Advancing Convective Severe Weather Outlooks into...
1
Advancing Convective Severe Weather Outlooks into Subseasonal-to-Seasonal Timescales Samantha Zito 1 , Cory Baggett 2 , Kyle Nardi 2 , Elizabeth Barnes 2 Stony Brook University 1 , Stony Brook, New York Colorado State University 2 , Fort Collins, Colorado Introduction Methods 1. MJO and QBO Influence Conclusion References Future Work Acknowledgment • While there is evidence of modulation of severe weather regimes at S2S timescales, it is important to link this modulation to actual observed storm reports of hail, tornadoes, etc. • An investigation should be made into how well numerical weather models forecast severe weather regimes at S2S timescales, and if their ability to forecast these regimes varies as a function of the MJO and QBO. • As in Mundhenk et.al. (2017), an empirical prediction scheme that uses the MJO and QBO as predictors can be developed to improve convective severe weather outlooks at S2S timescales. This work has been supported by the National Science Foundation Research Experiences for Undergraduates Site in Climate Science at Colorado State University under the cooperative agreement No. AGS-1461270. Every year, convective severe weather poses a threat to society, in money and lives. Tornadoes, hail, lightning, and flash floods disrupt the safety of our day to day lives. On April 27 th , 2011 a severe weather outbreak decimated the southeast United States. Almost 200 reported tornadoes killed more than 300 people and cost over 4 billion dollars in damages (Knupp et. al., 2014). With such extreme impacts, it would be beneficial to society to have as much lead time as possible to determine when and where convective severe weather regimes are likely to occur. This study aims to identify sources of predictability that can foretell a greater likelihood of severe weather at subseasonal-to-seasonal timescales (S2S; ~3 to 6 weeks). Recent work has shown that knowledge of the Madden-Julian Oscillation (MJO) and the Quasi-Biennial Oscillation (QBO) may serve as a potential source of predictability (Baggett et. al., 2017). • European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data (1979-2015) • Outgoing longwave radiation based MJO Index (OMI; Kiladis et. al., 2013) • QBO index as defined in Yoo and Son (2016) • Performed a composite analysis of CAPE, wind shear, and CS06 over February-May (FMAM) and March-June (MAMJ) according to MJO phases with amplitudes > 1 in combination with QBO easterly and westerly periods • The 7 day running means of these parameters are composited at lags out to 6 weeks following these MJO and QBO events Without taking the QBO into account, only negative anomalies of CS06 can be seen across the United States, indicating a reduced likelihood of severe weather. However, when parsing by QBO phase different regimes emerge. During the easterly QBO composite, there is a greater likelihood for severe weather over the Mississippi River Valley compared to the westerly QBO composite. In contrast, there is a greater likelihood for severe weather over Tuscaloosa, AL during the westerly QBO period. Perhaps not coincidentally, the MJO had propagated strongly through phase 6 during a westerly QBO period approximately 4 weeks before the April 27 th , 2011 severe weather outbreak. Baggett, Cory F., Elizabeth A. Barnes, Eric D. Maloney, and Bryan D. Mundhenk. "Advancing Atmospheric River Forecasts into Subseasonal-to-Seasonal Timescales." Geophysical Research Letters (2017) Barrett, Bradford S., and Brittany N. Henley. "Intraseasonal Variability of Hail in the Contiguous United States: Relationship to the Madden–Julian Oscillation." Monthly Weather Review 143.4 (2015): 1086-103. Kiladis, George N., Juliana Dias, Katherine H. Straub, Matthew C. Wheeler, Stefan N. Tulich, Kazuyoshi Kikuchi, Klaus M. Weickmann, and Michael J. Ventrice. "A Comparison of OLR and Circulation-Based Indices for Tracking the MJO." Monthly Weather Review 142.5 (2014): 1697-715. Web. Knupp, Kevin R., Todd A. Murphy, Timothy A. Coleman, Ryan A. Wade, Stephanie A. Mullins, Christopher J. Schultz, Elise V. Schultz, Lawrence Carey, Adam Sherrer, Eugene W. Mccaul, Brian Carcione, Stephen Latimer, Andy Kula, Kevin