The Arctic is currently warming at a rate three to four times greater than the global
average. This region also has important implications for sea level rise and the global radiation
balance. Therefore, the Arctic requires special attention in the study of meteorological
conditions, which is often accomplished through the use of reanalysis models. Reanalysis
models are weather forecasting systems that use a combination of observational data and
numerical models to produce a best guess of atmospheric variables at high resolution and for
a large period. When data from these reanalysis models are used, it is crucial to know the
uncertainty and potential biases that are associated with each parameter. In this study, we
used data from 17 meteorological stations located in the Arctic to validate the performance
of the ERA5 reanalysis model. The surface meteorological variables include air temperature
at 2m, relative humidity, surface pressure, wind speed/direction, and shortwave downward
radiation. We then performed a climatology of the Arctic region using ERA5 data. The
validation results show that the temperature (R2
mean = 0.94 with σR2 = 0.02) and surface
pressure (R2
mean= 0.98 with σR2 = 0.02) predictions are very accurate. However, the predictions
of relative humidity are worse with large variations (R2
mean = 0.42 with σR2 = 0.20).
Finally, the performance of the wind speed prediction (R2
mean = 0.49 with σR2 = 0.12) is
quite variable depending on the topographic situation of the station and the validation of
shortwave downward radiation flux gives satisfactory results (R2
mean = 0.87 with σR2 = 0.03).
Compared to other studies, the temporal resolution used (hourly in this study versus daily or
monthly in other studies) shows a strong influence on the quality of the wind and shortwave
radiation predictions and no influence on the temperature evaluation. The climatology of
the surface variables shows the spatial and seasonal variations of the stations distributed
in all regions of the Arctic. This study, unprecedented in the higher number of years used,
the number of variables considered and its high temporal resolution, provides the scientific
community with recommendations on the confidence to be placed in each of the surface
variables produced by ERA5. The climatology provides a good overview of common weather
conditions in the Arctic that can be used to contextualize future measurement campaigns.