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High-Resolution MPAS Simulations for Analysis of Climate Change Effects on Weather Extremes ALLISON MICHAELIS, GARY LACKMANN, & WALT ROBINSON Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University GEWEX


  1. High-Resolution MPAS Simulations for Analysis of Climate Change Effects on Weather Extremes ALLISON MICHAELIS, GARY LACKMANN, & WALT ROBINSON Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University GEWEX CONVECTION-PERMITTING CLIMATE MODELING WORKSHOP II 6 SEPTEMBER 2018

  2. Motivation  Current General Circulation Models (GCMs):  Too coarse for TCs, extreme weather events, issues with blocking  Regional Modeling with Pseudo-Global Warming (PGW):  Limited by lateral boundary conditions  High-resolution Time Slice Experiments:  Can be limited by SST representation  Our Method:  MPAS with high-resolution analyzed SSTs using pseudo-PGW/pseudo-time slice methods 1

  3. Model for Prediction Across Scales (MPAS) Simulations  MPAS v. 5.1  Variable resolution mesh: 15-km over NH expanding out to 60-km*  Physics choices:  WSM6 (MP) 15-km  YSU (PBL)  Tiedtke (CP)  CAM (radiation)  Initial conditions and SST 60-km field:  ERA-Interim Reanalysis 2 *Thanks to Michael Duda for creating this mesh

  4. Model for Prediction Across Scales (MPAS) Simulations  Selected 10 simulation years to sample range of ENSO phases  Simulations run from March 1 st of year 1 through mid-May of year 2 – first month discarded 3

  5. MPAS Simulations – Future  Simulate same 10 years under future thermodynamic conditions MPAS ERA-Interim initial MPAS conditions init_atmosphere atmosphere Future simulation CMIP5 21-member Ensemble March Average Temperature Change (K) *CO 2 adjusted to 2080–2099 (RCP 8.5) minus 1980–1999 936 ppm 4

  6. MPAS Simulations – Future  Future SST and sea ice fields 1 Current Future 5

  7. MPAS Simulations – Future  Future SST and sea ice fields  Create pseudo-daily sea ice fields from monthly average CMIP5 ensemble mean – historical and RCP 8.5 future emissions scenario 1 Current Future 6

  8. Model for Prediction Across Scales (MPAS) Simulations  Selected 10 simulation years to sample range of ENSO phases  Simulations run from March 1 st of year 1 through mid-May of year 2 – first month discarded Strongest Strongest La Niña El Niño 2010 1988 2011 2013 2001 2005 1992 1994 2015 1997 Ran full 14.5 month Output Output spin-up simulation from from March 1 st March 1 st Output from March 1989 2011 1 st 2014 7

  9. MPAS Simulations  Completed 10 sets (current and future) of simulations  2010, 1988, 2011, 2013, 2001, 2005, 1992, 1994, 2015, 1997  Output has been post-processed  Interpolate fields (temperature, height, winds, etc.) to pressure levels  Interpolate output to a 0.15º x 0.15º lat-lon grid  Saving output for Northern Hemisphere only  Select results shown today from (mostly) present-day simulations  2-m temperature, zonal mean temperature  Midlatitude jet features, tropical precipitation  Tropical cyclones 8

  10. 2-m Temperature (K) – March CMIP5 Ensemble Mean (20 year mean of 21 ensemble members) MPAS 10-yr Mean 9

  11. Zonal Mean Temperature (K) – March CMIP5 Ensemble Mean 10 MPAS 10-yr Mean

  12. Sea-Level Pressure Variance (hPa 2 ) – DJF ERA-Interim 10-yr Climatology r > 0.95 MPAS 10-yr Mean 11

  13. Tropical Precipitation (mm/day) – Annual TRMM 19-yr Climatology MPAS 10-yr Mean 12

  14. Tropical Cyclone Tracking  TempestExtremes tracking algorithm (Ullrich and Zarzycki 2017)  Tunable Parameters:  2 hPa closed SLP contour within 2º of center  -15 m closed 300–500-hPa thickness contour within 6º of center  Maximum offset from SLP minimum: 1.1º  Maximum search latitude for candidate storms: 60ºN  Maximum travel distance within 6-h: 6º  Minimum lifetime: 2 days  Allows for up to 12-h gaps in trajectories  Must be over water for at least 12-h  Must have at least 2 (non-consecutive) days of 10-m winds ≥ 14 m/s (~31 mph) 13

  15. Tropical Cyclone Density IBTrACS Number of Cyclones per 1ºx1º box 10-yr Climatology per 10 years MPAS 10-yr Mean 14

  16. Tropical Cyclone Strength TS Minimum SLP (hPa) 1 2 3 4 5 TS 5 1 2 3 4 Maximum 10m Wind Speed (kts) 15

  17. Summary  Future MPAS simulations reproduce two key warming signatures  Arctic amplification and tropical upper-tropospheric warming  Large-scale, seasonal mean fields realistically represented in MPAS simulations  e.g., midlatitude storm tracks, tropical precipitation  TC activity generated in all Northern Hemispheric basins  Storms simulated across full intensity spectrum 16

  18. Ongoing Projects TC Seasonality Persistent Anomalies Extratropical Transition of TCs Extreme Precipitation along US East Coast 17

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