Analyses of El Niño and La Niña Events and Their Effects on Local Climate Using JMP

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1 Analyses of El Niño and La Niña Events and Their Effects on Local Climate Using JMP Discovery Summit 2011 Denver, CO September 13-16, 2011 Dr. Rich Giannola JHU/APL Laurel, MD

2 Outline JHU/APL overview Objectives El Niño/La Niña discussion Data Analyses with JMP 9.0 Conclusions References 2

Division of The Johns Hopkins University

DHS IC Critical Contributions to Critical

3 APL in Brief What Are We? Who Are We? Who Are Our Sponsors? What Is Our Goal? Division of The Johns Hopkins University University Affiliated Research Center Technically skilled and operationally oriented Objective and independent DoD NASA DHS IC Critical Contributions to Critical Challenges Laboratory Statistics (GFY2011) Employees: ~5,000 Staff Revenues: ~$1.1B 3

APL JHU Whiting School of Engineering Graduate

Homewood Montgomery Campus APL Education Center

Professionals Program Graduate program for part-time

APL-based programs offered Applied and Computational

Electrical and Computer Engineering Information

Systems Engineering Technical Management ~Half of

4 APL JHU Whiting School of Engineering Graduate Education in Engineering and the Applied Sciences JHU Homewood Montgomery Campus APL Education Center Dorsey Center Johns Hopkins Engineering for Professionals Program Graduate program for part-time students Over 12,000 degrees awarded since 1964 Eight APL-based programs offered Applied and Computational Mathematics Applied Physics Computer Science Electrical and Computer Engineering Information Assurance (new) Information Systems Engineering Systems Engineering Technical Management ~Half of faculty are APL employees Over 2,200 students currently enrolled Southern Maryland Aberdeen Center 4

5 Objectives Collect long-term time series data to use for analysis in JMP 9.0 El Niño and La Niña indicators (indices) Temperature, precipitation, and snowfall data for selected U.S. locations (airports) Use JMP 9.0 to explore behavior of El Niño and La Niña Use various JMP 9.0 platforms to relate El Niño/La Niña events with observed temperature and precipitation anomalies* Determine if anomalies from selected U.S. locations are consistent with documented anomalies over larger, regional areas *An anomaly to meteorologists is a departure from average or normal conditions computed over a specified period of time. 5

6 El Niño/La Niña Discussion El Niño (EN) and La Niña (LN) are phases of a naturally occurring climate cycle originating in the equatorial Pacific Ocean that affect short-term climate around the world Phenomena result from ocean-atmosphere interactions Irregular occurrence (~ 3-5 years) and duration (EN: 9-12 months; LN: 1-3 years) El Niño The Child The Christ Child The Little Boy A warmer than normal sea surface temperature (SST) occurrence across the central and eastern equatorial Pacific Ocean La Niña The Little Girl A cooler than normal SST occurrence across the central and eastern equatorial Pacific Ocean 6

7 El Niño/La Niña Discussion (cont d) Atmosphere-ocean is a coupled system, so impacts of El Niño and La Niña are felt worldwide Equatorial Pacific Ocean pumps large amounts of heat and moisture into the upper atmosphere, affecting tropical rainfall patterns and atmospheric winds Shape or position of ocean heating helps to set the paths of the jet stream that controls weather in mid-latitude zones 7

8 El Niño/La Niña Discussion (cont d) 8 Thermocline - boundary separating well-mixed surface waters from deeper, colder waters

9 El Niño/La Niña Discussion (cont d) 9

10 El Niño/La Niña Discussion (cont d) 10

11 El Niño/La Niña Discussion (cont d) 11

12 El Niño/La Niña Discussion (cont d) El Niño and La Niña are typically strongest from December-April when the equatorial SST is usually the warmest Impact on U.S. climate is greater in winter than summer, thus seasonal analyses here will be restricted to winter* Accurate long-lead forecasts of these events are important Agriculture Fishing industry Management of water resources, grain reserves, and energy *Winter in this analysis is defined from December through March 12

