Research Projects and Student Opportunities in Lu Group

Transcription

1 Introduction to Research August 24, 216 Research Projects and Student Opportunities in Lu Group Xian Lu Assistant Professor Atmospheric and Space Physics Physics Astronomy Clemson University 1

2 Learn about our Earth! Earth in the solar system Ø What are the fundamental physical processes on the Earth driving its dynamics and chemistry? Ø What is the link from the lower to upper atmosphere, and to the space? Ø How does the solar activity affect the Earth? What impacts does the evolution of the solar system have on the Earth in the long- term? Ø How accurate can models predict climate change? How timely can models predict solar storms? Will and how the models can mitigate potential hazardous effects? 2

3 Motivations Ø More understanding about the fundamental processes on the Earth help us understand the whole universe and navigate safely across space. Ø More knowledge about our planet help predict and prevent natural and human- caused hazards. Hurricane Ozone Hole Space Storm Ø Knowing more about the Earth help iden>fy other habitable planets. In 5 billion years, the Sun will start helium- burning process and turn into a red giant star. When it expands, the outer layer will reach the Earth. 3

4 My Current Active Projects My own research interests: 1) Atmospheric Wave Dynamics and Coupling 2) Magnetosphere- Ionosphere- Thermosphere Coupling and Space weather 1. NSF (PI): Explora>on of lower- atmosphere wave forcing, ver>cal wave coupling and their impacts on the ionosphere and thermosphere variability using WAM, lidar and ISR (1 Journal Papers, 5 Conference Papers, >2 Conference Presenta>ons) 2. NSF (Co- I): Characterizing Gravity Waves and their Effects on the Antarc>c Ozone Layer 3. NASA (PI): Signatures of Energy Dissipa>on in the Magnetosphere- Ionosphere- Thermosphere Coupled System 4

5 Three Important Waves Ø Gravity wave (GW) Period: minutes to hours Scale: 1 km A few thousand km Ø Tides Period: Subharmonics of a solar day Planetary scale Ø Planetary wave (PW) Period: several days or staaonary Planetary scale 5

6 Why Waves? A gravity wave roll over Tama, Iowa Iowa Environmental Mesonet Webcam. Simulation by high-resolution WACCM Ø In the middle and upper atmosphere, waves are ubiquitous and dominant. Ø They transport momentum and energy, control the mean status and variability. Ø They are the message carrier between lower- atmosphere weather and space weather. Ø They are closely related to remarkable atmospheric phenomena such as thunderstorms, sudden stratospheric warming, Antarc>c ozone hole, and etc. 6

7 What have we learned from Project 1? P1: Explora>on of lower- atmosphere wave forcing, ver>cal wave coupling and their impacts on the ionosphere and thermosphere variability using WAM, lidar and ISR Atmospheric waves are playing a relay game in the Antarc>c Planetary Waves Inertial Gravity Waves Aurora- Related Tides 3 km ~6 km ~1 km ~14 km Upward Ver>cal Wave Coupling MIT Coupling 7

8 Lidar Temperatures at McMurdo (78S), Antarctica 3-1 h Persistent IGWs MLT (above 7 km): Inertial-Gravity Waves Stratosphere (3-6 km): Planetary Waves Chen et al., JGR, 213, 216 8

9 What questions have left from Project 1? Two mysteries: 1) What are the sources for the persistent and strong IGWs? Tools: Lidar Observa>on+ Ray- tracing model + General circula>on models (GCMs) 2) How do PWs survive their cri>cal level filtering and exist in the lower thermosphere (up to 11 km)? Tools: PW model+ GCMs+Satellite/Lidar Observa>on 9

10 Student Opportunity 1 Ray Tracing Model: Search for Wave Source Wave1 (k, l, m ) Detected at (x, y, z ) Background Winds (U, V) Group Velocity (Cx, Cy, Cz) Polar Vortex Instability? Wave(k, l, m) Detected at (x, y, z) at former step Topography+ Secondary Waves? Resonant Vibration of Ross Ice Shelf? 1

