Engineering PapersSearch

DOE OSTI · 3173647

Model-observation discrepancies in Arctic moisture intrusions: causes and pathways for improved simulation

Abstract

Arctic moisture intrusions (MIs), narrow filaments of strong moisture transport, are key drivers of poleward moisture flux and Arctic weather extremes, yet their representation in climate models is poorly understood. Using a new Arctic MI detection algorithm, we document persistent biases across three CMIP generations (CMIP3–CMIP6): models overestimate MI occurrence over the Pacific sector and underestimate it over the Atlantic sector. These errors stem from misrepresented midlatitude westerly jets, with an equatorward North Atlantic jet associated with too few Atlantic MIs, and a poleward, weakened North Pacific jet linked to too many Pacific MIs. Experiments that correct sea surface temperature and sea ice concentration biases and increase atmospheric resolution improve jet structure and MI statistics, while a cloud-locking simulation indicates that better high-frequency cloud–radiation–circulation interactions can yield further gains. Our results clarify pathways to reducing long-standing MI and jet biases, providing guidance for improving simulations of Arctic and midlatitude climate.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ma, Weiming [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Feldl, Nicole [Univ. of California, Santa Cruz, CA (United States)], Wang, Hailong [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Chen, Gang [Univ. of California, Los Angeles, CA (United States)], Lubis, Sandro W. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Qian, Yun [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Harrop, Bryce E. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]. 2026-04-24. Model-observation discrepancies in Arctic moisture intrusions: causes and pathways for improved simulation. https://doi.org/10.1038/s41612-026-01400-0

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES