Smart Ocean Climatology For Naval Warfare Tom Murphree, Ph.D. Naval Postgraduate School (NPS) murphree@nps.edu

1. Smart Ocean Climatology For Naval Warfare Tom Murphree, Ph.D. Naval Postgraduate School (NPS) murphree@nps.edu http://wx.met.nps.navy.mil/smart-climo/reports.php Brief Presented at DoD Climate Conference AFCCC/14WS and FNMOD Asheville, NC 06 November 2007 Updated 10 November 2007 Smart Climo 2, murphree@nps.edu, Nov07

2. Co-Authors • Tom Murphree, NPS • Mark LaJoie, Lt Col, USAF • Adam Stepanek, Capt, USAF • Damon Vorhees, Capt, USAF • Joel Feldmeier, LCDR, USN • Chris Hanson, Capt, USAF • Sarah Moss, Capt, USAF • David Meyer, (USN retired), NPS • Katherine Twigg, Lt, Royal Navy • Bob Tournay, Capt, USAF • Christi Montgomery, LT, USN • Allon Turek, LCDR, USN • Bruce Ford (USN retired), Clear Science, Inc. • Paul Frederickson, NPS • Dave Smarsh, Col, USAF • Karl Pfeiffer, Lt Col, USAF • Chuck Wash, NPS • NPS climatology course students • In Coordination and Collaboration with: • AFCCC/14WS, USAFE, AFRICOM, FNMOD, NAVO, ASW Directorate • Civilian climate research & operational climatology organizations Smart Climo 2, murphree@nps.edu, Nov07

3. Present State of DoD Climatology • Typical development of DoD climo products excludes many modern data sets and methods of climate analysis and forecasting. • Most DoD climatology products fail to account for advances in climate science and operational climatology during the last 30 years, including: • data sets • data analysis • modeling • monitoring • forecasting • This lag in DoD climatology has created significant gaps in climatological support for war fighters. • Course correction: Start applying smart climatology: State-of-the-art basic and applied climatology that directly supports DoD operations Smart Climo 2, murphree@nps.edu, Nov07

4. Tier 3 Decision Layer Tier 2 Performance Layer Tier 1 Environment Layer Observations Satellites Fleet Data Initial and Boundary Conditions Battlespace on Demand Smart Climo 2, murphree@nps.edu, Nov07

5. Tier 3 Decision Layer Smart climatology based decision surfaces Tier 2 Performance Layer Smart climatological performance surfaces Tier 1 Environment Layer Smart climatological analyses & forecasts Observations Smart climatology data sets Satellites Fleet Data Initial and Boundary Conditions Battlespace on Demand Data and methods for generating smart climo products at all three tiers already exist. Now we must commit resources to developing these products. Smart Climo 2, murphree@nps.edu, Nov07

6. Align and Streamline: Get the environment right Present / Forecast / Ensembles S&T Develop the performance surface Visualize / Animate R&D Couple with Operations Research Asset/Sensor allocation Maneuver / Quantify Risk Acquisition Operations Battlespace on Demand Battlespace on Demand is the Naval Oceanography community’s strategy and guidance to: Start by using smart climatology to get the background environment right. Streamline by building on existing smart climatology data, methods, and products. Need to go beyond forecasting the environment to analyze and forecast the operational impacts and the best courses of action. Smart Climo 2, murphree@nps.edu, Nov07

7. KNOWLEDGE-CENTRIC WARFIGHTING FOCUSED Effectiveness Readiness Anti-Submarine Warfare Shaping Naval Special Warfare Smart climatology supports all warfighting areas, at all planning levels. increased emphasis Mine Warfare ISR Navigation maintain excellence Precise Time and Astrometry Fleet Operations increased efficiency Maritime Operations Aviation Operations Smart climatology is the foundation for all reachback cells and mission support center support. FNMOC NAVO USNO Naval Oceanography Enterprise Model Smart Climo 2, murphree@nps.edu, Nov07

8. First Principles • Know the ground • Fight on the ground of your choosing • Own the windward gage Create and Maintain an Information Advantage • Smart climatology is critical to: • Knowing the ground well in advance • Finding and keeping the windward gage well in advance

