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AGU Meeting , San Francisco, December 14-18, 2009

Evaluating parameterized variables in the Community Atmospheric Model along the GCSS Pacific cross-section during YOTC. Cécile Hannay, Dave Williamson, Rich Neale, Jerry Olson, Dennis Shea National Center for Atmospheric Research, Boulder. AGU Meeting , San Francisco, December 14-18, 2009.

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AGU Meeting , San Francisco, December 14-18, 2009

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  1. Evaluating parameterized variables in the Community Atmospheric Model along the GCSS Pacific cross-section during YOTC Cécile Hannay, Dave Williamson, Rich Neale, Jerry Olson, Dennis SheaNational Center for Atmospheric Research, Boulder AGU Meeting, San Francisco, December 14-18, 2009

  2. Several cloud regimes: stratocumulus, transition, deep convection The GCCS Pacific Cross-section • EUROCS project • JJA 1998 • GCSS intercomparison • JJA 1998/2003 • This study • YOTC: JJA 2008 • Observations • ISCCP dataSSM/I product • CloudSat + Calipso • GPCP and TRMM precipFlash Flux data • Reanalyses • ERA-Interim, Merra

  3. Observations along the cross-section Cloud fraction Low-level cloud CloudSat+Caliop ISCCP Precipitation LWCF SWCF __ TRMM---- GPCP CERES-EBAF CERES-EBAF

  4. CAM Methodology for the forecasts Forecast • Strategy If the atmosphere is initialized realistically, the error comes from the parameterizations deficiencies. • Advantages - Evaluate the forecast against observations on a particular day andlocation - Evaluate the nature of moist processes parameterization errors before longer-time scale feedbacksdevelop. • Limitations Accuracy of the atmospheric state ? Initialize realistically ECWMF analysis 5-day forecast Starting daily at 00 UT Evaluation AIRS, ISCCP, TRMM, GPCP, SSMI, CloudSat, Flash-Flux, ECWMF analyzes

  5. Starting date Individual forecasts Ensemble mean forecast: average data at the same “forecasttime” 7/3 7/2 1 2 3 Day of July Timeseries forecast: concatenate data at the same “forecasttime” (hours 0-24) from individual forecasts 7/1 0 1 2 3 Forecast time (days) Ensemble mean forecast and timeseries forecast

  6. Model versions • CAM = Community Atmospheric Model (NCAR) • 3 versions of CAM

  7. Highlights of the results • Climate bias appears very quickly in CAM • - where deep convection is active, error is set within 1 day • - 5-day errors are comparable to the mean climate errors • CAM3 - ITCZ: warm/wet bias of the upper troposphere too much precipitation and high level cloud - StCu: cloud too close to the coast and PBL too shallow • CAM4-t1 - ITCZ: CAM4-t1 reduces warm/wet bias of the upper troposphere dramatic improvement of precipitation • … but too little high-level cloud compared to observations • CAM4-t5 • - ITCZ: same improvements as with CAM4-t1 • - StCu: better PBL height and low-level cloud fraction … but underestimates high-level cloud and LWP

  8. Precipitation: Monthly means, June 2008 Forecast at day 5 Forecast at day 1 ____ _ _ _____________ TRMM GPCPCAM3CAM4-t1CAM4-t5 Timeseriesat the ICTZ • CAM3: overestimates the precipitation in the ITCZ • CAM4: reduction in the ITCZ precipitation at day 1 precipitation intensity increases later in the forecast

  9. Correlation with TRMM Precipitation timeseries, JJA 2008 Precip(mm/day) Forecast at day 1 TRMM CAM3 (0.50)CAM4-t1 (0.70)CAM4-t5 (0.66) Days (JJA) • At the ITCZ: • CAM3: overestimates the precipitation in the ITCZ rains all the time • CAM4: reduction in the ITCZ precipitationbetter correlation with observed precipitation underestimates strong events

  10. Correlation with TRMM Precipitation timeseries, JJA 2008 Precip(mm/day) Forecast at day 1 TRMM CAM3 (0.50)CAM4-t1 (0.70)CAM4-t5 (0.66) Days (JJA) Mixing parcelenv No mixing Allows mixing • At the ITCZ: • CAM3: overestimates the precipitation in the ITCZ rains all the time • CAM4: reduction in the ITCZ precipitationbetter correlation with observed precipitation underestimates strong events

  11. Correlation with TRMM Precipitation timeseries, JJA 2008 Precip(mm/day) Forecast at day 1 TRMM CAM3 (0.50)CAM4-t1 (0.70)CAM4-t5 (0.66) Days (JJA) Pres (mbar) Relative humidity Days (JJA) CAM4: precipitation better connected to mid-troposphere

  12. Correlation with TRMM Precipitation timeseries, JJA 2008 Precip(mm/day) Forecast at day 1 TRMM CAM3 (0.50)CAM4-t1 (0.70)CAM4-t5 (0.66) Forecast at day 5 TRMM CAM3 (0.19)CAM4-t1 (0.47)CAM4-t5 (0.46) Days (JJA) CAM4: correlation w/obs decreases in 5-day forecast

  13. Moisture profile in the stratocumulus regime Moisture in CAM4-t5 Moisture in CAM4-t1 Day 0 Day 1Day 2Day 3 CAM4-t5: PBL height is maintained CAM4-t1: PBL collapses

  14. Moisture profile in the stratocumulus regime Moisture in CAM4-t5 Moisture in CAM4-t1 Day 0 Day 1Day 3Day 5 CAM4-t5: PBL height is maintained CAM4-t1: PBL collapses Dry and surface-driven PBL scheme scheme based on prognostic TKE w/ explicit entrainment at top of PBL

  15. Water vapor budget in the stratocumulus regime Advective tendencies Total physics tendency: Qphys Qphys in CAM4-t1 Qphys in CAM4-t5 Total PBLshallow conv.cloud-water

  16. Conclusion • CAM forecasts allows for diagnosing parameterization errors in different cloud regimes • CAM3 (AR4) • - too much precipitation near ITCZ (deep convection scheme: no mixing between the parcel and its environment) - PBL too shallow in StCu (dry and surface-driven PBL scheme ) • CAM4-t1 (AR5) • dramatic improvement of precipitation in the early forecast with the new convection scheme (entrainment of environment) • CAM4-t5 (AR5) • - new PBL scheme produces deeper and better mixed PBLs(PBL scheme: prognostic TKE with explicit entrainment at top of PBL)

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