Laws, Patrick T. Marsh, and Kim Klockow. "Meteorological Overview of the Devastating 27 April 2011 Tornado Outbreak." Bulletin of the American Meteorological Society 95.7 (2014): 1041-062. Markowski, Paul, and Yvette Richardson. Mesoscale meteorology in midlatitudes. Chichester: Wiley-Blackwell, 2010. Mundhenk, Bryan D., Elizabeth A. Barnes, Eric D. Maloney, and Cory F. Baggett: Skillful Empirical Subseasonal Prediction of Landfalling Atmospheric River Activity using the Madden-Julian Oscillation and Quasi-Biennial Oscillation. npj Climate and Atmospheric Science, accepted 07/2017. Thompson, Daniel B., and Paul E. Roundy. "The Relationship between the Madden–Julian Oscillation and U.S. Violent Tornado Outbreaks in the Spring." Monthly Weather Review 141.6 (2013): 2087-095. Yoo, Changhyun, and Seok-Woo Son. "Modulation of the boreal wintertime Madden-Julian oscillation by the stratospheric quasi-biennial oscillation." Geophysical Research Letters 43.3 (2016): 1392-398. From the evidence presented here, the MJO and QBO, in combination, may serve as a potential source of predictability of severe weather regimes out to S2S timescales. • MJO – anomalous tropical convection that propagates eastward with a period of ~30 to 90 days – consisting of 8 phases that correspond to the geographical location of the convection. The convection generates a Rossby wave train that teleconnects to the weather in the midlatitudes (Thompson and Roundy, 2013). • QBO – zonal wind anomalies in the tropical stratosphere that cycle between easterly and westerly phases with a period of ~ 2 to 3 years. Recently, it has been shown that the QBO is capable of modulating the amplitude of the MJO (Yoo and Son, 2016). • CAPE – Convective Available Potential Energy, measure of the potential updraft strength in a convective storm. • 0 to 6 km vertical wind shear - difference between the 6 km and 0 km wind speeds, used as a crude proxy for the storm motion and storm type (Markowski and Richardson, 2010). • CS06 – product of CAPE and the magnitude of the 0 to 6 km vertical wind shear (Barrett and Henley, 2015). This modulation of severe weather regimes can be seen in other areas as well, such as Fort Collins, CO. During the easterly QBO, there is a greater likelihood of positive CS06 as compared to the westerly QBO. A clear propagating signal is seen during the easterly QBO, out through several weeks lag. Key Terms • A clear propagating signal in shear is visible for both phases of the QBO. However, the anomalies differ as a function of lag week and MJO phase. For week 4, MJO phase 6, positive anomalies exist for both phases of the QBO. • For CAPE, a propagating signal is visible, but it is not as clear as that in shear. Interestingly, the CAPE and shear anomalies are often opposite of each other during the westerly QBO • For CS06, there is a greater likelihood for severe weather during the westerly QBO. This is particularly evident 4 weeks following MJO phase 6, when the April 27 th , 2011 event occurred. This result alludes to the importance of using a parameter that combines CAPE and shear in identifying severe weather regimes. 2. Propagation of the Signal 3. Fort Collins, CO Source: ECHO Storm Team Tuscaloosa, AL Total CAPE and 0 to 6 km Wind Shear 00 Z April 28 th , 2011 CAPE (J/kg) 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Composite of CS06 Anomalies during Week 4 following MJO Phase 6 (FMAM) CS06 Anomaly (m 3 /s 3 ) -10000 -8000 -6000 -4000 -2000 0 2000 4000 6000 8000 10000 Tuscaloosa, AL (FMAM) Easterly QBO Westerly QBO Shear Anomaly (m/s) -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 CAPE Anomaly (J/kg) -50 -37.5 -25 -12.5 0 12.5 25 37.5 50 CS06 Anomaly (m 3 /s 3 ) -7500 -5625 -3750 -1875 0 1875 3750 5625 7500 Lag Week Lag Week MJO Phase MJO Phase MJO Phase 1 2 3 4 5 6 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 Fort Collins, CO (MAMJ) Easterly QBO Westerly QBO CS06 Anomaly (m 3 /s 3 ) -7000 -5250 -3500 -1750 0 1750 3500 5250 7000 MJO Phase 1 2 3 4 5 6 7 8 Lag Week 1 2 3 4 5 6 Lag Week 1 2 3 4 5 6 MJO Phase 8 7 6 5 4 3 2 1 Tuscaloosa, AL 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
Transcript of Advancing Convective Severe Weather Outlooks into...