9. Motivation for Improving Navy Atmospheric and Oceanic Climatology • Prior assessments have shown that DoD, and specifically Navy, climatology lags significantly behind the state of the science, and the gap is widening. • Improved, at-your-fingertips, smart climatology has the potential to significantly improve the situational awareness of METOC personnel, and the quality of all METOC products --- “A rising tide lifts all ships.” • Existing civilian ocean reanalysis products provide very good 4-D climatological representations of the atmosphere and ocean. • Greatly improved and much higher resolution civilian coupled atmosphere-ocean reanalysis products are scheduled for release in FY08-FY09. • The potential exists to develop state-of-the-science climatological visualization, analysis, forecasting, and training tools at a relatively low cost. ASW Smart Climo, Aug 07, murphree@nps.edu

10. Smart Climatology Existing Navy Climatology No corresponding Navy climatology of subsurface currents exists. NECC NEC a b ci = 5.0 cm/s Long term mean ocean currents (cm/s) for August in VS07 region, 0-300 m depth: (a) smart climatology and (b) existing Navy climatology (no significant subsurface current data available in Navy climatologies). Smart climatology developed from existing civilian 47-year global ocean reanalysis. Smart Ocean Climatology – Data Sets Ocean Currents Existing civilian smart climatology data sets provide descriptions of many variables that are not represented in existing Navy climatologies. Results from NPS Smart Climatology Program, murphree@nps.edu

11. Smart Climatology Existing Navy Climatology No corresponding Navy climatologies of sea surface height or surface currents exist. a b 20 cm/s Sea surface heights (cm) and surface currents (0-50 m; cm/s) for July in RIMPAC region: (a) smart climatology and (b) existing Navy climatology (no Navy climatologies exist for these variables). Smart climatology developed from existing civilian 47-year global ocean reanalysis. Smart Ocean Climatology – Data Sets Sea Surface Heights and Surface Currents Existing civilian smart climatology data sets provide descriptions of many variables that are not represented in existing Navy climatologies. Results from NPS Smart Climatology Program, murphree@nps.edu

12. b a Evaporation duct height (m) for September from: (a) NPS smart EDH climatology and (b) existing Navy climatology. NPS smart climatology developed from existing civilian multi-decadal atmospheric and oceanic reanalyses. Smart Ocean Climatology – Data Sets Evaporation Duct Heights Smart Climatology Traditional Climatology Smart climo uses modern data sets, modeling, and visualization tools to produce more detailed, accurate, and operationally useful products. Results from NPS Smart Climatology Program: NPS thesis research by K. Twigg, Lt, RN. Smart Climo 1, murphree@nps.edu, Nov07

13. Smart Climatological Surface Currents Long Term Mean La Nina Anomalies a b 1 cm/s 3 cm/s Surface currents in Gulf of Oman during Nov-Mar: (a) long term mean currents and (b) current anomalies during La Nina periods. Based on 47-year global ocean reanalysis. Note reversal of currents in Gulf of Oman. Smart Ocean Climatology – Analysis Methods Existing civilian smart climatology data sets allow development of conditional climatologies that account for deviations from long term means. Results from NPS Smart Climatology Program, murphree@nps.edu

14. Surface Radar Detection Ranges Surface Radar Cutoff Frequencies a b (a) Surface radar detection ranges (km) based on NPS smart climatology for September. Values shown are long term means for September,for a C-band radar at 30 ft and detection threshold of 150 dB. (b) Cutoff frequencies (GHz) for surface radar for September. Based on application of existing civilian multi-decadal atmospheric and oceanic reanalyses, and existing sensor performance aids. Smart Ocean Climatology – Analysis and Modeling Methods Smart Climatological Performance Surfaces Use of state-of-the-science data sets, analyses, and modeling can lead to substantial improvements in climate products. Need to develop related ocean climate environmental products, including, for example, smart climatological SLD products, and smart climatological performance surfaces for acoustic detection ranges and cutoff frequency. Results from NPS Smart Climatology Program: NPS thesis research by K. Twigg, Royal Navy. Results from NPS Smart Climatology Program, murphree@nps.edu

