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

2. Outline What do climate models simulate? What do climate models simulate? Parameterization Parameterization Issues for ground-based remote sensing Issues for ground-based remote sensing Some examples Some examples Combining ground and satellite 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 prognostic equations for u, v, w (or ω), T, q, p0 Set of diagnostic equations for sub-grid processes (parameterizations) Set of diagnostic equations for sub-grid processes (parameterizations) New hybrid prognostic schemes for condensed water content New hybrid prognostic schemes for condensed water content Implemented on a global mesh of fairly coarse resolution Implemented on a global mesh of fairly coarse resolution Marched forward in time subject to boundary conditions (solar energy, atmospheric chemical composition, aerosol) Marched forward in time subject to boundary conditions (solar energy, atmospheric chemical composition, aerosol)

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

5. IPCC Fourth Assessment Report

6. Borrowed from Dave Randall, CSU

8. Lessons for model - data comparisons GCM clouds are statistical aggregates 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) 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 Mesoscale and cloud scale dynamics are not represented in GCM Data scale is mismatched to model Data scale is mismatched to model MMF and global CRMs are changing this picture MMF and global CRMs are changing this picture

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

10. Daily average values

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

16. 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.

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

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

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

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

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

26. Occurrence Statistics Cloud Heights at ARM SGP Year 20001997-2002 From Mace and Hartsock, University of Utah

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

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

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

30. 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)

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

32. 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. 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 siteBased 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 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. 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.

33. 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

34. 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

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

36. Ground and Satellite Instrument Synergy CloudSat CloudSat Nadir-pointing mm radar in space provides a “curtain” of cloud propertiesNadir-pointing mm radar in space provides a “curtain” of cloud properties 4 km footprint and 250 m resolution4 km footprint and 250 m resolution Flies in A-Train Constellation with Aqua (MODIS, AIRS), CALIPSO, etc. Flies in A-Train Constellation with Aqua (MODIS, AIRS), CALIPSO, etc. Just beginning to analyze data Just beginning to analyze data

37. Manus Island 2000

38. CloudSat 2 months 10x10 box

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

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