Tartu Observatory

Gaia is a cornerstone mission in the science programme of the EuropeanSpace Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page.

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7.
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\nAims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release.
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\nMethods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue.
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\nResults. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the Hipparcos and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ~3000 Cepheid and RR Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr-1 for the proper motions. A systematic component of ~0.3 mas should be added to the parallax uncertainties. For the subset of ~94 000 Hipparcos stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr-1. For the secondary astrometric data set, the typical uncertainty of the positions is ~10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ~0.03 mag over the magnitude range 5 to 20.7.
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\nConclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data.

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.

Context. Measurement of the Galactic neutral atomic hydrogen (H i) column density, N H i , and brightness temperatures, T B , is of high scientific value for a broad range of astrophysical disciplines. In the past two decades, one of the most-used legacy H i datasets has been the Leiden/Argentine/Bonn Survey (LAB).

We provide ingredients and recipes for computing signals of TeV-scale Dark Matter annihilations and decays in the Galaxy and beyond. For each DM channel, we present the energy spectra of at production, computed by high-statistics simulations. We estimate the Monte Carlo uncertainty by comparing the results yielded by the Pythia and Herwig event generators. We then provide the propagation functions for charged particles in the Galaxy, for several DM distribution profiles and sets of propagation parameters. Propagation of e ± is performed with an improved semi-analytic method that takes into account position-dependent energy losses in the Milky Way. Using such propagation functions, we compute the energy spectra of e ± ,p̄ and d̄ at the location of the Earth. We then present the gamma ray fluxes, both from prompt emission and from Inverse Compton scattering in the galactic halo. Finally, we provide the spectra of extragalactic gamma rays. All results are available in numerical form and ready to be consumed.

We provide ingredients and recipes for computing signals of TeV-scale Dark Matter annihilations and decays in the Galaxy and beyond. For each DM channel, we present the energy spectra of e±, p̄, d̄, γ, ν e,µ,τ at production, computed by high-statistics simula-tions. We estimate the Monte Carlo uncertainty by comparing the results yielded by the Pythia and Herwig event generators. We then provide the propagation functions for charged particles in the Galaxy, for several DM distribution profiles and sets of propaga-tion parameters. Propagation of e ± is performed with an improved semi-analytic method that takes into account position-dependent energy losses in the Milky Way. Using such propagation functions, we compute the energy spectra of e±, p ̄ and d ̄ at the location of the Earth. We then present the gamma ray fluxes, both from prompt emission and from Inverse Compton scattering in the galactic halo. Finally, we provide the spectra of extragalactic gamma rays. All results are available in numerical form and ready to be consumed.

Summary Extensive within‐canopy light gradients importantly affect the photosynthetic productivity of leaves in different canopy positions and lead to light‐dependent increases in foliage photosynthetic capacity per area ( A A ). However, the controls on A A variations by changes in underlying traits are poorly known. We constructed an unprecedented worldwide database including 831 within‐canopy gradients with standardized light estimates for 304 species belonging to major vascular plant functional types, and analyzed within‐canopy variations in 12 key foliage structural, chemical and physiological traits by quantitative separation of the contributions of different traits to photosynthetic acclimation. Although the light‐dependent increase in A A is surprisingly similar in different plant functional types, they differ fundamentally in the share of the controls on A A by constituent traits. Species with high rates of canopy development and leaf turnover, exhibiting highly dynamic light environments, actively change A A by nitrogen reallocation among and partitioning within leaves. By contrast, species with slow leaf turnover exhibit a passive A A acclimation response, primarily determined by the acclimation of leaf structure to growth light. This review emphasizes that different combinations of traits are responsible for within‐canopy photosynthetic acclimation in different plant functional types, and solves an old enigma of the role of mass‐ vs area‐based traits in vegetation acclimation. Contents Summary 973 I. Introduction 973 II. Defining the structural, chemical and partitioning controls on foliage photosynthetic potentials 974 III. Construction of a global database on within‐canopy variation in leaf structural, chemical and photosynthetic characteristics 976 IV. Methodology for analysis of within‐canopy leaf trait variation: concepts and standardizations 978 V. Global variation in leaf characteristics through the canopies 981 VI. Trait scaling with light, plasticity and quantitative limitations 982 VII. Conclusions: the economics spectrum for the within‐canopy plasticity 989 Acknowledgements 989 References 990

