Phytoplankton niches estimated from CPR data in the North Atlantic

We combine phytoplankton occurrence data for 119 species from the continuous plankton recorder with climatological environmental variables in the North Atlantic to obtain ecological response functions of each species using the MaxEnt statistical method. These response functions describe how the probability of occurrence of each species changes as a function of environmental conditions and can be reduced to a simple description of phytoplankton realized niches using the mean and standard deviation of each environmental variable, weighted by its response function. Although there was substantial variation in the realized niche among species within groups, the envelope of the realized niches of North Atlantic diatoms and dinoflagellates are mostly separate in niche space.

Particle concentration and size distribution in the NGRIP ice core

A novel laser microparticle detector used in conjunction with continuous sample melting has provided a more than 1500 m long record of particle concentration and size distribution of the NGRIP ice core, covering continuously the period approximately from 9.5-100 kyr before present; measurements were at 1.65 m depth resolution, corresponding to approximately 35-200 yr. Particle concentration increased by a factor of 100 in the Last Glacial Maximum (LGM) compared to the Preboreal, and sharp variations of concentration occurred synchronously with rapid changes in the delta18O temperature proxy. The lognormal mode µ of the volume distribution shows clear systematic variations with smaller modes during warmer climates and coarser modes during colder periods. We find µ ~ 1.7 µm diameter during LGM and µ ~ 1.3 µm during the Preboreal. On timescales below several 100 years µ and the particle concentration exhibit a certain degree of independence present especially during warm periods, when µ generally is more variable. Using highly simplifying considerations for atmospheric transport and deposition of particles we infer that (1) the observed changes of µ in the ice largely reflect changes in the size of airborne particles above the ice sheet and (2) changes of µ are indicative of changes in long range atmospheric transport time. From the observed size changes we estimate shorter transit times by roughly 25% during LGM compared to the Preboreal. The associated particle concentration increase from more efficient long range transport is estimated to less than one order of magnitude.

Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.