15. Forecast Analysis Smart Ocean Climatology – Climate Prediction Short Term Climate Prediction, Iraq Precip, Oct-Dec Analyses of climate scale relationships (left) lead to short term climate predictions (right). Hindcast for Oct-Dec 2002, during moderate El Nino event, shows high (low) probability of above (below) normal precip. Lead time: six weeks, Verifying observed precip was 28% above normal. Conclusion from many such analyses and forecasts: Short term climate forecasts of T and precip in Southwest Asia have useful skill, especially compared to traditional climo. Apply to the ocean the short term climate prediction methods already used successfully for atmosphere. For example, develop smart climatology predictions of SLD, cutoff frequency, acoustic propagation. Results from NPS Smart Climatology Program: NPS thesis research by C. Hanson, Capt, USAF, and S. Moss, Capt, USAF. Smart Climo 1, murphree@nps.edu, Nov07

16. Initial Study of Ocean Climatology for Antisubmarine Warfare • At request of CAPT Jim Berdeguez, developed prototype smart climatology products for ASW in August 2007. • Compared Navy atmospheric and oceanic climatologies to climatologies based on existing civilian atmospheric and oceanic reanalysis data sets. • Assessed value of providing state-of-the-science ocean climatologies for planning and execution of ASW operations. • Made recommendations for how to proceed with providing climatological support for ASW operations. ASW Smart Climo, Aug 07, murphree@nps.edu Acknowledgements: CAPT Jim Berdeguez, CDR Van Gurley, Dennis Krynen, staff of ASW RBC

17. Objectives of Initial Study • Use existing, long term ,civilian, ocean reanalysis data to prepare, analyze, and display long term mean (LTM) surface and subsurface ocean fields. • Assess ability of reanalysis data to represent LTM oceanic patterns and processes important in ASW. • Conduct initial comparisons of oceanic and atmospheric based on existing civilian sector reanalyses and on existing Navy climatologies • Focus region and time: VS07 region, August long term mean conditions • In this initial study, we did not consider climate regimes and • variations, such as high wind stress and low wind stress regimes, El Nino / La Nina, Madden-Julian Oscillation, etc. But a more complete study would do so. Note that the 2007-2008 La Nina was developing, and anomalies characteristic of La Nina events were developing in the VS07 region during VS07 period, August 2007. ASW Smart Climo, Aug 07, murphree@nps.edu

18. Background Definitions and Concepts – Section 1 • Climate– Expected state of the environment based on scientific observations, analyses, theories, and models, and is not limited to just observational analyses. Expected state accounts for long term means and variations from these means that occur over long periods (e.g., anomalous trends and oscillations that occur over weeks, years, or longer). • Smart Climatology– Climatology applied to military uses that employs all relevant tools of modern climatology, such as: • Full suite of in situ and remote observational data sets • Reanalysis • Downscaling • Data access, mining, processing, and visualization tools • Modern statistical and dynamical analysis methods • Long term means and higher order statistics • Climate variations (e.g., regimes, trends, oscillations) • Climate system monitoring • Climate system modeling • Statistical and dynamical climate forecasting • Online, real-time, user-driven, data access, analysis, and display Smart Climatology State-of-the-science basic and applied climatology that directly supports DoD operations ASW Smart Climo, Aug 07, murphree@nps.edu 18

19. Background Definitions and Concepts – Section 2 Reanalysis- The analysis of climate system components using modern analysis processes to analyze past and present states of the climate system. Reanalysis is the same as standard atmospheric or oceanic analysis, except that it applies a consistent set of analysis procedures to all times in the reanalysis period. Oceanic and atmospheric reanalyses yield gridded data sets that are temporally and spatially continuous (i.e., no temporal or spatial gaps). For many reanalyses, the reanalysis period is several decades long, and the reanalysis region is the global ocean or atmosphere. Reanalyses can have relatively high temporal and spatial resolution (e.g., mesoscale reanalyses: hourly and 10 km; global reanalyses: six hourly and one degree). Reanalysis fields are a major source of climate information since they fill in the spatial and temporal gaps in climatologies based strictly on observations. Traditional observational climatologies either leave these gaps unfilled or use statistical methods (e.g., interpolation, correlation) techniques to fill in these gaps. Reanalyses use state-of-the-science data assimilation and oceanic and/or atmospheric models to fill in these gaps, and develop a dynamically balanced description of the climate system. Reanalysis data include many derived fields (momentum, energy, and mass fluxes; sea surface heights; currents; precipitation; soil moisture, etc.) for which direct observations may be difficult to obtain. ASW Smart Climo, Aug 07, murphree@nps.edu 19

