Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region

Wang, Jiali; Huang, Chenfu; Peco, Kyle; Xue, Pengfei; Chakraborty, TC; Yang, Zhao; Qian, Yun; Hetland, Robert

doi:10.15485/3013004

Title: Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region

Dataset
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Abstract

This data package contains observation-based and model-simulated datasets (all provided in NetCDF format) for evaluating how wintertime lake-ice representation affects regional weather and climate over the Laurentian Great Lakes (freshwater lake ecosystem) during the high–ice-cover winter of 2009. The observational component includes: (1) Stage IV gridded precipitation at 4 km, hourly resolution for January–February 2009 over the Great Lakes region (radar–gauge multisensor precipitation analyses); (2) Great Lakes Surface Environmental Analysis (GLSEA) satellite-derived lake-ice coverage at 1.3 km, daily resolution for the 2009 winter months, providing ice coverage over Lakes Superior, Michigan, Huron, Erie, and Ontario; and (3) in situ measurements at the Standard Rock site on Lake Superior from the Great Lakes Evaporation Network (GLEN) at hourly resolution, including near-surface atmospheric variables and sensible and latent heat fluxes (air–lake exchange) at a fixed point location. The modeling component provides corresponding fields from two simulations, both archived at 4 km, hourly resolution: a standalone Weather Research Forecasting model (WRF) run driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), and a two-way coupled model using WRF and the Finite Volume Community Ocean Model (WRF-FVCOM, a 3-D hydrodynamic lake model). These outputs include variables relevant to air–lake interactionmore »« less

Authors:

Wang, Jiali; Huang, Chenfu; Peco, Kyle; Xue, Pengfei; Chakraborty, TC; Yang, Zhao; Qian, Yun; Hetland, Robert

Argonne National Laboratory
Michigan Technological University
Pacific Northwest National Laboratory (PNNL)

Publication Date:: Tue Dec 31 19:00:00 EST 2024

DOE Contract Number:: AC02-05CH11231

Research Org.:: COMPASS-GLM

Sponsoring Org.:: ESS-DIVE; U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > AIR TEMPERATURE; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND DIRECTION; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND SPEED; EARTH SCIENCE > ATMOSPHERE > CLOUDS > CLOUD DYNAMICS > MOISTURE FLUX; EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT; EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION PROFILES > LATENT HEAT FLUX; EARTH SCIENCE > CLIMATE INDICATORS > TERRESTRIAL HYDROSPHERE INDICATORS > RIVER/LAKE ICE FREEZE; EARTH SCIENCE > OCEANS > SEA ICE; Great Lakes; Regional climate; lake ice; surface_upward_sensible_heat_flux

OSTI Identifier:: 3013004

DOI:: https://doi.org/10.15485/3013004

Citation Formats


                    Wang, Jiali, Huang, Chenfu, Peco, Kyle, Xue, Pengfei, Chakraborty, TC, Yang, Zhao, Qian, Yun, and Hetland, Robert. Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region.  United States: N. p., 2024. 
        Web.  doi:10.15485/3013004.

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                    Wang, Jiali, Huang, Chenfu, Peco, Kyle, Xue, Pengfei, Chakraborty, TC, Yang, Zhao, Qian, Yun, & Hetland, Robert. Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region.  United States.  doi:https://doi.org/10.15485/3013004

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                    Wang, Jiali, Huang, Chenfu, Peco, Kyle, Xue, Pengfei, Chakraborty, TC, Yang, Zhao, Qian, Yun, and Hetland, Robert. 2024.  
"Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region".  United States.  doi:https://doi.org/10.15485/3013004.  https://www.osti.gov/servlets/purl/3013004. Pub date:Tue Dec 31 19:00:00 EST 2024

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@article{osti_3013004,

  title        = {Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region},

  author       = {Wang, Jiali and Huang, Chenfu and Peco, Kyle and Xue, Pengfei and Chakraborty, TC and Yang, Zhao and Qian, Yun and Hetland, Robert},

  abstractNote = {This data package contains observation-based and model-simulated datasets (all provided in NetCDF format) for evaluating how wintertime lake-ice representation affects regional weather and climate over the Laurentian Great Lakes (freshwater lake ecosystem) during the high–ice-cover winter of 2009. The observational component includes: (1) Stage IV gridded precipitation at 4 km, hourly resolution for January–February 2009 over the Great Lakes region (radar–gauge multisensor precipitation analyses); (2) Great Lakes Surface Environmental Analysis (GLSEA) satellite-derived lake-ice coverage at 1.3 km, daily resolution for the 2009 winter months, providing ice coverage over Lakes Superior, Michigan, Huron, Erie, and Ontario; and (3) in situ measurements at the Standard Rock site on Lake Superior from the Great Lakes Evaporation Network (GLEN) at hourly resolution, including near-surface atmospheric variables and sensible and latent heat fluxes (air–lake exchange) at a fixed point location. The modeling component provides corresponding fields from two simulations, both archived at 4 km, hourly resolution: a standalone Weather Research Forecasting model (WRF) run driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), and a two-way coupled model using WRF and the Finite Volume Community Ocean Model (WRF-FVCOM, a 3-D hydrodynamic lake model). These outputs include variables relevant to air–lake interaction and lake-effect processes (e.g., near-surface temperature, humidity, wind, precipitation, and surface turbulent fluxes), enabling direct comparison with the observational datasets. Users can analyze and visualize these NetCDF files with common tools such as Python (e.g., xarray, netCDF4, numpy, pandas), NCO/CDO, Panoply, or ncview; NetCDF variables can also be converted to other formats (e.g., CSV, GeoTIFF) using these utilities.},

  doi          = {10.15485/3013004},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Dec 31 19:00:00 EST 2024},

  month        = {Tue Dec 31 19:00:00 EST 2024}

}

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DOI: https://doi.org/10.15485/3013004

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