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Diurnal Warming and Associated Uncertainties

Diurnal Warming and Associated Uncertainties. Gary A. Wick NOAA ESRL/PSD New Chair, GHRSST DVWG. Outline. The diurnal warming problem Recent results and research directions Specific results related to uncertainties in physical modeling of diurnal warming. What is the Problem?.

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Diurnal Warming and Associated Uncertainties

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  1. Diurnal Warming and Associated Uncertainties Gary A. Wick NOAA ESRL/PSD New Chair, GHRSST DVWG

  2. Outline • The diurnal warming problem • Recent results and research directions • Specific results related to uncertainties in physical modeling of diurnal warming Wick et al.

  3. What is the Problem? • SST varies with time and depth • Need to account for DW to: • Reference an SST value to another time • Combination of observations from different times of the day • Reference values to a different depth • Construction of foundation analyses • Regression against observations at depth Wick et al.

  4. Why the Difficulty? • Warming is a complicated function of multiple parameters • Models still uncertain • Not all parameters easily measured from space • Need complete time history of forcing parameters • Sampling of available parameters is not continuous • All parameters subject to measurement uncertainties • Observations/validation still insufficient Wick et al.

  5. A Fundamental Question Can we estimate diurnal warming with enough skill to improve SST products? • To what degree is added complexity desired or justified? • Physical models vs. empirical parameterizations • At what point is the data insufficient? • Requires a detailed understanding of uncertainties Wick et al.

  6. Progress to Date Work within the GHRSST DVWG and independent research has led to: • Improved models • Improved understanding and characterization of DW events • Improved resources for evaluation of models • Aladin • Tropical Warm Pool+ • Initial diurnal warming analyses Wick et al.

  7. POSH • Profiles of Surface Heating (POSH) • F96 • Absorption • Reduce accumulated heat/momentum • Structured profiles of temperature within the warm layer (CG empirical or Kantha/Clayson (WICK) profiles)

  8. Dimensionless DW profile NonDim Heat Content NonDim Depth (z)

  9. Comparison throughout the day

  10. DW Diurnal warming 2004-2010 EXPERIMENTAL PRODUCTS Available on request from 2004

  11. In 2011 Hourly DW available in real time On the zone herewith See LeBorgne et al 2010 Proceedings EUMETSAT conference, Cordoba • Questions: • Hourly values every hour? Which delay • Or daily files with 24 fields?

  12. DVWG Priorities • Provision of diurnal warming analyses • Guidance on recommended/consensus approaches • Improved estimates of uncertainties in diurnal warming products Wick et al.

  13. Joint DVWG, HL-TAG, ST-VAL Workshop • When • 28th February to 2nd March 2011 • Where • University of Colorado, Boulder, USA • Why • To allow more time to address the key scientific issues • To recognise the high degree of commonality between each group

  14. Outline format • Three main topics • High latitude SST estimation • Retrieval, cloud masking, sea-ice analyses, both Arctic and Antarctic regions • Diurnal variability observation & analyses • Arctic, SEVIRI, POSH, shallow water, TWP+ • SST uncertainty characterisation & SSES • Uncertainty budgets, next generation radiometers, Argo, SSES • Main plenary talks (20 min), with open discussion (5 min highlight talks) and working breakout sessions • Open mainly to DVWG, HL-TAG and ST-VAL groups • 16 people confirmed with another 14 likely to attend • Some oral slots not yet taken

  15. For more information • Contact • Gary Corlett (gkc1@le.ac.uk), Gary Wick (Gary.A.Wick@noaa.gov) or Jacob Hoeyer (jlh@dmi.dk) • On the web • http://www.ghrsst.org/Joint-DVWG,-HL-TAG-and-ST-VAL-Workshop-2011.html

  16. Intercomparison of the Uncertainty in Diurnal Warming Estimates from Physical Mixed Layer Models G. A. Wick1 and S. L. Castro2 with C. Merchant, A. Harris, C.A. Clayson, C. Gentemann, and Y. Kawai 1NOAA ESRL 2CCAR, Univ. of Colorado

  17. Context • GHRSST Diurnal Variability Working Group • Providing recommended approaches to estimating the amount of diurnal warming present in satellite observations • Sub-effort to compare the ability of existing models to reproduce observations of diurnal warming • Constructing the SST Error Budget • What is the contribution of diurnal warming to the uncertainty of satellite SST products • Primarily relevant to SST analyses Wick et al.

