Browsing by Author "Johnston, Alice S. A."
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Item Open Access Greater local cooling effects of trees across globally distributed urban green spaces(Elsevier, 2023-11-24) Kim, Jiyoung; Khouakhi, Abdou; Corstanje, Ronald; Johnston, Alice S. A.Urban green spaces (UGS) are an effective mitigation strategy for urban heat islands (UHIs) through their evapotranspiration and shading effects. Yet, the extent to which local UGS cooling effects vary across different background climates, plant characteristics and urban settings across global cities is not well understood. This study analysed 265 local air temperature (TA) measurements from 58 published studies across globally distributed sites to infer the potential influence of background climate, plant and urban variables among different UGS types (trees, grass, green roofs and walls). We show that trees were more effective at reducing local TA, with reductions 2–3 times greater than grass and green roofs and walls. We use a hierarchical linear mixed effects model to reveal that background climate (mean annual temperature) and plant characteristics (specific leaf area vegetation index) had the greatest influence on cooling effects across UGS types, while urban characteristics did not significantly influence the cooling effects of UGS. Notably, trees dominated the overall local cooling effects across global cities, indicating that greater tree growth in mild climates with lower mean annual temperatures has the greatest mitigation potential against UHIs. Our findings provide insights for urban heat mitigation using UGS interventions, particularly trees across cities worldwide with diverse climatic and environmental conditions and highlight the essential role of trees in creating healthy urban living environments for citizens under extreme weather conditions.Item Open Access Temperature thresholds of ecosystem respiration at a global scale(Nature Publishing, 2021-02-22) Johnston, Alice S. A.; Meade, Andrew; Ardö, Jonas; Arriga, Nicola; Black, Andy; Blanken, Peter D.; Bonal, Damien; Brümmer, Christian; Cescatti, Alessandro; Dušek, Jiří; Graf, Alexander; Gioli, Beniamino; Goded, Ignacio; Gough, Christopher M.; Ikawa, Hiroki; Jassal, Rachhpal; Kobayashi, Hideki; Magliulo, Vincenzo; Manca, Giovanni; Montagnani, Leonardo; Moyano, Fernando E.; Olesen, Jørgen E.; Sachs, Torsten; Shao, Changliang; Tagesson, Torbern; Wohlfahrt, Georg; Wolf, Sebastian; Woodgate, William; Varlagin, Andrej; Venditti, ChrisEcosystem respiration is a major component of the global terrestrial carbon cycle and is strongly influenced by temperature. The global extent of the temperature–ecosystem respiration relationship, however, has not been fully explored. Here, we test linear and threshold models of ecosystem respiration across 210 globally distributed eddy covariance sites over an extensive temperature range. We find thresholds to the global temperature–ecosystem respiration relationship at high and low air temperatures and mid soil temperatures, which represent transitions in the temperature dependence and sensitivity of ecosystem respiration. Annual ecosystem respiration rates show a markedly reduced temperature dependence and sensitivity compared to half-hourly rates, and a single mid-temperature threshold for both air and soil temperature. Our study indicates a distinction in the influence of environmental factors, including temperature, on ecosystem respiration between latitudinal and climate gradients at short (half-hourly) and long (annual) timescales. Such climatological differences in the temperature sensitivity of ecosystem respiration have important consequences for the terrestrial net carbon sink under ongoing climate change.Item Open Access Three questions to ask before using model outputs for decision support(Nature Publishing Group, 2020-09-30) Grimm, Volker; Johnston, Alice S. A.; Thulke, H.-H.; Forbes, V. E.; Thorbek, P.Decision makers must have sufficient confidence in models if they are to influence their decisions. We propose three screening questions to critically evaluate models with respect to their purpose, organization, and evidence. They enable a more transparent, robust, and secure use of model outputs.