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

Abstract

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.

Full text

atmosphere
Article
An Assessment of ERA5 Reanalysis for Antarctic Near-Surface
Air Temperature
Jiangping Zhu 1,2 , Aihong Xie 1, *, Xiang Qin 1, *, Yetang Wang 3, Bing Xu 1,2 and Yicheng Wang 4


Citation: Zhu, J.; Xie, A.; Qin, X.;
Wang, Y.; Xu, B.; Wang, Y. An
Assessment of ERA5 Reanalysis for
Antarctic Near-Surface Air
Temperature. Atmosphere 2021,12,
217. https://doi.org/10.3390/
atmos12020217
Received: 23 December 2020
Accepted: 28 January 2021
Published: 5 February 2021
Publisher’s Note: MDPI stays neutral
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Attribution (CC BY) license (https://
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4.0/).
1State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources,
Chinese Academy of Sciences, Lanzhou 730000, China; zhujiangping@nieer.ac.cn (J.Z.);
xubing19@mails.ucas.ac.cn (B.X.)
2University of Chinese Academy of Sciences, Beijing 100049, China
3College of Geography and Environment, Shandong Normal University, Jinan 250014, China;
yetangwang@sdnu.edu.cn
4Lanzhou Central Meteorological Observatory, Lanzhou 730000, China; wangyicheng_climate@outlook.com
*Correspondence: xieaihong@nieer.ac.cn or xieaih@lzb.ac.cn (A.X.); qinxiang@lzb.ac.cn (X.Q.);
Tel.: +86-0931-4967338 (A.X.); +86-0931-8278034 (X.Q.)
Abstract:
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.
Keywords: ERA5; ERA-Interim; Antarctica; air temperature; in situ observations
1. Introduction
Antarctica, the southernmost continent on earth, plays a central role in the global
climate system [
1
–
3
]. Many studies have explored climate change in Antarctica [
4
–
8
] and its
influence, including melting of the Antarctic ice sheet and subsequent sea level rise [
9
]. The
temperature increase is an important reason for the melting of the ice sheet [
10
]. However,
the temperature variability in Antarctica is still not obvious compared with the warming
trend of global average temperature [
11
]. For instance, the Antarctic Peninsula is well
known to be one of the most rapidly warming regions across the Antarctic continent [
12
],
but this trend has been absent since 1998 [
13
]. West Antarctica is the key region for heat
and moisture in Antarctica, and it shows a significant warming trend, while there is
no significant temperature change in East Antarctica [
14
,
15
]. In Antarctica, long-term
observational datasets are scarce, and most weather stations are located at coastal areas [
16
]
(Figure 1), which limits climate research in Antarctica.
Atmosphere 2021,12, 217. https://doi.org/10.3390/atmos12020217 https://www.mdpi.com/journal/atmosphere

Atmosphere 2021,12, 217 2 of 23
Atmosphere 2021, 12, x FOR PEER REVIEW 2 of 25
(a)
(b)
Figure 1. (a) Map of Antarctica and the thick black lines outline the boundaries of the three main Antarctic regions; (b)
Spatial distribution of 41 stations cited in the text. Note that the numbers refer to the lists in Table 1.
Figure 1.
(
a
) Map of Antarctica and the thick black lines outline the boundaries of the three main Antarctic regions;
(b) Spatial distribution of 41 stations cited in the text. Note that the numbers refer to the lists in Table 1.

Atmosphere 2021,12, 217 3 of 23
Table 1.
List of the selected meteorological stations including station names, latitude, longitude, elevation, and
record lengths.
Number Station Names Latitude Longitude Elevation (m) Record Lengths Manual or AWS
1 Amundsen–Scott 90S 0E 2835 1979–2018 Manual
2 Arturo Prat 62.5S 59.7W 5 1979–2018 Manual
3 Belgrano II 77.9S 34.6W 256 1980–2018 Manual
4 Bellingshausen 62.2S 58.9W 16 1979–2018 Manual
5 Butler Island 72.2S 60.2W 91 1986–2018 AWS
6 Byrd 80.0S 119.4W 1515 1980–2018 Manual
7 Cape Ross 76.7S 163.0E 201 1990–2018 AWS
8 Casey 66.3S 110.5E 42 1979–2018 Manual
9 Davis 68.6S 78.0E 13 1979–2018 Manual
10 Dome C II 75.1S 123.4E 3280 1995–2018 AWS
11 Dumont d’Urville 66.7S 140.0E 43 1979–2018 Manual
12 Esperanza 63.4S 57.0W 13 1979–2018 Manual
13 Faraday 65.4S 64.4W 11 1979–2018 Manual
14 Ferrell 77.9S 170.8E 45 1980–2018 AWS
15 Gill 80.0S 178.6W 30 1985–2018 AWS
16 Great Wall 62.2S 59.0W 10 1985–2018 Manual
17 Halley 75.5S 26.4W 30 1979–2018 Manual
18 Jubany 62.2S 58.6W 4 1985–2019 Manual
19 King Sejong 62.2S 58.7W 11 1988–2018 Manual
20 Manuela 74.9S 163.7E 80 1984–2018 AWS
21 Marambio 64.2S 56.7W 198 1979–2018 Manual
22 Marble Point 77.4S 163.7E 120 1980–2018 AWS
23 Marilyn 80.0S 165.1E 75 1987–2018 AWS
24 Mario Zucchelli 74.7S 164.1E 92 1987–2018 Manual
25 Marsh 62.2S 58.9W 10 1979–2018 Manual
26 Mawson 67.6S 62.9E 16 1979–2018 Manual
27 McMurdo 77.9S 166.7E 24 1979–2018 Manual
28 Mirny 66.5S 93.0E 30 1979–2018 Manual
29 Molodeznaja 67.7S 45.9E 40 1979–2018 Manual
30 Neumayer 70.7S 8.4W 50 1981–2018 Manual
31 Novolazarevskaya 70.8S 11.8E 119 1979–2018 Manual
32 O’Higgins 63.3S 57.9W 10 1979–2018 Manual
33 Orcadas 60.7S 44.7W 6 1979–2018 Manual
34 Palmer 64.3S 64.0W 8 1979–2018 Manual
35 Rothera 67.5S 68.1W 32 1979–2018 Manual
36 San Martin 68.1S 67.1W 4 1979–2018 Manual
37 Schwerdtfeger 79.9S 170.0E 60 1985–2018 AWS
38 Scott Base 77.9S 166.7E 16 1979–2018 Manual
39 Syowa 69.0S 39.6E 21 1979–2018 Manual
40 Vostok 78.5S 106.9E 3490 1979–2018 Manual
41 Zhongshan 69.4S 76.4E 18 1989–2018 Manual
AWS, automatic weather station.
Reanalysis datasets can provide a numerical description of the recent climate and
describe complete and multivariate atmospheric conditions by combining a fixed data
assimilation system with global observations and satellite data [
2
,
17
,
18
]. Over the past
decades, reanalysis products have been widely used in many research fields, especially in
areas with sparse observations [
19
–
21
]. 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
–
28
]. Al-
though 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
–
33
]. ERA5 is the latest atmospheric reanaly-
sis produced by the European Center for Medium-Range Weather Forecasts (ECMWF). As

