ALBERT

Description: Historical understanding of marine biological dynamics has been limited by sparse in situ observations and the fact that dedicated ocean color satellite remote sensing only began in 1997. From these observations, it has become clear that physical oceanography controls biological variability over seasonal to interannual time scales. To quantify how multidecadal, climate-scale patterns impact biological productivity, the strong correlation with sea surface temperature and sea surface height is utilized to reconstruct a retrospective 51-yr time series of surface chlorophyll, the pigment measured by ocean color satellites. The canonical correlation analysis statistical reconstruction demonstrates greatest skill away from land and within about 10° of the equator where chlorophyll variance is greatest and predominantly associated with El Niño–Southern Oscillation dynamics. Differences in chlorophyll patterns between east or central Pacific El Niño events are observed, with larger declines east of 180° for east Pacific events and west of 180° for central Pacific events. Additionally, small but significant decadal variations in chlorophyll patterns are observed corresponding to the Pacific decadal oscillation. Decadal changes in chlorophyll west of 180° are consistent with increased stratification, whereas changes between 110°–140°W may be related to long-term shoaling of the nutrient-bearing equatorial undercurrent.

Description: This study formulates the design of optimal observing networks for past surface climate conditions as the solution to a data assimilation problem, given a realistic proxy system model (PSM), paleoclimate observational uncertainties, and a network of current and proposed observing sites. The method is illustrated with the design of optimal networks of coral δ18O records, chosen among candidate sites, and used to jointly infer sea surface temperature (SST) and sea surface salinity (SSS) fields from the Community Climate System Model, version 4, last millennium simulation over the 1850–2005 period. It is shown that an existing paleo-observing network accounts for approximately 20% of the SST variance, and that adding 25 to 100 optimal pseudocoral sites would boost this fraction to 35%–52%. Characterizing the SST variance alone, or jointly with the SSS, leads to similar optimal networks, which justifies using coral δ18O records for SST reconstructions. In contrast, the network design for reconstructing SSS alone is fundamentally different, emphasizing the hydroclimatic centers of action of El Niño–Southern Oscillation. In all cases, network design depends strongly on the amplitude of the observational error, so replicates may be more beneficial than the exploration of new sites; these replicates tend to be chosen where proxies are already informative of the large-scale climate field(s). Finally, extensions to other types of paleoclimatic observations are discussed, and a path to operationalization is outlined.

Description: Canonical correlation analysis (CCA) is evaluated for paleoclimate field reconstructions in the context of pseudoproxy experiments assembled from the millennial integration (850–1999 c.e.) of the National Center for Atmospheric Research Community Climate System Model, version 1.4. A parsimonious method for selecting the order of the CCA model is presented. Results suggest that the method is capable of resolving multiple (3–13) climatic patterns given the estimated proxy observational network and the amount of observational uncertainty. CCA reconstructions are compared to those derived from the regularized expectation maximization method using ridge regression regularization (RegEM-Ridge). CCA and RegEM-Ridge yield similar skill patterns that are characterized by high correlation regions collocated with dense pseudoproxy sampling areas in North America and Europe. Both methods also produce reconstructions characterized by spatially variable warm biases and variance losses, particularly at high pseudoproxy noise levels. RegEM-Ridge in particular is subject to significantly larger variance losses than CCA, even though the spatial correlation patterns of the two methods are comparable. Results collectively indicate the importance of evaluating the field performance of methods that target spatial climate patterns during the last several millennia and indicate that the results of currently available climate field reconstructions should be interpreted carefully.

