I have a strong interest in the study of the seasonal cycle of the atmosphere/ocean system. Since generally speaking the seasonal cycle is of much larger amplitude than interannual variations, it would seem that a better understanding of the former may aid in understanding the latter. The construction of anomalies (from the seasonal cycle), while a useful simplification in many contexts, is nonetheless an artificiality since the physical system responds to the total (seasonal + anomaly) signal.
Singularities & The Semiannual Cycle
My earliest work in this area (Lanzante 1980; Lanzante and Harnack, 1982a) involved the “January thaw” which was purported by folklore to be a brief period of warming in eastern North America during mid-winter. Subsequent work (Lanzante 1983a; Lanzante 1985) placed this “singularity” in the broader context of the low-frequency evolution of the large-scale general circulation of the atmosphere. It appears that the “January thaw” as well as another singularity inspired by folklore, “Indian summer”, are associated with the global semi-annual cycle which has its largest signal in the region of the Asiatic monsoon. Several mechanisms have been proposed for semiannual signals but the actual cause(s) and nature of the connections between tropical and extratropical regions have yet to be determined.
Oceanic Mixed Layer
The extratropical oceans provide another venue for study of the seasonal cycle, although not related to the above. In some regions of the extratropical oceans the upper oceanic thermal structure undergoes interesting seasonal variations. In these regions (for example the extratropical North Pacific) during the cold season the mixed layer of the ocean is deep (~ several hundred meters) in response to both mechanically (wind) and thermally (latent/sensible heat flux) induced vertical mixing. As the warming season progresses both types of forcing decrease due to diminished cyclonic activity and vertical gradients. During spring a transition to a much shallower mixed layer (~10’s of meters) may occur rather suddenly. For my M.S. thesis work I attempted to employ a physically motivated (Lanzante and Harnack, 1983) statistical scheme to try to predict summer SST in the extratropical North Pacific based on the consequences of the reduction in mixed layer depth. While this exercise was not successful, it produced some useful diagnostic results. It was doomed by the simplicity of the approach and the limitations of the available data. Subsequent work by Alexander and Deser (1995), using a simple physical model seems to confirm the basic thesis.
Some years later I returned to this area in a collaborative effort with Mike Alexander of ESRL, located in Boulder, CO, and Gabriel Lau of GFDL through the NOAA/Universities Collaborative Project for Climate Diagnosis using General Circulation Models. Mike has extensive experience with simple models of the oceanic mixed layer. Mike and his colleague Jamie Scott coupled one such model to a GFDL R30 atmospheric model for use in diagnostic studies. I performed several sizable ensembles of experiments using this coupled model. A number of diagnostic analyses of these experiments were performed by affiliated personnel. Results from these experiments were reported in an extensive review paper written by Mike (Alexander et al. 2002).
My research has not involved this area for a while but I hope in the future to return to analyzing the ensembles of coupled model experiments described above. In addition, I plan to examine the nature of the seasonal cycle from another angle (motivated by current real-time monitoring of the seasonal cycle of surface temperature) using observed data. In this context I hope to show that much of the evolution of the seasonal cycle/short-term climate anomalies can be characterized by jumps (or regime changes). I have performed some analyses on this project although other demands have put this on the back burner. In the future I would like to extend this new approach to GCM studies as well. Additionally, I would like to build on some of my earlier studies of singularities through more comprehensive data analyses as well as GCM simulations.
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