Near-Surface Biases in ERA5 Over the Canadian Prairies

Alan K Betts,Raymond L Desjardins,Darren Z Chan

doi:10.3389/fenvs.2019.00129

Abstract

We quantify the biases in the diurnal cycle of air temperature in ERA5, using hourly climate station data for four stations in Saskatchewan, Canada. Compared with ERA-Interim, the biases in ERA5 have been greatly reduced, and show no differences with snow cover. We compute fits to the ERA5 mean air temperature biases based on ERA5 effective cloud albedo. They can be used to improve the ERA5 diurnal cycle of air temperature for modeling agricultural processes. Diurnally, ERA5 has a negative wind speed bias, which increases quasi-linearly with wind speed, and is greater in the daytime than at night. We evaluate ERA5 precipitation against the original climate station precipitation data, and a second generation adjusted precipitation dataset by Mekis and Vincent [2011]. For the warm season, ERA5 has a high bias of 8±9% above the Mekis dataset. ERA5 is -22±7% below the Mekis estimate in winter, suggesting that their correction with snow may be too large. It is likely that the ERA5 precipitation bias is small, which is encouraging for agricultural modelling. Data from a BSRN site near Regina shows that the biases in the downwelling shortwave and longwave radiation estimates in ERA5 are small, and have changed little from ERA-Interim. We showed that the annual cycle of the Saskatchewan surface energy and water budgets in ERA5 are realistic. In particular the damping of extremes in summer precipitation by the extraction of soil water is comparable in ERA5 to our earlier observational estimate based on gravity satellite data.

Highlights

A series of papers have analyzed the coupling of the seasonal and diurnal climatology of the Canadian Prairies to land-surface properties, such as snow cover and agricultural cropping, as well as to reflective cloud cover (Betts et al, 2013a,b, 2014a,b, 2015, 2016; Betts and Tawfik, 2016) using hourly climate data from 15 stations in the Canadian Prairies
By comparing data from four climate stations on the Saskatchewan Prairies with corresponding grid-points in the ERA5 reanalysis, we have explored the model biases in the mean diurnal cycle of temperature, represented by together with maximum (Tx), Tn, Tm, and diurnal range of temperature (DTR)
First we binned the biases by opaque cloud cover, separating the warm season without snow cover, and the cold season with snow cover to make a direct comparison with an earlier study using ERI (Betts and Beljaars, 2017)

Summary

INTRODUCTION

A series of papers have analyzed the coupling of the seasonal and diurnal climatology of the Canadian Prairies to land-surface properties, such as snow cover and agricultural cropping, as well as to reflective cloud cover (Betts et al, 2013a,b, 2014a,b, 2015, 2016; Betts and Tawfik, 2016) using hourly climate data from 15 stations in the Canadian Prairies. For an analysis of the future, whether seasonal or longer term, all weather/climate data required as input by an agricultural model must be obtained from global circulation models (GCMs) Over time these GCMs used for forecasting weather and climate have improved, but forecast output variables still have biases for specific regions and/or specific times of the year. The reason for taking the very short range to study model biases is that we would like to be as close as possible to the large scale analysis, so errors can be attributed to the model formulation and uncertainty in land surface variables rather than to errors in the large scale flow It is well-known from operational verification that systematic errors in near surface variables like 2-m temperature and dew point (which are not used by the atmospheric analysis) are already present right from the start of the forecast (e.g., Haiden et al, 2016). The ERA5 grid-boxes are 0.25 × 0.25 degrees, corresponding to about 27.8 km in latitude and 17.5 km in longitude at 51◦N; much smaller than ERI, which had a spatial resolution of about 80 km

METHODS

Findings

CONCLUSIONS