1. Diurnal Variability in Coastal Shallow Waters Xiaofang ‘Bonnie’ Zhu, Peter Minnett Feb 28, 2011 Boulder CO

2. Motivation 1.Validate satellite SST using in-situ data 2.Merge satellite data taken at different times of day 3.Improve coral bleaching prediction 1.Validate satellite SST using in-situ data 2.Merge satellite data taken at different times of day 3.Improve coral bleaching prediction Coral study findings: 1.Very high temperature even for a short period of time could be lethal (Dunn et al 2004). 2.Diurnal warming peak coupled with seasonal high temperature might be important (Fitt et al 2001). Coral study findings: 1.Very high temperature even for a short period of time could be lethal (Dunn et al 2004). 2.Diurnal warming peak coupled with seasonal high temperature might be important (Fitt et al 2001). Current Coral Bleaching Warning System HOTSPOT DEGREE HEATING WEEK

3. Content 1.Determine the amplitude, timing of diurnal variability in coastal shallow water (<20m) using in-situ data 2.Understand the relationship between DW and environmental forcing using in-situ data 3.Test the applicability of previous empirical DW models in coastal areas 4. Test a 1-D layered diffusion model (Jacobs et al 2008)

4. 1.Amplitude, timing and vertical structure of diurnal variability in coastal shallow water (<20m): In-situ Dataset Hourly T Profile Data: NOAA/ICON ( Integrated Coral Observing Network) StationsPuerto Rico (LPPR1) Bahamas (CMRC3) St Croix, USVI (SRVI2) Little Cayman Cayman Island (LCIY2) Water depth6.5m CTD installation depth deep:5m Shallow:1m Deep:5m Shallow:2.3 m Deep:5.3m Shallow:1.7m Deep:4.6m New logger depth 0.5mN/A 0.2m,0.5m, 0.8m, 6.3m

5. 30 min Resolution Bottom T Measurement : GBR (Great Barrier Reef) 85 stations in total usually 2-3 stations of different types at one coral location Note all measurements are taken at the water bottom Station types Reef Flat (FL) Reef Slope (SL) Deep Reef Slope (DSL) Water depth 0-0.5m5 - 9 mAbout 20 m. 7 Automatic Weather  Station 85 Coral Bottom  T measurement

6. DW Amplitude and timing: ICON stations Shallow: 0.65±0.21K, peak 15.3±1.5LT Deep: 0.41±0.14K, peak 18±2.3LT Shallow: 0.44±0.20K, peak 16.7±1.9LT Deep: 0.31±0.14K, peak 16.3±1.9LT Deep: 0.27±0.13K, peak 16±2.6LT Maximum tidal currents of over 2m/s, DW not dominant

7. DW Amplitude :GBR stations Station types24 Reef Flat stations 24 Reef Slope Stations 5 Deep Reef Slope Stations Water depth0-0.5m5 - 9 mAbout 20 m. For January 2003 (summer) Note all measurements are taken at the water bottom

8. Note, not all coastal dataset have obvious daily warming Strong current case Strong upwelling case

9. Relate daily max/mean T with daily max/mean forcing ICON data LPPR_1LPPR_5SRVI_1.7SRVI_5.3LCIY_4.6 Qmax 0.380.290.380.120.19 AvgWind 0.11-0.05-0.040.480.05 AvgRain -0.07-0.010.150.08-0.07 Correlation with Qmax makes sense. Correlation with wind makes sense as well? PathFinder data SRVI T at 1.7m depth WHY? Gentemann et al 2003 2. Analysis of DW and environmental analysis: ICON station

10. The complex influence of wind on sub-surface T Wind  from 0 to intermediate, subsurface T  Wind  from intermediate to max, subsurface T  The wind W, depends on measuring depth of T SkinDeEP Profile, Gentemann et al 2009 W SurfaceSubsurface>1m Insolation  SST  Subsurface T  Wind Speed  SST  Latent heat loss+ heat transport downward ? Function(wind speed, depth) Gentemann’s findingOur findings