Atmosphere 2021,12, 217 4 of 23
the fifth generation of ECMWF reanalysis, ERA5 contains most of the parameters available
in ERA-Interim, and it has many innovative features. The ERA-Interim dataset is at a
0.7 degree resolution and ERA5 provides a much higher resolution of 0.25 degree [
34
]. An
assessment of ERA5 in the southern Antarctic Peninsula-Ellsworth Land Region, with a
specific focus on its application for ice core data, has been performed [
34
], while no research
paper has assessed the applicability of ERA5 for Antarctic near-surface temperature. There-
fore, our objective is to assess the performance of ERA5 in terms of Antarctic near-surface
air temperature and to investigate whether ERA5 is a useful reanalysis for future research
and model development in Antarctica.
2. Materials and Methods
More than 100 automatic weather stations (AWSs) have been installed in Antarctica
to measure basic meteorological parameters [
16
,
35
]. For the AWS data, there are three
important issues: (1) most AWSs fail to produce complete and accurate data for the
harsh environment in Antarctica; (2) many AWSs have moved short distances from their
original location, even several times, during their operation period; and (3) because snow
accumulates on the surface, the height of some AWSs above the ground slightly changes,
which normally results in slightly lower temperature readings [
31
,
35
,
36
]. For these reasons,
the project called the Reference Antarctic Data for Environmental Research (READER) was
undertaken by the Scientific Committee on Antarctic Research (SCAR). The observational
data we used were collected by 9 AWSs and 32 manual stations. The data we used in the
present study were all from the READER project and cover at least 20 years. Locations of the
manned stations and the AWSs are shown in Figure 1, and we provide detailed information
on the compared observations in Table 1. Observational data can be downloaded at
https://legacy.bas.ac.uk/met/READER/surface [
37
]. The ice sheet mask in Figure 1is
derived from the Antarctic Digital Database (ADD), available at http://www.add.scar.org/
index.jsp [38].
ERA5 reanalysis, as a part of the implementation of the EU-funded Copernicus Climate
Change Service, uses the Cycle 41r2 Integrated Forecasting System (IFS) and provides
data on the global weather and climate. Both ERA5 and ERA-Interim are produced by
the ECMWF and use a four-dimensional variational assimilation system. Compared with
ERA-Interim, ERA5 benefits from many improvements in the observation operators as well
as a decade of developments in model physics, core dynamics, and data assimilation. Thus,
ERA5 replaces the highly successful ERA-Interim reanalysis, which provides data from
1 January 1979 to 31 August 2019. Similarly, ERA5 reanalysis extends to 1 January 1950,
and it provides hourly data on many atmospheric, land surface, and sea state parameters
together with estimates of uncertainty for the first time. Compared with ERA-Interim,
ERA5 has a number of new features: (1) the significantly enhanced horizontal resolution
is a 31 km grid spacing in ERA5 but 79 km in ERA-Interim; (2) for radiance data, ERA5
uses version 11 of the Radiative Transfer for the TIROS Operational Vertical Sounder
(RTTOV-11) as the observation operator and an all-sky approach instead of the RTTOV-7
and clear-sky approach in ERA-Interim; (3) the number of observations assimilated in
ERA5 has increased remarkable relative to ERA-Interim; (4) ERA5 contains hourly output
throughout and an uncertainty estimate of 63 km grid spacing; (5) ERA5 includes forcings
for total solar irradiance, ozone, greenhouse gases, and some aerosols; and (6) ERA5
parameters are accumulated from previous post-processing, whereas those in ERA-Interim
are accumulated from the beginning of the forecast [39,40].
ERA5 data are available free on the following web interface: https://cds.climate.
copernicus.eu/#!/search?text=ERA5&type=dataset [
41
]. ERA-Interim data can be down-
loaded at https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ [42].
Because the deviation between the grid and actual elevation can introduce temperature
biases in the reanalyses, we adjust the temperature of the reanalysis grid box over the
stations using the temperature environmental vertical lapse rate of 6.5
◦
C km
−1
that is the
average vertical decline rate of air temperature in the troposphere [16].

Atmosphere 2021,12, 217 5 of 23
To compare the measured temperature with ERA reanalyses, it is necessary to extract
the monthly temperature from reanalyses, and we take the nearest grid data as the com-
parative data instead of interpolating the data to where the stations located for different
interpolation methods may introduce errors caused by assumptions. We selected the corre-
lation coefficient (R), mean bias (MB), and the ratio of the standard deviations (RSD) as
indicators to examine the performance of ERA5 and ERA-Interim for Antarctic near-surface
temperature. Parameter calculations are executed only for the overlapping time spans. The
significance of the correlations is determined by the standard t test, and F test is used to
estimate the significance of trends, and the significant is at the 95% confidence interval.
3. Results
3.1. The Performance of ERA5 over Antarctica
The correlation coefficients between ERA5 and observations are significant for all
months, and they are higher than 0.82, and those of ERA-Interim are more than 0.84,
indicating a stronger linear relationship of monthly temperature. Figure 2displays the
MB between the monthly mean air temperature measured at the 41 weather stations and
the ECMWF reanalysis output data, and we also plot the RSD in this figure. For ERA5,
warm bias prevails between May and September. ERA5 has a monthly bias between
−
0.44 and 1.19
◦
C, and the extreme values appear in August and December, respectively.
ERA-Interim has a cold bias in all months, and this characteristic is different from that
of ERA5. In particular, a large contrast in ERA5 and ERA-Interim bias occurs in winter
(June–August (JJA)).
Atmosphere 2021, 12, x FOR PEER REVIEW 6 of 25
Figure 2. Mean bias (MB, °C) and ratio of standard deviations (RSD) of monthly near-surface temperature simulation from
ERA5 and ERA-Interim.
Figure 3. Time series of annual, autumn (MAM), winter (JJA), spring (SON), and summer (DJF) mean temperature anom-
alies from stations and the corresponding values of ERA5, and ERA-Interim.
The correlations, mean bias, and ratio of SDs between observations and the corre-
sponding data from ERA reanalyses at Antarctic coastal and inland stations are shown in
Figure 4, and the correlations pass the test of significance (p < 0.05). ERA5 presents the
−2
−1
0
1
2
Annual
Stations
ERA5
ERA-Interim
−2
−1
0
1
2
3
−3
−2
−1
0
1
2
3
1980 1985 1990 1995 2000 2005 2010 2015
−4
−3
−2
−1
0
1
2
3
−2
−1
0
1
2
MAM
−2
−1
0
1
2
3
−4
−3
−2
−1
0
1
2
3
4
5
1980 1985 1990 1995 2000 2005 2010 2015
−3
−2
−1
0
1
2
3
−2
−1
0
1
2
JJA
−3
−2
−1
0
1
2
3
4
−6
−5
−4
−3
−2
−1
0
1
2
3
4
5
1980 1985 1990 1995 2000 2005 2010 2015
Yea r
−6
−5
−4
−3
−2
−1
0
1
2
3
4
5
−2
−1
0
1
2
SON
−2
−1
0
1
2
3
−4
−3
−2
−1
0
1
2
3
4
1980 1985 1990 1995 2000 2005 2010 2015
−3
−2
−1
0
1
2
3
−1
0
1
DJF
−2
−1
0
1
2
−3
−2
−1
0
1
2
1980 1985 1990 1995 2000 2005 2010 2015
−2
−1
0
1
West Antarctica
East Antarctica
Antarctica
Antarctic Peninsula
Figure 2.
Mean bias (MB,
◦
C) and ratio of standard deviations (RSD) of monthly near-surface temperature simulation from
ERA5 and ERA-Interim.