20. Background Definitions and Concepts – Section 3 Evaluating Reanalyses - Several organizations have produced and are producing reanalyses of the atmosphere and ocean for use in basic and applied climatology (e.g., NOAA, ECMWF, NASA, AFCCC, NRL, universities). But there can be large differences between reanalyses in terms of their data and methods, temporal and spatial coverage, quality control procedures, verification, and accuracy. The net result is that some reanalyses are much more useful and reliable than others, and some reanalyses should be used with a fair bit of caution. These differences among reanalyses reflect the complexity and high cost of developing an extensive and high quality reanalysis. ASW Smart Climo, Aug 07, murphree@nps.edu 20

21. Background Definitions and Concepts – Section 4 Long Term Mean (LTM)- The mean of many observations collected over a long period of time (the base period). The standard period for calculating a LTM is 30 years. LTMs are commonly based on observations made at the same location and same time of day but at many different dates (e.g., T at 00Z on 01 January of 30 consecutive years). LTMs are common and very useful quantities in climatology. But LTMs do not represent many important temporal and spatial aspects of the climate system, such as climate variation trends and oscillations. These are better represented by other types of means and by higher order statistical quantities (e.g., select composite means, variance, standard deviation, temporal clusters, principal components). ASW Smart Climo, Aug 07, murphree@nps.edu 21

22. Background Definitions and Concepts – Section 5 • Methods for Generating Climatologies • 1. Observations • Average all available observations for a given location and time of year. • Pros: Based strictly on observations, with no influence of statistical or dynamical methods for filling in temporal and spatial data gaps. • Cons: In data sparse regions (e.g., most of the ocean), there can be major data gaps. • 2. Observations with statistically-based filling of gaps • Use all available observations with data gaps filled via statistical methods (e.g., interpolation, correlation). Relies, in effect, on statistical model to fill in gaps. ManyNavy operational climatologiesgenerated using this method. • Pros: Relies on observations alone to develop statistical tools for filling gaps • Cons: Limited by the number of observations and the number of observed variables, especially in data sparse regions. No explicit check for dynamic consistency. • Observations with reanalysis-based filling of gaps • Uses all available observations with data gaps filled by data assimilation and dynamical models. This method used by many state-of-the-artcivilian operational climatologies. Reanalysis climatologies shown in this initial study use this method. • Pros: Data assimilation and dynamical models are well tested and yield dynamically balanced results. • Cons: In data sparse regions, results are strongly dependent on model. ASW Smart Climo, Aug 07, murphree@nps.edu 22

23. Initial Demonstration Case – Section 1 Oceanic Reanalysis Data Set Used: Simple Ocean Data Assimilation (SODA), version 1.4.3: - Spatial coverage: ~global (0-360E, 75.25S-89.25N) - Temporal coverage: 1958-2004, 47 years - Horizontal resolution: variable but ~ 0.3 x 0.3 degrees * - Vertical resolution: 40 levels, from 5 m to 5374 m 10 levels in the upper 100 m 20 levels in the upper 500 m - Observational input variables: surface winds, T, S, SSH, heat and freshwater fluxes - Reanalyzed output variables: x, y, SSH, T, S, u, v - Model based on Parallel Ocean Program numerics and similar to z level models used by multiple researchers - Data analyzed: 528 individual monthly means, Jan58–Dec04* horiz res: 0.5 x 0.5 degrees all levels, but focus on upper 500 m base period for calcualting LTMs: 1968-1996 * See notes section of this slide for more information. ASW Smart Climo, Aug 07, murphree@nps.edu

