Planck early results II : The thermal performance of Planck

Peer reviewe

Planck early results V : The Low Frequency Instrument data processing

Peer reviewe

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Planck early results. II. The thermal performance of Planck

The performance of the Planck instruments in space is enabled by their low operating temperatures, 20 K for LFI and 0.1 K for HFI, achieved through a combination of passive radiative cooling and three active mechanical coolers. The scientific requirement for very broad frequency coverage led to two detector technologies with widely different temperature and cooling needs. Active coolers could satisfy these needs; a helium cryostat, as used by previous cryogenic space missions (IRAS, COBE, ISO, Spitzer, AKARI), could not. Radiative cooling is provided by three V-groove radiators and a large telescope baffle. The active coolers are a hydrogen sorption cooler (<20 K), a 4He Joule-Thomson cooler (4.7 K), and a 3He-4He dilution cooler (1.4 K and 0.1 K). The flight system was at ambient temperature at launch and cooled in space to operating conditions. The HFI bolometer plate reached 93 mK on 3 July 2009, 50 days after launch. The solar panel always faces the Sun, shadowing the rest of Planck, and operates at a mean temperature of 384 K. At the other end of the spacecraft, the telescope baffle operates at 42.3 K and the telescope primary mirror operates at 35.9 K. The temperatures of key parts of the instruments are stabilized by both active and passive methods. Temperature fluctuations are driven by changes in the distance from the Sun, sorption cooler cycling and fluctuations in gas-liquid flow, and fluctuations in cosmic ray flux on the dilution and bolometer plates. These fluctuations do not compromise the science data

Planck Early Results. VII. The Early Release Compact Source Catalogue

A brief description of the methodology of construction, contents and usage of the Planck Early Release Compact Source Catalogue (ERCSC), including the Early Cold Cores (ECC) and the Early Sunyaev-Zeldovich (ESZ) cluster catalogue is provided. The catalogue is based on data that consist of mapping the entire sky once and 60% of the sky a second time by Planck, thereby comprising the first high sensitivity radio/submillimetre observations of the entire sky. Four source detection algorithms were run as part of the ERCSC pipeline. A Monte-Carlo algorithm based on the injection and extraction of artificial sources into the Planck maps was implemented to select reliable sources among all extracted candidates such that the cumulative reliability of the catalogue is ≥90%. There is no requirement on completeness for the ERCSC. As a result of the Monte-Carlo assessment of reliability of sources from the different techniques, an implementation of the PowellSnakes source extraction technique was used at the five frequencies between 30 and 143 GHz while the SExtractor technique was used between 217 and 857GHz. The 10σ photometric flux density limit of the catalogue at |b| > 30◦ is 0.49, 1.0, 0.67, 0.5, 0.33, 0.28, 0.25, 0.47 and 0.82 Jy at each of the nine frequencies between 30 and 857 GHz. Sources which are up to a factor of ∼2 fainter than this limit, and which are present in “clean” regions of the Galaxy where the sky background due to emission from the interstellar medium is low, are included in the ERCSC if they meet the high reliability criterion. The Planck ERCSC sources have known associations to stars with dust shells, stellar cores, radio galaxies, blazars, infrared luminous galaxies and Galactic interstellar medium features. A significant fraction of unclassified sources are also present in the catalogs. In addition, two early release catalogs that contain 915 cold molecular cloud core candidates and 189 SZ cluster candidates that have been generated using multifrequency algorithms are presented. The entire source list, with more than 15000 unique sources, is ripe for follow-up characterisation with Herschel, ATCA, VLA, SOFIA, ALMA and other ground-based observing facilities

Upscaling wetland methane emissions from the FLUXNET‐CH4 eddy covariance network (UpCH4 v1.0): model development, network assessment, and budget comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253)