Retreat characteristics and geometries for 59 glaciers in West Greenland between 2000-2009

Recent changes in the dynamics of Greenland's marine terminating outlet glaciers indicate a rapid and complex response to external forcing. Despite observed ice front retreat and recent geophysical evidence for accelerated mass loss along Greenland's northwestern margin, it is unclear whether west Greenland glaciers have undergone the synchronous speed-up and subsequent slow-down as observed in southeastern glaciers earlier in the decade. To investigate changes in west Greenland outlet glacier dynamics and the potential controls behind their behavior, we derive time series of front position, surface elevation, and surface slope for 59 marine terminating outlet glaciers and surface speeds for select glaciers in west Greenland from 2000 to 2009. Using these data, we look for relationships between retreat, thinning, acceleration, and geometric parameters to determine the first-order controls on glacier behavior. Our data indicate that changes in front positions and surface elevations were asynchronous on annual time scales, though nearly all glaciers retreated and thinned over the decade. We found no direct relationship between retreat, acceleration, and external forcing applicable to the entire region. In regard to geometry, we found that, following retreat, (1) glaciers with grounded termini experienced more pronounced changes in dynamics than those with floating termini and (2) thinning rates declined more quickly for glaciers with steeper slopes. Overall, glacier geometry should influence outlet glacier dynamics via stress redistribution following perturbations at the front, but our data indicate that the relative importance of geometry as a control of glacier behavior is highly variable throughout west Greenland.

Spatial distribution of key zooplankton species using continuous plankton recorder (CPR) data from the North Atlantic (2000-2009)

The continuous plankton recorder (CPR) survey is an upper layer plankton monitoring program that has regularly collected samples, at monthly intervals, in the North Atlantic and adjacent seas since 1946. Water from approximately 6 m depth enters the CPR through a small aperture at the front of the sampler and travels down a tunnel where it passes through a silk filtering mesh of 270 µm before exiting at the back of the CPR. The plankton filtered on the silk is analyzed in sections corresponding to 10 nautical miles (approx. 3 m**3 of seawater filtered) and the plankton microscopically identified (Richardson et al., 2006 and reference therein). In the present study we used the CPR data to investigate the current basin scale distribution of C. finmarchicus (C5-C6), C. helgolandicus (C5-C6), C. hyperboreus (C5-C6), Pseudocalanus spp. (C6), Oithona spp. (C1-C6), total Euphausiida, total Thecosomata and the presence/absence of Cnidaria and the Phytoplankton Colour Index (PCI). The PCI, which is a visual assessment of the greenness of the silk, is used as an indicator of the distribution of total phytoplankton biomass across the Atlantic basin (Batten et al., 2003). Monthly data collected between 2000 and 2009 were gridded using the inverse-distance interpolation method, in which the interpolated values were the nodes of a 2 degree by 2 degree grid. The resulting twelve monthly matrices were then averaged within the year and in the case of the zooplankton the data were log-transformed (i.e. log10 (x+1).

Spring snowmelt and midwinter thaw and refreeze north of 60°N based on Seawinds QuikScat 2000-2009

The scatterometer SeaWinds on QuikSCAT provided regular measurements at Ku-band from 1999 to 2009. Although it was designed for ocean applications, it has been frequently used for the assessment of seasonal snowmelt patterns aside from other terrestrial applications such as ice cap monitoring, phenology and urban mapping. This paper discusses general data characteristics of SeaWinds and reviews relevant change detection algorithms. Depending on the complexity of the method, parameters such as long-term noise and multiple event analyses were incorporated. Temporal averaging is a commonly accepted preprocessing step with consideration of diurnal, multi-day or seasonal averages.