Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign
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| Title: | Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign |
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| Authors: | William Rudisill, Alan Rhoades, Zexuan Xu, Daniel R. Feldman |
| Source: | Bulletin of the American Meteorological Society, vol 105, iss 7 BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, vol 105, iss 7 |
| Publisher Information: | American Meteorological Society, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Atmospheric sciences, Model evaluation/ performance, Climate change science, Climate, Temperature, 0207 environmental engineering, Mountain meteorology, 02 engineering and technology, 01 natural sciences, Physical Geography and Environmental Geoscience, Atmospheric Sciences, Climate Action, Earth Sciences, Meteorology & Atmospheric Sciences, Astronomical and Space Sciences, 0105 earth and related environmental sciences |
| Description: | Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates. |
| Document Type: | Article |
| File Description: | application/pdf |
| ISSN: | 1520-0477 0003-0007 |
| DOI: | 10.1175/bams-d-23-0082.1 |
| DOI: | 10.1175/bams-d-23-0082.s1 |
| Access URL: | https://escholarship.org/content/qt1906r408/qt1906r408.pdf https://escholarship.org/uc/item/1906r408 https://escholarship.org/uc/item/2qs7g9nt https://escholarship.org/content/qt2qs7g9nt/qt2qs7g9nt.pdf |
| Rights: | CC BY URL: http://www.ametsoc.org/PUBSReuseLicenses |
| Accession Number: | edsair.doi.dedup.....9af7a469554b8d6f150afacd7b5d71ac |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates. |
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| ISSN: | 15200477 00030007 |
| DOI: | 10.1175/bams-d-23-0082.1 |