Atmosphere 2021,12, 217 6 of 23
As shown in Table 2, for the whole Antarctic ice sheet, there are significant correlations
between ERA5 and observations for all annual and seasonal temperature. Compared to
ERA-Interim, ERA5 exhibits lower performance only in summer (December–February
(DJF)). In this season, the significant correlation coefficient of ERA5 is 0.84, and the corre-
sponding value of ERA-Interim is 0.86. Table 3shows the bias between ERA reanalyses
and observations. In general, ERA5 performs worst in summer, with the largest bias of
1.06
◦
C and the lowest correlation coefficient. Over the whole of Antarctica, ERA5 has
good performance in autumn (March–May (MAM)) and spring (September–November
(SON)), with low bias of 0.16 and 0.38
◦
C, and the highest significant correlation coefficients
are 0.90 and 0.93, respectively. Different from the lowest bias for ERA5, which occurs in
MAM, ERA-Interim has the lowest bias of 0.52
◦
C in JJA. The greatest bias between ERA
reanalyses and observations is shown in DJF, with the cold bias of 1.06 and 0.76
◦
C for ERA5
and ERA-Interim, respectively. In Antarctica, a cold bias prevails for annual and seasonal
mean temperatures in ERA-Interim, whereas ERA5 shows a warm bias in JJA, when the
largest difference between ERA reanalyses is captured. Figure 3shows the time series of
the annual and seasonal mean temperature anomalies with respect to the 1979–2018 mean
from 41 observation locations and the corresponding value of ERA reanalyses. Generally,
there are no clear anomaly differences between ERA5 and ERA-Interim in Antarctica. ERA
reanalyses can reflect common interannual variability, and they can capture the abrupt
changes occurring in Antarctica.
Table 2.
Correlation between observations and ERA reanalyses for annual, autumn (MAM), winter
(JJA), spring (SON), and summer (DJF) mean temperatures in Antarctica, East Antarctica, West
Antarctica, and the Antarctic Peninsula.
Antarctica East
Antarctica
West
Antarctica
Antarctic
Peninsula
Annual ERA5 0.92 0.89 0.93 0.94
ERA-Interim 0.91 0.88 0.91 0.96
MAM ERA5 0.90 0.88 0.93 0.92
ERA-Interim 0.90 0.88 0.90 0.93
JJA ERA5 0.92 0.91 0.93 0.92
ERA-Interim 0.91 0.90 0.88 0.94
SON ERA5 0.93 0.91 0.97 0.93
ERA-Interim 0.93 0.91 0.96 0.95
DJF ERA5 0.84 0.85 0.89 0.82
ERA-Interim 0.86 0.87 0.88 0.83
Note: The correlation coefficients are all significant at the 95% confidence interval.
Table 3.
Bias (
◦
C) between ERA reanalyses and observations for annual, autumn (MAM), winter (JJA),
spring (SON), and summer (DJF) mean temperature in Antarctica, East Antarctica, West Antarctica,
and the Antarctic Peninsula.
Antarctica East
Antarctica
West
Antarctica
Antarctic
Peninsula
Annual ERA5 0.34 0.51 −0.66 0.58
ERA-Interim 0.64 0.97 0.03 0.23
MAM ERA5 0.16 0.07 −0.77 0.67
ERA-Interim 0.64 0.93 0.20 0.15
JJA ERA5 −0.28 −0.22 −1.25 0.10
ERA-Interim 0.52 0.74 0.17 −0.04
SON ERA5 0.38 0.59 −0.69 0.63
ERA-Interim 0.63 0.96 −0.06 0.33
DJF ERA5 1.06 1.53 −0.01 0.96
ERA-Interim 0.76 1.25 −0.20 0.53

Atmosphere 2021,12, 217 7 of 23
Atmosphere 2021, 12, x FOR PEER REVIEW 6 of 25
Figure 2. Mean bias (MB, °C) and ratio of standard deviations (RSD) of monthly near-surface temperature simulation from
ERA5 and ERA-Interim.
Figure 3. Time series of annual, autumn (MAM), winter (JJA), spring (SON), and summer (DJF) mean temperature anom-
alies from stations and the corresponding values of ERA5, and ERA-Interim.
The correlations, mean bias, and ratio of SDs between observations and the corre-
sponding data from ERA reanalyses at Antarctic coastal and inland stations are shown in
Figure 4, and the correlations pass the test of significance (p < 0.05). ERA5 presents the
−2
−1
0
1
2
Annual
Stations
ERA5
ERA-Interim
−2
−1
0
1
2
3
−3
−2
−1
0
1
2
3
1980 1985 1990 1995 2000 2005 2010 2015
−4
−3
−2
−1
0
1
2
3
−2
−1
0
1
2
MAM
−2
−1
0
1
2
3
−4
−3
−2
−1
0
1
2
3
4
5
1980 1985 1990 1995 2000 2005 2010 2015
−3
−2
−1
0
1
2
3
−2
−1
0
1
2
JJA
−3
−2
−1
0
1
2
3
4
−6
−5
−4
−3
−2
−1
0
1
2
3
4
5
1980 1985 1990 1995 2000 2005 2010 2015
Yea r
−6
−5
−4
−3
−2
−1
0
1
2
3
4
5
−2
−1
0
1
2
SON
−2
−1
0
1
2
3
−4
−3
−2
−1
0
1
2
3
4
1980 1985 1990 1995 2000 2005 2010 2015
−3
−2
−1
0
1
2
3
−1
0
1
DJF
−2
−1
0
1
2
−3
−2
−1
0
1
2
1980 1985 1990 1995 2000 2005 2010 2015
−2
−1
0
1
West Antarctica
East Antarctica
Antarctica
Antarctic Peninsula
Figure 3.
Time series of annual, autumn (MAM), winter (JJA), spring (SON), and summer (DJF) mean temperature anomalies
from stations and the corresponding values of ERA5, and ERA-Interim.
The correlations, mean bias, and ratio of SDs between observations and the corre-
sponding data from ERA reanalyses at Antarctic coastal and inland stations are shown
in Figure 4, and the correlations pass the test of significance (p< 0.05). ERA5 presents
the highest correlations in spring for both coastal and inland stations, with significant
correlation coefficients of 0.93 and 0.94, respectively. Compare to ERA-Interim, ERA5 has
higher correlations in MAM and JJA at coastal stations, and lower correlations at inland
stations in these seasons. ERA-Interim always shows cold bias at coastal stations and warm
bias at inland stations; somewhat differently, ERA5 has cold bias in DJF at inland stations,
indicating that the cold biases seen previously for the whole continent are caused by the
coastal stations. The mean bias at coastal stations is always smaller than that of inland
stations in all annual and seasonal mean temperature, and both of the ERA reanalyses
show this feature. For coastal stations, biases in ERA5 are smaller than in ERA-Interim for
all seasons with the exception of austral summer. In MAM, ERA-Interim has the greatest
bias of 0.99 and
−
3.25
◦
C for coastal and inland stations, respectively. For ERA5, only
inland stations have an SD value higher than the observations in MAM, and the RSD of
ERA-Interim is greater than 1 in autumn, winter, and annually at coastal stations.

Atmosphere 2021,12, 217 8 of 23
Atmosphere 2021, 12, x FOR PEER REVIEW 8 of 25
Figure 4. Correlation coefficient (R), MB (°C), and RSD of autumn (MAM), winter (JJA), spring (SON), summer (DJF), and
annual mean temperatures for ERA5 and ERA-Interim at Antarctic coastal and inland stations. Note: The correlation co-
efficients are all significant at the 95% confidence interval.
3.2. The Performance of ERA5 over the Three Subregions in Antarctica
The trend of Antarctic temperature is unclear now, and the tendency and the number
of weather stations may differ among regions, therefore, we divided Antarctica into three
subregions, including East Antarctica, West Antarctica, and the Antarctic Peninsula (Fig-
ure 1) to further explore the performance of ERA5. The correlation coefficients, bias, and
ratio of the SDs of ERA5 and the differences in these variables between ERA5 and ERA-
Interim for monthly temperature measurements at 41 meteorological stations are shown
in Figure 5. It is worth noting that the correlation coefficients here are all significant at the
95% confidence interval. For monthly temperature, the correlation coefficients between
ERA5 and observations were generally high, with correlations higher than 0.95 at every
one of the 41 stations selected, and the high correlations related to the temperature data
have been assimilated into the reanalyses. The difference between the correlation coeffi-
cients of ERA5 and ERA-Interim is fairly small, and the division between them is less than
Figure 4.
Correlation coefficient (R), MB (
◦
C), and RSD of autumn (MAM), winter (JJA), spring (SON), summer (DJF),
and annual mean temperatures for ERA5 and ERA-Interim at Antarctic coastal and inland stations. Note: The correlation
coefficients are all significant at the 95% confidence interval.
3.2. The Performance of ERA5 over the Three Subregions in Antarctica
The trend of Antarctic temperature is unclear now, and the tendency and the num-
ber of weather stations may differ among regions, therefore, we divided Antarctica into
three subregions, including East Antarctica, West Antarctica, and the Antarctic Peninsula
(Figure 1)
to further explore the performance of ERA5. The correlation coefficients, bias,
and ratio of the SDs of ERA5 and the differences in these variables between ERA5 and ERA-
Interim for monthly temperature measurements at 41 meteorological stations are shown in
Figure 5. It is worth noting that the correlation coefficients here are all significant at the 95%
confidence interval. For monthly temperature, the correlation coefficients between ERA5
and observations were generally high, with correlations higher than 0.95 at every one of
the 41 stations selected, and the high correlations related to the temperature data have
been assimilated into the reanalyses. The difference between the correlation coefficients
of ERA5 and ERA-Interim is fairly small, and the division between them is less than 0.01.
As shown in Figure 5b, ERA-Interim shows a higher linear relationship at stations located
in East Antarctica and most stations in the Antarctic Peninsula, whereas ERA5 exhibits a
stronger linear relationship with the observations on the Ross Ice Shelf. For ERA5, warm
bias prevails for stations located at the interior of the East Antarctica, and there is no
distinct pattern of bias over the coastal East Antarctica, West Antarctica, and Antarctic

