Global Simulation of Snow Algal Blooming by Coupling a Land Surface and Newly Developed Snow Algae Models

Onuma, Yukihiko; Yoshimura, Kei; Takeuchi, Nozomu

doi:10.1029/2021jg006339

Cited by 11 publications

(7 citation statements)

References 68 publications

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“…Blooms first appeared in late June or early July in most regions and years (fig. S5 and data S1 to S3), consistent with previous studies using direct observations ( 4 ) and simulations ( 27 ). The Sentinel-2 images reveal that the blooms moved upslope throughout the season, as expected due to rising snowline and melt of snow at higher elevation (fig.…”

Section: Resultssupporting

confidence: 90%

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Satellite mapping of red snow on North American glaciers

Engstrom,

Quarmby

2023

Sci. Adv.

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Red snow caused by blooms of microalgae darkens the surface of summer snowfields, increasing snowmelt. To assess the contribution of red snow to supraglacial snowmelt in northwestern North America, we systematically mapped the 2019–2022 distribution of blooms by applying supervised classification to 6158 satellite images. Blooms occurred on 5% of the total glaciated area, heavily affecting many glaciers in years of prolonged snow cover duration. Individual glaciers had up to 65% of their surface area affected by bloom in one melt season, which we estimate caused as much as 3 cm of snow meltwater equivalent averaged across the glacier surface. These results demonstrate appreciable snowmelt caused by red snow albedo over vast areas of North American glaciers.

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Section: Resultssupporting

confidence: 90%

“…S3), where air temperatures are cold enough for snow cover to persist well into summer, yet warm enough to provide liquid meltwater. The presence of liquid meltwater in the snowpack is likely a key limiting factor for snow algal growth ( 4 , 16 , 27 ).…”

Section: Resultsmentioning

confidence: 99%

Satellite mapping of red snow on North American glaciers

Engstrom,

Quarmby

2023

Sci. Adv.

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“…Snow algae also play important roles in snow melt and glacier retreat 51 , but nearly all ESMs neglect snow algae effects on snow. Although snow algae blooming and distributions have been successfully implemented in the Minimal Advanced Treatments of Surface Interaction and Runoff land surface model (MATSIRO) 52 , more observations and modeling are needed to evaluate and improve MATSIRO performance before further applications. Better constraining the projections LAP impacts will benefit from the long-term field measurements and hyperspectral remote sensing satellite missions, e.g., the Surface Biology and Geology mission led by NASA 53 .…”

Section: Discussionmentioning

confidence: 99%

A cleaner snow future mitigates Northern Hemisphere snowpack loss from warming

Hao

Bisht

Wang

et al. 2022

Preprint

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Light-absorbing particles (LAP) such as black carbon and dust deposited on seasonal snowpack can result in snow darkening, earlier snowmelt, and regional climate change. However, the future deposition and surface radiative forcing of LAP in snow and their contributions to snowpack change remain unclear. Here, using Earth System Model simulations, we show significant reduction in black carbon deposition in the future. The reduced deposition decreases the December-May average LAP-induced radiative forcing in snow over the Northern Hemisphere from 1.3 W m-2 during 1995-2014 to 0.65 W m-2 and 0.49 W m-2 by 2081-2100 under the SSP126 and SSP585 scenarios, respectively. The reduced black carbon contamination in snow over the Tibetan Plateau will alleviate future snowpack loss due to climate change by 52.1±8.0% and 8.0±1.1% for the two scenarios. Our findings highlight a cleaner snow future and its benefits for future water availability from snowmelt.

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“…The datasets include three- or six-hourly information on surface air temperature, surface air pressure, downward radiation (shortwave and longwave), humidity, wind speed and precipitation rate. As the datasets have a low horizontal resolution (0.5° × 0.5° globally), they were corrected with elevation information at the study sites following the protocol of Onuma and others (2022b). The temperature lapse rate for correction was assumed to be −7.80 × 10 −3 K m −1 , which was estimated from field observations of the Qaanaaq Ice Cap in late July 2012 (Sugiyama and others, 2014).…”

Section: Study Sites and Methodsmentioning

confidence: 99%

“…In mountain snowpacks or upper accumulation areas of glaciers, the cell abundance of green algae on the snow surface exponentially increases with snow melting, as reported by field observations worldwide, such as in Greenland, Alaska and Japan (Takeuchi, 2013; Onuma and others, 2016, 2018); the authors proposed that temporal changes in the abundance of snow algal cells on surface snow could be expressed using a simple differential logistic growth equation known as the ‘snow algae model’. The latest version of the snow algae model is used to reproduce global spatiotemporal changes in snow algae abundance (Onuma and others, 2022b). This model can reproduce the exponential growth of snow algae during snow melting using their initial cell concentration, growth rate and carrying capacity, which limits algal growth.…”

Section: Introductionmentioning

confidence: 99%

Modeling seasonal growth of phototrophs on bare ice on the Qaanaaq Ice Cap, northwestern Greenland

et al. 2022

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Glacier phototroph blooms on the surfaces of ice sheets and glaciers cause albedo reduction, leading to increased melting rates. We observed seasonal changes in the abundance of phototrophs on the Qaanaaq Ice Cap in northwestern Greenland from June to August 2014, and reproduced these changes using numerical and empirical models. The phototroph community on the ice surface mainly consisted of the glacier alga Ancylonema nordenskioldii and the cyanobacterium Phormidesmis priestleyi. The glacier alga appeared on the ice surface in late June, after which its abundance increased exponentially throughout the melting period. A logistic growth model designed for snow algal growth reproduced the measured exponential increases, suggesting that growth could be explained using the model as a function of the ice melting duration. Cyanobacteria appeared and their abundance increased in late July but did not change exponentially thereafter. The abundance of cyanobacteria was explained with an empirical model expressed as a function of the amount of mineral dust on the bare ice surface. Our numerical and empirical models for reproducing glacier algae and cyanobacteria could be useful for quantifying the albedo reduction caused by their growth and the melt rates of the Greenland ice sheet and glaciers in the future.

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Global Simulation of Snow Algal Blooming by Coupling a Land Surface and Newly Developed Snow Algae Models

Cited by 11 publications

References 68 publications

Satellite mapping of red snow on North American glaciers

Satellite mapping of red snow on North American glaciers

A cleaner snow future mitigates Northern Hemisphere snowpack loss from warming

Modeling seasonal growth of phototrophs on bare ice on the Qaanaaq Ice Cap, northwestern Greenland

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