Evaluation of reanalysis air temperature and precipitation in high‐latitude Asia using ground‐based observations

Lan, Huiting; Guo, Dali; Hua, Wei; Pepin, Nick; Sun, Jianqi

doi:10.1002/joc.7937

Cited by 12 publications

(4 citation statements)

References 56 publications

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“…ERA5 is a new-generation product widely used in meteorological and oceanographic data analysis [42,43]. It has a smaller bias than other datasets and has been verified for its accuracy at high latitudes by many scholars [44][45][46]. Compared to other datasets, ERA5 has been proven by many scholars to have good accuracy and high degree of fitting.…”

Section: Era5 Reanalysis Datamentioning

confidence: 99%

Interdecadal Variation Trend of Arctic Wind Energy

Wang,

Wu,

et al. 2023

Energies

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The Arctic’s navigation and utilization have garnered global interest due to the economic and strategic significance of its open shipping routes. The feasibility and cost-effectiveness of Arctic wind power generation depend largely on the potential of wind energy, which in turn influences the construction of new ports. In order to effectively harness polar wind energy, we must understand and adapt to its ever-changing rules. This study leverages ERA5′s 40-year wind field data to estimate the Theil–Sen Median slope and perform Mann–Kendall trend analysis. We consider factors such as wind power density, effective wind speed occurrence, energy level frequency, stability, and resource reserves to comprehensively analyze the intergenerational variations in Arctic wind energy resources. Our findings indicate that Northeast Passage, Davis Strait, and Baffin Bay possess favorable wind power density (1~2 W/m2·yr−1), effective wind speed occurrence (0.1~0.2%·yr−1), energy level frequency (0.1~0.2%·yr−1), stability (−0.005 yr−1), and resource reserves (1 kWh/m2·yr−1). However, these indicators are inferior in the Barents Sea, Canada’s northern archipelagos, and Greenland’s vicinity, where wind energy is relatively poor and unfavorable for development. Autumn dominates the annual change trend of Arctic wind energy, while spring and summer show no significant trends.

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Section: Era5 Reanalysis Datamentioning

confidence: 99%

Interdecadal Variation Trend of Arctic Wind Energy

Wang,

Wu,

et al. 2023

Energies

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“…The mean 2‐metre air temperature is the most accurately reproduced climate variable in ERA‐Interim, irrespective of study location, with correlation values consistently over 0.90 and mean biases of 0.80°C–1.50°C (e.g., Demchev et al, 2020; Grankina et al, 2019; Lan et al, 2023). In these studies, the validations were conducted on a daily timescale before the mean was taken either seasonally (Demchev et al, 2020; Grankina et al, 2019) or annually (Lan et al, 2023). Note that we expect the correlation values to increase as the timescale increases as daily extreme maxima/minima have less overall impact on the correlations of longer periods.…”

Section: Introductionmentioning

confidence: 98%

“…Previous recent studies of ERA5's performance have also been conducted in remote regions of Russia: Matveeva and Sidorchuk (2020) validated the daily mean 2‐metre air temperature, TP and SD on the Yamal Peninsula from 1985 to 2019, Voropay et al (2021) evaluated the total monthly precipitation with station data across South Siberia from 1979 to 2015, and Lan et al (2023) used daily temperature and precipitation variables grouped at an annual timescale to compare several reanalysis datasets, including ERA‐Interim and ERA5, across Northern Siberia from 2000 to 2018.…”

Section: Introductionmentioning

confidence: 99%

“…The bias in ERA5 is consistently around 0.50°C, whereas in ERA‐Interim this increases to 0.96°C, and ERA5 performs best during the summer months and worst during the winter months. When considering the maximum and minimum temperatures, Lan et al (2023) found that both ERA‐Interim and ERA5 perform better reproducing temperature maxima. For ERA‐Interim the absolute bias of the maxima is 1.29°C less than the minima (−1.73°C/3.02°C) and the correlation is increased by 0.06 (0.90/0.84), whereas for ERA5 the absolute bias of the maxima is 0.12°C less than the minima (−2.35°C/2.47°C) and the correlation is increased by 0.08 (0.93/0.85).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the performance of key ERA‐Interim, ERA5 and ERA5‐Land climate variables across Siberia

