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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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Presentation Transcript
slide1
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

the gccs pacific cross section

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
observations along the cross section
Observations along the cross-section

Cloud fraction

Low-level cloud

CloudSat+Caliop

ISCCP

Precipitation

LWCF

SWCF

__

TRMM---- GPCP

CERES-EBAF

CERES-EBAF

methodology for the forecasts

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

ensemble mean forecast and timeseries forecast

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
model versions
Model versions
  • CAM = Community Atmospheric Model (NCAR)
  • 3 versions of CAM
highlights of the results
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
precipitation monthly means june 2008
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
precipitation timeseries jja 2008

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
precipitation timeseries jja 20081

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
precipitation timeseries jja 20082

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

precipitation timeseries jja 20083

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

moisture profile in the stratocumulus regime
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

moisture profile in the stratocumulus regime1
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

water vapor budget in the stratocumulus regime
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

conclusion
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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