Mineralogical composition of the upper 300m of Hole 302-M0002A

During the Arctic Coring Expedition (ACEX), a 428-m-thick sequence of Upper Cretaceous to Quaternary sediments was penetrated. The mineralogical composition of the upper 300 m of this sequence is presented here for the first time. Heavy and clay mineral associations indicate a major and consistent shift in provenance, from the Barents-Kara - western Laptev Sea region, characterized by presence of common clinopyroxene, to the eastern Laptev-East Siberian seas in the upper part of the section, characterized by common hornblende (amphibole). Sea ice originating from the latter source region must have survived at least one summer melt cycle in order to reach the ACEX drill site, if considering modern sea ice trajectories and velocities. This shift in mineral assemblages probably represents the onset of a perennial sea ice cover in the Arctic Ocean, which occurred at about 13 Ma, thus suggesting a coeval freeze in the Arctic and Antarctic regions.

Abundance of mesozooplankton collected in October and November 1991 at 20 stations in the South Aegean Sea during O91

The O91- Mesozooplankton dataset is based on samples collected in mid October-mid November 1991 at 20 stations in the South Aegean, the SE.Ionian Sea and in NW Levantine. Samples were collected at discrete layers (from the surface till 300m. These data are published. Sampling volume was estimated by multiplying the mouth area with the wire length. The entire sample (for deep layers) or aliquot of Taxon-specific mesozooplankton abundance (1/4) (for the upper layer) was analyzed under the binocular microscope. Copepod and cladoceran species were identified and enumerated; the other zooplankters were identified and enumerated at higher taxonomic level (commonly named as zooplankton groups). Taxonomic identification was done by I.Siokou-Frangou, E.Christou, and N.Fragopoulu, using the relevant taxonomic literature. The entire sample (for deep layers) or aliquot of Mesozooplankton total abundance (1/4) (for the upper layer) was analyzed under the binocular microscope. All zooplankters were enumerated.

Global Homogeneous Response Units

The concept of homogenous response units (HRU) was designed as a general concept for the delineation of basic spatial units. Only those characteristics of landscape, which are relatively stable over time (even under climate change) and largely unsusceptible to anthropogenic influence, were selected. The HRU can be seen as a basic spatial framework for the implementation of climate change and land management alternative scenarios into global modeling and therefore is a basic input for delineation of landscape units. HRUs are defined based on classifications of altitude (five classes: 1 (0 - 300m), 2 (300 - 600m), 3 (600 - 1100m), 4 (1100 - 2500m), 5 (> 2500m)), slope (seven classes(degrees): 1 (0 - 3), 2 (3 - 6), 3 (6 - 10), 4 (10 - 15), 5 (15 - 30), 6 (30 - 50), 7 (> 50)) and soil composition (five classes: 1 (sandy), 2 (loamy), 3 (clay), 4 (stony), 5 (peat)). e.g. HRU111 refers to Altitude class 1: 0-300m; Slope class 1: 0-3 degrees; and Soil class 1: sandy. Areas of non-soil are assigned 88. HRUs have a spatial resolution of approximately 10 km**2.

Pan-European land cover/use map for 2009

GlobCorine demonstrated an automatic service that can generate in a consistent way land cover / land use maps and land change indicators, based on a CLC-compatible legend. CLC is derived from a visual identification and classification of landscape objects using high resolution images. This methodology provides high thematic accuracy but limits the update rate since it is time-consuming. Therefore, the project evaluated the use of MERIS FR time series, processed automatically to provide a more frequent update of CLC-compatible maps. GlobCorine built upon the experience and resources available through the GlobCover project, to tune the classification chain and adapt it to the EEA needs, covering the pan-European area (including the Mediterranean basin and the European Russia), although the system could be potentially extendable globally. The project delivered two CLC-compatible pan-European land cover maps in less than two years, demonstrating efficient and quick production. The first map is based on Envisat MERIS fine resolution (300m) mode data acquired between end 2004 and mid 2006, while the second used full-year 2009 data. GlobCorine is an initiative of ESA with the partnership of EEA and is implemented by Universite' catholique de Louvain - UCL.

Downscaled land surface temperatures obtained by a particle smoothing approach, with links to NetCDF files

This dataset contains continuous time series of land surface temperature (LST) at spatial resolution of 300m around the 12 experimental sites of the PAGE21 project (grant agreement number 282700, funded by the EC seventh Framework Program theme FP7-ENV-2011). This dataset was produced from hourly LST time series at 25km scale, retrieved from SSM/I data (André et al., 2015, doi:10.1016/j.rse.2015.01.028) and downscaled to 300m using a dynamic model and a particle smoothing approach. This methodology is based on two main assumptions. First, LST spatial variability is mostly explained by land cover and soil hydric state. Second, LST is unique for a land cover class within the low resolution pixel. Given these hypotheses, this variable can be estimated using a land cover map and a physically based land surface model constrained with observations using a data assimilation process. This methodology described in Mechri et al. (2014, doi:10.1002/2013JD020354) was applied to the ORCHIDEE land surface model (Krinner et al., 2005, doi:10.1029/2003GB002199) to estimate prior values of each land cover class provided by the ESA CCI-Land Cover product (Bontemps et al., 2013) at 300m resolution . The assimilation process (particle smoother) consists in simulating ensemble of LST time series for each land cover class and for a large number of parameter sets. For each parameter set, the resulting temperatures are aggregated considering the grid fraction of each land cover and compared to the coarse observations. Miniminizing the distance between the aggregated model solutions and the observations allow us to select the simulated LST and the corresponding parameter sets which fit the observations most closely. The retained parameter sets are then duplicated and randomly perturbed before simulating the next time window. At the end, the most likely LST of each land cover class are estimated and used to reconstruct LST maps at 300m resolution using ESA CCI-Land Cover. The resulting temperature maps on which ice pixels were masked, are provided at daily time step during the nine-year analysis period (2000-2009).