EUV Spectra of the Full Solar Disk: Analysis and Results of the Cosmic Hot Interstellar Plasma Spectrometer (CHIPS)

We analyze EUV spectra of the full solar disk from the Cosmic Hot Interstellar Plasma Spectrometer (CHIPS) spanning a period of two years. The observations were obtained via a fortuitous off-axis light path in the 140 -- 270 Angstrom passband. The general appearance of the spectra remained relatively stable over the two-year time period, but did show significant variations of up to 25% between two sets of Fe lines that show peak emission at 1 MK and 2 MK. The variations occur at a measured period of 27.2 days and are caused by regions of hotter and cooler plasma rotating into, and out of, the field of view. The CHIANTI spectral code is employed to determine plasma temperatures, densities, and emission measures. A set of five isothermal plasmas fit the full disk spectra well. A 1 -- 2 MK plasma of Fe contributes 85% of the total emission in the CHIPS passband. The standard Differential Emission Measures (DEMs) supplied with the CHIANTI package do not fit the CHIPS spectra well as they over-predict emission at temperatures below log(T) = 6.0 and above log(T) = 6.3. The results are important for cross-calibrating TIMED, SORCE, SOHO/EIT, and CDS/GIS, as well as the recently launched Solar Dynamics Observatory.Comment: 27 Pages, 13 Figure

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives