Can Temperature Extremes in East Antarctica be Replicated from ERA Interim Reanalysis?

Xie, Aihong; Wang, Shimeng; Xiao, Cunde; Kang, Shichang; Gong, Juanjuan; Ding, Minghu; Li, Chuanjin; Dou, Tingfeng; Ren, Jiawen; Qin, Dahe

doi:10.1657/aaar0015-048

Cited by 6 publications

(5 citation statements)

References 54 publications

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“…The IWV issues in ERA-Interim may be linked to the large surface air temperature biases of the reanalysis diagnosed by Bracegirdle and Marshall (2012), from coastal station observations which are related to its too-smooth orography. In addition, Xie et al (2016), showed that the replicability of daily and annual variance of surface air temperature in this reanalysis decreases from the coast to the interior of the continent. These results also support the findings of Parracho et al (2018) that the IWV variability and trends in ERA-Interim reanalysis are more realistic near the coast where in situ observations are assimilated than in the interior where the reanalysis mainly relies on satellite observations and short-term model forecasts.…”

Section: Analysis Of Outlying Sitesmentioning

confidence: 72%

Consistency and representativeness of integrated water vapour from ground-based GPS observations and ERA-Interim reanalysis

Bock

Parracho

2019

Atmos. Chem. Phys.

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Abstract. This study examines the consistency and representativeness differences of daily integrated water vapour (IWV) data from ERA-Interim reanalysis and GPS observations at 120 global sites over a 16-year period (1995–2010). Various comparison statistics are analysed as a function of geographic, topographic, and climatic features. A small (±1 kg m−2) bias is found in the reanalysis across latitudes (moist in northern and southern midlatitudes and dry in the tropics). The standard deviation of daily IWV differences is generally below 2 kg m−2 but peaks in the northern and southern storm-track regions. In general, the larger IWV differences are explained by increased representativeness errors, when GPS observations capture some small-scale variability that is not resolved by the reanalysis. A representativeness error statistic is proposed which measures the spatiotemporal variability in the vicinity of the GPS sites, based on reanalysis data at the four surrounding grid points. It allows to predict the standard deviation of daily IWV differences with a correlation of 0.73. In general, representativeness differences can be reduced by temporal averaging and spatial interpolation from the four surrounding grid points. A small number of outlying cases (15 sites) which do not follow the general tendencies are further examined. It is found that their special topographic and climatic features strongly enhance the representativeness errors (e.g. steep topography, coastlines, and strong seasonal cycle in monsoon regions). Discarding these sites significantly improves the global ERA-Interim and GPS comparison results. The selection of sites a priori, based on the representativeness error statistic, is able to detect 11 out of the 15 sites and improve the comparison results by 20 % to 30 %.

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Section: Analysis Of Outlying Sitesmentioning

confidence: 72%

Consistency and representativeness of integrated water vapour from ground-based GPS observations and ERA-Interim reanalysis

Bock

Parracho

2019

Atmos. Chem. Phys.

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“…At present, a number of available global reanalyses have been released by China [22], Europe [18], the United States [23], and Japan [24]. Many studies have assessed the performance of these datasets in different regions [25][26][27][28]. Although the estimation for ERA-Interim shows that the largest differences are found in the polar regions, particularly in Antarctica, where this reanalysis dataset differs to the greatest extent in terms of both absolute temperatures and anomalies from measured near-surface air temperatures [29,30], some studies have generally found that ERA-Interim performs well in representing Antarctic temperature [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of ERA5 Reanalysis for Antarctic Near-Surface Air Temperature

et al. 2021

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The European Center for Medium-Range Weather Forecasts (ECMWF) released its latest reanalysis dataset named ERA5 in 2017. To assess the performance of ERA5 in Antarctica, we compare the near-surface temperature data from ERA5 and ERA-Interim with the measured data from 41 weather stations. ERA5 has a strong linear relationship with monthly observations, and the statistical significant correlation coefficients (p < 0.05) are higher than 0.95 at all stations selected. The performance of ERA5 shows regional differences, and the correlations are high in West Antarctica and low in East Antarctica. Compared with ERA5, ERA-Interim has a slightly higher linear relationship with observations in the Antarctic Peninsula. ERA5 agrees well with the temperature observations in austral spring, with significant correlation coefficients higher than 0.90 and bias lower than 0.70 °C. The temperature trend from ERA5 is consistent with that from observations, in which a cooling trend dominates East Antarctica and West Antarctica, while a warming trend exists in the Antarctic Peninsula except during austral summer. Generally, ERA5 can effectively represent the temperature changes in Antarctica and its three subregions. Although ERA5 has bias, ERA5 can play an important role as a powerful tool to explore the climate change in Antarctica with sparse in situ observations.

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“…In this paper, we have introduced the PANDA AWS network which can monitor the meteorology from Zhongshan to beyond Panda S with high spatial and temporal resolution. The data collected during the past decades are reliable after calibration and homogenization, and have been used widely in meteorological and climate change research in Antarctica (e.g., Xie et al, 2016, Ding et al 2021a. The data can also be used to derive surface energy balance, assimilated into reanalyzes, and used to evaluate climate models and to validate satellite data.…”

Section: Discussionmentioning

confidence: 99%

The PANDA automatic weather station network between the coast and Dome A, East Antarctica

Ding

Zou

Sun

et al. 2022

Preprint

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Abstract. This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network which includes eleven automatic weather stations (AWS) across Prydz Bay-Amery Ice Shelf-dome area from the coast to the summit of the East Antarctica ice sheet. The ~1460 km transect from Zhongshan to Panda S station follows roughly along ~77° E longitude and covers all geographic and climatic units of East Antarctica. Initial inland observation, near the coast, started in the 1996/1997 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1-hour intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real-time via the ARGOS system. Data quality is generally very reliable and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol, and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the network. This dataset, which is publicly available, is planned to be updated on a near-real time and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at https://doi.org/10.11888/Atmos.tpdc.272721 (Ding et al., 2022).

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Can Temperature Extremes in East Antarctica be Replicated from ERA Interim Reanalysis?

Cited by 6 publications

References 54 publications

Consistency and representativeness of integrated water vapour from ground-based GPS observations and ERA-Interim reanalysis

Consistency and representativeness of integrated water vapour from ground-based GPS observations and ERA-Interim reanalysis

An Assessment of ERA5 Reanalysis for Antarctic Near-Surface Air Temperature

The PANDA automatic weather station network between the coast and Dome A, East Antarctica

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