Clelland,

Marshall,

Baxter

2024

Intl Journal of Climatology

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Reanalysis datasets provide a continuous picture of the past climate for every point on Earth. They are especially useful in areas with few direct observations, such as Siberia. However, to ensure these datasets are sufficiently accurate they need to be validated against readings from meteorological stations. Here, we analyse how values of six climate variables—the minimum, mean and maximum 2‐metre air temperature, snow depth (SD), total precipitation and wind speed (WSP)—from three reanalysis datasets—ERA‐Interim, ERA5 and ERA5‐Land—compare against observations from 29 meteorological stations across Siberia and the Russian Far East on a daily timescale from 1979 to 2019. All three reanalyses produce values of the mean and maximum daily 2‐metre air temperature that are close to those observed, with the average absolute bias not exceeding 1.54°C. However, care should be taken for the minimum 2‐metre air temperature during the summer months—there are nine stations where correlation values are <0.60 due to inadequate night‐time cooling. The reanalysis values of SD are generally close to those observed after 1992, especially ERA5, when data from some of the meteorological stations began to be assimilated, but the reanalysis SD should be used with caution (if at all) before 1992 as the lack of assimilation leads to large overestimations. For low daily precipitation values the reanalyses provide good approximations, however they struggle to attain the extreme high values. Similarly, for the 10‐metre WSP; the reanalyses perform well with speeds up to 2.5 ms−1 but struggle with those above 5.0 ms−1. For these variables, we recommend using ERA5 over ERA‐Interim and ERA5‐Land in future research. ERA5 shows minor improvements over ERA‐Interim, and, despite an increased spatial resolution, there is no advantage to using ERA5‐Land.

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Elevation‐dependent warming and possible‐driving mechanisms over global highlands

Abbas,

Daramola,

2024

Intl Journal of Climatology

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Elevation‐dependent warming (EDW) has been a topic of intense debate due to limited observed data in global highland areas. This study aims to fill this gap by utilizing CRU and ERA5 datasets from 1981 to 2021 to explore the trends of climate change and its elevation dependency. The anomalies of temperature indicators (Tmean, Tmax, and Tmin) in both ERA5 and CRU showed significant warming trends over global highlands. Moreover, the response of temperature indicators to elevation across global highlands is not spatially uniform. The linear regression model based on the elevation showed significant warming signals for the temperature indicators at various elevations over the global highlands. On a regional scale, Tmean and Tmax predominantly showed linear EDW over EU highlands, while Tmean in Asian highlands exhibited EDW signals at 4–5 km. Tmin showed EDW at 2.5–5.5 km with ERA5 and 3–5 km with CRU. In the Andes, EDW was prominent at 2.5–4 km. Overall, EDW signals are evident in all studied regions but vary across them. While assessing the driving factors, the results of this study indicate that total column water vapour (TCWV), snow depth (SD), snow albedo, and normalized difference vegetation index (NDVI) correlated positively with the temperature indicators. These findings emphasize the significance of elevation‐specific interactions between environmental factors and temperature in forecasting temperature changes in mountainous areas. Additionally, temperature exhibited coherence with teleconnection indices from the Atlantic and Pacific Oceans. Asian and European (EU) highlands exhibited interzonal coherence with the Pacific and Atlantic Oceans, while North American (NA) highlands showed coherence, followed by South American (SA) highlands. These findings provide a comprehensive understanding of EDW and its implications for highland regions globally, including the potential for more severe depletion of snow/ice resources in a warmer future.

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Evaluation of reanalysis air temperature and precipitation in high‐latitude Asia using ground‐based observations

Cited by 12 publications

References 56 publications

Interdecadal Variation Trend of Arctic Wind Energy

Interdecadal Variation Trend of Arctic Wind Energy

Evaluating the performance of key ERA‐Interim, ERA5 and ERA5‐Land climate variables across Siberia

Elevation‐dependent warming and possible‐driving mechanisms over global highlands

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