11 Lidar Temperatures at McMurdo (78S), Antarctica 3-1 h Persistent IGWs MLT (above 7 km): Inertial-Gravity Waves Stratosphere (3-6 km): Planetary Waves Chen et al., JGR, 213,

12 Zonal Wind MERRA Assimilation Movie lasts for 4 days Meridional Wind Planetary Waves Drive Polar Temperature, Winds and Ozone Variability Temperature Ozone 12

13 What have we learned about polar PWs? Altitude (km) (b) June 2 day Wave Latitude (Deg) Altitude (km) (a) Mean Zonal Wind q m 2 >.5 1 m 2 = m 2 < Latitude (Deg) Lu et al., JGR, 213 Ø Eastward traveling planetary waves with respective to grounds are dominant in the stratosphere Ø Wave source: Large wind shears of polar vortex cause instability. Ø Refractive index becomes imaginary preventing wave propagation. 13

14 Mystery: how PWs reach upper atmosphere? Altitude (km) Lu et al., 216b Altitude (km) Days (May 214) day wave (a) Filtered T Pert (K) Lidar Altitude (km) Critical level filtering? day wave Altitude (km) Altitude (km) Zonal Mean Wind (May) Critical level filtering Latitude (Deg) PW Model Amplitude At McMurdo Phase At McMurdo Altitude (km) 1 8 Critical level filtering? Amplitude (K) Phase (Radian) Normalized Amplitude Phase

15 Numerical Equations to Study PWs Because it is a wave form along longitude, it becomes a 3- D problem as a function of latitude, altitude and time. 15

16 Numerical Scheme Forward Time Difference: Space Difference second order differential equation ψ t = ψ i, j 2 ψ n n+1 n ψ i, j n Δt z 2 = ψ i, j+1 2ψ n i, j Δz 2 n +ψ i, j 1 Δt =1.5h Δz = 2km Δφ = 5 z Grid Points! " 1 A 1,1 2 A 1,1 1 A 2,1 2 A 2,1 3 A 2,1 1! A 1,2 2 A 3,1 3 A 3,1 4 " A 3,1 Matrix Method 2! A 2,2 3 A 4,1 4 A 4,1 3! A 3,2 4 A 5,1!!!!!!!! 4! A 4,2!!!!!!!!!!!!!!!!!! " " " " " " " " " " " " " " " " " " " " " " n*m A n*m,n*m A :[n m] [n m] ψ :[n m] 1 B :[n m] 1 A = A(ψ n, Δt, Δz) B = B(ψ n, Δt, Δz) n*m A n*m,n*m! $!!! %! " ψ 1,1 ψ 2,1 ψ 3,1 ψ n,1 ψ 1,2 ψ 2,2 ψ 3,2 ψ n,2 ψ 1,m ψ 2,m ψ 3,m ψ n,m $!! =!!! % " 16 B 1,1 B 2,1 B 3,1 B n,1 B 1,2 B 2,2 B 3,2 B n,2 B 1,m B 2,m B 3,m B n,m $ %

17 Simulation of PW Generation Coupling Altitude (km) Vorticity Gradient (May) Instability x Latitude (Deg) Wave Source: Instability of the Mean Winds Lu et al., CEDAR,

18 Potential Mechanisms? A QG model confirms the wave generaaon mechanism in the stratosphere and produces first- order wave structure. It doesn t give clear explanaaon for the existence of the waves in the upper atmosphere. 1. Direct ver>cal propaga>on? 2. Mirror mechanism: PWs modulate GW ac>vity in the lower atmosphere which propagate and project the PW signatures in the upper atmosphere? 3. In- situ instability in the upper atmosphere enhances the PWs? 18

19 Next step: Implement GW effects + Gravity Wave Effects + More Realistic Mean winds Because it is a wave form along longitude, it becomes a 3- D problem as a function of latitude, altitude and time. 19

