Earth System Science Data, Volume 13, Issue 7

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EMDNA: an Ensemble Meteorological Dataset for North America
Guoqiang Tang | Martyn P. Clark | Simon Michael Papalexiou | Andrew J. Newman | Andy Wood | Dominique Brunet | Paul H. Whitfield

Abstract. Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, “equally plausible” ensemble members are used to approximate the probability distribution, hence the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1∘ spatial resolution (approx. 10 km grids) from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at (Tang et al., 2020a).

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FLUXNET-CH<sub>4</sub>: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
Kyle Delwiche | Sara Knox | Avni Malhotra | Etienne Fluet‐Chouinard | Gavin McNicol | Sarah Féron | Zutao Ouyang | Dario Papale | Carlo Trotta | Eleonora Canfora | You Wei Cheah | Danielle Christianson | Ma. Carmelita R. Alberto | Pavel Alekseychik | Mika Aurela | Dennis Baldocchi | Sheel Bansal | David P. Billesbach | Gil Bohrer | Rosvel Bracho | Nina Buchmann | David I. Campbell | Gerardo Celis | Jiquan Chen | Weinan Chen | Housen Chu | Higo J. Dalmagro | Sigrid Dengel | Ankur R. Desai | Matteo Detto | Han Dolman | Elke Eichelmann | Eugénie Euskirchen | D. Famulari | Kathrin Fuchs | Mathias Goeckede | Sébastien Gogo | Mangaliso J. Gondwe | Jordan P. Goodrich | Pia Gottschalk | Scott L. Graham | Martin Heimann | Manuel Helbig | Carole Helfter | Kyle S. Hemes | Takashi Hirano | David Y. Hollinger | Lukas Hörtnagl | Hiroyasu Iwata | Adrien Jacotot | Gerald Jurasinski | Minseok Kang | Kuno Kasak | John S. King | Janina Klatt | Franziska Koebsch | Ken W. Krauss | Derrick Y.F. Lai | Annalea Lohila | Ivan Mammarella | Luca Belelli Marchesini | Giovanni Manca | Jaclyn Hatala Matthes | Trofim C. Maximov | Lutz Merbold | Bhaskar Mitra | Timothy H. Morin | Eiko Nemitz | Mats Nilsson | Shuli Niu | Walter C. Oechel | Patricia Y. Oikawa | Kaori Ono | Matthias Peichl | Olli Peltola | Michele L. Reba | Andrew D. Richardson | William J. Riley | Benjamin R. K. Runkle | Youngryel Ryu | Torsten Sachs | Ayaka Sakabe | Camilo Rey‐Sánchez | Edward A. G. Schuur | Karina V. R. Schäfer | Oliver Sonnentag | Jed P. Sparks | Ellen Stuart-Haëntjens | Cove Sturtevant | Ryan C. Sullivan | Daphne Szutu | Jonathan E. Thom | M. S. Torn | Eeva‐Stiina Tuittila | J. Turner | Masahito Ueyama | Alex Valach | Rodrigo Vargas | Andrej Varlagin | Alma Vázquez‐Lule | Joseph Verfaillie | Timo Vesala | George L. Vourlitis | Eric J. Ward | Christian Wille | Georg Wohlfahrt | Guan Xhuan Wong | Zhen Zhang | Donatella Zona | Lisamarie Windham‐Myers | Benjamin Poulter | Robert B. Jackson

Abstract. Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.