Advancing Convective Severe Weather Outlooks into Subseasonal-to-Seasonal Timescales
Samantha Zito1, Cory Baggett2, Kyle Nardi2, Elizabeth Barnes2
Stony Brook University1, Stony Brook, New York Colorado State University2, Fort Collins, Colorado
Introduction
Methods
1. MJO and QBO Influence
Conclusion
References
FutureWork
Acknowledgment
• While there is evidence of modulation of severe weather regimes at S2S timescales, it is important to link this modulation to actual observed storm reports of hail, tornadoes, etc.
• An investigation should be made into how well numerical weather models forecast severe weather regimes at S2S timescales, and if their ability to forecast these regimes varies as a function of the MJO and QBO.
• As in Mundhenk et.al. (2017), an empirical prediction scheme that uses the MJO and QBO as predictors can be developed to improve convective severe weather outlooks at S2S timescales.
This work has been supported by the National Science Foundation Research Experiences for Undergraduates Site in Climate Science at Colorado State University under the cooperative agreement No. AGS-1461270.
Every year, convective severe weather poses a threat to society, in money and lives. Tornadoes, hail, lightning, and flash floods disrupt the safety of our day to day lives. On April 27th, 2011 a severe weather outbreak decimated the southeast United States. Almost 200 reported tornadoes killed more than 300 people and cost over 4 billion dollars in damages (Knupp et. al., 2014).
With such extreme impacts, it would be beneficial to society to have as much lead time as possible to determine when and where convective severe weather regimes are likely to occur. This study aims to identify sources of predictability that can foretell a greater likelihood of severe weather at subseasonal-to-seasonal timescales (S2S; ~3 to 6 weeks). Recent work has shown that knowledge of the Madden-Julian Oscillation (MJO) and the Quasi-Biennial Oscillation (QBO) may serve as a potential source of predictability (Baggett et. al., 2017).
• European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data (1979-2015)
• Outgoing longwave radiation based MJO Index (OMI; Kiladis et. al., 2013) • QBO index as defined in Yoo and Son (2016) • Performed a composite analysis of CAPE, wind shear, and CS06 over February-May
(FMAM) and March-June (MAMJ) according to MJO phases with amplitudes > 1 in combination with QBO easterly and westerly periods
• The 7 day running means of these parameters are composited at lags out to 6 weeks following these MJO and QBO events
Without taking the QBO into account, only negative anomalies of CS06 can be seen across the United States, indicating a reduced likelihood of severe weather. However, when parsing by QBO phase different regimes emerge. During the easterly QBO composite, there is a greater likelihood for severe weather over the Mississippi River Valley compared to the westerly QBO composite. In contrast, there is a greater likelihood for severe weather over Tuscaloosa, AL during the westerly QBO period. Perhaps not coincidentally, the MJO had propagated strongly through phase 6 during a westerly QBO period approximately 4 weeks before the April 27th, 2011 severe weather outbreak.
Baggett, Cory F., Elizabeth A. Barnes, Eric D. Maloney, and Bryan D. Mundhenk. "Advancing Atmospheric River Forecasts into Subseasonal-to-Seasonal Timescales." Geophysical Research Letters (2017)
Barrett, Bradford S., and Brittany N. Henley. "Intraseasonal Variability of Hail in the Contiguous United States: Relationship to the Madden–Julian Oscillation." Monthly Weather Review 143.4 (2015): 1086-103.
Kiladis, George N., Juliana Dias, Katherine H. Straub, Matthew C. Wheeler, Stefan N. Tulich, Kazuyoshi Kikuchi, Klaus M. Weickmann, and Michael J. Ventrice. "A Comparison of OLR and Circulation-Based Indices for Tracking the MJO." Monthly Weather Review 142.5 (2014): 1697-715. Web.
Knupp, Kevin R., Todd A. Murphy, Timothy A. Coleman, Ryan A. Wade, Stephanie A. Mullins, Christopher J. Schultz, Elise V. Schultz, Lawrence Carey, Adam Sherrer, Eugene W. Mccaul, Brian Carcione, Stephen Latimer, Andy Kula, Kevin Laws, Patrick T. Marsh, and Kim Klockow. "Meteorological Overview of the Devastating 27 April 2011 Tornado Outbreak." Bulletin of the American Meteorological Society 95.7 (2014): 1041-062.