Transpiration and ozone uptake rates were measured simultaneously in sunflower leaves at different stomatal openings and various ozone concentrations. Ozone uptake rates were proportional to the ozone concentration up to 1500 nanoliters per liter. The leaf gas phase diffusion resistance (stomatal plus boundary layer) to water vapor was calculated and converted to the resistance to ozone multiplying it by the theoretical ratio of diffusion coefficients for water vapor and ozone in air (1.67). The ozone concentration in intercellular air spaces calculated from the ozone uptake rate and diffusion resistance to ozone scattered around zero. The ozone concentration in intercellular air spaces was measured directly by supplying ozone to the leaf from one side and measuring the equilibrium concentration above the other side, and it was found to be zero. The total leaf resistance to ozone was proportional to the gas phase resistance to water vapor with a coefficient of 1.68. It is concluded that ozone enters the leaf by diffusion through the stomata, and is rapidly decomposed in cell walls and plasmalemma.

We present the first all-sky sample of galaxy clusters detected blindly by the Planck satellite through the Sunyaev-Zeldovich (SZ) effect from its six highest frequencies. This early SZ (ESZ) sample is comprised of 189 candidates, which have a high signal-to-noise ratio ranging from 6 to 29. Its high reliability (purity above 95%) is further ensured by an extensive validation process based on Planck internal quality assessments and by external cross-identification and follow-up observations. Planck provides the first measured SZ signal for about 80% of the 169 previouslyknown ESZ clusters. Planck furthermore releases 30 new cluster candidates, amongst which 20 meet the ESZ signal-to-noise selection criterion. At the submission date, twelve of the 20 ESZ candidates were confirmed as new clusters, with eleven confirmed using XMM-Newton snapshot observations, most of them with disturbed morphologies and low luminosities. The ESZ clusters are mostly at moderate redshifts (86% with z below 0.3) and span more than a decade in mass, up to the rarest and most massive clusters with masses above 1 10 15 M .

Sunlit and shaded leaf separation proposed by Norman (1982) is an effective way to upscale from leaf to canopy in modeling vegetation photosynthesis. The Boreal Ecosystem Productivity Simulator (BEPS) makes use of this methodology, and has been shown to be reliable in modeling the gross primary productivity (GPP) derived from CO 2 flux and tree ring measurements. In this study, we use BEPS to investigate the effect of canopy architecture on the global distribution of GPP. For this purpose, we use not only leaf area index (LAI) but also the first ever global map of the foliage clumping index derived from the multiangle satellite sensor POLDER at 6 km resolution. The clumping index, which characterizes the degree of the deviation of 3‐dimensional leaf spatial distributions from the random case, is used to separate sunlit and shaded LAI values for a given LAI. Our model results show that global GPP in 2003 was 132 ± 22 Pg C. Relative to this baseline case, our results also show: (1) global GPP is overestimated by 12% when accurate LAI is available but clumping is ignored, and (2) global GPP is underestimated by 9% when the effective LAI is available and clumping is ignored. The clumping effects in both cases are statistically significant (p < 0.001). The effective LAI is often derived from remote sensing by inverting the measured canopy gap fraction to LAI without considering the clumping. Global GPP would therefore be generally underestimated when remotely sensed LAI (actually effective LAI by our definition) is used. This is due to the underestimation of the shaded LAI and therefore the contribution of shaded leaves to GPP. We found that shaded leaves contribute 50%, 38%, 37%, 39%, 26%, 29% and 21% to the total GPP for broadleaf evergreen forest, broadleaf deciduous forest, evergreen conifer forest, deciduous conifer forest, shrub, C4 vegetation, and other vegetation, respectively. The global average of this ratio is 35%.