20 Student Opportunity II Ø Implement gravity wave effects or parameteriza>on into the planetary wave model. Ø Implement different background winds from GCMs with data assimila>on (e.g. SD- WACCM) into the PW model. Ø Study the ver>cal coupling process of planetary waves and their interac>ons with the mean and gravity waves. 2

21 2. NSF (Co- I): Characterizing Gravity Waves and their Effects on the Antarc>c Ozone Layer 21

22 Long-lasting Cold-Pole Problem Temperature Polar Stratospheric Clouds Ozone Depletion hinders the accurate prediction of ozone formation and concentration. Ozone simulation significantly affect predictions of climate change, which are critical to assess the future inhabitability of the Earth. One candidate for Cold Pole Problem: missing wave drag in the models. (Model South Pole Characteriza>on of GWs Valida>on High- Resolu>on Model (WRF) Global GWs GW Parameteriza>on in Climate Model Tan et al., CEDAR, 21 Climate Change Predic>on NSF- funded Project (Co- I): Characterizing Gravity Waves and their Effects on the AntarcAc Ozone Layer 22

23 Altitude (km) Linear Fit Winter Mean E pm Gravity Wave Potential Energy Density (b) Winter Mean E pv Linear Fit E pm (z) = 1! g $ 2 " N (z)% Growth Rate (Scale Height) 2! T '(z) $ " T (z)% PED Per Mass (J/kg) m Spectrum (211) (km) Gravity Wave Vertical Wavenumber Spectra m Spectrum (212) (km) F(m k ) = Δz f (m k ) 2 N = Δz N N n=1 x(z n )e Lu et al., JGR, 215a (n 1)(k 1) 2 2π i N 1 1 PSD (m) PSD (m) PED Per Volume (J/m 3 ) Vertical Wavenumber (km 1 ) Vertical Wavenumber (km 1 ) PSD Slopes Lu et al., JGR, 215a 23

24 Student Opportunity III Analyze the gravity wave characteris>cs and extend the database of their poten>al energy densi>es and ver>cal wavenumber spectra to 6 ( ) years. Analyze the gravity wave characteris>cs using the same method in high- resolu>on model (WRF) and compare with the observa>ons. Help the team to improve the gravity wave parameteriza>on in the climate models. 24

25 3. NASA (PI): Signatures of Energy Dissipa>on in the Magnetosphere- Ionosphere- Thermosphere Coupled System 25

26 Magnetosphere Energy Dissipation Sun- Earth System Magnetosphere Credits: NASA Credits: Dan Su We see aurora because of the magnetosphere- ionosphere- thermosphere (MIT) coupling 2

27 Wave III Atmospheric Tides Diurnal Composi>on at McMurdo Amplitude Fong, Lu, Chu et al., JGR, 214 Super- exponential increases of tidal amplitudes and Kp dependence illustrate a magnetic source for the tide. 27

28 CTIPe model simula>on of Tides Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics Fong, Chu, Lu et al., GRL,

29 Joule Heating Adiabatic Heating Joule+Adiabatic Heating Joule+Adiabatic Heating Ion Drag Off Fong, Chu, Lu et al., GRL,

30 Conclusions for Tidal Source Ø Joule heating is a minor contributor due to the counteraction by Joule- heating- induced adiabatic cooling.! Ø Adiabatic cooling/heating associated with Hall ion drag is the dominant source. Ø Sum of total dynamical effects and Joule heating explains ~8% of temperature diurnal amplitude. 3

