Measuring oxygen fluxes in a European beech forest - results from the OXYFLUX project

<p>Ecosystem assimilation and respiration result in anti-correlated fluxes of oxygen (O<sub>2</sub>) and carbon dioxide (CO<sub>2</sub>). While the ecosystem O<sub>2</sub>:CO<sub>2</sub> molar exchange ratio is usually assumed constant at ≈1.1 on longer timescales, variations for individual ecosystem compartments or shorter timescales have been reported in the past. We hypothesize that these exchange ratio variations can reveal information about underlying biotic and abiotic processes in plants or soil that cannot be inferred from traditional net ecosystem exchange measurements. To date, oxygen measurements have not been widely implemented in ecosystem research due to the technical challenge of detecting very small variations (ppm-level) against an atmospheric background of ≈21% (≈210,000 ppm).</p><p>We evaluate the performance and applicability of two commercial oxygen analyzers Integrated into custom-built gas handling and calibration systems, and report first results from measurements of O<sub>2</sub>:CO<sub>2</sub> exchange ratios in a managed European beech forest in central Germany.</p><p>System 1, consisting of a relatively slow response differential fuel cell O<sub>2</sub> analyzer (Oxzilla FC-2, Sable Systems Inc., USA) together with a non-dispersive infrared CO<sub>2</sub> analyzer (LI-840, LI-COR Biosciences, USA), was used to simultaneously measure O<sub>2</sub> and CO<sub>2 </sub>mole fractions in air sampled from soil, stem, and branch chambers. Chambers were operated in an open flow-through steady-state design aimed at equilibrium mole fractions within a few hundred ppm of atmospheric background. Using a multiplexer valve design, we measured chambers sequentially by directing chamber air at a controlled flow rate to the gas analyzing system.</p><p>Preliminary analysis of August to December 2018 data show that chamber-based flux estimates for O<sub>2</sub> and CO<sub>2</sub> were anti-correlated at all times, and that the O<sub>2</sub>:CO<sub>2</sub> molar exchange ratios (defined as ‑Δ[O<sub>2</sub>]/Δ[CO<sub>2</sub>]) varied considerably over time and between the different ecosystem compartments (soil, stems, and branches) with a median (interquartile range) of 0.94 (0.75 to 1.09).</p><p>In system 2, CO<sub>2</sub>, O<sub>2</sub> and water vapor (H<sub>2</sub>O) measurements were performed with a fast response (5 Hz) gas analyzer using tunable infrared laser direct absorption spectroscopy (TILDAS, Aerodyne Research Inc., USA). We measured fluctuations in O<sub>2</sub>:CO<sub>2</sub> exchange ratios in air sampled at 1.5 times the canopy height, i.e. a typical eddy covariance set-up.</p><p>Analysis of the high-frequency data revealed instrumental noise levels of ≈±12 ppm O<sub>2</sub>. Fourier transformation of high-frequency data obtained during well-mixed boundary layer conditions indicate that turbulent fluctuations of the O<sub>2</sub> signal were insufficiently resolved when compared to the CO<sub>2</sub> power spectra. When averaging high-frequency data to 2-min aggregates, instrumental noise was reduced to ≈±1 ppm, similar to the precision of system 1. At this timescale, contemporaneous measurements of above-canopy air revealed agreement between the fuel cell and the laser systems, both in O<sub>2</sub> mole fraction (R<sup>2</sup> = 0.6 slope = 0.7, MAE = 1.6 ppm) and in estimated O<sub>2</sub>:CO<sub>2</sub> exchange ratios of 1.01 and 0.97 for system 1 and 2, respectively.</p><p>Our presentation will expand on the applicability of both O<sub>2</sub> and CO<sub>2 </sub>measurement systems with regard to micrometeorological flux techniques. Specifically, we elucidate on the potential of using O<sub>2 </sub>flux measurements as a constraint for estimating ecosystem-scale gross primary production.</p><!-- COMO-HTML-CONTENT-END -->

Distinguishing the albedo of exoplanets from stellar activity

Context. Light curves show the flux variation from the target star and its orbiting planets as a function of time. In addition to the transit features created by the planets, the flux also includes the reflected light component of each planet, which depends on the planetary albedo. This signal is typically referred to as phase curve and could be easily identified if there were no additional noise. As well as instrumental noise, stellar activity, such as spots, can create a modulation in the data, which may be very difficult to distinguish from the planetary signal. Aims. We analyze the limitations imposed by the stellar activity on the detection of the planetary albedo, considering the limitations imposed by the predicted level of instrumental noise and the short duration of the obervations planned in the context of the CHEOPS mission. Methods. As initial condition, we have assumed that each star is characterized by just one orbiting planet. We built mock light curves that included a realistic stellar activity pattern, the reflected light component of the planet and an instrumental noise level, which we have chosen to be at the same level as predicted for CHEOPS. We then fit these light curves to try to recover the reflected light component, assuming the activity patterns can be modeled with a Gaussian process. Results. We estimate that at least one full stellar rotation is necessary to obtain a reliable detection of the planetary albedo. This result is independent of the level of noise, but it depends on the limitation of the Gaussian process to describe the stellar activity when the light curve time-span is shorter than the stellar rotation. As an additional result, we found that with a 6.5 magnitude star and the noise level of CHEOPS, it is possible to detect the planetary albedo up to a lower limit of Rp = 0.03 R*. Finally, in presence of typical CHEOPS gaps in the simulations, we confirm that it is still possible to obtain a reliable albedo.

