritsch parameterizations have opposite sensitivities over land & ocean

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

9 Observations: NAME

10 North American Monsoon Observed Precipitation NAME Higgins & Shi Gochis et al  1  gridded fields

11 The NAME Field Campaign

12 Intraseasonal to Interannual Variations: An “ephemeral” courtship

13 Empirical studies of decade-scale predictability variations (I) winter precip  summer precip lag correlations correlations most pronounced pre-1930 and post-1965 Hu & Feng (J Climate 2002)

14 Emergent Successes:

15 Simulation of moisture surges & low-level jets Berbery & Fox-Rabinowitz (J. Climate 2003) surge no-surge precip: NAMS, Great Plains

16 Simulation of heavy precipitation events Kunkel et al. (J. Hydromet. 2002) timing of intense events interannual variability of intense events interannual envelope of intense precip holds promise; precise timing is elusive

17 Seattle Working Group Questions: What guidance can be derived from PACS/GAPP science to improve simulation of convective precipitation in climate models? What are the highest priorities for improvements in sustained observations, derived products, and/or information dissemination, to achieve PACS/GAPP science goals? What is the best strategy for improvements in operational climate prediction of warm season precipitation? What is the optimum role for dynamical models?

18 Recommendations:

19 Priorities for Ongoing & Future Research Activities: Short Term Encourage organization of dynamical prediction efforts: –Focus on establishing predictors & predictands –Ascertain time scales of predictability Develop new diagnostics and forecasts metrics: –Utilize the diurnal cycle as a principal focal point of diagnostic and simulation research –Improve characterization of intraseasonal and seasonal regimes which generate warm season precipitation –Advance understanding of local-remote forcing linkages

20 Priorities for Ongoing & Future Research Activities: Short Term Demonstrate critical components of enhanced observing systems: –Link EOP projects to future long-term observing network enhancements (e.g. TAO array) –Improve a priori coordination of EOP’s with operational centers (e.g. NAME-NCEP) –Continue and improve coordination of single EOPs with variety of programmatic research goals (GEWEX, DOE, CLIVAR, etc.)

21 Priorities for Ongoing & Future Research Activities: Long Term Explore focused engagement of observational- diagnostic-model development communities to think about improved techniques for simulating warm season precipitation Improve ties with operational communities to define the time-scales of predictability and elucidate avenues of significant opportunity

22 Priorities for Ongoing & Future Research Activities: Obs. & Data Motivate data mining as a priority to enhance longer term records Improve and implement metadata requirements for PACS/GAPP datasets Cloud microphysics and aerosols currently underrepresented in warm season precipitation research priorities

23 Priorities for Ongoing & Future Research Activities: Linkages Strengthen ties to NASA’s Global Precipitation Monitoring Project Improve ties with groups studying warm season precip. over oceans (e.g. CLIVAR-EPIC) Improve linkages to NOAA RISA’s program to explore fruitful applications of PACS/GAPP research Improve connections to other monsoon-related programs (CLIVAR-VAMOS; S. America, GEWEX; Asia)

24 THE END!

25 Sensitivity of a JAS precip in a global GCM to interannually varying SST Farrara & Yu (J. Climate 2002)... not much

26 Empirical studies of decade-scale predictability variations (III) spring snow  summer precip lag correlations negative correlation most pronounced during the period Lo & Clark (J Climate 2002)

27 Diagnostics of snow-summer precip relationship Matsui et al. (J. Climate 2003)... but T sfc is poorly correlated with summer precip to the south April snow cover is inversely related with southern Rockies T sfc through June (though not with later T sfc )...

28 SST in the Gulf of California modulating North American monsoon precipitation Mitchell et al. (J Climate 2002)

29 NAME Model Assessment Project Surface Temperature simulations

30 Sensitivity of MM5 to choice of model physics/ convective parameterization (II) Xu & Small (JGR 2002) Kain-Fritsch: too wet, not enough interannual variability Grell: superior, but also sensitive to choice of radiation code

31 Empirical studies of decade-scale predictability variations (II) winter  summer precip lag correlations negative correlation most pronounced during the period Kim (J Climate 2002)

32 Discussion [1]: Modeling Deep Convection GAPP research has explored the large sensitivity of current dynamical models to choices of surface treatment, convective parameterization and physics packages These sensitivities are surely important for modeling efforts outside GAPP, e.g. IPCC climate change simulations so... What guidance can be derived from GAPP science to improve simulation of convective precipitation in climate models?

33 Discussion [2]: Observations Warm season precipitation is poorly sampled (in time and space) relative to the principal time/space scales of variability Simulations of warm season precipitation are sensitive to surface conditions that are sampled even more poorly (e.g. land surface fluxes) so... What are the highest priorities for improvements in sustained observations, derived products, and/or information dissemination, to achieve GAPP science goals?

34 Discussion [3]: Predictability Operational seasonal predictability of warm season precipitation is close to zero GAPP empirical research on seasonal prediction has suggested new pathways to predictability, but also demonstrated that observed interannual lead/lag relationships are not temporally stationary Current global model sensitivity to prescribed summer SST anomalies is problematic so... What is the best strategy for improvements in operational climate prediction of warm season precipitation? What is the optimum role for dynamical models?


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