The relatively high spatial resolution, short revisit time and red-edge spectral band (705 nm) of the ESA Sentinel-2 Multi Spectral Imager makes this sensor attractive for monitoring water quality of coastal and inland waters. Reliable atmospheric correction is essential to support routine retrieval of optically active substance concentration from water-leaving reflectance. In this study, six publicly available atmospheric correction algorithms (Acolite, C2RCC, iCOR, l2gen, Polymer and Sen2Cor) are evaluated against above-water optical in situ measurements, within a robust methodology, in two optically diverse coastal regions (Baltic Sea, Western Channel) and from 13 inland waterbodies from 5 European countries with a range of optical properties. The total number of match-ups identified for each algorithm ranged from 1059 to 1668 with 521 match-ups common to all algorithms. These in situ and MSI match-ups were used to generate statistics describing the performance of each algorithm for each respective region and a combined dataset. All ACs tested showed high uncertainties, in many cases >100% in the red and >1000% in the near-infra red bands. Polymer and C2RCC achieved the lowest root mean square differences (~0.0016 sr−1) and mean absolute differences (~40–60% in blue/green bands) across the different datasets. Retrieval of blue-green and NIR-red band ratios indicate that further work on AC algorithms is required to reproduce the spectral shape in the red and NIR bands needed to accurately retrieve the chlorophyll-a concentration in turbid waters.

Abstract We analyze the parsec-scale jet kinematics from 2007 June to 2013 January of a sample of γ -ray bright blazars monitored roughly monthly with the Very Long Baseline Array at 43 GHz. In a total of 1929 images, we measure apparent speeds of 252 emission knots in 21 quasars, 12 BL Lacertae objects (BLLacs), and 3 radio galaxies, ranging from 0.02 c to 78 c ; 21% of the knots are quasi-stationary. Approximately one-third of the moving knots execute non-ballistic motions, with the quasars exhibiting acceleration along the jet within 5 pc (projected) of the core, and knots in BLLacs tending to decelerate near the core. Using the apparent speeds of the components and the timescales of variability from their light curves, we derive the physical parameters of 120 superluminal knots, including variability Doppler factors, Lorentz factors, and viewing angles. We estimate the half-opening angle of each jet based on the projected opening angle and scatter of intrinsic viewing angles of knots. We determine characteristic values of the physical parameters for each jet and active galactic nucleus class based on the range of values obtained for individual features. We calculate the intrinsic brightness temperatures of the cores, , at all epochs, finding that the radio galaxies usually maintain equipartition conditions in the cores, while ∼30% of measurements in the quasars and BLLacs deviate from equipartition values by a factor >10. This probably occurs during transient events connected with active states. In the Appendix, we briefly describe the behavior of each blazar during the period analyzed.

The cosmic web is one of the most striking features of the distribution of galaxies and dark matter on the largest scales in the Universe. It is composed of dense regions packed full of galaxies, long filamentary bridges, flattened sheets and vast low-density voids. The study of the cosmic web has focused primarily on the identification of such features, and on understanding the environmental effects on galaxy formation and halo assembly. As such, a variety of different methods have been devised to classify the cosmic web -depending on the data at hand, be it numerical simulations, large sky surveys or other. In this paper, we bring 12 of these methods together and apply them to the same data set in order to understand how they compare. In general, these cosmic-web classifiers have been designed with different cosmological goals in mind, and to study different questions. Therefore, one would not a priori expect agreement between different techniques; however, many of these methods do converge on the identification of specific features. In this paper, we study the agreements and disparities of the different methods. For example, each method finds that knots inhabit higher density regions than filaments, etc. and that voids have the lowest densities. For a given web environment, we find a substantial overlap in the density range assigned by each web classification scheme. We also compare classifications on a halo-by-halo basis; for example, we find that 9 of 12 methods classify around a third of group-mass haloes (i.e. M halo 10 13.5 h -1 M ) as being in filaments. Lastly, so that any future cosmic-web classification scheme can be compared to the 12 methods used here, we have made all the data used in this paper public.

