2.2 ERA5

ERA5 is a dataset created by the European Centre for Medium-Range Weather Forecasts (ECMWF) and managed by Copernicus Climate Change Services (C3S). To produce a more precise spatial and temporal resolution compared to ERA-Interim, ERA5 uses advanced techniques like 4D-Var and a high-resolution numerical weather model (Hersbach  et al., (2020)). ERA5 assimilates a broad range of observational data, including satellite measurements, ground-based weather stations, and ocean buoys, thus, improving the accuracy of the initial conditions used in weather models. This dataset plays a significant role in weather forecasting by assimilating observational data, offering high-resolution information, maintaining consistency in data records, providing global coverage, and aiding in model validation. All of these factors contribute to the accuracy and reliability of temperature forecasts (Hersbach et al., (2020) ; Yu  et al., (2021) ; McNicholl et al., (2022)).

    2.3 Air Temperature and ERA5

Several studies have assessed ERA5 efficiency both in terms of air temperature data and air temperature trends (Almeida and Coelho, (2023) ; Yilmaz, (2023)). According to them, ERA5 has a tendency to slightly underestimate air temperature in some regions, possesses  a greater accuracy with simulations across flatter areas in contrast to locations of high altitude and complex, uneven terrain patterns (Almeida and Coelho, (2023)). While it may be best to be cautious for short term environmental studies, it is overall really effective to describe air temperature in Europe (Almeida and Coelho, (2023)). Focusing more on temperature trends, ERA5 is shown to be consistent with observed trends with a better accuracy over long term period, its trends can be on average slightly higher than observed but to a negligible level of difference (Yilmaz, (2023)). Factors such as time period, location of study, biases in ground observation and inhomogeneities can introduce trends and variability in the dataset that are inconsistent with observed values (Almeida and Coelho, (2023)). In light of these points, Almeida and Coelho (2023) suggest carrying out assessments of reanalysis datasets under different climatic conditions to eliminate as much uncertainty as possible,  however, all in all, studies still agree that ERA5 can be highly trusted with air temperature. Therefore the data must be simulated to determine if an outlying data point is truly incorrect (whether from an alternate data set or a typo), if it is genuinely a novel change in data (e.g., freak events, creating a novel area for research), or if it is due to an unrecorded change in station location.  A step-by-step approach used in the data manipulation, calibration and verification of this process is shown below. 

    2.4 Analysis Method 

We used the Copernicus Climate Change Service (C3S) Climate Data Store (https://climate.Copernicus.eu/climate-reanalysis) to obtain hourly 2-m air temperature from ERA5-Land surface, which the European Centre provides for Medium-Range Weather Forecasts (ECMWF). The data was downloaded on December 7, 2023, in NetCDF format (CDS, n.d.) for a single month in September 2013 and September 2014; it was available at 0.250 (31 km) gridded resolution in the latitude 49–50 degrees and longitude 8–9 degrees.