Permafrost thaw can accelerate climate warming by releasing carbon from previously frozen soil in the form of greenhouse gases. Rainfall extremes have been proposed to increase permafrost thaw, but the magnitude and duration of this effect are poorly understood. Here we present empirical evidence showing that one extremely wet summer (+100 mm; 120% increase relative to average June-August rainfall) enhanced thaw depth by up to 35% in a controlled irrigation experiment in an ice-rich Siberian tundra site. The effect persisted over two subsequent summers, demonstrating a carry-over effect of extremely wet summers. Using soil thermal hydrological modelling, we show that rainfall extremes delayed autumn freeze-up and rainfall-induced increases in thaw were most pronounced for warm summers with mid-summer precipitation rainfall extremes. Our results suggest that, with rainfall and temperature both increasing in the Arctic, permafrost will likely degrade and disappear faster than is currently anticipated based on rising air temperatures alone.
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http://dx.doi.org/10.1038/s41467-022-29248-x | DOI Listing |
Halophilic proteins possess unique structural properties and show high stability under extreme conditions. This distinct characteristic makes them invaluable for application in various aspects such as bioenergy, pharmaceuticals, environmental clean-up, and energy production. Generally, halophilic proteins are discovered and characterized through labor-intensive and time-consuming wet lab experiments.
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FSCN Research Center, Organic Chemistry, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden.
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Department of Civil and Environmental Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea. Electronic address:
Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presence of synthetic chemical substances in aquatic systems has emerged as a growing concern. Moreover, limited machine-learning (ML) approaches exist for analyzing environmental data, and the increasing complexity of ML models has made it challenging to understand predictor-outcome relationships. In particular, understanding complex interactions among multiple variables remains challenging.
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Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Azurem Campus, 4800-058 Guimaraes, Portugal.
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View Article and Find Full Text PDFPlant Biol (Stuttg)
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Department of Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany.
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