13 Data El Niño and La Niña indicators (indices) were obtained from NOAA/NWS/CPC* web site Several indices are available Primary indices used here are SST and atmospheric sea level pressure (SLP) Indices are provided by month and season (3-month running means) Indices are often relative to climatic anomalies Climatologists use 30-year means as normal This analysis used as normal Normals from were released too late to use here *NOAA/NWS/CPC National Oceanic and Atmospheric Administration/National Weather Service/Climate Prediction Center 13

14 Data (cont d) Equatorial Pacific Ocean area divided into four El Niño SST regions Region used for El Niño/La Niña definition 14

15 Data (cont d) Oceanic Niño Index (ONI) 3-month running mean of ERSST.v3b SST* anomalies in the Niño 3.4 region El Niño episode: ONI +0.5 C for at least 5 consecutive months La Niña episode: ONI 0.5 C for at least 5 consecutive months Southern Oscillation Index (SOI) is based on the atmospheric SLP difference between Tahiti and Darwin, Australia SOI is large and negative during El Niño SOI is large and positive during La Niña Note: JMP 9.0 Help Sample Data Time Series has an older SOI data set called Southern Oscillation ( ) El Niño/Southern Oscillation (ENSO) refers to the combined oceanatmospheric cycle *Extended Reconstructed Sea Surface Temperature 15

16 Data (cont d) Temperature, precipitation, and snowfall data from were obtained from NNDC* web site for selected U.S. airports BWI Baltimore-Washington Int l Airport, MD DEN Denver Stapleton Int l Airport, CO DLH Duluth Int l Airport, MN HOU Houston Hobby Airport, TX LEX Bluegrass Airport, KY SEA Seattle-Tacoma Int l Airport, WA SFO San Francisco Int l Airport, CA TPA Tampa Int l Airport, FL Completeness of data = 99.3% (13,815/13,908 observations) Missing data supplemented with NOAA/NCDC Local Climatological Data publication or by interpolation from nearby stations *NNDC NOAA National Data Centers 16

17 Data (cont d) Selected U.S. Airports for Analyses 17

18 Analyses with JMP 9.0 Primary JMP data table used in analyses has 94 columns of data 18

19 Analyses with JMP 9.0 (cont d) JMP platforms used in analysis: Analyze Distribution Fit Y by X Bivariate Fit Modeling Time Series Graph Bubble Plot Chart Scatterplot Matrix Analyses will explore data characteristics and relationships over the 61-year period and the winter seasons 19

20 Analyses with JMP 9.0 (cont d) Time series temperature anomalies for 61-year period (all locations) No cycles evident (as expected) Similar results found for precipitation and snowfall anomalies DLH 20

21 Analyses with JMP 9.0 (cont d) Bivariate fit of airport temperature trends (61 years) shows possible evidence of climate change SEA SFO 21

22 Analyses with JMP 9.0 (cont d) Bivariate fit temperature summary Location Slope ( F/yr) ANOVA (Prob > F) BWI LEX TPA DLH DEN HOU SEA * SFO * *Statistically significant at 95% confidence level 2.4 F increase in 61 years 22

23 Analyses with JMP 9.0 (cont d) Bivariate fit of SST trends (61 years) also shows possible evidence of climate change 0.4 C increase in 61 years Statistically significant at 95% confidence level 23

24 Analyses with JMP 9.0 (cont d) Chart of seasonal ONI illustrating irregularity of ENSO over 61 years 0.5 C La Niña Threshold +0.5 C El Niño Threshold 24

25 Analyses with JMP 9.0 (cont d) Chart showing inverse relationship between SOI SLP (red) and Niño 3.4 SST (blue) over 61 years 25

26 Analyses with JMP 9.0 (cont d) Bubble plot animation illustrating inverse relationship between SOI SLP and Niño 3.4 SST over 61 years 26