Atmosphere 2021,12, 217 9 of 23
Peninsula stations. Compared with ERA-Interim, lower bias in ERA5 can be found at
26 stations, and the biggest difference between ERA5 and ERA-Interim occurs at McMurdo.
For ERA5, eight stations have a higher SD value than the observations (Belgrano II, Cape
Ross, Marble Point, Marilyn, Mario Zucchelli, O’Higgins, Rothera, and San Martin), and
only two stations have ERA5 SD values that are more than 20% lower (Dome C II and
Marambio). ERA-Interim RSD values are higher than that those of ERA5 at 32 stations, and
the SD value for ERA-Interim is higher than observations at 16 stations.
Atmosphere 2021, 12, x FOR PEER REVIEW 9 of 25
0.01. As shown in Figure 5b, ERA-Interim shows a higher linear relationship at stations
located in East Antarctica and most stations in the Antarctic Peninsula, whereas ERA5
exhibits a stronger linear relationship with the observations on the Ross Ice Shelf. For
ERA5, warm bias prevails for stations located at the interior of the East Antarctica, and
there is no distinct pattern of bias over the coastal East Antarctica, West Antarctica, and
Antarctic Peninsula stations. Compared with ERA-Interim, lower bias in ERA5 can be
found at 26 stations, and the biggest difference between ERA5 and ERA-Interim occurs at
McMurdo. For ERA5, eight stations have a higher SD value than the observations (Bel-
grano I I, Cape Ross, Marble P oint, Marilyn, Mario Zucchelli, O’Higgins, Rothera, and San
Martin), and only two stations have ERA5 SD values that are more than 20% lower (Dome
C II and Marambio). ERA-Interim RSD values are higher than that those of ERA5 at 32
stations, and the SD value for ERA-Interim is higher than observations at 16 stations.
Atmosphere 2021, 12, x FOR PEER REVIEW 10 of 25
Figure 5. R (a), bias (c), and RSD (e) of monthly air temperature simulation for 41 stations from ERA5 and the difference
of R (b), bias (d), and RSD (f) between ERA5 and ERA-Interim. Note: The correlation coefficients are all significant at the
95% confidence interval.
Figure 6 exhibits the correlations of ERA5 for annual and seasonal temperature and
the difference between these correlations and those for ERA-Interim. Generally, the cor-
relation coefficients between ERA5 and observations are fairly high for annual and sea-
sonal temperature, with significant correlation coefficients (p < 0.05) higher than 0.80 at
most stations. Compared with ERA-Interim, ERA5 exhibits winter temperature better,
with relatively high correlation coefficients at 21 stations. ERA5 always has a stronger
linear relationship with observations at Esperanza, Schwerdtfeger, and Scott Base, and the
biggest difference between ERA5 and ERA-Interim occurs at Scott Base for annual tem-
perature, where the correlation coefficient of ERA5 is 0 .23 h igh er th an t hat of ER A-I nte rim.
Figure 7 shows the same content as Figure 6 but for bias. In general, the bias values (<2.00
°C) are relatively low at stations located at the coastal area of East Antarctica. For annual
and seasonal temperature, ERA5 always has a warm bias at 8 stations (Amundsen–Scott,
Butler Island, Halley, Marambio, Mirny, Neumayer, Schwerdtfeger, and Scott Base), and
a cold bias is shown at 14 stations (Belgrano II, Byrd, Esperanza, King Sejong, Manuela,
Marble Point, Mario Zucchelli, Mawson, McMurdo, Novolazarevskaya, O’Higgins,
Palmer, Rothera, San Martin, and Zhongshan), and ERA5 shows warm biases at the inland
stations (Vostok, Amundsen–Scott, and Dome C II) for the non-summer mean tempera-
ture. The smallest bias of ERA5 occurs at Faraday in MAM, and the data from ERA5 is
lower than observations only by 0.01 °C. The greatest cold and warm bias can be found at
Mario Zucchelli and Halley in the austral winter, with values of −6.94 and 11.61 °C, re-
spectively. Compared with ERA-Interim, ERA5 exhibits lower bias at 10 stations (Dumont
d’Urville, Faraday, Gill, Manuela, Marilyn, Mawson, San Martin, Scott Base, and
Zhongshan) for all annual and seasonal temperatures, and higher bias always be found at
Esperanza, Halley, Marble Point, Mirny, O’Higgins, Rothera, and Schwerdtfeger. At Scott
Base station, warm bias is shown in all annual and seasonal temperatures in ERA5, while
ERA-Interim shows cold bias, and a cold bias of 3.96 °C occurs for winter temperature in
ERA-Interim, whereas ERA5 has a warm bias of 2.40 °C, which is the largest contrast be-
tween ERA5 and ERA-Interim. In particular, a large contrast in bias between autumn and
winter is exhibited at Mawson and McMurdo station, which represent as ERA-Interim
shows large bias larger than 5 °C, but the bias in ERA5 is lower than 1 °C. ERA5 bias is
bigger than ERA-Interim in JJA, and smaller bias are shown in SON and annual tempera-
ture at inland stations. Compared with ERA-Interim, the smaller bias in ERA5 occurs at
five stations (Zhongshan, Casey, Dumont d’Urville, Mawson, Neumayer) at the coastal
Figure 5.
R (
a
), bias (
c
), and RSD (
e
) of monthly air temperature simulation for 41 stations from
ERA5 and the difference of R (
b
), bias (
d
), and RSD (
f
) between ERA5 and ERA-Interim. Note: The
correlation coefficients are all significant at the 95% confidence interval.
Figure 6exhibits the correlations of ERA5 for annual and seasonal temperature and
the difference between these correlations and those for ERA-Interim. Generally, the correla-
tion coefficients between ERA5 and observations are fairly high for annual and seasonal
temperature, with significant correlation coefficients (p< 0.05) higher than 0.80 at most
stations. Compared with ERA-Interim, ERA5 exhibits winter temperature better, with
relatively high correlation coefficients at 21 stations. ERA5 always has a stronger linear
relationship with observations at Esperanza, Schwerdtfeger, and Scott Base, and the biggest
difference between ERA5 and ERA-Interim occurs at Scott Base for annual temperature,
where the correlation coefficient of ERA5 is 0.23 higher than that of ERA-Interim.
Figure 7