11. As a result, previous empirical models of SST and shallow subsurface T will never apply easily to our in-situ data Webster, Clayson & Curry 1996 SST Kawai &Kawamura 2002 SST and 1m T K&K 2002 Applicability of previous empirical model to ICON data

12. Turbulence diff. fluxes within layers Test a simple layered diffusion model (Jacobs et al 2008) Diurnal temperature fluctuations in an artificial small shallow water body. Jacobs et al. Int J Biometeorol (2008) 52:271–280 the author used an empirical relationship between Kw and depth found in lakes ( Henderson-Sellers 1984) with the stratification correction suggested. Kw is a function of wind speed, stratification and depth. Surface layer The ith layer, including bottom

13. Preliminary test of layered model Choose a simulation day (June 02 at ICON station SRVI2). Water depth 6.5m, time step 60s, Vertical layer number 20, assuming no bottom fluxes input, top layer temperature are used as surface temperature Water depth 6.5m, time step 60s, Vertical layer number 20, assuming no bottom fluxes input, top layer temperature are used as surface temperature Solar absorption: 9 band model ( Gentemann et al 2009) No bottom fluxes and reflection implemented at this stage

14. Measured data at SRVI2 Simulation Results Over predict the amplitude Did not lose heat fast enough Temperature time series does not repeat the shape of solar radiation Possible changes: 1.Add in bottom heat flux 2.Increase diffusivity 3.Add in bottom reflection, thus reduce stratification

15. Summary DW exist in coastal areas where upwelling/downwelling and tidal currents are not dominant. The amplitude can be significant even at the water bottom (2 degree at 5m depth) Wind influences subsurface T in a complex manner, thus the empirical diurnal amplitude model can not applied to our in- situ data directly A one-dimensional simple model can provide some insights into the warming evolution in shallow water.

16. Data In-situ T + envt. data ICON (T profile) GBR (bottom T) coastal MODIS/MTSAT/SEV ERI SST + satellite wind/insolation/de pth data Coastal SST optimization? Envt.data source? Model 1-D physical based model POSH model adapted to shallow water Empirical model (based on open ocean ones) Application Bleaching prediction refinement DHW+daily DW vs. DHW (dataset?) Coastal SST data merging Work in progress Future work, need input

17. Acknowledgements I would like to thank the following people for providing valuable in-situ data, as well as collaborating with the effort for extra T logger installation. Jim Hendee, Mike Jankulak, Lew Gramer (NOAA/AOML) Ray Berkelmans (Australia BOM/ AIMS) Miguel Izaguirre, Adam Chambers (UM/RSMAS) Carrie Manfrino (CCMI), Wessley Merten (U.Puerto Rico) for suggestions on modeling and satellite SST data source Chelle Gentemann (RSS) Andy Harris, Jon Mittaz （ NOAA/NESDIS/STAR ） NASA funding

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19. Temperature T seriesDaytime T seriesAfter dawn T increaseHourly T increaseVertical T difference Forcing Environmental dataCalculated fluxes T_LPPR_1T_ LPPR_5T_SRVI_1.7T_SRVI_5.3T_CMRC_2. 3 T_CMRC_5T_LCIY_4.6 Wind 0.100.07-0.12 -0.14 -0.41 Insolation 0.120.050.080.070.080.070.12 AirT 0.830.810.720.710.89 0.62 Relative Humidity 0.050.080.010.02N/A -0.08 Rain Amt 0.240.250.110.12N/A -0.10 Tidal Height 0.170.200.12 0.52 -0.02  Can reveal some but not all of the important forcing.  Apparently location specific  Few opportunities to use such empirical formula due to lack of high-resolution forcing data Example: ICON station Hourly correlation? USEFUL?

20. Reef Specific Temperature pattern

21. Dominant Frequency in T Sea Breeze Phenomenon(LPPR1 as an example)

22. shallow water diurnal warming: flux budget