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SoilTemp: A global database of near-surface temperature.
Global Change Biology ( IF 11.6 ) Pub Date : 2020-04-20 , DOI: 10.1111/gcb.15123
Jonas J Lembrechts 1 , Juha Aalto 2, 3 , Michael B Ashcroft 4, 5 , Pieter De Frenne 6 , Martin Kopecký 7, 8 , Jonathan Lenoir 9 , Miska Luoto 3 , Ilya M D Maclean 10 , Olivier Roupsard 11, 12 , Eduardo Fuentes-Lillo 13, 14, 15 , Rafael A García 13, 14 , Loïc Pellissier 16, 17 , Camille Pitteloud 16, 17 , Juha M Alatalo 18, 19 , Stuart W Smith 20, 21 , Robert G Björk 22, 23 , Lena Muffler 24, 25 , Amanda Ratier Backes 26, 27 , Simone Cesarz 27, 28 , Felix Gottschall 27, 28 , Joseph Okello 29, 30 , Josef Urban 31, 32 , Roman Plichta 31 , Martin Svátek 31 , Shyam S Phartyal 33, 34 , Sonja Wipf 35, 36 , Nico Eisenhauer 27, 28 , Mihai Pușcaș 37 , Pavel D Turtureanu 38 , Andrej Varlagin 39 , Romina D Dimarco 40 , Alistair S Jump 41 , Krystal Randall 42 , Ellen Dorrepaal 43 , Keith Larson 43 , Josefine Walz 43 , Luca Vitale 44 , Miroslav Svoboda 8 , Rebecca Finger Higgens 45 , Aud H Halbritter 46 , Salvatore R Curasi 47 , Ian Klupar 47 , Austin Koontz 48 , William D Pearse 48, 49 , Elizabeth Simpson 48 , Michael Stemkovski 48 , Bente Jessen Graae 20 , Mia Vedel Sørensen 20 , Toke T Høye 50 , M Rosa Fernández Calzado 51 , Juan Lorite 51 , Michele Carbognani 52 , Marcello Tomaselli 52 , T'ai G W Forte 52 , Alessandro Petraglia 52 , Stef Haesen 53 , Ben Somers 53 , Koenraad Van Meerbeek 53 , Mats P Björkman 22, 23 , Kristoffer Hylander 54 , Sonia Merinero 54 , Mana Gharun 55 , Nina Buchmann 55 , Jiri Dolezal 7, 56 , Radim Matula 8 , Andrew D Thomas 57 , Joseph J Bailey 58 , Dany Ghosn 59 , George Kazakis 59 , Miguel A de Pablo 60 , Julia Kemppinen 3 , Pekka Niittynen 3 , Lisa Rew 61 , Tim Seipel 61 , Christian Larson 61 , James D M Speed 62 , Jonas Ardö 63 , Nicoletta Cannone 64 , Mauro Guglielmin 65 , Francesco Malfasi 65 , Maaike Y Bader 66 , Rafaella Canessa 66 , Angela Stanisci 67 , Juergen Kreyling 24 , Jonas Schmeddes 24 , Laurenz Teuber 24 , Valeria Aschero 68, 69 , Marek Čiliak 70 , František Máliš 71 , Pallieter De Smedt 6 , Sanne Govaert 6 , Camille Meeussen 6 , Pieter Vangansbeke 6 , Khatuna Gigauri 72 , Andrea Lamprecht 73 , Harald Pauli 73 , Klaus Steinbauer 73 , Manuela Winkler 73 , Masahito Ueyama 74 , Martin A Nuñez 75 , Tudor-Mihai Ursu 76 , Sylvia Haider 26, 27 , Ronja E M Wedegärtner 20 , Marko Smiljanic 77 , Mario Trouillier 77 , Martin Wilmking 77 , Jan Altman 7 , Josef Brůna 7 , Lucia Hederová 7 , Martin Macek 7 , Matěj Man 7 , Jan Wild 7 , Pascal Vittoz 78 , Meelis Pärtel 79 , Peter Barančok 80 , Róbert Kanka 80 , Jozef Kollár 80 , Andrej Palaj 80 , Agustina Barros 69 , Ana C Mazzolari 69 , Marijn Bauters 29 , Pascal Boeckx 29 , José-Luis Benito Alonso 81 , Shengwei Zong 82 , Valter Di Cecco 83 , Zuzana Sitková 84 , Katja Tielbörger 85 , Liesbeth van den Brink 85 , Robert Weigel 25 , Jürgen Homeier 25 , C Johan Dahlberg 54, 86 , Sergiy Medinets 87 , Volodymyr Medinets 87 , Hans J De Boeck 1 , Miguel Portillo-Estrada 1 , Lore T Verryckt 1 , Ann Milbau 88 , Gergana N Daskalova 89 , Haydn J D Thomas 89 , Isla H Myers-Smith 89 , Benjamin Blonder 90, 91 , Jörg G Stephan 92 , Patrice Descombes 16, 17, 93 , Florian Zellweger 93 , Esther R Frei 35, 93 , Bernard Heinesch 94 , Christopher Andrews 95 , Jan Dick 95 , Lukas Siebicke 96 , Adrian Rocha 97 , Rebecca A Senior 98 , Christian Rixen 35 , Juan J Jimenez 99 , Julia Boike 100, 101 , Aníbal Pauchard 13, 14 , Thomas Scholten 102 , Brett Scheffers 103 , David Klinges 104 , Edmund W Basham 104 , Jian Zhang 105 , Zhaochen Zhang 105 , Charly Géron 1, 106 , Fatih Fazlioglu 107 , Onur Candan 107 , Jhonatan Sallo Bravo 108 , Filip Hrbacek 109 , Kamil Laska 109 , Edoardo Cremonese 110 , Peter Haase 111, 112 , Fernando E Moyano 96 , Christian Rossi 36, 113, 114 , Ivan Nijs 1
Affiliation  