Atmosphere 2021,12, 217 10 of 23
shows the same content as Figure 6but for bias. In general, the bias values (<2.00
◦
C)
are relatively low at stations located at the coastal area of East Antarctica. For annual
and seasonal temperature, ERA5 always has a warm bias at 8 stations (Amundsen–Scott,
Butler Island, Halley, Marambio, Mirny, Neumayer, Schwerdtfeger, and Scott Base), and
a cold bias is shown at 14 stations (Belgrano II, Byrd, Esperanza, King Sejong, Manuela,
Marble Point, Mario Zucchelli, Mawson, McMurdo, Novolazarevskaya, O’Higgins, Palmer,
Rothera, San Martin, and Zhongshan), and ERA5 shows warm biases at the inland stations
(Vostok, Amundsen–Scott, and Dome C II) for the non-summer mean temperature. The
smallest bias of ERA5 occurs at Faraday in MAM, and the data from ERA5 is lower than
observations only by 0.01
◦
C. The greatest cold and warm bias can be found at Mario
Zucchelli and Halley in the austral winter, with values of
−
6.94 and 11.61
◦
C, respectively.
Compared with ERA-Interim, ERA5 exhibits lower bias at 10 stations (Dumont d’Urville,
Faraday, Gill, Manuela, Marilyn, Mawson, San Martin, Scott Base, and Zhongshan) for
all annual and seasonal temperatures, and higher bias always be found at Esperanza,
Halley, Marble Point, Mirny, O’Higgins, Rothera, and Schwerdtfeger. At Scott Base station,
warm bias is shown in all annual and seasonal temperatures in ERA5, while ERA-Interim
shows cold bias, and a cold bias of 3.96
◦
C occurs for winter temperature in ERA-Interim,
whereas ERA5 has a warm bias of 2.40
◦
C, which is the largest contrast between ERA5 and
ERA-Interim. In particular, a large contrast in bias between autumn and winter is exhibited
at Mawson and McMurdo station, which represent as ERA-Interim shows large bias larger
than 5
◦
C, but the bias in ERA5 is lower than 1
◦
C. ERA5 bias is bigger than ERA-Interim
in JJA, and smaller bias are shown in SON and annual temperature at inland stations.
Compared with ERA-Interim, the smaller bias in ERA5 occurs at five stations (Zhongshan,
Casey, Dumont d’Urville, Mawson, Neumayer) at the coastal areas of East Antarctica in
all annual and seasonal temperature, and higher bias always at Mirny station. At Byrd
station located at West Antarctica, ERA5 represents bigger bias than that of ERA-Interim
except for autumn. McMurdo Station and Scott Base, both on Ross Island and only 3 km
apart. The temperature in ERA5 at McMurdo Station are always colder than observations,
whereas those at Scott Base are warmer, indicating that the temperature of ERA5 reanalysis
is influenced by small-scale topographical differences.
Atmosphere 2021, 12, x FOR PEER REVIEW 11 of 25
areas of East Antarctica in all annual and seasonal temperature, and higher bias always at
Mirny station. At Byrd station located at West Antarctica, ERA5 represents bigger bias
than that of ERA-Interim except for autumn. McMurdo Station and Scott Base, both on
Ross Island and only 3 km apart. The temperature in ERA5 at McMurdo Station are always
colder than observations, whereas those at Scott Base are warmer, indicating that the tem-
perature of ERA5 reanalysis is influenced by small-scale topographical differences.
In Figure 8, we plot six selected stations (Amundsen–Scott, Byrd, Marambio, Novo-
lazarevskaya, Scott Base, and Vostok) located in central Antarctica, West Antarctica, the
Antarctic Peninsula, the East Antarctica coast, the Ross Ice Shelf, and the interior of East
Antarctica, respectively. For the six stations, ERA5 has slightly lower correlation (R < 0.8)
at Amundsen–Scott and Novolazarevskaya, as shown in Figure 6i, and the bias of ERA5
is larger than that of ERA-Interim only at Byrd. At Novolazarevskaya station, the data
from ERA5 has a poor linear relationship with observations before 2000, but subsequently
fit the observation data well. The temperature from ERA5 shows obvious disparity at Scott
Base and Vostok relative to ERA-Interim. For Marambio, ERA5 shows the same warming
as station data during the period.
Figure 6. Cont.

Atmosphere 2021,12, 217 11 of 23
Atmosphere 2021, 12, x FOR PEER REVIEW 12 of 25
Figure 6. R of ERA5 for austral seasons of autumn (a), winter (c), spring (e), summer (g), annual mean (i), and the difference
of it between ERA5 and ERA-Interim in autumn (b), winter (d), spring (f), summer (h), and annual mean (j). Note: The
correlation coefficients are all significant at the 95% confidence interval.
Figure 6.
R of ERA5 for austral seasons of autumn (
a
), winter (
c
), spring (
e
), summer (
g
), annual
mean (
i
), and the difference of it between ERA5 and ERA-Interim in autumn (
b
), winter (
d
), spring
(
f
), summer (
h
), and annual mean (
j
). Note: The correlation coefficients are all significant at the 95%
confidence interval.

Atmosphere 2021,12, 217 12 of 23
Atmosphere 2021, 12, x FOR PEER REVIEW 13 of 25
Atmosphere 2021, 12, x FOR PEER REVIEW 14 of 25
Figure 7. Bias (°C) of ERA5 for austral seasons of autumn (a), winter (c), spring (e), summer (g), annual mean (i), and the
difference of it between ERA5 and ERA-Interim in autumn (b), winter (d), spring (f), summer (h), and annual mean (j).
Figure 7.
Bias (
◦
C) of ERA5 for austral seasons of autumn (
a
), winter (
c
), spring (
e
), summer (
g
),
annual mean (
i
), and the difference of it between ERA5 and ERA-Interim in autumn (
b
), winter (
d
),
spring (f), summer (h), and annual mean (j).

Atmosphere 2021,12, 217 13 of 23
In Figure 8, we plot six selected stations (Amundsen–Scott, Byrd, Marambio, No-
volazarevskaya, Scott Base, and Vostok) located in central Antarctica, West Antarctica, the
Antarctic Peninsula, the East Antarctica coast, the Ross Ice Shelf, and the interior of East
Antarctica, respectively. For the six stations, ERA5 has slightly lower correlation (R < 0.8)
at Amundsen–Scott and Novolazarevskaya, as shown in Figure 6i, and the bias of ERA5 is
larger than that of ERA-Interim only at Byrd. At Novolazarevskaya station, the data from
ERA5 has a poor linear relationship with observations before 2000, but subsequently fit
the observation data well. The temperature from ERA5 shows obvious disparity at Scott
Base and Vostok relative to ERA-Interim. For Marambio, ERA5 shows the same warming
as station data during the period.
ERA5 has average annual temperature biases of 0.51,
−
0.66, and 0.58
◦
C in East Antarc-
tica, West Antarctica, and the Antarctic Peninsula (Table 3), respectively. The corresponding
biases from ERA-Interim are 0.97, 0.03, and 0.23
◦
C, which indicates a more accurate per-
formance of ERA5 for annual temperature over East Antarctica. Table 2summarizes the
correlation between ERA reanalyses and observations over the Antarctica and its three sub-
regions for annual and seasonal temperature means. Generally, we can conclude that ERA5
performs better over East Antarctica with exception of DJF, and it exhibits a high linear
relationship with observations and small bias in other cases. Compared with ERA-Interim,
ERA5 has a lower correlation and higher bias in DJF in East Antarctica, and the highest bias
in ERA reanalyses occurs in this season, with the cold bias of 1.53 and 1.25
◦
C for ERA5
and ERA-Interim, respectively. Over West Antarctica, warm bias and higher correlation
coefficients prevail for annual and seasonal temperature means in ERA5. Especially, the
highest correlation coefficient of ERA5 is 0.97, occurring in West Antarctica in SON, and the
corresponding value of ERA-Interim is 0.96. The biggest difference between temperature
from ERA5 and observations in West Antarctica is found in JJA, with a warm bias of 1.25
◦
C.
In the Antarctic Peninsula, ERA5 always shows cold bias, and ERA-Interim exhibits a warm
bias in JJA. In general, a higher bias and lower correlation coefficients of ERA5 relative
to ERA-Interim are observed in this area. For ERA5, lower performance in the Antarctic
Peninsula is shown in DJF, with high bias values and low liner relationship with stations
records. We conclude that ERA5 performs well in representing East Antarctic and West
Antarctic temperature, especially in SON, with the bias lower than 0.80
◦
C and correlation
coefficients higher than 0.90. The anomalies from ERA5 and ERA-Interim coincide with
common variability from observations over the three subregions, especially in the Antarctic
Peninsula (Figure 3). The mean autumn temperature over East Antarctica exhibits a shift
change in circa 2002, and the change is also found at Antarctica and West Antarctica in
MAM. However, this phenomenon does not occur in the Antarctic Peninsula.
Atmosphere 2021, 12, x FOR PEER REVIEW 14 of 25
Figure 7. Bias (°C) of ERA5 for austral seasons of autumn (a), winter (c), spring (e), summer (g), annual mean (i), and the
difference of it between ERA5 and ERA-Interim in autumn (b), winter (d), spring (f), summer (h), and annual mean (j).
Figure 8. Cont.

