Global Simulation of Snow Algal Blooming by Coupling a Land Surface and Newly Developed Snow Algae Models. Issue 2 (2nd February 2022)

• Record Type: Journal Article
• Authors: Onuma, Y.; Yoshimura, K.; Takeuchi, N.
• Is Part Of: Journal of geophysical research. Volume 127:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-02
• Subjects: snow algae -- red snow -- snow -- numerical modeling -- global simulation -- land surface model; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2021JG006339
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Snow algae are found from spring to summer on snowfields and glaciers throughout the world. Their blooming darkens snow surfaces, reducing snow surface albedo and accelerating melting. Uncertainties remain, however, regarding the blooming season and global distribution of these algae. To reproduce snow algal bloom temporal and geographical variability, we improved an existing snow algae model using a land surface model calibrated with a reanalysis dataset of the global atmosphere. Snowfall and daylight length data for selected model locations were also incorporated. To evaluate its performance, we used in situ observational data from 15 polar to alpine area sites. The improvements made in this study allowed the reconstruction of detailed snow algal blooming reports from various locations worldwide, and the results suggested that the major factors affecting the appearance of snow algal blooming were the snow melting period duration and algal growth interruption by new snow cover. We then incorporated the updated snow algae model into a land surface model and performed a global simulation. In this case, our simulation suggested that red snow could appear on snowfields during the melting season but only in the absence of frequent new snowfalls, and if the snow cover persists long enough to allow prolonged algal growth. This simulation has the potential to be used for global prediction of future red snow phenomena, which are likely to synchronize with global climate change. Plain Language Summary: Snow algae are commonly found on snow surfaces worldwide. Their blooming changes the surface color to red from white due to their red cells, accelerating melting during summer. Uncertainties remain, however, regarding the blooming season and global distribution of these algae. To reproduce snow algal bloom temporal and geographical variability, we improved an existing snow algae model and evaluated its performance using in situ observational data from 15 polar to alpine area sites. We then incorporated the updated snow algae model into a global land surface model and performed a global algae simulation. The simulation suggests that the timing of red snow appearance is likely to be a balance between the duration of snow melting and the timing of the snowfall event. Our numerical simulation of snow algal blooming has the potential to be used for global prediction of future red snow phenomena, which are likely to synchronize with global climate change. Key Points: Red snow algae model was updated with new observational data from 15 snowfields and incorporated into a land surface model Revised model simulations achieved good agreement with red snow observations at snowfields worldwide, from polar to mid‐latitudes Global simulation suggests that timing of red snow appearance is likely to be a balance between snow melting duration and snowfall timing