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long‐term average thermal conditions at coarse spatial resolutions only. Hence, many climate‐forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold‐air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free‐air temperatures, microclimatic ground and near‐surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near‐surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.

中文翻译:

土壤温度:近地表温度的全球数据库。

当前对生态学中空间上明确的模式和过程的分析和预测通常依赖于从标准化气象站插值的气候数据。这些内插的气候数据仅代表粗略的空间分辨率下的长期平均热状况。因此,许多以精细的时空分辨率运行的强迫气候因素被忽略了。这对于观察高度的影响(例如植被,雪和土壤特征)以及在其暴露于辐射,湿气和风的环境中有所不同(例如地形,辐射强迫或冷空气池)的影响尤其重要。由于生活在地面附近的生物与这些微气候条件的关系比与自由空气温度的关系更为紧密,需要微气候的地面和近地表数据来提供有关人为气候变化下这类生物的命运以及它们所生活的生态系统功能的现实预测。为填补这一关键空白,我们着重呼吁人们呼吁温度时间系列提交给GeoTemp,SoilTemp是一个地理空间数据库计划,旨在汇编来自世界各地的土壤和近地表温度数据。目前,该数据库包含来自51个国家/地区的所有关键生物群落的7538个温度传感器的时间序列。该数据库将为增进全球对微气候的了解铺平道路,并弥合现有气候数据与以与大多数生物和生态系统过程相关的精细时空分辨率的气候之间的差距。为了填补这一关键空白,我们着重呼吁将温度时间序列提交给GeoTemp,这是一个地理空间数据库计划,该计划收集了来自世界各地的土壤和近地表温度数据。目前,该数据库包含来自51个国家/地区的所有关键生物群落的7538个温度传感器的时间序列。该数据库将为增进全球对微气候的了解铺平道路,并弥合现有气候数据与以与大多数生物和生态系统过程相关的精细时空分辨率的气候之间的差距。为了填补这一关键空白,我们着重呼吁将温度时间序列提交给GeoTemp,这是一个地理空间数据库计划,该计划收集了来自世界各地的土壤和近地表温度数据。目前,该数据库包含来自51个国家/地区的所有关键生物群落的7538个温度传感器的时间序列。该数据库将为增进全球对微气候的了解铺平道路,并弥合现有气候数据与以与大多数生物和生态系统过程相关的精细时空分辨率的气候之间的差距。一项地理空间数据库计划,用于汇编来自世界各地的土壤和近地表温度数据。目前,该数据库包含来自51个国家/地区的所有关键生物群落的7538个温度传感器的时间序列。该数据库将为增进全球对微气候的了解铺平道路,并弥合现有气候数据与以与大多数生物和生态系统过程相关的精细时空分辨率的气候之间的差距。一项地理空间数据库计划,用于汇编来自世界各地的土壤和近地表温度数据。目前,该数据库包含来自51个国家/地区的所有关键生物群落的7538个温度传感器的时间序列。该数据库将为增进全球对微气候的了解铺平道路,并弥合现有气候数据与以与大多数生物和生态系统过程相关的精细时空分辨率的气候之间的差距。
更新日期:2020-04-20
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