Verification of ERA-Interim and ERA5 Reanalyses Data
			on Surface Air Temperature in the Arctic

Demchev, Denis; Kulakov, M.; Makshtas, Alexander; Makhotina, Irina; Filchuk, Kirill; Фролов, И. Э.

doi:10.3103/s1068373920110035

Cited by 17 publications

(20 citation statements)

References 2 publications

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“…In our study, CARRA and ERA5 were compared with daily mean SAT observations at three locations for recent periods (mainly after 2010, observations were used in CARRA, but not in ERA5 due to land-fraction below 50%), and for earlier periods (mainly before 2010, observations not available in either CARRA or ERA5) in northern and eastern Svalbard. Further, the representation of SAT over sea ice has earlier been shown to be a weakness in many reanalyses 26 , 27 , 29 , 30 . Therefore, we additionally investigated how the presence of nearby sea ice impacted differences in SAT between observations and the two reanalyses at ten observation sites (Table S3 ).…”

Section: Methodsmentioning

confidence: 99%

“…The scarcity of available near-surface in-situ observations in the region makes reanalyses more dependent on model assumptions compared to more data-rich regions, and it both hampers the validation and reduces the confidence of the reanalyses in this region. Recently, it has been shown that a warm bias is present at the surface and over sea ice in most reanalyses 25 – 30 .…”

Section: Introductionmentioning

confidence: 99%

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Exceptional warming over the Barents area

Isaksen

Nordli

Иванов

et al. 2022

Sci Rep

131

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In recent decades, surface air temperature (SAT) data from Global reanalyses points to maximum warming over the northern Barents area. However, a scarcity of observations hampers the confidence of reanalyses in this Arctic hotspot region. Here, we study the warming over the past 20–40 years based on new available SAT observations and a quality controlled comprehensive SAT dataset from the northern archipelagos in the Barents Sea. We identify a statistically significant record-high annual warming of up to 2.7 °C per decade, with a maximum in autumn of up to 4.0 °C per decade. Our results are compared with the most recent global and Arctic regional reanalysis data sets, as well as remote sensing data records of sea ice concentration (SIC), sea surface temperature (SST) and high-resolution ice charts. The warming pattern is primarily consistent with reductions in sea ice cover and confirms the general spatial and temporal patterns represented by reanalyses. However, our findings suggest even a stronger rate of warming and SIC-SAT relation than was known in this region until now.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exceptional warming over the Barents area

Isaksen

Nordli

Иванов

et al. 2022

Sci Rep

131

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“…Air temperature values for the 20-year study are from the Era-Interim 0.125 • × 0.125 • , 6-hourly, reanalysis [22]. 2 m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions [23], who compares air temperature measurements from ground-based weather stations, drifting buoys and the North Pole drifting stations, shows the insignificant overestimation of 2 m temperature in the ERA-Interim reanalysis compilation with observations from coastal stations (the bias does not exceed 0.5 • C), i.e., data from the ground-based meteorological stations are assimilated quite effective. However, the discrepancy between the reanalysis and drifting buoys observations is greater and ranged from −6.95 • C to 0.78 • C and depend on the type of buoy used and the season-in the cold season the difference is much greater.…”

Section: Methodsmentioning

confidence: 99%

Svalbard’s Mesoscale Environmental Factor Impact on the Wind Field

Kitowska

Petelski

2021

Atmosphere

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The mesoscale circulation along the west shore of Spitsbergen is largely controlled by the difference in temperature between the glaciers and surface sea temperatures. We describe how the mesoscale effect influences the atmospheric circulation patterns. The conducted research was based on reanalysis data, model data, and atmospheric measurements; wind data from different sources and scales were compared and analysed. We discuss the situations wherein the mesoscale effect can be identified by analysing the wind direction or its velocity. This study shows the role of the mesoscale effect on the wind in the Svalbard region. Different situations according to the atmospheric patterns taken from a catalogue of 21 circulation types for each day created for Svalbard are analysed and compared with cases of land-sea breeze type circulation for the 20-year period between 1994 and 2013. It is proved that even if it is not possible to distinguish this mesoscale effect based on the difference between local and large-scale wind directions, this factor can be observed by studying the wind speeds. It is claimed that as long as there are glaciers on Spitsbergen, there will be a mesoscale land-sea breeze type circulation controlled by the difference in air temperature over land and water.

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“…The ERA5 launched by ECMWF is currently the most powerful global climate atmospheric reanalysis tool, which provides hourly data and uncertainty estimates of various atmospheric, land and ocean state parameters. Additionally, it has also been widely used for atmospheric parameter estimation [36][37][38]. Existing studies [36,[38][39][40] have shown the accuracy and applicability of wind speed and temperature in ERA5 data.…”

Section: Era5 Accuracy Analysismentioning

confidence: 99%

“…Additionally, it has also been widely used for atmospheric parameter estimation [36][37][38]. Existing studies [36,[38][39][40] have shown the accuracy and applicability of wind speed and temperature in ERA5 data. Hersbach H et al [41] as well showed the good performance of ERA5 data.…”

Section: Era5 Accuracy Analysismentioning

confidence: 99%

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

et al. 2021

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Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for PM2.5 concentration estimation is an important means for estimating regional PM2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R2 of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m−3, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R2 of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability.

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Verification of ERA-Interim and ERA5 Reanalyses Data on Surface Air Temperature in the Arctic

Cited by 17 publications

References 2 publications

Exceptional warming over the Barents area

Exceptional warming over the Barents area

Svalbard’s Mesoscale Environmental Factor Impact on the Wind Field

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

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