Atmosphere 2021,12, 217 14 of 23
Atmosphere 2021, 12, x FOR PEER REVIEW 15 of 25
Atmosphere 2021, 12, x FOR PEER REVIEW 16 of 25
Figure 8. Comparisons between station time series and ERA5 and ERA-Interim at six selected stations located in central
Antarctica, West Antarctica, Antarctic Peninsula, East Antarctica coast, Ross Ice Shelf, and the interior of East Antarctica.
ERA5 has average annual temperature biases of 0.51, −0.66, and 0.58 °C in East Ant-
arctica, West Antarctica, and the Antarctic Peninsula (Table 3), respectively. The corre-
sponding biases from ERA-Interim are 0.97, 0.03, and 0.23 °C, which indicates a more ac-
curate performance of ERA5 for annual temperature over East Antarctica. Table 2 sum-
marizes the correlation between ERA reanalyses and observations over the Antarctica and
its three subregions for annual and seasonal temperature means. Generally, we can con-
clude that ERA5 performs better over East Antarctica with exception of DJF, and it exhibits
a high linear relationship with observations and small bias in other cases. Compared with
ERA-Interim, ERA5 has a lower correlation and higher bias in DJF in East Antarctica, and
the highest bias in ERA reanalyses occurs in this season, with the cold bias of 1.53 and 1.25
°C for ERA5 and ERA-Interim, respectively. Over West Antarctica, warm bias and higher
correlation coefficients prevail for annual and seasonal temperature means in ERA5. Es-
pecially, the highest correlation coefficient of ERA5 is 0.97, occurring in West Antarctica
in SON, and the corresponding value of ERA-Interim is 0.96. The biggest difference be-
tween temperature from ERA5 and observations in West Antarctica is found in JJA, with
a warm bias of 1.25 °C. In the Antarctic Peninsula, ERA5 always shows cold bias, and
ERA-Interim exhibits a warm bias in JJA. In general, a higher bias and lower correlation
coefficients of ERA5 relative to ERA-Interim are observed in this area. For ERA5, lower
performance in the Antarctic Peninsula is shown in DJF, with high bias values and low
liner relationship with stations records. We conclude that ERA5 performs well in repre-
senting East Antarctic and West Antarctic temperature, especially in SON, with the bias
lower than 0.80 °C and correlation coefficients higher than 0.90. The anomalies from ERA5
and ERA-Interim coincide with common variability from observations over the three sub-
regions, especially in the Antarctic Peninsula (Figure 3). The mean autumn temperature
over East Antarctica exhibits a shift change in circa 2002, and the change is also found at
Antarctica and West Antarctica in MAM. However, this phenomenon does not occur in
the Antarctic Peninsula.
In Figure 9, we compare the spatial trend of austral seasons for the whole of Antarc-
tica from ERA5 and ERA-Interim during the period 1979–2018. In MAM, the trend of
ERA5 is in broad agreement with that from ERA-Interim; both of them show significant
warming trends in the western Antarctic Peninsula, while the trend of ERA-Interim in this
region shows greater warming than that of ERA5. In JJA, there is an obvious warming
trend on the Ross Ice Shelf in ERA-Interim, while the trend value of ERA5 is lower than
that of ERA-Interim. Significant warming trends are prevalent in East Antarctica in SON,
and the trend is stronger and broader than shown by ERA-Interim. Over all seasons, the
greatest difference between the trend of ERA5 and ERA-Interim occurs in DJF. In this sea-
son, ERA5 reveals a slight warming trend over the area in the interior of East Antarctica,
whereas ERA-Interim shows a significant cooling trend in that region. The annual and
seasonal mean temperature trends are summarized for the Antarctic subregions in Table
Figure 8.
Comparisons between station time series and ERA5 and ERA-Interim at six selected stations
located in central Antarctica, West Antarctica, Antarctic Peninsula, East Antarctica coast, Ross Ice
Shelf, and the interior of East Antarctica.

Atmosphere 2021,12, 217 15 of 23
In Figure 9, we compare the spatial trend of austral seasons for the whole of Antarctica
from ERA5 and ERA-Interim during the period 1979–2018. In MAM, the trend of ERA5 is
in broad agreement with that from ERA-Interim; both of them show significant warming
trends in the western Antarctic Peninsula, while the trend of ERA-Interim in this region
shows greater warming than that of ERA5. In JJA, there is an obvious warming trend on
the Ross Ice Shelf in ERA-Interim, while the trend value of ERA5 is lower than that of
ERA-Interim. Significant warming trends are prevalent in East Antarctica in SON, and the
trend is stronger and broader than shown by ERA-Interim. Over all seasons, the greatest
difference between the trend of ERA5 and ERA-Interim occurs in DJF. In this season, ERA5
reveals a slight warming trend over the area in the interior of East Antarctica, whereas
ERA-Interim shows a significant cooling trend in that region. The annual and seasonal
mean temperature trends are summarized for the Antarctic subregions in Table 4. Annual
and seasonal temperature trends in ERA5 and ERA-Interim are statistically significant in
East Antarctica, and the cooling trends in observations in SON do not pass the significance
test. The trends in ERA reanalyses and observations are all negative in East Antarctica in
all annual and seasons, and the fastest cooling trend appears in MAM, and the cooling
rate of this season is more than 1
◦
C per decade. In West Antarctica, the ERA5 trends
are similar to observation trends, whereas there is a difference between ERA5 trends and
ERA-Interim in SON, as reflected in a warming trend in ERA-Interim while a cooling trend
is observed in ERA5. ERA5 exhibits a significant cooling trend in annual data, MAM,
and JJA, and the trends from ERA-Interim always fail to pass the significance test. It
is also worth mentioning that the ERA5 shows a faster cooling rate than ERA-Interim
and observations in West Antarctica. Over the Antarctic Peninsula, trends of annual and
seasonal temperature means in ERA reanalyses and observations are not significant. ERA5
presents a warming trend with the exception of DJF, as is the case for ERA-Interim and
station records. Compared with ERA-Interim, the difference between ERA5 trends and
observations in the Antarctic Peninsula is relatively small in JJA and DJF.
Atmosphere 2021, 12, x FOR PEER REVIEW 17 of 25
4. Annual and seasonal temperature trends in ERA5 and ERA-Interim are statistically sig-
nificant in East Antarctica, and the cooling trends in observations in SON do not pass the
significance test. The trends in ERA reanalyses and observations are all negative in East
Antarctica in all annual and seasons, and the fastest cooling trend appears in MAM, and
the cooling rate of this season is more than 1 °C per decade. In West Antarctica, the ERA5
trends are similar to observation trends, whereas there is a difference between ERA5
trends and ERA-Interim in SON, as reflected in a warming trend in ERA-Interim while a
cooling trend is observed in ERA5. ERA5 exhibits a significant cooling trend in annual
data, MAM, and JJA, and the trends from ERA-Interim always fail to pass the significance
test. It is also worth mentioning that the ERA5 shows a faster cooling rate than ERA-In-
terim and observations in West Antarctica. Over the Antarctic Peninsula, trends of annual
and seasonal temperature means in ERA reanalyses and observations are not significant.
ERA5 presents a warming trend with the exception of DJF, as is the case for ERA-Interim
and station records. Compared with ERA-Interim, the difference between ERA5 trends
and observations in the Antarctic Peninsula is relatively small in JJA and DJF.
Figure 9. Cont.

