Mesoscale Convective Systems Under Climate Change

Transcription

1 North American Scale Convection-Permitting Climate Modeling Mesoscale Convective Systems Under Climate Change AF Prein, K Ikeda, C Liu, R Rasmussen, R Bullock, S Trier, GJ Holland, M Clark RAL Retreat, Dec. 5 th 2017, NCAR, Boulder, CO

2 Why are MCSs important? Fritsch et al. 1986: MCSs contribute between 30 70% to the warm season precipitation (April September) in region between the Rocky mountains and the Mississippi River. [Feng et al. 2016, Nature Com.] [Carbone and Tuttle, 2008, Journal of Clim.]

3 Why are MCSs important? Most major flooding events during the warm season are caused by MCSs West Virginia 2016 Rainfall x Heavy Rainfall West Virginia Area Rate Rainfall Flood α Volume Severity System Moovement Speed 2016 Louisiana Phoenix Phoenix AZ 2014 Louisiana

4 Simulating MCSs with Climate Models

5 Convective Diurnal Cycle Afternoon Peak Noon Peak Nighttime Peak Observations WRF 36 km Traditional climate models are not able to simulate realistic propagating convective systems [e.g., Mooney et al. 2017, Journal of Clim.]

6 Simulation Domain and Setup WRF 4 km 1359 x 1015 grid cells 13 years ( ) ERA-Interim Liu et al. 2016, Clim. Dyn. Physics Microphysics Thompson aerosol-aware [Thompson and Eidhammer 2014] Radiation RRTMG [Iacono et al. 2008] Land-surface model NOAH-MP Boundary layer YSU [Hong et al. 2006] Spectral Nudging U, V, T, and ZG above the PBL

7 CONUS Project Team Project Lead Roy Rasmussen RAL/HAP Experiment Designing and WRF Modeling Data Analysis and Management Changhai Liu Jimy Dudhia Liang Chen, Sopan Kurkute Kyoko Ikeda, Changhai Liu, Andreas Prein, Andrew Newman, Aiguo Dai RAL/HAP MMM University of Saskachewan RAL/HAP MMM Microphysics Greg Thompson RAL/HAP LSM modeling Fei Chen, Mike Barlage RAL/HAP Hydrology modeling David Gochis RAL/HAP Snow Physics Martyn Clark RAL/HAP Dynamical Downscaling Ethan Gutmann RAL/HAP Social Impacts Dave Yates RAL/HAP

8 Convective Diurnal Cycle Afternoon Peak Noon Peak Nighttime Peak Observations WRF 36 km WRF 4 km Improved simulationed diurnal cycle of of the amount, intensity, and frequency of precipitation [Rasmussen et al Scaff et al. submitted]

9 MCSs in current climate simulation [Prein et al. 2017, Climate Dynamics]

10 MCS tracks: observed vs. modeled WRF - current climate STAGE4 - observations All MCS tracks from 13-years ( ) Tracks fade out after 7-days

11 MCS in Texas during March 2007 Modeled Observed (stage-iv) Method for Object- Based Diagnostic Evaluation (MODE) Time Domain (MTD)

12 MCS in Texas during March 2007 Observed (stage-iv) Modeled MCS Characteristics Speed Lifetime Size Maximum Intensity 4 km WRF model is able to simulate the precipitation form MCSs realistically Total Precipitation

13 MCS attributes JJA Central U.S. Observation Model MCS MCS MCS Maximum Movement MCS Precipitation Lifetime Size Precipitation Speed Volume Superior representation of MCS attributes But underestimation of MCS frequency MCSs per Year probability []

14 Future MCSs [Prein et al. 2017, Nat. Clim. Change]

15 WRF Future Climate Simulation Pseudo Global Warming (PGW) [Schär et al. 1996] Monthly averaged climate change perturbations from 19 CMIP5 GCMs Delta 2071 to to 2005 RCP8.5 Thermodynamic response of climate change No changes in weather patterns / moisture convergence No issues with internal variability

16 MCS Tracks & Intensities End Current of century climate At least 4-times more extreme (>90 mm/h max. rainfall) MCSs in North America

17 Changes in MCS characteristics MCS Size PDF MCS Speed PDF Large size large precipitation volume Increasing Precipitation intensity MCS Volume PDF High Impact MCSs MCS Volume PDF Medium size low precipitation volume

18 MCS total precipitation Mid Atlantic +30 % maximum Maximum rain rate +30 % approximately Clausius Clapeyron relation Size of > 10 mm/h Area +88 % Volume within > 10 mm/h Area +105 %

19 Changes in MCS Dynamics and Thermodynamics Mid Atlantic Rotated MCS environments More Favorable + Increased CAPE Changes in the Inflow Changes in CAPE Less Favorable Increasing stability Less Rel. Humidity

20 Changes in MCS Dynamics and Thermodynamics Mid Atlantic Rotated MCS environments >4km High Flash Flood Risk [Doswell 2015] More Favorable + Increased CAPE + Higher cloud top + Increased vertical moisture transport + Deeper warm cloud layer Less Favorable Increasing stability Less Rel. Humidity

21 Conclusion

22 Thank You!

23 MCS tracks: current vs. future climate WRF - current climate ( ) WRF - future climate end of century - RCP8.5

24 MCS characteristics JJA Central U.S. Observation Model current Model future MCS MCS precipitation MCS maximum MCS size movement Lifetime of track precipitation hourly volume length speed precipitation area significant increase significant significant no increase significant no significant increase combination no significant of rainfall in large in changes MCSs extreme volume changes precipitation changes and intensity increases

25 MCS total precipitation Mid Atlantic Adding 6 times the Hudson river to rainfall over New York +30 % maximum +60 % volume

26 Storm total precipitation Mid Atlantic ff xx = II ee cccc + oo Increase in Volume Increase in Intensity (I) +30 % Decrease in Scaling (c) +30 %

27 Method for Object-based Diagnostic Evaluation (MODE) Time Domain (MODE-TD) Gridded precipitation Hourly on 4 km grid Smoothed 32 km square filter Masked with threshold Threshold 5 mm/h [Davis et al. 2005] Original data gets filtered by mask objects

28 MODE Time Domain (MTD) Extension of the MODE tool to the time dimension [Randy Bullock, NCAR]

29 Method for Object-based Diagnostic Evaluation (MODE) Time Domain (MODE-TD) Smoothing Radius: 8, 16, and 32 km Low High Radius Large Smooth Few Large Detailed Many Small Smoothed Few Small Detailed Few Low Threshold High Threshold: 2.5 and 5 mm/h [adapted form Randy Bullock]