Validating NAVO’s Navy Coastal Ocean Model LT Giancarlo Waghelstein OC-3570 Summer 2009 Cruise

Validating NAVO’s Navy Coastal Ocean Model (NCOM) Introduction Data Collection Results Conclusion Recommendations

All information taken from CNMOC INST 3140 Introduction What is the NAVO G-Navy Ocean Coastal Model? Physics based fluid dynamics ocean model that resolves mesoscale features Temp, salinity, currents, elevation Derives 3D sound speed for ASW, object drift track for MIW/environmental concerns Uses statistics, satellite data, and physics to interpolate ocean structure between sparse insitu observations Up to 42 vertical layers 1/8˚ horizontal resolution Higher resolution in AOIs FNMOC NOGAPS atmospheric forcing (winds, heat fields) Mention oil spills Classified resolution – if I told you I’d have to kill you All information taken from CNMOC INST 3140

Data Collection 28 CTD casts along 2 tracks (SC & SNB runs) were used to compare salinity & temperature depth profiles from NCOM’s expected values (56 depth profiles for those keeping track) CTD collection times taken throughout the course of the cruise had to be matched as closely as possible to spatially and temporally model runs (12 hr τ) OPOC Results.xlsx TIP: Learn to use for loops in Matlab. They are your friends. No seriously. Don’t look at me like I don’t know what I’m talking about. Just do it.

CTD Tracks

Results: Salinity (Whiskey Tango Foxtrot!) Green – CTD Cast Blue – Model Forecast Station 5-8 show a significant under forecast of salinity temperatures. (Maybe use a correction in model output? Seems to be a fairly consistent under-forecast of expected salinity values)

Results: Salinity (Whiskey Tango Foxtrot!) Green – CTD Cast Blue – Model Forecast Fairly consistent over all 4 stations so we don’t expect standard deviation between Observed-Model difference Is it a function of space? Or a function of time? Need to find which model runs stations 5-8 came from

Results: Salinity Green – CTD Cast Blue – Model Forecast Stations 12-15 show a much smaller “over forecast” of expected salinity values

Results: Salinity Green – CTD Cast Blue – Model Forecast Note the over forecast in the thermocline layer (0-75m) Once again- this “better” data could be due to the model’s ability to forecast better in the relative op area or perhaps the model run at the given time of the observations was more accurate due to oparea mirroring BC better or insitu data become available in later runs to populate model background field.

Results: Temperature Red – CTD Cast Blue – Model Forecast Looks like fairly good temperature predictions with these two examples. Barely any MLD? Summer time heating and light winds present very little chance for turbulence to increase MLD

Results: Temperature Red – CTD Cast Blue – Model Forecast Still “looks” like the model might be better at forecasting T but at consider the x variation in the two profiles. Salinity changes between roughly 33.5-35 PSU, while Temp variations range from 5 – 22 Deg C

Results: Temperature (Ruh Roh Rorge!) Red – CTD Cast Blue – Model Forecast What happened? We see a large over forecast error in expected vs observed values. Fairly good at predicting depth of MLD throughout each scenario whether its 0-15m

Results: Temperature (Ruh Roh Rorge!) Red – CTD Cast Blue – Model Forecast Over-forecast situation seems to permeate from surface to just below seasonal thermocline

Conclusions: Show Me the Money! Entire Water Column Thermocline Taking the averaged difference between the 28 stations’ observed and expected values and plotted across depth shows where the model over the course of 3 days was most accurate. Positive & large values in the shallower depths indicate the model under-forecasted (which we noted earlier). At deeper depths (300-1200m) we see improvement in the forecast. Depths lower than 1000m can be thrown away due to much fewer collection points than >1000m

Conclusions Entire Water column thermocline Large over-forecast in expected values throughout the thermocline leads to large negative values in first graph. Look how close the model performed at deeper depths!

Conclusions Standard Deviation Salinity Temperature No surprises here- standard deviations show that once again the model performed better at depths between 300-1000m

Conclusions Spatial vs. Temporal Better Model Salinity Results Over-forecast TC Better Model Temp Results 07 26 1200 07 27 0000 Model Under-forecasted Salinity Model Over-forecast Temp 07 28 1200 07 25 1200 07 26 0000 07 26 1200

Theories Model needs improvement in predicting seasonal thermocline temp Currently too warm Model needs improvement in predicting seasonal thermocline salinity Currently not saline enough Data More obs Better data in absence of obs Use more accurate climo instead?? Better BC? Bathy