LCDR Joel W. Feldmeier, USN Master’s Thesis Presentation September 9, 2005 Co-Advisors:

1. Climatic Variations of the California Current System: Application of Smart Climatology to the Coastal Ocean LCDR Joel W. Feldmeier, USN Master’s Thesis Presentation September 9, 2005 Co-Advisors: Professor T. Murphree Professor R. Tokmakian

2. Outline • Motivation • Scientific • Naval/Military • Smart Climatology • Overview • As applied to this research • Parallel Ocean Climate Model 4C (POCM 4C) • Specifications • Known biases • Geographic Focus Area: California Current System (CCS) • Long Term Mean (LTM) and LTM as represented in model • Conditions during El Nino (EN)/La Nina (LN) • EN/LN in model • Comparison of EN and LN within the model and with published work • Sample comparison of traditional vs. smart climatology along hydrographic line P • Discussion and Conclusions

3. Motivation • Scientific • General: Desire to examine air-sea interaction on climatological time scales (months to years) • How is energy transferred back and forth between the air and sea? How is this expressed? • What sorts of predictable variability is found in this interaction? • Specific: By using knowledge of atmospheric climate variability can we make meaningful statements about what is going on in the ocean simultaneously?

4. Naval Motivation Help lay the groundwork for a methodology to back-up, enhance, or supplement ocean products already in use, such as MODAS. Traditional Climatology Dynamic MODAS In situ Data Ocean Temperature depth vs. latitude cross section After Fox et al. (2002b)

5. Ultimate Aim • Provide the best ocean climatology possible, leading to: • Better initial and boundary conditions for models • Insight into climatic variability • Improved analyses and forecasts • Better environmental tools for naval planners • This research is a background effort

6. Smart Climatology Concepts • Traditional Climatology • Get as much data as possible and take straight (~30 year) averages • Smart Climatology • Goal is to do better than traditional climatology • Use and time based (e.g. a tool for planning) • Look at state of the atmosphere/ocean and selectively pick what data to average • Utilize modern analysis and model resources • Some prediction Smart Climatology – Climatology that accounts for modern developments in climatology, such as analyses of anomalous trends and oscillations, reanalysis, downscaling, and climate forecasting.

7. Climate Indices • A way of characterizing the climate phase of the atmosphere • Most Familiar Example – the Southern Oscillation Index (SOI) useful for characterizing state of El Nino • NOI: Basic form NOI = SLPA(NPH) – SLPA(Darwin)

8. NOI Time Series LN LN EN EN EN From: www.pfel.noaa.gov Note: not all EN and LN events annotated

9. The Parallel Ocean Climate Model (POCM 4C) • Global Scale Model • 20 vertical layers • Average ¼ degree horizontal resolution • Initialized by Levitus Climatology • Heat, freshwater and momentum (wind) fluxes are derived from ECMWF 15 year reanalysis (1979-93) and ECMWF operational fields (1994-98) • For this project • Had data from 1979 – 1998 • 20 year average of POCM data was the ‘traditional’ climatology • Selected composites of POCM data were the ‘smart climatology’

10. POCM 4C Known Trends • Strengths • general circulation • Western Boundary Currents and associated eddies • Location, timing, and variance character of major currents • Mean overturning heat flux realistically resolved • Variation character of Sea Surface Temperature (SST) • Model Sea Surface Height (SSH) correlates to tidal gauge approx. same as satellite altimetry • Issues • Magnitude of current variations weak by factor of 2-4 • May overestimate SST in some locations • Salinity not resolved as well as SST • Underestimates SSH in eddy rich areas • Model effective viscosity may be too large

11. Hypotheses • EN/LN composites of ocean parameters will show realistic and identifiable anomaly patterns in temperature, salinity, currents, and SSH. • The differences between the EN/LN composites and the Long Term Mean (LTM) state of the CCS will be significant and should be accounted for when naval planners consider tasking such as ASW operations, UUV and AUV operations, search and rescue, and transits.