27 Analyses with JMP 9.0 (cont d) Bivariate fit illustrating inverse relationship between SOI SLP and Niño 3.4 SST (all winters) Statistically significant at 95% confidence level 27

28 Analyses with JMP 9.0 (cont d) Bubble plot animation showing inverse relationship between Tahiti and Darwin SLP s (all winters) 28

29 Analyses with JMP 9.0 (cont d) Bivariate fit showing inverse relationship between Tahiti and Darwin SLP s (all winters) Statistically significant at 95% confidence level 29

30 Analyses with JMP 9.0 (cont d) Scatterplot matrix showing relationship between ONI and seasonal temperature anomalies during El Niño winters 30

31 Analyses with JMP 9.0 (cont d) Chart of DLH El Niño winter temperature and precipitation anomalies 31

32 Analyses with JMP 9.0 (cont d) Chart of SEA La Niña winter temperature and precipitation anomalies 32

33 Analyses with JMP 9.0 (cont d) Distribution* of DLH El Niño winter temperature anomalies *Distributions used 61-year means 33 Mean is statistically different from zero at 95% confidence level

34 Analyses with JMP 9.0 (cont d) Distribution of SFO El Niño winter precipitation anomalies 34 Mean is statistically different from zero at 95% confidence level

35 Analyses with JMP 9.0 (cont d) Distribution test mean summary for El Niño winter temperature anomalies Location Mean Temp. Anomaly ( F) Test Mean (Prob > t ) BWI LEX TPA * DLH * DEN HOU * SEA * SFO *Statistically significant from zero at 95% confidence level 35

36 Analyses with JMP 9.0 (cont d) Distribution test mean summary for El Niño winter precipitation anomalies Location Mean Precip. Anomaly (in.) Test Mean (Prob > t ) BWI LEX * TPA * DLH DEN HOU * SEA SFO * *Statistically significant from zero at 95% confidence level 36

37 Analyses with JMP 9.0 (cont d) Distribution test mean summary for El Niño winter snowfall anomalies Location Mean Snowfall Anomaly (in.) Test Mean (Prob > t ) BWI DLH * DEN *Statistically significant from zero at 95% confidence level 37

38 Analyses with JMP 9.0 (cont d) Distribution test mean summary for La Niña winter temperature anomalies Location Mean Temp. Anomaly ( F) Test Mean (Prob > t ) BWI LEX TPA DLH * DEN HOU * SEA * SFO * *Statistically significant from zero at 95% confidence level 38

39 Analyses with JMP 9.0 (cont d) Distribution test mean summary for La Niña winter precipitation anomalies Location Mean Precip. Anomaly (in.) Test Mean (Prob > t ) BWI LEX * TPA < * DLH DEN HOU SEA SFO *Statistically significant from zero at 95% confidence level 39

40 Analyses with JMP 9.0 (cont d) Distribution test mean summary for La Niña winter snowfall anomalies Location Mean Snowfall Anomaly (in.) Test Mean (Prob > t ) BWI DLH DEN

41 Analyses with JMP 9.0 (cont d) Distribution test mean summary for neutral winter temperature anomalies Location Mean Temp. Anomaly ( F) Test Mean (Prob > t ) BWI LEX TPA DLH DEN HOU SEA SFO * *Statistically significant from zero at 95% confidence level 41

42 Analyses with JMP 9.0 (cont d) Distribution test mean summary for neutral winter precipitation anomalies Location Mean Precip. Anomaly (in.) Test Mean (Prob > t ) BWI LEX TPA DLH DEN HOU SEA SFO

43 Analyses with JMP 9.0 (cont d) Distribution test mean summary for neutral winter snowfall anomalies Location Mean Snowfall Anomaly (in.) Test Mean (Prob > t ) BWI DLH DEN

44 Analyses with JMP 9.0 (cont d) Statistically significant correlations found with Distribution test mean in JMP 9.0: El Niño Winter La Niña Winter Location Temp. Precip. Temp. Precip. BWI LEX - Dry - Wet TPA Cool Wet - Dry DLH Warm - Cool - DEN HOU Cool Wet Warm - SEA Warm - Cool - SFO - Wet Cool - 44