Atmosphere 2021,12, 217 16 of 23
Atmosphere 2021, 12, x FOR PEER REVIEW 18 of 25
Figure 9. Comparison of the trends between ERA5 and ERA-Interim over the Antarctica for austral seasons of autumn (a),
winter (b), spring (c), and summer (d) during the period 1979–2018. The gray shaded areas with trends significant at the
95% confidence level.
Table 4. Trends (°C/decade) of annual, autumn (MAM), winter (JJA), spring (SON), and summer
(DJF) mean temperature from Stations, ERA5, and ERA-Interim calculated for the 1979–2018 pe-
riod. The bold font shows that the trend is significant at the 95% confidence interval.
East Antarctica West Antarctica Antarctic Peninsula
ERA5
−0.70 ± 0.24 −0.42 ± 0.37 0.18 ± 0.23
Annual ERA-Interim −0.69 ± 0.23 −0.06 ± 0.36 0.18 ± 0.24
Stations
−0.56 ± 0.29 −0.31 ± 0.33 0.17 ± 0.26
ERA5
−1.20 ± 0.33 −0.72 ± 0.64 0.24 ± 0.32
MAM ERA-Interim −1.10 ± 0.33 −0.23 ± 0.60 0.17 ± 0.31
Stations
−1.03 ± 0.34 −0.55 ± 0.59 0.19 ± 0.34
ERA5
−0.86 ± 0.43 −0.80 ± 0.59 0.20 ± 0.53
JJA ERA-Interim −0.74 ± 0.44 −0.26 ± 0.61 0.27 ± 0.55
Stations
−0.69 ± 0.50 −0.62 ± 0.57 0.17 ± 0.56
ERA5
−0.52 ± 0.29 −0.29 ± 0.47 0.05 ± 0.28
SON ERA-Interim −0.48 ± 0.29 0.12 ± 0.48 0.06 ± 0.30
Stations −0.33 ± 0.36 −0.16 ± 0.48 0.10 ± 0.33
Figure 9. Comparison of the trends between ERA5 and ERA-Interim over the Antarctica for austral
seasons of autumn (
a
), winter (
b
), spring (
c
), and summer (
d
) during the period 1979–2018. The gray
shaded areas with trends significant at the 95% confidence level.
Table 4.
Trends (
◦
C/decade) of annual, autumn (MAM), winter (JJA), spring (SON), and summer
(DJF) mean temperature from Stations, ERA5, and ERA-Interim calculated for the 1979–2018 period.
The bold font shows that the trend is significant at the 95% confidence interval.
East Antarctica West Antarctica Antarctic
Peninsula
ERA5 −0.70 ±0.24 −0.42 ±0.37 0.18 ±0.23
Annual ERA-Interim −0.69 ±0.23 −0.06 ±0.36 0.18 ±0.24
Stations −0.56 ±0.29 −0.31 ±0.33 0.17 ±0.26
ERA5 −1.20 ±0.33 −0.72 ±0.64 0.24 ±0.32
MAM ERA-Interim −1.10 ±0.33 −0.23 ±0.60 0.17 ±0.31
Stations −1.03 ±0.34 −0.55 ±0.59 0.19 ±0.34
ERA5 −0.86 ±0.43 −0.80 ±0.59 0.20 ±0.53
JJA ERA-Interim −0.74 ±0.44 −0.26 ±0.61 0.27 ±0.55
Stations −0.69 ±0.50 −0.62 ±0.57 0.17 ±0.56
ERA5 −0.52 ±0.29 −0.29 ±0.47 0.05 ±0.28
SON ERA-Interim −0.48 ±0.29 0.12 ±0.48 0.06 ±0.30
Stations −0.33 ±0.36 −0.16 ±0.48 0.10 ±0.33
ERA5 −0.39 ±0.21 −0.12 ±0.30 −0.03 ±0.09
DJF ERA-Interim −0.56 ±0.18 −0.02 ±0.31 −0.07 ±0.10
Stations −0.38 ±0.22 −0.06 ±0.27 −0.05 ±0.11
Figure 10 illustrates the regression trends of seasonal mean temperature from six select
stations from the ERA reanalyses and Antarctic observations as illustrated in Figure 8. At
the Byrd, Marambio, Novolazarevskaya, and Scott Base stations, ERA5 exhibits the same
trend as ERA-Interim in seasonal temperature. At Amundsen–Scott and Vostok, a warming
trend is captured by ERA5 and observations in SON and DJF, whereas a cooling trend is

Atmosphere 2021,12, 217 17 of 23
found in ERA5 in MAM and JJA, in contrast to the warming trend in observations. There is
no significant trend at Byrd, and ERA5 displays a different sign compared with observations
in JJA and DJF. At Marambio, the same sign is captured by ERA5 and observation with
exception of DJF. At Novolazarevskaya and Scott Base, the warming trend in ERA5 is
prevalent for all seasons, while observation shows a cooling trend in MAM and DJF. At
Vostok, ERA5 agrees well with the observed warming trend in SON, and divergence is
shown between ERA-Interim and station records, except for austral spring.
Atmosphere 2021, 12, x FOR PEER REVIEW 19 of 25
ERA5
−0.39 ± 0.21 −0.12 ± 0.30 −0.03 ± 0.09
DJF ERA-Interim −0.56 ± 0.18 −0.02 ± 0.31 −0.07 ± 0.10
Stations
−0.38 ± 0.22 −0.06 ± 0.27 −0.05 ± 0.11
Figure 10 illustrates the regression trends of seasonal mean temperature from six se-
lect stations from the ERA reanalyses and Antarctic observations as illustrated in Figure
8. At the Byrd, Marambio, Novolazarevskaya, and Scott Base stations, ERA5 exhibits the
same trend as ERA-Interim in seasonal temperature. At Amundsen–Scott and Vostok, a
warming trend is captured by ERA5 and observations in SON and DJF, whereas a cooling
trend is found in ERA5 in MAM and JJA, in contrast to the warming trend in observations.
There is no significant trend at Byrd, and ERA5 displays a different sign compared with
observations in JJA and DJF. At Marambio, the same sign is captured by ERA5 and obser-
vation with exception of DJF. At Novolazarevskaya and Scott Base, the warming trend in
ERA5 is prevalent for all seasons, while observation shows a cooling trend in MAM and
DJF. At Vostok, ERA5 agrees well with the observed warming trend in SON, and diver-
gence is shown between ERA-Interim and station records, except for austral spring.
Figure 10. Comparison of trends between reanalyses and observations at six selected stations as illustrated in Figure 8 for
austral seasons of autumn (a), winter (b), spring (c), and summer (d) during the period 1979–2018. AS = Amundsen–Scott;
BY = Byrd; MA = Marambio; NO = Novolazarevskaya; SB = Scott Base; VO = Vostok. “*” represents that the trend is sig-
nificant at the 95% confidence interval.
Figure 10.
Comparison of trends between reanalyses and observations at six selected stations as illustrated in Figure 8for
austral seasons of autumn (
a
), winter (
b
), spring (
c
), and summer (
d
) during the period 1979–2018. AS = Amundsen–Scott;
BY = Byrd; MA = Marambio; NO = Novolazarevskaya; SB = Scott Base; VO = Vostok. “*” represents that the trend is
significant at the 95% confidence interval.
4. Discussion
ERA5 has the ability to approximate the Antarctic near-surface temperature measured
by weather stations. Although ERA reanalyses can accurately estimate the temperature,
they always exhibit bias compared with the observations for the assimilation observation
data as well as biases in boundary forcing, and reanalysis presents average data for the
grid cell, while the measured data are from a specific point [
28
,
43
]. A previous study
assessed the applicability of eight types of reanalysis datasets in Antarctica, and the
results showed that ERA-Interim has the highest correlations among them and can capture
the interannual variability in monthly air temperature, which results from the higher

