Validation of CESM Land–Atmosphere Coupling Using Observationally Derived Metrics

Validation of CESM Land–Atmosphere Coupling Using Observationally Derived Metrics | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 28 February 2025 V1 Latest version Share on Validation of CESM Land–Atmosphere Coupling Using Observationally Derived Metrics Authors : Nazanin Tavakoli 0009-0001-1501-3184 [email protected] and Paul Dirmeyer 0000-0003-3158-1752 Authors Info & Affiliations https://doi.org/10.22541/au.174077730.07572432/v1 216 views 188 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract A key challenge in climate model validation is the limited availability of observationally-based datasets on a global scale. This research addresses this gap by developing global land-atmosphere (LA) coupling metrics derived from gridded observational data while accounting for stochastic errors in soil moisture satellite measurements. These metrics are then validated against global LA coupling metrics derived from the Community Earth System Model (CESM). This dual approach bridges the gap between observational data and model performance.Global observationally-gridded LA coupling metrics are constructed using soil moisture time series from Soil Moisture Active Passive (SMAP L3) satellite, the Climate Change Initiative (ESA CCI v08.1), and soil moisture time series derived through machine learning trained with in-situ measurements (SoMo.ml). Observation-based surface heat fluxes are sourced from the Global Land Evaporation Amsterdam Model (GLEAM). For validation, simulations from the Atmospheric Model Intercomparison Project (AMIP), the Community Land Model (CLM), and the coupled CESM are utilized, ensuring a comprehensive comparison framework.Since soil moisture variability typically resembles a first-order Markov process, it enables the estimation of random errors in soil moisture time series. This characteristic enables the correction of global observationally-gridded LA coupling metrics, making them more robust when accounting for stochastic errors in satellite-based soil moisture data. Two categories of metrics are employed in this study: Soil Moisture Metrics and Bivariate Metrics.Soil Moisture Metrics focus on corrected soil moisture memory and the identification of key breakpoints, such as the wilting point and critical soil moisture, which help delineate regime distributions. On the other hand, Bivariate Metrics examine the corrected Pearson correlation coefficient between soil moisture and surface fluxes—latent heat flux (LE), sensible heat flux (H), and evaporative fraction (EF). This enables the derivation of the Coupling Index, which quantifies the strength of land-atmosphere interaction.To conduct a comprehensive comparison between observational LA coupling metrics and model-based estimates, we apply Soil Moisture Metrics (memory, breakpoints, regime distribution) and Bivariate Metrics (correlations, Coupling Index) to the CESM model, AMIP simulations, and the CLM model. This comparison identifies global regimes and hotspots for LA interactions, highlights regions where model biases are most pronounced, and interprets spatial patterns and seasonal variations in coupling metrics. Ultimately, our findings provide a framework for assessing model performance and can potentially improve parameterizations and climate model predictions. Supplementary Material File (ncar group meeting 2025-abstract.pdf) Download 95.80 KB File (tavakoli_lmwg_2025.pdf) Download 2.72 MB Information & Authors Information Version history V1 Version 1 28 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cesm model validation corrected observational gridded la coupling metrics global soil moisture-evaporative fraction regime distribution land atmosphere interaction soil moisture memory Authors Affiliations Nazanin Tavakoli 0009-0001-1501-3184 [email protected] View all articles by this author Paul Dirmeyer 0000-0003-3158-1752 View all articles by this author Funding Information National Aeronautics and Space Administration 80NSSC20K1803 - 80NSSC21K1801 Metrics & Citations Metrics Article Usage 216 views 188 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Nazanin Tavakoli, Paul Dirmeyer. Validation of CESM Land–Atmosphere Coupling Using Observationally Derived Metrics. Authorea . 28 February 2025. DOI: https://doi.org/10.22541/au.174077730.07572432/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. 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