24. Initial Demonstration Case – Section 2 Atmospheric Reanalysis Data Set Used: NCEP Global Atmospheric Reanalysis: Temporal resolution: six hours; spatial resolution: 2.5 x 2.5 degrees. Available at all standard tropospheric and stratospheric levels. Available from Jan 1948 – present. We worked with data from Jan 1970-present. Provides all standard atmospheric variables, and many less standard ones. Available at menu driven data downlaod, analysis, and display sites provided by NOAA. Note: new NCEP global coupled atmospheric-oceanic reanalysis to be available in FY08-FY09, at 0.25 x 0.25 degree resolution in atmosphere, 0.5 x 0.5 degree resolution in ocean. ASW Smart Climo, Aug 07, murphree@nps.edu

25. Initial Demonstration Case – Section 3 Navy Climatology Data Sets Used: Ocean: Generalized Digital Environmental Model(GDEM), Version 3.0, provided by Dennis Krynen of NAVO. Contains long term mean (LTM) values based on data from many decades but especially most recent decades. Temporal and spatial resolution: one month; 0.25 x 0.25 degrees, at 78 levels from 0-6600 m, and ~global. Variables available: T, S, SVP. Based on observations and statistical methods to fill data gaps. Atmosphere: Surface Marine Gridded Climatology (SMGC) Version 2.0, as provided in the environmental background section of the VS07 page at the Navy Oceanography Portal on the SIPRnet. Spatial resolution: temporal resolution: one month; spatial resolution: 1 x 1 degrees. Based on observations and statistical interpolation methods to fill gaps. Alternate Navy atmospheric climatology for future investigation: Global Marine Climatic Atlas (GMCA), V. 2.0. For further details on SMGC and GMCA, see: https://navy.ncdc.noaa.gov/marine-servlet/marinegridded.html https://navy.ncdc.noaa.gov/private/products/fleet/gmca.html ASW Smart Climo, Aug 07, murphree@nps.edu

26. Initial Demonstration Case – Section 4 VS07 Region Plus: 135E-160E, 5N-20N VS07 Period: August ASW Smart Climo, Aug 07, murphree@nps.edu

27. LTM Surface Wind Stress, August, From Reanalysis August LTM Wind Stress Magnitude and Direction (Base Period 1968-1996) N Pacific High Monsoon Trough E-erly trades SW-erly trades ITCZ N/m2 Note implied relationships between LTM patterns for wind stress and the LTM patterns for, and variability of (and uncertainty in), atmospheric convection, clouds, wind driven mixing, forcing of surface and internal ocean waves, MLD, SLD, BLG, atmospheric EDH, atmospheric EM propagation, etc. There are very large differences between the LTM winds associated with these stresses and the SMGC 2.0 winds provided for VS07 (not shown but available at VS07 page of Navy Oceanography Portal). The reanalysis winds are very similar to those from many prior observational studies, while the SMGC winds for VS07 are quite different. Thus, the reanalysis winds appear to be realistic, while the SMGC winds provided for VS07 are very questionable. From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

28. LTM Precipitation Rate, August, From Reanalysis mm/day Note high precipitation in monsoon trough and ITCZ. Prior studies have shown that precipitation in this region can have a significant impact on upper ocean structure (e.g., formation of low salinity and high temperature layers in upper few meters), which may then impact SVPs, SLDs, and other upper ocean acoustic features. Precipitation over the ocean is not available in any Navy climatologies. From NCEP atmospheric reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

29. LTM Temperature at 5 m, August, From Reanalysis oC • T ranges from low 29s to high 29s. • Warmest water roughly coincident with surface wind convergence zones (monsoon trough, ITCZ). From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

30. LTM Temperature at 4 m, August, from GDEM .0 oC • Comparisons of reanalysis T at 5 m and GDEM T at 4 m show: • GDEM cooler by ~0.5oC. This is a surprisingly large difference, since near-surface T is relatively well observed. Cooler GDEM due to efforts in development of GDEM to accentuate ML? • GDEM has more small scale structure (e.g., bulls eyes, patchy patterns). Result of GDEM • statistical methods used to fill in gaps? ASW Smart Climo, Aug 07, murphree@nps.edu