  18. Approach • Diurnal warming models tested for both idealized and real forcing • Models initially considered • COARE warm layer model • Modified Kantha-Clayson • POSH • Generalized Ocean Turbulence Model (GOTM) – k-epsilon approach • Tested models with common solar penetration model • Used common skin layer treatment • Used common vertical grid • Fluxes computed with COARE model and used in other models Wick et al.

  19. Approach • Diurnal warming models tested for both idealized and real forcing • Models initially considered • COARE warm layer model • Modified Kantha-Clayson • POSH • Generalized Ocean Turbulence Model (GOTM) – k-epsilon approach • Tested models with common solar penetration model • Used common skin layer treatment • Used common vertical grid • Fluxes computed with COARE model and used in other models Wick et al.

  20. Idealized Forcing • Goal to compare behavior at low wind speeds and assess sensitivity to factors including solar penetration model and environmental conditions • Constant wind speed from 0.5 – 10 m/s • Peak insolation from 50 – 1000 W/m2 • Conditions representative of tropics, mid-latitudes, and high-latitudes • Models run for 5 days Wick et al.

  21. Model Simulations • Tropical conditions with u = 3 m/s, Qspeak = 800 W/m2 Wick et al.

  22. Modeled Warming at the Skin • Tropical conditions • Results shown at 13:30 on day 3 of simulation • Warming computed relative to 25-m depth COARE Modified Kantha-Clayson Wick et al.

  23. Modeled Warming at 1-m Depth COARE Modified Kantha-Clayson • Tropical conditions • Results shown at 13:30 on day 3 of simulation • Warming computed relative to 25-m depth Wick et al.

  24. Sensitivity to Other Fluxes Tropics Mid-latitudes Results shown for Modified Kantha-Clayson model Wick et al.

  25. Sensitivity to Other Fluxes Mid-latitudes – Tropics Results shown for Modified Kantha-Clayson model Wick et al.

  26. Sensitivity to Solar Absorption 3-Band from Fairall 9-Band Modified Kantha-Clayson model, tropical conditions Wick et al.

  27. Sensitivity to Solar Absorption 3-Band – 9-Band Modified Kantha-Clayson model, tropical conditions Wick et al.

  28. Real Forcing • Goal to evaluate absolute accuracy relative to observed warming and sensitivity to temporal resolution of forcing parameters • ETL cruise database from the R/V Ronald H. Brown • Validation against sea-snake near-surface temperature • Data courtesy C. Fairall • R/V Ronald H. Brown cruises with the CIRIMS • Validation against skin temperature observations from the CIRIMS • CIRIMS data courtesy A. Jessup • Models forced with continuous meteorological observations from the ship Wick et al.

  29. Real Simulations • Example of visual comparison shown here • Models ability to reproduce observations varies notably with conditions Skin Validation Wick et al.

  30. Absolute Accuracy • Models run for entire set of cruises and differences relative to observations binned as a function of local solar time • Results shown for wind speeds less than 4 m/s • Mean bias can be reduced to small levels but RMS differences of O(1K) remain even with full forcing data Modified Kantha-Clayson COARE Wick et al.

  31. Impact of Forcing Resolution • Simulations re-run for Modified Kantha-Clayson model and forcing data sampled at 6-hour intervals • Significant degradation observed in both bias and RMS relative to observations Full Resolution Interpolated 6-Hourly Data Wick et al.

  32. Conclusions • Modeled diurnal warming highly variable at lowest wind speeds • Significant sensitivity to solar penetration model • Warming also exhibits some sensitivity to background fluxes • Bias in warming predictions can be largely removed with tuned models • RMS uncertainty in predicted warming on O(1K) at low wind speeds Wick et al.

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