Intensity Mapping Foreground Cleaning with Generalized Needlet Internal Linear Combination

Intensity mapping (IM) is a new observational technique to survey the large-scale structure of matter using spectral emission lines. IM observations are contaminated by instrumental noise and astrophysical foregrounds. The foregrounds are at least three orders of magnitude larger than the searched signals. In this work, we apply the Generalized Needlet Internal Linear Combination (GNILC) method to subtract radio foregrounds and to recover the cosmological HI and CO signals within the IM context. For the HI IM case, we find that GNILC can reconstruct the HI plus noise power spectra with 7.0% accuracy forz= 0.13 − 0.48 (960 − 1260 MHz) and ℓ ≲ 400, while for the CO IM case, we find that it can reconstruct the CO plus noise power spectra with 6.7% accuracy forz= 2.4 − 3.4 (26 − 34 GHz) and ℓ ≲ 3000.

Cosmology with the cosmic microwave background temperature-polarization correlation

We demonstrate that the cosmic microwave background (CMB) temperature-polarization cross-correlation provides accurate and robust constraints on cosmological parameters. We compare them with the results from temperature or polarization and investigate the impact of foregrounds, cosmic variance, and instrumental noise. This analysis makes use of the Planck high-ℓ HiLLiPOP likelihood based on angular power spectra, which takes into account systematics from the instrument and foreground residuals directly modelled using Planck measurements. The temperature-polarization correlation (TE) spectrum is less contaminated by astrophysical emissions than the temperature power spectrum (TT), allowing constraints that are less sensitive to foreground uncertainties to be derived. For ΛCDM parameters, TE gives very competitive results compared to TT. For basic ΛCDM model extensions (such as AL, ∑mν, or Neff), it is still limited by the instrumental noise level in the polarization maps.

Random uncertainties of flux measurements by the eddy covariance technique

Large variability is inherent to turbulent flux observations. We review different methods used to estimate the flux random errors. Flux errors are calculated using measured turbulent and simulated artificial records. We recommend two flux errors with clear physical meaning: the flux error of the covariance, defining the error of the measured flux as 1 standard deviation of the random uncertainty of turbulent flux observed over an averaging period of typically 30 min to 1 h duration; and the error of the flux due to the instrumental noise. We suggest that the numerical approximation by Finkelstein and Sims (2001) is a robust and accurate method for calculation of the first error estimate. The method appeared insensitive to the integration period and the value 200 s sufficient to obtain the estimate without significant bias for variety of sites and wide range of observation conditions. The filtering method proposed by Salesky et al. (2012) is an alternative to the method by Finkelstein and Sims (2001) producing consistent, but somewhat lower, estimates. The method proposed by Wienhold et al. (1995) provides a good approximation to the total flux random uncertainty provided that independent cross-covariance values far from the maximum are used in estimation as suggested in this study. For the error due to instrumental noise the method by Lenschow et al. (2000) is useful in evaluation of the respective uncertainty. The method was found to be reliable for signal-to-noise ratio, defined by the ratio of the standard deviation of the signal to that of the noise in this study, less than three. Finally, the random uncertainty of the error estimates was determined to be in the order of 10 to 30 % for the total flux error, depending on the conditions and method of estimation.

Random uncertainties of flux measurements by the eddy covariance technique

Large variability is inherent to turbulent flux observations. We review different methods used to estimate the flux random errors. Flux errors are calculated using measured turbulent and simulated artificial records. We recommend two flux errors with clear physical meaning: the flux error of the co-variance, defining the error of the measured flux as one standard deviation of the random uncertainty of turbulent flux observed over an averaging period of typically 30 min to 1 hour duration; and the error of the flux due to the instrumental noise. We suggest that the numerical approximation by Finkelstein and Sims (2001) is a robust and accurate method for calculation of the first error estimate. The method appeared insensitive to the integration period and the value 200 s sufficient to obtain the estimate without significant bias for variety of sites and wide range of observation conditions. The error proposed by Wienhold et al. (1995) is a good approximation to the total flux random uncertainty provided that independent cross-covariance values far from the maximum are used in estimation as suggested in this study. For the error due to instrumental noise the method by Lenschow et al. (2000) is useful in evaluation of the respective uncertainty. The method was found to be reliable for signal-to-noise ratio, defined by the ratio of the standard deviation of the signal to that of the noise in this study, less than three. Finally, the random uncertainty of the error estimates was determined to be in the order of 10 to 30 % for the total flux error depending on the conditions and method of estimation.

Blind Search for Variability in Planck Data

The sky is full of variable and transient sources on all time scales, from milliseconds to decades. Planck's regular scanning strategy makes it an ideal instrument to search for variable sky signals in the millimetre and submillimetre regime, on time scales from hours to several years. A precondition is that instrumental noise and systematic effects, caused in particular by non-symmetric beam shapes, are properly removed. We present a method to perform a full sky blind search for variable and transient objects at all Planck frequencies.