Long-term Analysis of SST off the coast of New Jersey

by Sage Lichtenwalner

Abstract

Sea Surface Temperature in the coastal regions has yet to be explored thoroughly.   As the emphasis of research today shifts to this region, it is imperative that we adjust our global remote sensing systems to be able to measure as efficiently in this region.  We have performed an analysis of a seven year data-set for a 120km transect perpendicular to the coast of New Jersey.  In plotting monthly averages of temperature and climatic variability we have shown that coastal temperature fluxes can be witnessed in the satellite data.  We have found methods for identifying periods of scientific interest without the tedious need to search through thousands of images.

Background - Click Here

Hypothesis:  We can locate periods of high-scientific interest for specified locations through a statistical analysis of long-term sea surface temperature trends.

Methodology - Click Here

Results - Click Here

Discussion

• We have found that we can easily find positions and time periods of great variability.   However, this comes with many limitations.  Generally, most extreme cases of variability are not annually occurring events, and thus we must be careful not to average our data over all years.  Also, in some cases, these events do occur annually, in which case they might be minimized by our fitting mechanism.  It is important to look at both the range in variability as well as the average.  While the average is useful for determining the general anomaly, the range shows us the overall variability occurring within the area of interest.
• Many common coastal effects can be observed in our analysis of the data.   Upwellings, SST fronts, and Gulf Stream effects can all be found in the analysis.   This method provides a quick an easy way to find interesting features, without having to eyeball thousands of images.
• While much research has been made on the analysis of SST, applying SST to biological phenomena, and global climate systems, very little has been done to long-term coastal applications.  This avenue of research provides many future projects of a more specific nature.

Future Research

This project lends itself to many avenues of future research including:

• A more complex fit function may be appropriate to model the seasonal and inter-annual temperature fluxes.  Along the lines of basic fits, sin² yields higher R² values as do model equations with more terms.  Unfortunately, the scarcity of the data does not avail this data-set to a Fourier Analysis, however from a physical perspective, an appropriate function may be created.  It is also interesting to note that a non-standard function (like a saw-tooth) may also be appropriate to account for warming/cooling trends especially prevalent in the off-shore stations.
• Correlation with buoy or other in situ temperature data would help validate the AVHRR SST in the coastal zone.
• It would be advantageous to correlate this data with other factors such as precipitation, winds, sea surface albedo, an upwelling index, and atmospheric temperature.   Doing so might lead to explanations of why short-term variability occurs.
• Obtaining data prior to 1993 would help to build a larger climate data-set, and may suppress SST fluctuations on the scale of a few years or more.
• We have also not adequately determined a Cloud-clearing process.  Data in the coastal zone does not subject well to the NASA Pathfinder algorithms and clouds are hard to filter using a 1D perspective.  A statistical method should be developed to fit the raw data, look at residuals and eliminate data that appears to be tampered by clouds.

References

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Kitchens, Larry J.  Exploring Statistics 2nd. Ed.  Duxbury Press, Brooks/Cole Publishing Company (Pacific Groove, CA) 1998.

Ullman DS, PC Cornillon.  Satellite-derived sea surface temperature fronts on the continental shelf off the northeast US coast.  JGR-Oceans 1999, Vol 104, Iss C10, pp 23459-23478.

Walton, C.C., WG Pichel, JF Sapper, DA May.  The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites.  JGR-Oceans 1998, Vol 103, Iss C12, pp. 27999-28012.