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The Three Themes: Regional Climate Change and Energy Modeling Outstanding Scientific Problems

The Three Themes: Regional Climate Change and Energy Modeling Outstanding Scientific Problems Infusion of Data into Models. Outstanding Science Problems. Zhang Marat K. Lin. Vogelmann Miller Jensen Wagener. Colle. Chang. Liu Daum Guo. NY Blue Center. Riemer. McGraw Schwartz

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The Three Themes: Regional Climate Change and Energy Modeling Outstanding Scientific Problems

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  1. The Three Themes: Regional Climate Change and Energy Modeling Outstanding Scientific Problems Infusion of Data into Models

  2. Outstanding Science Problems Zhang Marat K. Lin Vogelmann Miller Jensen Wagener Colle Chang Liu Daum Guo NY Blue Center Riemer McGraw Schwartz Lewis Chang Wang Reisman Bhatt

  3. Aerosol Modeling Aerosol Indirect Effect on Clouds Cloud Processes for Weather Cloud Climate Feedback and Turbulence Coastal Turbulent Mixing

  4. • • • Quantifying Aerosol Forcing of Climate Change (McGraw) Radiative forcing components from the 2007 IPCC Report: Largest contributions to uncertainty are due to aerosols and aerosol-cloud interaction. Performing ensemble climate model runs of several months duration with and without aerosols will allow better quantification of this forcing, insight into the quantities and processes on which it depends.

  5. BNL APPROACH: REPRESENTING AEROSOLS IN CLIMATE MODELS USING THE QUADRATURE METHOD OF MOMENTS (QMOM) Illustration of a Simple Case: Blue Gene provides the ensemble GCM simulation capability

  6. n(log dp) mm Multiscale Aerosol Models (Riemer) Mesoscale Regional scale Global scale Microscale Particle scale

  7. Stochastic particle resolved aerosol code • Explicitly track composition of all particles in a parcel, with random coagulation events, interleaved with chemistry. • Current serial code is implemented in Fortran 95. • Compute 10 minutes of simulation time using 107 particles and a gravitational kernel in about 5 minutes of CPU time on a PC. • Parallel version is needed to enable very large particle numbers (1010 and higher) and faster computation, especially when coupled to chemistry and transport models. • Flexible communication topology using MPI.

  8. Mesoscale aerosol modeling • Use WRF-chem (standard community model) to model aerosol transport, dynamics and chemistry on the mesoscale.

  9. Aerosol Indirect Effect • Aerosol-cloud-climate interactions: • Largest uncertainties in climate studies • Aerosols: tiny particles in the air • Clouds: water drops/ice crystals • Require sophisticated models

  10. Clouds are microscopic droplets Macroscopic view of clouds Mean droplet radius ~ 10 mm Microscopic Zoom in Droplet Radius (mm) n(r) n(r) (cm-3mm-1)

  11. Bulk Cloud Physics • Cloud properties: • mass concentration • number concentration • radar reflectivity … Observed Modeled (Fan et al., 2007)

  12. Size-bin Cloud Physics • Cloud particle sizes: nm to cm • Spectrum: M1,M2,. . . Mi,. . . (Tao et al., 2003)

  13. Importance of Clouds in Weather Research (Colle) Many effects of clouds on climate and weather are largely unknown/uncertain (observations lacking, models at coarse resolution have poor representation of clouds). Most important problem confronting dynamicists and modelers today. Cloud-resolving (Dh ~ O(100 m)) simulations of cloud systems are needed to understand cloud dynamics and to improve parameterizations - a computing challenge. cloud- mixing eddies cloud systems planetary waves synoptic systems clouds >106 meters 105 - 106 meters 102 - 104 meters meters to 100’s meters

  14. Reflectivity Forecast Composite NEXRAD Radar 4-km WRF Reflectivity Forecast Observed Reflectivity Vertical Motion (5-km) (Courtesy of G. Bryan, NCAR/MMM)

  15. Vertical cross-section of tracer concentration). x = 4000 m Simulations using x = 4 km to x = 125 m x = 1000 m x = 250 m (Courtesy of G. Bryan, NCAR/MMM)

  16. Fundamental Hurricane Research

  17. Meso-/Cloud-Scale Model (WRF) Hurricane Katrina Reflectivity at Landfall 29 Aug 2005 14 Z 4 km WRF, 62 h forecast Mobile AL Radar (Courtesy of B. Skamarock, NCAR/MMM)

  18. Cloud-ClimateFeedback (Zhang)

  19. Conv. moistening evap. cooling PBL deepens turb. mostening conv. recovers conv. drying turb. mostening PBL drying

  20. LES Simulations as Benchmark (Marat Khairoutdinov) Plan: SAM LES GCE LES CAM High Resolution and Physical Ensembles

  21. Water mass exchange between the continental shelf and the Gulf Stream (Wang)

  22. Problems Modeling of Some Key Missing Processes Specific Consequence of Multi-scale Turbulent Interactions Models Aerosol Models GCE CRM/LES (3d bin microphysics) SAM LES (large domains) WRF POP

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