Description: North American Mesoscale (NAM) model forecasts of the occurrence, magnitude, depth, and persistence of ingredients previously shown to be useful in the diagnosis of banded and/or heavy snowfall potential are examined for a broad range of 25 snow events, with event total snowfall ranging from 10 cm (4 in.) to over 75 cm (30 in.). The ingredients examined are frontogenetical forcing, weak moist symmetric stability, saturation, and microphysical characteristics favorable for the production of dendritic snow crystals. It is shown that these ingredients, previously identified as being critical indicators for heavy and/or banded snowfall in major storms, are often found in smaller snowfall events. It is also shown that the magnitude, depth, and persistence of these ingredients, or combinations of these ingredients, appear to be good predictors of event total snowfall potential. In addition, a relationship is demonstrated between temporal trends associated with one of the ingredients (saturated, geostrophic equivalent potential vorticity) and event total snowfall. Correlations between forecast values of these ingredients and observed snowfall are shown to decrease substantially as forecast lead time increases beyond 12 h. It is hypothesized that model forecast positioning and timing errors are primarily responsible for the lower correlations associated with longer-lead forecasts. This finding implies that the best forecasts beyond 12 h may be produced by examining the diagnostics of heavy snow ingredients from a single, high-resolution model to determine snowfall potential, then using ensemble forecasting approaches to determine the most probable location and timing of any heavy snow.

Description: Two banded, heavy snowstorms that occurred over the northern mid-Atlantic region are compared and contrasted. On 6–7 January 2002, a narrow, intense band of heavy snow was observed, along with several other weaker bands, embedded within a large area of moderate snow. On 19–20 January 2002, a single, broader band of heavy snow was observed, embedded within a broken area of light snow. The synoptic-scale settings associated with these two storms were strikingly dissimilar. In the first case, strong quasigeostrophic (QG) forcing for ascent was present just to the south of the heavy snowfall area. A highly amplified longwave trough was located over the Mississippi River valley, while a compact shortwave trough moved northward, up the east side of the longwave trough. The result was robust cyclogenesis off of the mid-Atlantic coast. In the second case, the relatively weaker QG forcing for ascent was located much farther southwest of the snowband. The flow aloft was much less amplified, with weaker cyclogenesis occurring off of the mid-Atlantic coast. Analysis of the frontal scale environments for both cases indicated that the snowbands were each associated with the collocation of midtropospheric frontogenesis and reduced stability. In the first case, evidence is shown that a layer of potential symmetric instability (PSI) was located just above a deep, sloping zone of frontogenesis, in the presence of deep near-saturated conditions. In the second case, evidence is shown that a layer of potential instability (PI), associated with rapidly decreasing relative humidity with height, was located just above a shallow, sloping zone of frontogenesis. In addition, it is shown that a particularly favorable thermal environment for snowflake growth and accumulation became collocated with the heavy snowband. It is hypothesized that the differences in the intensity and horizontal extent of the bands observed with these two events resulted from differing atmospheric responses associated with the areal extent of large-scale and frontogenetical forcing, moisture availability, degree of instability, and specific thermal profiles.

Description: The spatial distribution of snowfall accumulation accompanying winter storms is a product of both snowfall rate and duration. Winter storms are commonly associated with mesoscale snowbands that can strongly modulate snowfall accumulation. Although the development of mesoscale snowbands can usually be anticipated, snowband residence time at a fixed location is often a forecasting challenge. However, given that snowband residence time is related to characteristics of band motion, an improved understanding of band motion presents an opportunity to improve snowfall-accumulation forecasts. This study investigates environmental features associated with specific snowband motion characteristics. Using radar reflectivity data, snowband events in the northeast United States spanning a 6-yr period are categorized according to a band-motion classification scheme, with this scheme consisting of laterally translating, hybrid, laterally quasi-stationary, and pivoting snowbands. On the basis of this classification, composite analysis is performed to identify common environmental features associated with particular band-motion categories. Results indicate that snowband motion is related to cyclone-relative band position, the confluence/diffluence and curvature of midlevel streamlines, and the distribution of horizontal temperature advection. Snowband motion is also related to hodograph shape, as well as to the across- and along-isotherm components of the Q vector. Composite results are supplemented with case studies, which suggest that laterally quasi-stationary and pivoting snowbands can favor distinct gradients in snowfall accumulation. The present study proposes that snowband motion warrants consideration during the forecasting process and, to that end, conceptual models are presented to synthesize key findings for operational application.