Investigating Snowpack-Shrub Interactions in the Arctic Tundra using Machine Learning and Process Models

Investigating Snowpack-Shrub Interactions in the Arctic Tundra using Machine Learning and Process Models | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 October 2025 V1 Latest version Share on Investigating Snowpack-Shrub Interactions in the Arctic Tundra using Machine Learning and Process Models Authors : Isabella Lu 0009-0002-1222-854X [email protected] , Linnia Hawkins , Katie Dagon , Sudhanshu Kumar , and Sarah Ryu Authors Info & Affiliations https://doi.org/10.22541/au.176124783.32634509/v1 125 views 91 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The Community Land Model (CLM), the land component of the Community Earth System Model (CESM), simulates key terrestrial processes including carbon cycling, photosynthesis, and snowpack dynamics. While CLM is an indispensable tool for advancing ecosystem process understanding, it is computationally expensive to run. We developed machine learning emulators that efficiently approximate CLM outputs for plant, snow, and soil variables across different parameter settings, enabling efficient exploration of model behavior and hypothesis testing. In this study, we apply these emulators to investigate feedbacks between snowpack, shrub growth, and soil conditions in Arctic Tundra ecosystems. After running sensitivity analysis with our emulators, we identified that increased snowpack suppresses plant growth, while lower snowpack supports plant growth, suggesting a negative feedback loop. To validate these findings we performed CLM simulations with fixed plant parameters and varying snowpack parameters, and results confirmed our emulator predictions, strengthening evidence for a negative snow-vegetation feedback loop in CLM. Our results suggest that CLM does not capture the positive feedback loop between snowpack and plant growth observed in Arctic ecosystems, and we further investigate the mechanisms governing the feedback between snowpack and vegetation. This work demonstrates how machine learning and process models can be used in tandem to improve understanding of ecosystem processes. Supplementary Material File (lu.isabella.sympoisum poster.pdf) Download 1.81 MB Information & Authors Information Version history V1 Version 1 23 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords arctic community earth system model (cesm) emulator machine learning snowpack Authors Affiliations Isabella Lu 0009-0002-1222-854X [email protected] Columbia University View all articles by this author Linnia Hawkins Columbia University View all articles by this author Katie Dagon National Center for Atmospheric Research View all articles by this author Sudhanshu Kumar Auburn University View all articles by this author Sarah Ryu University of California View all articles by this author Metrics & Citations Metrics Article Usage 125 views 91 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Isabella Lu, Linnia Hawkins, Katie Dagon, et al. Investigating Snowpack-Shrub Interactions in the Arctic Tundra using Machine Learning and Process Models. Authorea . 23 October 2025. DOI: https://doi.org/10.22541/au.176124783.32634509/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176124783.32634509/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0067d8c1839df94',t:'MTc3OTU2NDc4Ng=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();