31. LTM Temperature at 5 m, August, from Reanalysis oC • This slide is the same as slide 29, but with a different color range used to highlight the • major patterns in the reanalysis T at 5 m data. • Compare to GDEM T at 4 m using color range that highlights major patterns (slide 32). From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

32. LTM Temperature at 5 m, August, from GDEM oC • This slide is the same as slide 30, but with a different color range used to highlight the major • patterns in the GDEM T at 4 m data. • Compare to reanalysis T at 5 m using a color range that highlights major patterns (slide 31). • Note the larger amount of small scale structure (e.g., bulls eyes, patchy patterns) in • GDEM than in the reanalysis (slide 31), probably due to the statistical methods used in • GDEM to fill in data gaps. ASW Smart Climo, Aug 07, murphree@nps.edu

33. LTM Temperature at 96 m, August, From Reanalysis oC • Warm water at ~11-18N associated with: • - Deep main thermocline • - W-ward transport of relatively warm water by NECC • T structures strongly associated with meridional shear in wind stress. From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

34. LTM Temperature at 96 m, August, From GDEM oC • Comparisons of T from reanalysis at 96 m and GDEM at 95 m show: • Overall patterns similar. • GDEM cooler than reanalysis, but difference much smaller than near surface. • GDEM has more small scale structure. Result of GDEM statistical methods used to fill in gaps? ASW Smart Climo, Aug 07, murphree@nps.edu

35. LTM Temperature Along 140E, August, From Reanalysis CI = 1.0 oC • Near-surface isothermal layer in upper 10-25 m. • Large meridional variations. • Main thermocline depth: 125-150 m at 5-10N; 150-250 m at 10-15N; 50-100 m at 15-20N. • Thermal structure consistent with velocity structure (see ocean current slides). From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

36. LTM Temperature Along 140E, August, From GDEM CI = 1.0 oC • Comparisons of T from reanalysis and GDEM show: • Overall patterns similar, but reanalysis warmer than GDEM, especially near surface. • GDEM near-surface isothermal layer deeper by ~10-35 m. Due to efforts in development of GDEM to accentuate ML? • GDEM has more fine scale structure. Result of GDEM statistical methods used to fill in gaps? ASW Smart Climo, Aug 07, murphree@nps.edu

37. LTM Temperature Profiles, August, From Reanalyses ASW Smart Climo, Aug 07, murphree@nps.edu 37 From SODA oceanic reanalysis

38. LTM Temperature Profiles, August, From GDEM GDEM up to 0.5oC cooler than reanalyses in upper 30 m, with largest differences closest to surface. This is a surprisingly large difference, since near-surface T is relatively well known. Is this due to efforts in development of GDEM to accentuate ML?This difference would lead to deeper SLD in GDEM than in reanalysis. ASW Smart Climo, Aug 07, murphree@nps.edu ASW Smart Climo, Aug 07, murphree@nps.edu Note: Near surface temperatures observed during VSO7 were, in general, ~0.5oC warmer than the long term mean reanalysis temperatures, and ~0.5-1.0oC warmer than the GDEM temperatures. The warmer waters appears to have been part of the 2007-08 La Nina event that was developing during August 2007.

39. Hypothesized Temperature Profiles, August Schematic T Profiles for Two Hypothesized Regimes, and for LTM of the Two Regimes Hypothesized Regime 1: Occurs during and soon after periods of high insolation and low winds (clear and calm conditions). May be enhanced if immediately preceded by high precipitation (e.g., surface freshening). Leads to shallower SLD. Regime 1 Profile Regime 2 Profile LTM of Regime 1 and 2 Profiles Hypothesized Regime 2: Occurs during and soon after periods of low insolation and high winds (e.g., deep convection with strong winds). May be enhanced if immediately preceded by high insolation, low-moderate winds (e.g., high evaporation, salinification). Leads to deeper SLD. If hypothesized profiles are common occurrences (and they probably are), then: (1) the reanalysis T profiles (shown in prior slide and similar to LTM profile in this slide) are probably realistic LTM profiles; and (2) the GDEM profiles (see prior slide) are probably not realistic LTM profiles, although they may be good depictions of Regime 2 conditions. This hypothesis can be tested using regime-based conditional climatologies developed from atmospheric and oceanic reanalyses (e.g., separate climatologies for high and low wind regimes). If a LTM represents opposing regimes, then two or more conditional climatologies may be more useful than a single LTM climatology. The high temporal resolution of reanalyses greatly facilitates development of such conditional climatologies. ASW Smart Climo, Aug 07, murphree@nps.edu

