Collocations of selected in situ (ICOADS ship) observations and centred 5-day samples of analysis (ERA Interim) and global (HadGEM2) and local (WRF) numerical model surface marine temperature, humidity, and wind data ...

• Main Authors: Danielson, Rick, Zhang, Minghong, Perrie, William
• Format: Dataset
• Language: unknown
• Published: Canadian Cryospheric Information Network 2020
• Subjects: North Atlantic
• Online Access: https://dx.doi.org/10.21963/13169; https://www.polardata.ca/pdcsearch?doi_id=13169

In situ surface marine observations of the International Comprehensive Ocean-Atmosphere Data Set (ICOADS Version 3; Freeman et al. 2017) are taken as a reference for numerical model diagnoses of visibility between January 1979 and December 2004. To ensure that observations are of high quality, we consider only a full range of valid variables (wind speed and direction, sea level pressure, air, dew point, and sea surface temperature, present weather, and visibility) and the strictest ICOADS trimming (i.e., values of air and sea surface temperature, zonal and meridional wind component, sea level pressure, and relative humidity are within 2.8 standard deviations of a smoothed monthly climatology). About one percent of these observations are excluded if any duplication of all variables (including latitude and longitude) are found within a few hours of an existing observation. Also, observations are excluded if any type of precipitation was falling at the time visibility was observed. This is done to emphasize ... : A conventional parameterization of midlatitude warm fog occurrence, based on in situ observations, is employed to estimate marine surface visibility in the Arctic and North Atlantic from three datasets: an ensemble member of the Hadley Earth System (HadGEM2) model and a nested regional WRF simulation that follow historical and future emissions scenarios for 1979-2100, and the ERA Interim reanalysis for 1979-2004. Over large scales (of an entire year and region), all three gridded datasets agree well in terms of variables like surface air temperature, whose systematic differences seem small by comparison with its predicted change over the course of this century. On the other hand, systematic differences are more apparent in large-scale estimates of relative humidity and visibility. Large differences are attributed to a sensitivity to representation bias that is inherent in the formulation of each individual model and analysis. ...