Chemical reaction rates and entrainment within the Endeavour Ridge hydrothermal plume

The aging of the hydrothermal plume over the Endeavour segment of the Juan de Fuca Ridge was estimated by measuring the²²²Rn³He ratio in the plume as it dispersed. Despite uncertainties in the source function of hydrothermal input, it wa determined that the relative sequence of removal from the plume isH₂ > Δc >²²²Rn>CH₄ Mn, whereΔc is a measure of particle concentration and the mean life of²²²Rn is 5.5 days. H₂ is removed from the plume within hours of input while Mn is not removed within the two-week timescale of the radon-helium clock.Entrainment of bottom water within the buoyant plume may introduce additional chemical signatures into the spreading effluent layer over that which would be introduced by hydrothermal discharge alone. This is particularly significant for those chemical species which are not greatly enriched in the vent fluids relative to bottom water concentration and which display a nutrient-like profile in the deep ocean. Thus we found that significant fractions of the Si and²²⁶Ra anomalies in the plume were not of hydrothermal origin but were derived from entrained bottom water which has a higher concentration of these elements than ambient water at plume height.     

Partition of Forecast Error into Positional and Structural Components

Weather manifests in spatiotemporally coherent structures. Weather forecasts hence are affected by both positional and structural or amplitude errors. This has been long recognized by practicing forecasters(cf., e.g.,Tropical Cyclone track and intensity errors). Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors, most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error.The Forecast Error Decomposition(FED) method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field. The total error is then partitioned into three orthogonal components:(a) large scale positional,(b) large scale structural, and(c) small scale error variance.The use of FED is demonstrated over a month-long MSLP data set. As expected, positional errors are often characterized by dipole patterns related to the displacement of features, while structural errors appear with single extrema, indicative of magnitude problems. The most important result of this study is that over the test period, more than 50% of the total mean sea level pressure forecast error variance is associated with large scale positional error. The importance of positional error in forecasts of other variables and over different time periods remain to be explored.