The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables

Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

Introduction of a Nonlinearity Measure for Principal Component Models

This is the first paper that introduces a nonlinearity test for principal component models. The methodology involves the division of the data space into disjunct regions that are analysed using principal component analysis using the cross-validation principle. Several toy examples have been successfully analysed and the nonlinearity test has subsequently been applied to data from an internal combustion engine.

Cytohesin Binder and Regulator (Cybr) is Not Essential for T- and Dendritic-Cell Activation and Differentiation

Cybr (also known as Cytip, CASP, and PSCDBP) is an interleukin-12-induced gene expressed exclusively in hematopoietic cells and tissues that associates with Arf guanine nucleotide exchange factors known as cytohesins. Cybr levels are dynamically regulated during T-cell development in the thymus and upon activation of peripheral T cells. In addition, Cybr is induced in activated dendritic cells and has been reported to regulate dendritic cell (DC)-T-cell adhesion. Here we report the generation and characterization of Cybr-deficient mice. Despite the selective expression in hematopoietic cells, there was no intrinsic defect in T- or B-cell development or function in Cybr-deficient mice. The adoptive transfer of Cybr-deficient DCs showed that they migrated efficiently and stimulated proliferation and cytokine production by T cells in vivo. However, competitive stem cell repopulation experiments showed a defect in the abilities of Cybr-deficient T cells to develop in the presence of wild-type precursors. These data suggest that Cybr is not absolutely required for hematopoietic cell development or function, but stem cells lacking Cybr are at a developmental disadvantage compared to wild-type cells. Collectively, these data demonstrate that despite its selective expression in hematopoietic cells, the role of Cybr is limited or largely redundant. Previous in vitro studies using overexpression or short interfering RNA inhibition of the levels of Cybr protein appear to have overestimated its immunological role.

Generation of macroscopic superposition states with small nonlinearity

We suggest a scheme to generate a macroscopic superposition state ("Schrodinger cat state") of a free-propagating optical field using a beam splitter, homodyne measurement, and a very small Kerr nonlinear effect. Our scheme makes it possible to reduce considerably the required nonlinear effect to generate an optical cat state using simple and efficient optical elements.