12. California Current System (CCS) LTM

13. CCS • Compared the 4 seasons as resolved in POCM 4C with currents, SSH, temperature, salinity with conceptual models, published observations, and traditional climatology • Examined how the Spring and Fall transitions were resolved in POCM 4C • Allowed further characterization of POCM 4C trends and biases in the CCS region • Will show here only a sample of comparisons done

14. Seasonal Wind Stress Patterns AL AL Winter Spring NPH NPH a) b) AL AL NPH Fall Summer NPH c) d)

15. Conceptual Seasonal Current Variability From Strub and James (2000)

16. Winter Temperature Patterns Model LTM Winter ¼ degree Levitus Winter Cross-section Cross-section a) b) c) d)

17. Comparison of Model and Expected Currents DC CC SCE a) POCM LTM Winter (DJF) Current Streamlines b) After Strub and James (2000)

18. Seasonal Variation in V-component of Current CC DC Winter Location of cross-sections shown CUC CC Summer CUC

19. Evaluation of Model SSH Simulation

20. Found POCM 4C Trends in CCS • POCM resolves CC, CUC, and DC in realistic locations • POCM captures seasonal current variability • POCM resolves realistic surface temperature and salinity • Biases of note: • POCM resolves currents generally too weak • CC in POCM further offshore than observations imply from Jan-Jun • Max. equatorward speeds of CC in POCM further offshore than observed • DC not well resolved along Central California Coast • DC further north in Spring than expected • General warm and salty bias in upper 500 m of water column • Cool bias along coast at surface • Columbia River outflow weaker than observations • Conclusion: POCM depiction of CCS suitable for use in this smart climatology study

21. CCS EN/LN

22. Atmospheric Climate Patterns From: www.pfel.noaa.gov

23. Choice of Data for Composites • EN Composite: • Average of November to March from • 1982-83, 1991-92, 1994-95, 1997-98 • All ‘strong’ EN events • Composite EN NOI = -5.891 • LN Composite • Average of November to March from • 1984-85, 1987-88, 1988-89, 1989-90 • Only 1988-89 a ‘strong’ LN • Composite LN NOI = 2.030

24. EN/LN Wind Stress (WS) Anomaly EN WS Anomaly LN WS Anomaly Anomalous wind stress forcing patterns in model during EN/LN. Consistent with those reported in other EN/LN studies (e.g. Schwing et al. 2002b).

25. CCS during EN/LN From Strub and James (2002b)

26. Model Current Streamlines Model EN Current Model LN Current a) b) Model LN Current Anomaly Model EN Current Anomaly c) d)

27. Model Cross-sections of V- current at 36 deg N Model EN Current Model LN Current Model LN Current Anomaly Model EN Current Anomaly

28. SSH Comparisons Model EN Altimetry - EN a) b) Model LN Altimetry - LN c) d)

29. Model vs. Reynolds SST POCM EN SST Anom. POCM LN SST Anom. EN SST Anom. From Reynolds SST LN SST Anom. From Reynolds SST Note: Reynolds SST at 1 deg resolution Consistent with EN/LN anomaly patterns previously reported, e.g.Schwing et al. 2002b.

30. EN SST and Wind Stress Anomalies EN SST Anom. EN WSC Anom. a) b) Note: WSC = Wind Stress Curl EN SST and WS Anom. • Notice WSC along the coast (reversed colorbar) • Notice WS along coast c)

31. LN SST and Wind Stress Anomalies LN SST Anom. LN WSC Anom. a) b) LN SST and WS Anom. • Notice WSC along the coast (reversed colorbar) • Notice WS along coast c)

32. Hydrographic Line P Figure from Fisheries and Oceans Canada Focus area for this study

33. Temperature Anomalies: Observed EN Month vs. Model EN Composite Nov 82 NOI = -5.642 cool warm warm cool (Figure from Fisheries and Oceans Canada) warm EN Composite NOI = -5.891 Compare with above warm

34. Temperature Anomalies: Observed EN Month vs. Model LN Composite Nov 82 NOI = -5.642 cool warm warm warm (Figure from Fisheries and Oceans Canada) cool LN Composite NOI = 2.030 Compare with above cool

