1. Sandra L. Castro Candidacy for Promotion to Associate Research Professor ASEN Research Seminar, November 30 th 2012

4.  Motivation  Spatial variability in SSTs at different depths in the water column  Variability in SST due to the presence of the near-surface diurnal thermocline (different from the seasonal thermocline)  Currently funded projects

5.  Motivation  Spatial variability in SSTs at different depths in the water column  Variability in SST due to the presence of the near-surface diurnal thermocline (different from the seasonal thermocline)  Currently funded projects

6. Q net  ( -  x  z )  This skin layer is generally present, and is ~ 10 µm thick.  It is usually cooler than the water below by ~ 0.3 K

7. IR Radiom Buoys NightDay

8. 1mm - 5 m -1 m What do radiometers measure and why the near-surface thermal structure of the ocean matters 10μm

9. VOS or SOO drifting or moored buoy research vessel Polar-orbiting infrared radiometer Polar-orbiting microwave radiometer Platforms for Measuring SST Geostationary orbit Infrared radiometer Argo Floats

10. Processes Affecting SST Measurement Uncertainties Sensor calibration Atmospheric correction Aerosols Cloud detection Water vapor Precipitation Skin-bulk model  T Skin + (1-  )T Sky 10 μm 5 m ? Cloud Processes Detector, transducer, amplifier, digitiser Surface emissivity effects Scattering & absorption by stratospheric dust Absorption by Water vapor, etc. Thermal microlayer Diurnal thermocline SST =c0 + Σcj Tb TbTb Digital signal, SST TOA radiance (brightness temperatures), T b Bulk temperature, T bulk Water-leaving radiance Skin temperature, T S Temperature Measure TSTS T bulk Diurnal Warming model T skin T bulk 5 cm

11. http://www.sstscienceteam.org/white_paper.html  NASA SST Science Team developed White Paper on contributions to the SST uncertainty budget  Diurnal and spatial variability identified as most critical physical processes contributing to the high resolution upper ocean variability

12.  Motivation  Spatial variability in SSTs at different depths in the water column  Variability in SST due to the presence of the near-surface diurnal thermocline (different from the seasonal thermocline)  Currently funded projects

14. 14 What do users require? –T bulk for thermal capacity, deep convection and for existing bulk flux parameterisations. –T S for air-sea interaction processes, better for fluxes In situ measurements: T bulk is conventionally observed –But at what depth? Strictly we should record T z and z. –May be compromised by  T diurnal Shipborne radiometry with sky correction can measure T skin Satellite observations –All IR satellites “see” only T Skin –T S is precisely defined at the surface. –T S atmospheric algorithms are fundamentally-based Independent of in situ calibration Require in situ T S for validation only –T bulk algorithms have hybrid function Sensitive to definition of T bulk Near-surface SST gradients introduce uncertainties in the SST error budget  Tcool represented as a globally applied bias correction Need Physical models of  Tdiurnal Require in situ calibration (buoy network)

15.  Since IR satellite retrievals sensitive to the skin temperature, it was believed improvements in regression algorithm accuracy possible through use of in situ skin measurements

18.  We evaluated parallel skin and subsurface MCSST-type models using coincident in situ skin and SST-at-depth measurements from research-quality ship data.  RMS accuracy of skin and SST-at-depth regression equations directly compared

19. ROYAL CARIBBEAN EXPLORER OF THE SEAS SKIN SST: M-AERI INTERFEROMETER (RSMAS) SST-AT-DEPTH: SEA-BIRD THERMOMETER (SBE-38) @ 2M NOAA R/V RONALD H. BROWN SKIN SST: CIRIMS RADIOMETER (APL) SST-AT-DEPTH: SEA-BIRD THERMOMETER (SBE-39) @ 2M Satellite IR: 2003-2005 AVHRR/N17 GAC and LAC NAVOCEANO BTs

20. Collocation window: 25 km and 4 hours All Mean Min Time

22. RMSE differences equivalent to removing an independent error in the bulk SST measurements % Accuracy Improvement

27.  The issue of the Point-to-Pixel Sampling Characteristics of Remotely Sensed SSTs  Satellite sensors see an “average” value of the radiation emanating from the footprint, whereas in situ instruments measure the emission at single points on the ground  Sub-pixel variability long acknowledged as source of uncertainty in satellite validation, but magnitude largely unquantified

28.  To test this hypothesis, we added increasing levels of noise to the SST-at- depth, such that: RMSE bulk SST = RMSE skin SST  Attempt to decompose the supplemental noise into individual contributions from the two effects using Variogram techniques.

29. Added discrete realizations of white noise processes to SST-at-depth: Note that, for equal number of observations: RMSE SST-at-depth < RMSE SST skin, but RMSE SST skin < RMSE SST emulated buoy Build RMSE curve for noise-degraded SST- at-depth. Where the RMSE (skin) intercepts the curve, corresponds to the supplemental noise needed for equivalence in RMSE

30. Added Noise:  LAC SST: σ ~ O ( 0.09 – 0.14 K )  GAC SST: σ ~ O ( 0.14 – 0.17 K )

31. Supplemental Noise: σ ~ O ( 0.1 – 0.2 K )

32.  From literature: M-AERI: 0.079°C and CIRIMS: 0.081°C  IR Radiometers: σ~ O(0.08°K)  Thermometers: σ~ O(0.01 K)  Added Noise: σ~ O(0.08°K)  From the data: Empirical distributions for the measurement uncertainty of M-AERI skin SST and coincident SST-at-depth support the required supplemental noise obtained from values reported in the literature!!