Atmospheric correction over inland and coastal waters is one of the major remaining challenges in aquatic remote sensing, often hindering the quantitative retrieval of biogeochemical variables and analysis of their spatial and temporal variability within aquatic environments. The Atmospheric Correction Intercomparison Exercise (ACIX-Aqua), a joint NASA – ESA activity, was initiated to enable a thorough evaluation of eight state-of-the-art atmospheric correction (AC) processors available for Landsat-8 and Sentinel-2 data processing. Over 1000 radiometric matchups from both freshwaters (rivers, lakes, reservoirs) and coastal waters were utilized to examine the quality of derived aquatic reflectances (ρ̂w). This dataset originated from two sources: Data gathered from the international scientific community (henceforth called Community Validation Database, CVD), which captured predominantly inland water observations, and the Ocean Color component of AERONET measurements (AERONET-OC), representing primarily coastal ocean environments. This volume of data permitted the evaluation of the AC processors individually (using all the matchups) and comparatively (across seven different Optical Water Types, OWTs) using common matchups. We found that the performance of the AC processors differed for CVD and AERONET-OC matchups, likely reflecting inherent variability in aquatic and atmospheric properties between the two datasets. For the former, the median errors in ρ̂w560 and ρ̂w664 were found to range from 20 to 30% for best-performing processors. Using the AERONET-OC matchups, our performance assessments showed that median errors within the 15–30% range in these spectral bands may be achieved. The largest uncertainties were associated with the blue bands (25 to 60%) for best-performing processors considering both CVD and AERONET-OC assessments. We further assessed uncertainty propagation to the downstream products such as near-surface concentration of chlorophyll-a (Chla) and Total Suspended Solids (TSS). Using satellite matchups from the CVD along with in situ Chla and TSS, we found that 20–30% uncertainties in ρ̂w490≤λ≤743nm yielded 25–70% uncertainties in derived Chla and TSS products for top-performing AC processors. We summarize our results using performance matrices guiding the satellite user community through the OWT-specific relative performance of AC processors. Our analysis stresses the need for better representation of aerosols, particularly absorbing ones, and improvements in corrections for sky- (or sun-) glint and adjacency effects, in order to achieve higher quality downstream products in freshwater and coastal ecosystems.

We introduce the 4-metre Multi-Object Spectroscopic Telescope (4MOST), a new high-multiplex, wide-field spectroscopic survey facility under development for the four-metre-class Visible and Infrared Survey Telescope for Astronomy (VISTA) at Paranal. Its key specifications are: a large field of view (FoV) of 4.2 square degrees and a high multiplex capability, with 1624 fibres feeding two low-resolution spectrographs ($R = λ/Δλ\sim 6500$), and 812 fibres transferring light to the high-resolution spectrograph ($R \sim 20\,000$). After a description of the instrument and its expected performance, a short overview is given of its operational scheme and planned 4MOST Consortium science; these aspects are covered in more detail in other articles in this edition of The Messenger. Finally, the processes, schedules, and policies concerning the selection of ESO Community Surveys are presented, commencing with a singular opportunity to submit Letters of Intent for Public Surveys during the first five years of 4MOST operations.

The Radiation Transfer Model Intercomparison (RAMI) initiative benchmarks canopy reflectance models under well‐controlled experimental conditions. Launched for the first time in 1999, this triennial community exercise encourages the systematic evaluation of canopy reflectance models on a voluntary basis. The first phase of RAMI focused on documenting the spread among radiative transfer (RT) simulations over a small set of primarily 1‐D canopies. The second phase expanded the scope to include structurally complex 3‐D plant architectures with and without background topography. Here sometimes significant discrepancies were noted which effectively prevented the definition of a reliable “surrogate truth,” over heterogeneous vegetation canopies, against which other RT models could then be compared. The present paper documents the outcome of the third phase of RAMI, highlighting both the significant progress that has been made in terms of model agreement since RAMI‐2 and the capability of/need for RT models to accurately reproduce local estimates of radiative quantities under conditions that are reminiscent of in situ measurements. Our assessment of the self‐consistency and the relative and absolute performance of 3‐D Monte Carlo models in RAMI‐3 supports their usage in the generation of a “surrogate truth” for all RAMI test cases. This development then leads (1) to the presentation of the “RAMI Online Model Checker” (ROMC), an open‐access web‐based interface to evaluate RT models automatically, and (2) to a reassessment of the role, scope, and opportunities of the RAMI project in the future.

