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Applications to Global Climate Modeling

Applications to Global Climate Modeling. Tom Ackerman Lecture II.7b. Outline. What do climate models simulate? Parameterization Issues for ground-based remote sensing Some examples Combining ground and satellite. Global Climate Model Construction (atmosphere only).

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Applications to Global Climate Modeling

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  1. Applications to Global Climate Modeling Tom Ackerman Lecture II.7b

  2. Outline • What do climate models simulate? • Parameterization • Issues for ground-based remote sensing • Some examples • Combining ground and satellite

  3. Global Climate Model Construction (atmosphere only) • Set of prognostic equations for u, v, w (or ω), T, q, p0 • Set of diagnostic equations for sub-grid processes (parameterizations) • New hybrid prognostic schemes for condensed water content • Implemented on a global mesh of fairly coarse resolution • Marched forward in time subject to boundary conditions (solar energy, atmospheric chemical composition, aerosol)

  4. Climate model evaluation • Simulate current climate very well • Large-scale circulation patterns • TOA energy balance • Seasonal progression • What don’t we simulate well? • Regional climate • Smaller scale dynamical features – MJO • Cloud properties • Diurnal cycle of convection • Stratiform cloud properties

  5. IPCC Fourth Assessment Report

  6. Borrowed from Dave Randall, CSU

  7. Borrowed from Dave Randall, CSU

  8. Lessons for model - data comparisons • GCM clouds are statistical aggregates • GCMs really care only about the large-scale impacts of clouds – vertical transport of momentum and moisture, heating, radiation balance, precipitation (same principle is true for surface properties) • Mesoscale and cloud scale dynamics are not represented in GCM • Data scale is mismatched to model • MMF and global CRMs are changing this picture

  9. Uses of Ground-based Data • Radiation budget • Cloud properties • Heating rates • Single column models and cloud resolving models • Initial condition GCMS • Classification studies

  10. Daily average values

  11. Uses of Ground-based Data • Radiation budget • Cloud properties • Heating rates • Single column models and cloud resolving models • Initial condition GCMS • Classification studies

  12. Manus Island 2000 McFarlane, S. A., J. H. Mather, and T. P. Ackerman (2007), Analysis of tropical radiative heating profiles: A comparison of models and observations, J. Geophys. Res.

  13. Uses of Ground-based Data • Radiation budget • Cloud properties • Heating rates • Single column models and cloud resolving models • Initial condition GCMS • Classification studies

  14. Initial Conditions Single Column Model Forecast Model Global Cloud Resolving Model Cloud System Resolving Model Borrowed from Dave Randall, CSU

  15. ARM data compared to Cloud-resolving model (CRM) and single column model (SCM) extracted from weather forecasting model

  16. Cloudnet results – comparison of observations with operation models From Illingworth et al. (2007) BAMS See also Cloudnet web page

  17. Climate Change Prediction Program (CCPP)-ARM Parameterization Testbed (CAPT) Unique: Implementing GCM in NWP framework initialize with high-frequency analyses (ERA40) run short term forecasts model stays close to observations From Mace and Hartsock, University of Utah CAPT Program

  18. From Mace and Hartsock, University of Utah Occurrence StatisticsCloud Heights at ARM SGP 1997-2002 Year 2000

  19. From Mace and Hartsock, University of Utah Conclusions - Occurrence Statistics • GCMs predict cirrus at lower occurrence frequency • Thicker cirrus cloud layers produced by the models (higher cloud top heights) • Smaller mean IWC values predicted in GCMs (IWP more consistent with observed values) • Microphysics more variable between seasons in GCM predicted cirrus • Strong sensitivity of microphysics to large-scale motions in GCMs (stronger than obs cold season)

  20. Uses of Ground-based Data • Radiation budget • Cloud properties • Heating rates • Single column models and cloud resolving models • Initial condition GCMS • Classification studies

  21. Classification Studies • Composite data using some set of criteria • Analyze features within composite class (cloud features in our case) • Composite data using same set • Analyze same features within composite class • Compare data and model • Helps identify cause of feature differences • Work in progress with clouds – largely working with data at this point

  22. ARM SGP Diurnal Composites Distinguishes late afternoon/early evening convection from nocturnal convection; latter are largely affected by the eastward propagating precipitation events, originated in the Rocky mountains. Precipitation ARSCL Cloud Fraction All Cases Weak or None Daytime (1800 LST) Nocturnal (0300 LST)

  23. Next step • Run 2D cloud resolving model from MMF for 3 years forced by weather analyses • Composite diurnal precipitation • Compare with data

  24. Cluster Analysis Marchand et al., 2006, JAS • Created an objective atmospheric classification using a simple competitive (or self-organizing) neural network and classified the atmosphere into 25 possible states. • Based on 17 months of analysis data from the Rapid Update Cycle (RUC) model – used because data was stored in a convenient form over an approximately 600 km by 600 km region centered over the SGP site • Analyzed vertical profiles of cloud occurrence obtained from the ARM cloud-radar • Goal was to evaluate whether or not the profiles of cloud occurrence, when aggregated according to the large-scale atmospheric state, were temporal stable and distinct in a statistically meaningful way.

  25. Clusters based on 17 months of data around ARM SGP site divided into 3-hour time blocks Blue line = fractional cloud occurrence as function of height Black line = level passes statistical significance test

  26. Comparison of clouds occurrence for two different winters:96-97 (red) and 97-98 (blue) Percentage = amount of time that state was occupied in each winter Black line = level passes statistical significance test

  27. Next steps • Run 2D cloud resolving model from MMF for 3 years forced by weather analyses • Cluster states • Compare cloud data within each state to model cloud • Carry out cluster analysis on GCM field and compare clouds with data • Repeat cluster analysis using CloudSat data

  28. Ground and Satellite Instrument Synergy • CloudSat • Nadir-pointing mm radar in space provides a “curtain” of cloud properties • 4 km footprint and 250 m resolution • Flies in A-Train Constellation with Aqua (MODIS, AIRS), CALIPSO, etc. • Just beginning to analyze data

  29. Manus Island 2000

  30. CloudSat 2 months 10x10 box

  31. Concluding thoughts • Using ground-based data to evaluate GCMs is a relatively new field • Lots to learn and lots to do • Onus is on the data community – GCM groups too small and overworked • Statistics, statistics, statistics • MMF and GCRM changing the paradigm • Most fertile research will combine ground-based and satellite data – not really being done yet

  32. Thank you for your attention! Dave and I hope this has been useful and informative. Questions are welcome!

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