33. M-AERI CIRIMS The variogram (Cressie, 1993, Kent et al., 1999) is a means by which it is possible to isolate the individual contributions from the 2 sources of variability, since the behavior at the origin yields an estimate of the measurement error variance, while the slope gives an indication of the changes in natural variability with separation distance.

34. We fitted a linear variogram model by weighted least squares to both skin SST and SST-at-depth with separation distances up to 200 km, and extrapolated to the origin to obtain the variance at zero lag. M-AERI Measurement Uncertainty Spatial Variability

35. MethodVariogramGraphic Error Contribution Measurement Uncertainty Spatial Variability Total VariabilitySupplemental Noise M-AERI0.100.070.120.15 CIRIMS0.07 0.100.23 C & M0.120.100.160.17  Variogram estimates provide strong support to the notion that the combined role of differences in measurement uncertainty and spatial variability between the skin and SST-at-depth account for the range of required subsurface supplemental noise found graphically  Measurement uncertainty estimates are consistent with the noise required to reconcile the accuracy differences between thermometers and IR radiometers (σ~O(0.08 K)) On spatial scales of O(25 km):

36. MethodVariogramGraphic Error Contribution Measurement Uncertainty Spatial Variability Total VariabilitySupplemental Noise M-AERI0.100.070.120.15 CIRIMS0.07 0.100.23 C & M0.120.100.160.17  Agreement between Variogram estimates and supplemental noise levels provide strong support to the notion that the combined role of differences in measurement uncertainty and spatial variability between the skin and SST-at-depth are responsible for the lack of accuracy improvement in skin-only regressions  Measurement uncertainty estimates are consistent with the noise required to reconcile the accuracy differences between thermometers and IR radiometers (σ~O(0.08 K))  Measurement uncertainty and spatial variability contribute in equal measure to the overall uncertainty budget On spatial scales of O(25 km):

38.  The role of spatial variability in the uncertainty budget arises in part because inadequacies in point-to-pixel comparisons  Sparse radiometric sampling along a single track does not provide full coverage of the spatial variability within the satellite footprint.  The satellite measurement is a spatial average over the IFOV. This integration might be smoothing out the enhanced variability of the skin, making the variability across the pixel more representative of the less variable point measurements of the SST-at-depths  To better understand and quantify these effects, we require increased observations of sub-pixel satellite SST variability. In particular, direct observations of spatial variability as a function of measurement depth are needed.

39.  Motivation  Vertical variability in SST associated with the near-surface thermal structure of the ocean  Variability in SST associated with the presence of the diurnal thermocline  Current funded projects

40. 40 Emphasis on synergy benefits of multi-sensor SST products In principle, the merging and analysis of complementary satellite and in situ measurements can deliver SST products with enhanced spatial and temporal coverage Many analyses currently available, but most ignore daytime obs

41. Skin SST Foundation SST  Predawn SST  Previous night composite  SST observations at winds greater than 6 m/s Common definitions:

42.  In recent years, improvements in the accuracy and sampling of geostationary satellites have enabled better characterization of diurnal warming from space  Average difference between MTSAT(skin) and RAMSSA (fnd) for the 1 degree box around 151.5  E, 0.5  S on 1 Jan 2009

43. Monthly climatologies of maximum diurnal warming observed from Geostationary MSG/Seviri SSTs for February and June Previous night composite used as Foundation SST Amplitudes are small on average but can be significant at low winds

44. ΔT diurnal Average effect on fluxes Clayson and Bogdanoff (2012) Flux w/ diurnal correction – Flux w/o diurnal correction (W/m2)

45. Maximum effect on fluxes (1998 - 2007) Clayson and Bogdanoff (2012)

47. APPROACH Detailed Physical Models Wick’s Modified Kantha-Clayson GOTM COARE Parametric Models Castro LUT CG03 HR Forcing from Cruises HR Forcing from NWP and Satellite data

48.  Wick Modified Kantha-Clayson  Second moment turbulence closure model with enhanced treatment of mixing near the surface  Run with 1-minute resolution and fine vertical grid  Generalized Ocean Turbulence Model (GOTM)  Used 2 different included turbulence schemes  K-epsilon  Mellor-Yamada  Run with 1-minute resolution on same vertical grid  COARE  Warm-layer and cool skin portions of the COARE 3.0 model with included flux computations  Forced with temporal resolution of available forcing data  Solar Penetration Models  3-band absorption model from COARE (Fairall et al., 1996)  9-band absorption model from Paulson and Simpson (1981) warming Wick and Castro

49. NOAA/ESRL Cruises (courtesy C. Fairall)  NOAA R/V R. H. Brown cruises from 1992-2000  Detailed eddy covariance flux measurements  SST-at-depth from the Sea Snake (5-50 cm) CIRIMS Cruises (Courtesy A. Jessup)  NOAA R/V R. H. Brown cruises from 2003-2005  HR Skin SSTs from the CIRIMS  Through-the-Hull SST-at-depth (2-3 m) Over 300 diurnal warming events!