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We correct a few mistakes of the original version of this work (notably related to the computations of extragalactic gamma rays), while at the same time improving and upgrading other aspects (notably as a consequence of the discovery of the higgs boson at the LHC). A brief list of the main changes is:
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\n- We include a higgs boson channel hh with mass mh = 125 GeV. All previous channels hmhm are removed.
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\n- We correct the formulæ for the computation of extragalactic gamma rays (fixing in particular the redshift dependence) as well as the numerical computations (also including a corrected impact of absorption).
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\n- We provide a new version of the Optical Depth function, employing updated models of Extragalactic Background Light (EBL) and fixing the redshift dependence.
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\nAll these corrections and updates are reflected on the numerical ingredients provided on the website; they correspond to Release 2.0.

Atmospheric reanalyses were validated against tethersonde sounding data on air temperature, air humidity and wind speed, collected during the drifting ice station Tara in the central Arctic in April–August 2007. The data were not assimilated into the reanalyses, providing a rare possibility for their independent validation, which was here made for the lowermost 890 m layer. The following reanalyses were included in the study: the European ERA‐Interim, the Japanese JCDAS, and the U.S. NCEP‐CFSR, NCEP‐DOE, and NASA‐MERRA. All reanalyses included large errors. ERA‐Interim was ranked first; it outperformed the other reanalyses in the bias and root‐mean‐square‐error (RMSE) for air temperature as well as in the bias, RMSE, and correlation coefficient for the wind speed. ERA‐Interim suffered, however, from a warm bias of up to 2°C in the lowermost 400 m layer and a moist bias of 0.3 to 0.5 g kg −1 throughout the 890 m layer. The NCEP‐CFSR, NCEP‐DOE, and NASA‐MERRA reanalyses outperformed the other reanalyses with respect to 2‐m air temperature and specific humidity and 10‐m wind speed, which makes them, especially NCEP‐CSFR, better in providing turbulent flux forcing for sea ice models. Considering the whole vertical profile, however, the older NCEP‐DOE got the second highest overall ranking, being better than the new NCEP‐CFSR. Considering the whole group of reanalyses, the largest air temperature errors surprisingly occurred during higher‐than‐average wind speeds. The observed biases in temperature, humidity, and wind speed were in many cases comparable or even larger than the climatological trends during the latest decades.

Late-spring frosts (LSFs) affect the performance of plants and animals across the world's temperate and boreal zones, but despite their ecological and economic impact on agriculture and forestry, the geographic distribution and evolutionary impact of these frost events are poorly understood. Here, we analyze LSFs between 1959 and 2017 and the resistance strategies of Northern Hemisphere woody species to infer trees' adaptations for minimizing frost damage to their leaves and to forecast forest vulnerability under the ongoing changes in frost frequencies. Trait values on leaf-out and leaf-freezing resistance come from up to 1,500 temperate and boreal woody species cultivated in common gardens. We find that areas in which LSFs are common, such as eastern North America, harbor tree species with cautious (late-leafing) leaf-out strategies. Areas in which LSFs used to be unlikely, such as broad-leaved forests and shrublands in Europe and Asia, instead harbor opportunistic tree species (quickly reacting to warming air temperatures). LSFs in the latter regions are currently increasing, and given species' innate resistance strategies, we estimate that ∼35% of the European and ∼26% of the Asian temperate forest area, but only ∼10% of the North American, will experience increasing late-frost damage in the future. Our findings reveal region-specific changes in the spring-frost risk that can inform decision-making in land management, forestry, agriculture, and insurance policy.