35. Temperature Anomalies: Observed LN Month vs. Model EN Composite Feb 99 NOI = 3.174 cool warm cool cool (Figure from Fisheries and Oceans Canada) warm EN Composite NOI = -5.891 Compare with above warm

36. Temperature Anomalies: Observed LN Month vs. Model LN Composite Feb 99 NOI = 3.174 cool warm cool warm (Figure from Fisheries and Oceans Canada) cool LN Composite NOI = 2.030 Compare with above cool

37. Discussion/Conclusions • EN and LN variations consistently opposite each other • Reinforces idea that they are opposing phases of same variation even in the extra-tropics • Shows that an atmospherically based climate index is also characterizing the ocean to some extent • NOI based EN/LN composites from POCM 4C capture well the character of large EN/LN variations over the CCS • NOI based EN/LN composites from POCM 4C show weaker variations than those seen in in situ data • Mixed results when attempting to look at specific hydrographic lines on smaller scales

38. Future Work • Somewhat hampered by 20 year model period -> re-do project/similar project with • Longer model period • Use of different, perhaps air-ocean coupled model such as new NOAA CFS • Look at slightly shorter time-scales, perhaps down to one month • E.g. for comparison with hydrographic data • Examine a WBC region with a different climate index • Stronger currents and eddies • May be easier to see large variations

39. Final Thoughts • Naval Relevance • Recall this research a background effort, not intended to be directly applicable • Real-world ocean variability can be reflected in smart-climatology • Anticipate changes in position of ocean temperature and salinity fronts • ASW, MIW • Anticipate changes in position of ocean currents • ASW, Search and Rescue, AUV/UUV Operations, Transits, Amphibious warfare, Special Warfare • Relevant for long-range planning and ocean modeling

40. Naval Motivation Possibilities Dynamic MODAS (or other product) Enhanced with Smart Climatology Traditional Climatology In situ Data Smart Climatology Further Research Warranted Ocean Temperature depth vs. latitude cross section After Fox et al. (2002b)

41. Questions?

42. Extra Slides

43. From Matthews et al. (1992)

45. CC ~900 km CC DC CC ~44 N ~44 N CUC CUC DC ~140 km ~273 km CC ~900 km a) b) c) d)

47. Selection of NOI Based Composite EN/LN from POCM • Decided to focus on Nov-Mar -> period of most impact of EN/LN felt in CCS • Used NOI values from NOAA PFEL website • Averaged over 56 separate 5 month periods • Started with Nov 1948 – Mar 1949 • Ended with Nov 2003 – Mar 2004 • Average Nov to Mar value over 56 year period: • -0.011 • Median Nov to Mar value over 56 year period: • 0.787 • Standard Deviation of Nov to Mar average values over 56 year period: • 2.6327

48. Ten Most Negative Average Nov to Mar NOI Values • 1982 -8.291 • 1997 -6.6648 • 1977 -5.2068 • 1991 -4.5694 • 1994 -4.0388 • 1957 -3.6552 • 1979 -3.5722 • 1992 -3.2742 • 1968 -2.4828 • 1985 -2.4118 Years used in current (as of 7/5/05) NOI based EN Composite EN Composite Avg = -5.891 Note: Year value refers to the November year – e.g 1982 corresponds to Nov 82 – Mar 83

49. Ten Most Positive Average Nov to Mar NOI Values • 1952 2.1666 • 1949 2.238 • 1963 2.255 • 1971 2.6006 • 1970 2.8632 • 1954 3.0132 • 1948 3.1146 • 1988 3.3132 • 1998 3.4674 • 1975 4.2506 Out of top 10 positive values only this year available from POCM for NOI based LN composite

50. More Positive NOI Nov to Mar Values • 1989 1.2282 • 1960 1.2858 • 1959 1.3058 • 1950 1.3242 • 1955 1.5186 • 1964 1.5494 • 1984 1.5774 • 1956 1.6984 • 1974 1.8538 • 1976 1.9912 • 1987 2.0036 These years used for POCM NOI based LN composite LN Composite Avg = 2.0303