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

Dataset ·
DOI:https://doi.org/10.15485/3013004· OSTI ID:3013004
 [1];  [2];  [1];  [2];  [3];  [3];  [3];  [3]
  1. Argonne National Laboratory
  2. Michigan Technological University
  3. Pacific Northwest National Laboratory (PNNL)
+ Show Author Affiliations

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.

Research Organization:
COMPASS-GLM
Sponsoring Organization:
ESS-DIVE; U.S. DOE > Office of Science > Biological and Environmental Research (BER)
DOE Contract Number:
AC02-05CH11231
OSTI ID:
3013004
Country of Publication:
United States
Language:
English

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Related Subjects

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