31 Mystery: T Inversion Layer (~13 km) During Storms th, McMurdo Event on May Discovery of T28 hermospheric Metal Layers at McMurdo (a) 372 nm Fe Density on 28 May McMurdo 155 cm (c) Temperature on 28 May McMurdo 15 (d) Temperature on 2 May McMurdo Altitude (km) Universal Time (h) (b) Fe Temperature on 28 May McMurdo 115 K 28 1 LIDAR MSIS Kp= LIDAR MSIS Altitude (km) 11 Altitude (km) 115 Altitude (km) The T increase and inversion is missing in the IT model 8 Kp=3 6 5 [Chu et al., GRL, 211] (a) (c) Neutral T at McMurdo W 9 W 55 9 E 5 S S W 6 E 4 S 15 S 6 15 S 4 W 18 E 8 E E 3 E 1 Temperature (K) 6 6 W 3 9 E E 18 E S 15 W 12 W E 9 W 8 3 E W 12 Neutral T: (a) , UT = km (b) , (b) CTIPe Model Simula>on 3 W 8 Temperature (K) Altitude (km) 16 Universal Time (h) E Quiet Storm 2 Local Effects!!! Temperature (K) 31

32 Challenges for IT Models Most Ionosphere- Thermosphere (IT) models are sall driven by staasacal auroral maps and convecaon fields which may underesamate the local structure of heaang. Energy Flux Ion Drih by DMSP May 28, 211 DOY:148 Orbit: (DMSP F16) South - 5 o - 6 o - 7 o - 8 o 12 UT 11:32 SSUSI flux ( ergs/s/cm 2 ) It is time to go beyond a global statistical picture and use recent new observations to quantify how the magnetospheric energy is dissipated locally. 32

33 CTIPe Model with Enhanced Ionization Produces Observed Temperature Structure Without Enhanced Ioniza>on (a) 9 W 12 W 6 W 4 S 3 W 6 S 8 S (b) 3 E 6 E 12 E 9 E Altitude (km) (c) Neutral T at McMurdo Quiet Storm 15 W 18 E 15 E Temperature (K) With locally Enhanced Ioniza>on 33

34 Student Opportunity IV Realistic Energy Input Ionosphere- Thermosphere Coupling Models Assimila>on NASA and other Satellites Ground- Based Compare With Observations Constrain Model Study Physical Mechanisms Of MIT Coupling Explain Observations NASA- funded Project (PI): Signatures of Energy Dissipation in the Magnetosphere- Ionosphere- Thermosphere Coupled System 34

35 Student Opportunity to Develop Experimental Skills 35

36 Photo Credit Dr. Zhibin Yu McMurdo Lidar Campaign Observations at poles are generally sparse. McMurdo is in side of the polar vortex and on the poleward edge of auroral oval. Geophysic: 77.8 S E Geomagnetic: 8. S 33.9 W Summer From L to R: Dr. Xinzhao Chu, Dr. Xian Lu, Ian Barry, Muzhou Lu 36

37 Working on Lidar in Antarctica and Boulder Fe Boltzmann McMurdo, Antarctica Two Transmitters Two Receiving Telescopes Na Doppler Boulder, CO Transmitter Transmitter 37

38 Whole Spectrum of Research Skills Develop a full spectrum of skills for atmospheric science study that can be also transformed for other disciplines and also be beneficial for your post- PhD lives. Develop Research Proposals Instrumenta>on Technique Raw Data Retrieval Data Analysis Observa>ons Numerical Modeling Publish Papers Deliver Science In addi>on to the three current projects, more are expected, and you could be a part of the team to develop research proposals. 38

39 About Myself: Reality Easy- going, encourage and inspire Extensive experiences of mentoring students Respect + Guidance Good mentor (help students to be successful)+ friend (be supporave) 39

40 Summary and Contact Information Xian Lu 12A Kinard Lab, Projects: 1) Data analysis (Ground- based+satellite+models) 2) Numerical modeling WACCM (CESM), CTIPe, PW Model, GW ray- tracing model, WRF First step: Relocate the models to Clemson Palmeco system 3) Technique learning (Lidar remote sensing) Collaborators: NCAR, NOAA, NASA, University of Colorado Boulder, Virginia Tech, USU, UIUC, and etc. 4