40. LTM Sea Surface Height and Currents, 0-50 m, August, From Reanalysis Subtropical Gyre NEC NECC • Maximum speeds of about 25 cm/s in NEC, centered at ~11N. • Currents strongly linked to surface winds (speed, direction, and • horizontal shear). • SSH and currents not available from GDEM. 20 cm/s From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

41. LTM Temperature and Currents at 96 m, August, From Reanalysis oC • Note W-ward transport of relatively warm water by NECC. • Maximum speeds of about 20 cm/s in NEC, centered at ~12N. • Currents strongly linked to surface winds and upper ocean T-S structure. • Currents not available from GDEM. From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

42. LTM Zonal Current Along 140E, August, From Reanalysis NEC NECC CI = 5.0 cm/s • NECC max speeds > 25 cm/s at ~50 m. • Velocity structure consistent with thermal structure (see next slide). • Currents not available from GDEM. From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

43. LTM Temperature Along 140E, August, From Reanalysis CI = 1.0 oC • Note dynamic correspondence between thermal structure in this • slide and velocity structure in prior slide. From SODA oceanic reanalysis ASW Smart Climo, Aug 07, murphree@nps.edu

44. Preliminary Findings: Smart Climatology and VS07 • Overall T and S patterns in oceanic climatologies based on existing civilian reanalyses are similar to those in Navy climatologies. • But there are some surprisingly large differences in near-surface T magnitudes (GDEM cooler) that may be due to efforts during development of GDEM to accentuate mixed layer (e.g., avoid rounded off upper ocean T profiles). • GDEM has considerable small scale structure (e.g., bulls eyes, patchy patterns) that may be an artifact of the statistical processes used to fill in data gaps. • Some Navy marine atmospheric climatologies provide very poor representations of well known features of the lower tropospheric circulation (e.g., monsoon trough) that are important in atmospheric forcing of upper ocean. • Overall accuracy of climatologies based on existing civilian reanalyses appears to be equal to or greater than that of Navy climatologies.* • A complete comparative assessment is difficult because Navy climatologies do not provide a number of important variables that are available in reanalyses (e.g., SSH, currents, precipitation, estimates of deep convection). * See notes section of this slide for more details. ASW Smart Climo, Aug 07, murphree@nps.edu

45. Preliminary Findings: Smart Climatology and VS07 • Comparisons highlight inherent advantages of reanalysis-based atmospheric and oceanic climatologies over Navy climatologies, including: • Much higher temporal resolution • Spatial resolution that is equal to or greater than Navy climatologies • More variables (in some cases, many more) than Navy climatologies • Ability to explicitly account for complex dynamical relationships (e.g., interactions of clouds, radiation, winds, and surface heat fluxes) • Much greater functionality in conducting operational climate analysis and forecasting. Example: ability to develop conditional climatologies (e.g., upper ocean climatology for high and low wind stress regimes) • Informal comparisons of reanalysis-based long term mean (LTM) climatological fields with VS07 observations (not shown) were favorable, and indicate that the major features of the observed atmosphere and ocean were well represented by the reanalyses. ASW Smart Climo, Aug 07, murphree@nps.edu