45 Analyses with JMP 9.0 (cont d) El Niño La Niña 45

46 Conclusions Long-term time series data were successfully collected and manipulated for analysis in JMP 9.0 JMP 9.0 was an excellent resource to explore visual and statistical relationships between El Niño, La Niña, and local climate Temperature and precipitation anomalies from eight selected U.S. locations were consistent with documented anomalies over larger, regional areas 46

47 References Publications Arkin, P.A., 1982: The Relationship Between Interannual Variability in the 200 mb Tropical Wind Field and the Southern Oscillation. Monthly Weather Review, 110, Diaz, H.F. and Markgraf, V. (eds.), 2000: El Niño and the Southern Oscillation: Multiscale Variability and Global and Regional Impacts. New York: Cambridge University Press, 496 pp. Halpert, M.S. and C.F. Ropelewski, 1992: Surface Temperature Patterns Associated with the Southern Oscillation. Journal of Climate, 5, Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77, Kistler, R., 2001: The NCEP-NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation. Bulletin of the American Meteorological Society, 82, Philander, S.G.H., 1990: El Niño, La Niña, and the Southern Oscillation. International Geophysics Series, Volume 46. San Diego: Academic Press, 289 pp. Ropelewski, C.F. and M.S. Halpert, 1986: North American Precipitation and Temperature Patterns Associated with the El Niño/Southern Oscillation (ENSO). Monthly Weather Review, 114, Ropelewski, C.F. and M.S. Halpert, 1987: Global and Regional Precipitation Patterns Associated with the El Niño/Southern Oscillation. Monthly Weather Review, 115,

48 References Publications (cont d) Ropelewski, C.F. and M.S. Halpert, 1989: Precipitation Patterns Associated with the High Index Phase of the Southern Oscillation. Journal of Climate, 2, Rasmusson, E.M. and T.H. Carpenter, 1982: Variations in Tropical Sea Surface Temperature and Surface Wind Fields Associated with the Southern Oscillation/El Niño. Monthly Weather Review, 110, Smith, T.M., R.W. Reynolds, T.C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA s Historical Merged Land-Ocean Surface Temperature Analysis ( ). Journal of Climate, 21, Trenberth, K.E., 1991: General Characteristics of El Niño-Southern Oscillation. In Teleconnections Linking Worldwide Climate Anomalies Scientific Basis and Societal Impact, M.H. Glantz, R.W. Katz, and N. Nicholls, Eds., Cambridge University Press,

49 References Internet Sites NNDC monthly surface data (U.S. Airports): NOAA/NWS/CPC El Niño/Southern Oscillation diagnostic discussion: NOAA/NWS/CPC El Niño/Southern Oscillation home page: NOAA/NWS/CPC El Niño and La Niña indicators (indices): 49

50 References El Niño Relationships El Niño 50

51 References La Niña Relationships La Niña 51

52 References Historical Episodes 52

53 References Model Predictions IRI International Research Institute for Climate and Society 53

54 References SOI Definition Calculate mean SLP (P) and std. dev. of SLP ( P ) over 360 months for Tahiti (TAH) and Darwin (DAR) For each month m: TAH standardized SLP anomaly(m) = SSLP TAH (m) = (P TAH (m) P TAH ) / P(TAH) DAR standardized SLP anomaly(m) = SSLP DAR (m) = (P DAR (m) P DAR ) / P(DAR) SOI SLP anomaly(m) = SSLP TAH (m) SSLP DAR (m) SOI SLP standardized anomaly(m) = (SSLP TAH (m) SSLP DAR (m) ) / MSD, where MSD = monthly std. dev. 360 m=1 = [(SSLP TAH (m) SSLP DAR (m) ) 2 ] / 360) 54

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012

ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 23 April 2012 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index