Markowski, Paul, and Yvette Richardson. Mesoscale meteorology in midlatitudes. Chichester: Wiley-Blackwell, 2010. Mundhenk, Bryan D., Elizabeth A. Barnes, Eric D. Maloney, and Cory F. Baggett: Skillful Empirical Subseasonal Prediction of
Landfalling Atmospheric River Activity using the Madden-Julian Oscillation and Quasi-Biennial Oscillation. npj Climate and Atmospheric Science, accepted 07/2017.
Thompson, Daniel B., and Paul E. Roundy. "The Relationship between the Madden–Julian Oscillation and U.S. Violent Tornado Outbreaks in the Spring." Monthly Weather Review 141.6 (2013): 2087-095.
Yoo, Changhyun, and Seok-Woo Son. "Modulation of the boreal wintertime Madden-Julian oscillation by the stratospheric quasi-biennial oscillation." Geophysical Research Letters 43.3 (2016): 1392-398.
From the evidence presented here, the MJO and QBO, in combination, may serve as a potential source of predictability of severe weather regimes out to S2S timescales.
• MJO – anomalous tropical convection that propagates eastward with a period of ~30 to 90 days – consisting of 8 phases that correspond to the geographical location of the convection. The convection generates a Rossby wave train that teleconnects to the weather in the midlatitudes (Thompson and Roundy, 2013).
• QBO – zonal wind anomalies in the tropical stratosphere that cycle between easterly and westerly phases with a period of ~ 2 to 3 years. Recently, it has been shown that the QBO is capable of modulating the amplitude of the MJO (Yoo and Son, 2016).
• CAPE – Convective Available Potential Energy, measure of the potential updraft strength in a convective storm.
• 0 to 6 km vertical wind shear - difference between the 6 km and 0 km wind speeds, used as a crude proxy for the storm motion and storm type (Markowski and Richardson, 2010).
• CS06 – product of CAPE and the magnitude of the 0 to 6 km vertical wind shear (Barrett and Henley, 2015).
This modulation of severe weather regimes can be seen in other areas as well, such as Fort Collins, CO. During the easterly QBO, there is a greater likelihood of positive CS06 as compared to the westerly QBO. A clear propagating signal is seen during the easterly QBO, out through several weeks lag.
Key Terms • A clear propagating signal in
shear is visible for both phases of the QBO. However, the anomalies differ as a function of lag week and MJO phase. For week 4, MJO phase 6, positive anomalies exist for both phases of the QBO.
• For CAPE, a propagating
signal is visible, but it is not as clear as that in shear. Interestingly, the CAPE and shear anomalies are often opposite of each other during the westerly QBO
• For CS06, there is a greater
likelihood for severe weather during the westerly QBO. This is particularly evident 4 weeks following MJO phase 6, when the April 27th, 2011 event occurred. This result alludes to the importance of using a parameter that combines CAPE and shear in identifying severe weather regimes.
2. Propagation of the Signal
3. Fort Collins, CO
Source: ECHO Storm Team
Tuscaloosa,AL
Total CAPE and 0 to 6 km Wind Shear 00 Z April 28th, 2011
CAPE (J/kg)
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Composite of CS06 Anomalies during Week 4 following MJO Phase 6 (FMAM) CS06 Anomaly (m3/s3)
-10000 -8000 -6000 -4000 -2000 0 2000 4000 6000 8000 10000
Tuscaloosa, AL (FMAM) Easterly QBO Westerly QBO
Shear Anomaly (m/s)
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
CAPE Anomaly (J/kg)
-50 -37.5 -25 -12.5 0 12.5 25 37.5 50
CS06 Anomaly (m3/s3)
-7500 -5625 -3750 -1875 0 1875 3750 5625 7500
Lag Week Lag Week
MJO
Pha
se
MJO
Pha
se
MJO
Pha
se
1 2 3 4 5 6
1
2
3
4
5
6
7
8 1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
Fort Collins, CO (MAMJ) Easterly QBO Westerly QBO
CS06 Anomaly (m3/s3)
-7000 -5250 -3500 -1750 0 1750 3500 5250 7000
MJO
Pha
se
1
2
3
4
5
6
7
8
Lag Week 1 2 3 4 5 6
Lag Week 1 2 3 4 5 6
MJO
Phase
8 7 6 5 4 3 2 1
Tuscaloosa, AL
1 2 3 4 5 6
1 2 3 4 5 6 1 2 3 4 5 6
1 2 3 4 5 6 1 2 3 4 5 6