46. Preliminary Findings: Smart Climatology and VS07 • VS07 region in August has complex atmosphere-ocean dynamics. Many aspects of these dynamics are evident in reanalysis-based climatologies, but are missing in Navy climatologies. Examples: (a) strong wind stress convergence and curl, deep convection, and high spatial / temporal variability in the monsoon trough; (b) high spatial / temporal variability in MLD due to variability in atmospheric forcing of upper ocean; (c) NEC advection, shear, and eddies. • During VS07, there were several situations in which improved atmospheric and oceanic information, especially climate information, for the VS07 region would have aided ocean analysts and forecasters. Examples: • Improved information on climatology of: • Monsoon trough • Deep atmospheric convection and associated air-sea fluxes • On-going climate variations (e.g., emerging La Nina) • Improved climatology-based tools for evaluating in situ observations and model output • Improved access to and understanding of the use of satellite imagery in analyzing atmospheric forcing of ocean • Improved assessments of the accuracy of the atmospheric model output used to force ocean models ASW Smart Climo, Aug 07, murphree@nps.edu

47. Preliminary Findings: Smart Climatology and VS07 • VS07 strongly reinforced the concept: • Getting the atmosphere right is critical in getting the ocean right. • This is especially true for ASW, for which atmosphere-ocean interactions play a large role in shaping the upper ocean environment. • VS07 also reinforced a basic concept in analysis and forecasting: • To get the small scale right, you first have to get the large scale right. • This is because the mesoscale and smaller scale ocean features of importance in ASW are strongly related to larger scale and lower frequency variations of the atmosphere-ocean climate system (e.g., shifts in location and intensity of monsoon trough; anomalies in subtropical high and trade wind forcing, anomalies in SSH and currents; etc.). • Lessons learned from VS07 have wide application, especially to other areas with high variability. Examples: (a) east China seas regions with high temporal and spatial variability in atmospheric forcing throughout the year; (b) RIMPAC region with high spatial variability in wind stress. Conditional climatologies are well suited for describing this variability from a climatological perspective. Development of conditional climatologies is quite feasible using reanalyses but not with Navy climatologies. ASW Smart Climo, Aug 07, murphree@nps.edu

48. Preliminary Findings: Smart Climatology and VS07 • Reanalysis based climatologies, and related smart climatology data sets and methods, have the potential to greatly improve the Navy’s: • Access to state-of-the-science climate information • Quality control of observational data (e.g., BT data) • Development of ICs and BCs for ocean models • Development of standards for assessing skill of short range ocean models • Development of baselinestoward which to nudge ocean analyses and forecast models • Development of support products in data sparse regions • Development of long lead ocean forecasting systems • Contributions to war gaming and other simulations • Development of on the shelf, ready-to-go guidance to METOC personnel and their customers • Identification of characteristic climate regimes, and temporal and spatial patterns of variability • Education and training of METOC personnel • Environmental situational awareness of METOC personnel and their customers ASW Smart Climo, Aug 07, murphree@nps.edu

49. Preliminary Findings: Smart Climatology and VS07 • We recommend that the Navy focus on exploiting existing and forthcoming civilian atmospheric and oceanic reanalyses, rather than attempting to develop independent Navy reanalyses that largely duplicate existing civilian products. Developing accurate reanalyses is a complex and expensive process. Very useful civilian reanalyses are available at little or no cost, and are ready for application to Navy issues. One application to consider is using civilian reanalyses to derive Navy-relevant variables (e.g., use civilian oceanic reanalyses to derive corresponding acoustic variables; use civilian atmospheric and oceanic reanalyses to derive surface wave variables; etc.). • By exploiting existing civilian reanalyses, and those that will be released in the next several years, we can rapidly move Navy climatology forward. Focusing instead on developing new and duplicative Navy reanalyses would not allow such rapid progress, and would cost much more than applying existing and forthcoming civilian reanalyses. The benefits of including classified data in the development of new Navy reanalyses are likely to be small and heavily outweighed by high development costs and long development times. ASW Smart Climo, Aug 07, murphree@nps.edu

50. ASW Uses of Climatology – Present Approach In Situ Data LTM Climatology Dynamic MODAS From Feldmeier (2005), adapted from Fox et al. (2002). Ocean temperature, depth vs. latitude cross sections Overview of process presently used at NAVO: Assimilate near real time observations (remotely sensed and in situ). ASW Smart Climo, Aug 07, murphree@nps.edu