50. Models demonstrate ability to reproduce observed warming, but their relative performance varies notably with environmental conditions Wick and Castro Skin ValidationSub-skin Validation

51. Mean bias can be reduced to small levels but RMS differences of O(1K) remain even when the models are run with high resolution forcing fields Wick and Castro Modified Kantha-Clayson COARE GOTM

52.  1D Models are computationally expensive  Require high resolution forcing fields to produce reliable predictions  Not currently practical to implement in an operational framework

53.  Easily integrated into operational applications and revised  Can be tuned for specific regions  Look-up tables, if trained with actual observations of diurnal warming, can incorporate incomplete or unknown functional dependencies

55. Used detailed 2 nd moment closure turbulent mixed layer model based on Kantha and Clayson (1994) with added skin layer (Wick, 1995) – Wick’s Modified KC Model Forced with research cruise observations from the NOAA R/V Ronald H. Brown: ▪Eddy covariance flux measurements from the NOAA/ESRL flux campaigns (C. Fairall) ▪SST Observations from the Calibrated Infrared In Situ Measurement System (CIRIMS) radiometer (A. Jessup)

57. Fine Resolution LUT Coarse Resolution LUT

58. LUT Accuracy wrt 1D Model LUT Accuracy wrt Cruise Obs  LUT approach has minimal bias wrt to model from which they were derived and RMS up to ~ 0.6 ºC at peak  LUT have biases wrt to observations from which they were derived of ~ 0.2 ºC in the morning hours leading to peak and ~ -0.2 ºC after the peak. RMS varies from ~ 0.4 ºC at nighttime to ~ 1.0 ºC for daytime.

59.  ACCESS-R NWP Hourly forecasts of winds and fluxes at 0.375° res  MTSAT-1R Hourly Skin SSTs at 0.05° res  RAMSSA Skin and RAMSSA SSTfnd at 1/12° res Instantaneous WindIntegrated WindInsolation Peak Insolation

60. HourBiasSt DevHourBiasSt Dev 22-0.1010.4568-0.3490.761 23-0.1910.4459-0.2930.681 0-0.2470.44910-0.2430.699 1-0.2750.44211-0.1930.658 2-0.2890.45612-0.2380.712 3-0.2470.46013-0.1740.673 4-0.1740.46414-0.1510.704 5-0.0150.46315-0.1720.720 16-0.2270.791 17-0.3800.925 MTSAT Accuracy evaluation wrt drifting buoys

61. Distribution of winds < 3 m/s  Not all low winds resulted in DW events, probably due to clouds  Coastal events on the western coast not captured by low wind probability distribution  Other factors: air-sea temperature differences? Joint distribution of winds 0 in MTSAT

63. MTSAT-1R Observed DW CG03 ACCESS-R NWP Wind 1D Wick MODKC LUT Integ Wind, Integ Qs 3D Coupled Model HR CLAM

64. Results stratified by wind speed

65. Results stratified by diurnal warming amplitude

66.  ALADIN NWP Hourly forecasts of winds and fluxes at 0.1° res  SEVIRI Hourly Skin SSTs at 0.1° res Instantaneous Wind SST Analysis Insolation Peak Insolation

67. Seviri Δt diurnal ObsWick MODKC Δt diurnal COARE Δt diurnal Instant Wnd-Instant QsInstant Wnd-Integrated QsIntegrated Wnd-Integrated Qs LUT Δt diurnal

68.  Extreme diurnal warming events are not uncommon  Current models do have skill in predicting diurnal warming in an average sense  Different approaches best capture different aspects of observed diurnal warming  At low wind speeds random errors ~O(1 K)  A limiting factor is availability of time history of forcing data (fluxes, winds) from satellites

69.  For NWP-derived forcing data, simplified DW models produce as good or better results than more detailed approaches  More model improvements needed before we can use them obtained improve SST analyses by explicitly accounting for diurnal warming

70.  Near-surface SST variability one of biggest issues in constructing multi-sensor SST analyses  Variations in near-surface spatial variability impacts validation and interpretation of IR SST products  Greater spatial variability in the skin layer  IR spatial averages more representative of point subsurface measurements than skin  Diurnal variability potentially a significant source of uncertainty in referencing observations to a common time  Models work well in the mean but random errors approach 1 K

71.  Motivation  Vertical variability in SST associated with the near-surface thermal structure of the ocean  Variability in SST associated with the presence of the diurnal thermocline  Currently funded projects

72.  Evaluation of Diurnal Warming models in conjunction with the Australian BoM and NOAA NESDIS for application to their different operational SST products

74. NASA PO: Climatologies of ΔTdiurnal

75.  Demonstration of UAS capabilities to monitor Arctic Ice/SST conditions  Data Coordinator for the project  SST Spatial variability studies from airborne IR Radiometers flying at different elevations  Evaluation of SST analyses at high latitudes