Atmosphere 2021,12, 217 18 of 23
resolution, application of the 2 m temperature observations and background temperatures
in an optimal interpolation scheme [
16
,
18
]. There is only a small distinction between the
correlations of ERA5 and ERA-Interim, although ERA5 has higher spatial and temporal
resolutions, and more in situ observations and satellite data are used, and this phenomenon
may be caused by imprecisely measured data. The correlations of Climate Forecast System
Reanalysis (CFSR) and Modern-Era Retrospective Analysis for Research and Applications
updated version (MERRA-2) are high in Antarctica, and MERRA-2 can capture the monthly
temperature over the Antarctic inland stations [
16
], but their correlations are lower than
those of ERA5, which may be mainly related to the differences in dimensions of the
assimilation systems used. In Antarctica, the performance of 20th century reanalyses
including the ECMWF twentieth century reanalysis (ERA-20C), a new coupled 20th-century
climate reanalysis product (CERA-20C) and 20th-Century Reanalysis (20CR) from the
National Oceanic and Atmospheric Administration (NOAA) is not satisfactory and they
cannot capture the long-term temperature changes, and they show temporal changes due to
the limited number of observations [
16
], and ERA5 performs better than them. Japanese 55-
year Reanalysis (JRA-55) shows strong linear relationship with observations in the austral
winter months for both inland and coastal stations, with correlation coefficients lower than
0.90 [
16
], and it is lower than that of ERA5, for which correlation coefficients are higher
than 0.90 for inland stations and coastal mean in JJA. Reanalysis data shows minimum
correlations in austral summer in Antarctica, small bias in DJF at inland stations and cold
bias at coastal stations [
16
], and the same results also appear in ERA5. The warm bias in
ERA5 in winter at inland stations is greater than that in ERA-Interim, and the warm bias in
ERA-Interim is related to the overestimation of the surface turbulent sensible heat fluxes
under very stable conditions [
32
]. The following several factors contribute to the seasonal
variation. Air–sea–land interactions near the coast are difficult for the model to capture,
and the melting of sea ice and snow cover near the coast in summer complicates regional
temperature simulations because of albedo and thermal differences between coastal regions
and inland regions [
43
–
45
]. Our results agree with previous research showing that ERA-
Interim shows a warm bias in the interior of East Antarctica for annual temperature, and
the warm bias is most likely related to the limited ability to capture surface turbulent
fluxes [
28
,
31
]. Many observations of temperature measured in Antarctica suffer from warm
bias due to the solar radiation, especially in summer [
46
], indicating that ERA5 has a
smaller cold bias in this season. In particular, ERA5 has a slightly warm bias of 0.01
◦
C in
summer in West Antarctica, and ERA5 may show a larger warm bias actually. Although
ERA reanalyses perform well in representing East Antarctic temperature, the ECMWF
has flaws in parameterizing clouds, longwave radiation, and turbulent mixing in the cold
and stable atmosphere over East Antarctica [
45
,
47
]. The coastal regions of Antarctica have
complex terrains and landforms, with complicated and variable thermal properties, and
this may be a factor in the difference between the observations and the reanalysis data [
43
].
It is noteworthy that the manual temperature data have been assimilated into the ERA5,
so it is important to compare the data from ERA5 and AWS to make the comparison
independent. For this, we compare the data from AWSs in Table 1with the corresponding
data in ERA5 and ERA-Interim, and the result is shown in Table 5. Clearly, ERA5 has the
strongest linear relationship and the lowest bias with AWS in austral spring, and the strong
linear relation also can be captured when we do the comparison including manual stations.
ERA5 always has cold bias in all annual and seasonal temperature, while there is a warm
bias of 4.72 ◦C in ERA-Interim in SON.

Atmosphere 2021,12, 217 19 of 23
Table 5.
Correlation, bias (
◦
C), and RSD between data from AWSs and ERA reanalyses for annual,
autumn (MAM), winter (JJA), spring (SON), and summer (DJF) mean temperatures at Antarctica.
R Bias RSD
Annual ERA5 0.94 0.08 0.98
ERA-Interim 0.95 1.45 1.00
MAM ERA5 0.91 1.09 1.05
ERA-Interim 0.91 0.08 1.02
JJA ERA5 0.89 1.19 0.98
ERA-Interim 0.88 0.21 0.98
SON ERA5 0.96 0.03 0.97
ERA-Interim 0.96 −4.72 0.99
DJF ERA5 0.91 0.31 0.98
ERA-Interim 0.90 0.07 1.01
Note: The correlation coefficients are all significant at the 95% confidence interval.
ERA5 reveals a cooling trend in austral summer in the Antarctic Peninsula, and Turner
et al. (2016) have pointed out that the most rapid cooling trend occurs in the austral
summer, and this drop is related to more frequent cold, east-to-southeasterly winds. Sea ice
plays an important role in the temperature over the Antarctic Peninsula, and the changes
in sea ice extent and duration are mainly controlled by Southern Annular Mode (SAM)
and El Niño-Southern Oscillation (ENSO) [
13
,
48
,
49
]. The decline in stratospheric ozone
concentrations is a part reason for the increase in the circumpolar westerlies and may
account for the warming trends in the peninsula region in austral summer and autumn [
50
].
West Antarctic continental temperature increases primarily in austral winter and spring,
and research concluded that the increasing tropical sea surface temperature affects the
high-latitude atmospheric circulation in the Southern Hemisphere, which may accounts
for the West Antarctic warming [
50
]. In ERA5, East Antarctica displays warming trends in
spring, and significant warming is concentrated in the region above 80
◦
S latitude. A cooling
trend occurs in MAM and DJF, and the regional cooling is likely related to tropospheric
flow, which is promoted by atmospheric circulation changes [15,51,52]. The cooling trend
of ERA5 in East Antarctica indicates that ERA5 can describe the ozone-forced cooling of
the troposphere that has been observed in the region since the late 1970s [15].
There are several temperature reconstructions in Antarctica based on different meth-
ods [
11
,
53
,
54
]. Monthly near-surface temperature anomalies in Antarctica for the period
1958–2012, based on 15 monthly instrumental temperature observations in combination
with spatiotemporal temperature covariances, from CFSR (RECONCFSR) show the best
performance [
15
]. Figure 11 compares the annual temperature trends for the entire Antarc-
tic continent from RECONCFSR and ERA5 during the period 1979–2012. The trends of
ERA5 are highly different from those of RECONCFSR. Positive trends occur in almost all
grids of RECONCFSR over the Antarctic Peninsula, while ERA5 shows a negative trend
over the northern Antarctic Peninsula islands. The opposite trends are found over East
Antarctica; in particular, a significant cooling trend in central Antarctica is shown in the
ERA5 output. From 1979 to 2010, an extensive negative trend predominated East Antarctica
in 20CR, a positive trend was dominant in Antarctica in ERA-20C [
28
], while a positive
trend occurs in northern East Antarctica in ERA5 in comparison.

Atmosphere 2021,12, 217 20 of 23
Atmosphere 2021, 12, x FOR PEER REVIEW 22 of 25
Figure 11. Trends in annual mean temperature from RECONCFSR and ERA5 during the period 1979–2012. The gray
shaded areas with trends significant at the 95% confidence level.
5. Conclusions
Based on the monthly near-surface air temperature from 41 weather stations in Ant-
arctica, compared with that of ERA-Interim, the performance of ERA5 has been assessed
in all of Antarctica and the three subregions namely East Antarctica, West Antarctica, and
the Antarctic Peninsula. The variability in annual and seasonal mean temperature can be
reproduced by ERA5, although bias occurs. Over the whole of Antarctica, ERA5 presents
a cold bias with the exception of JJA, while ERA-Interim shows a cold bias in all annual
and seasonal means. The two reanalyses exhibit lower bias at coastal stations in all cases,
and the lowest correlation occurs in DJF. For all stations, the correlations between ERA5
and monthly observations are higher than 0.95, indicating a high linear relationship and
good performance, and the difference between ERA5 and ERA-Interim is lower than 0.01.
The significant correlations of ERA5 are higher than 0.80 at most stations in annual and
seasonal mean temperatures. Generally, ERA5 has the highest linearity in SON, with sig-
nificant correlation coefficients higher than 0.90, and the lowest linear relationship is
shown in DJF. There are regional differences in ERA5 capacity, with high correlations in
West Antarctica and the Antarctic Peninsula. Compared with ERA5, ERA-Interim has a
slightly higher linear relationship with observations in the Antarctic Peninsula. Warm bias
in ERA5 prevails for the stations located in the interior of the Antarctica, and the biases
for coastal stations are irregular. ERA5 shows a warm bias in East Antarctica except in
JJA, a cold bias in West Antarctica and a warm bias on the Antarctic Peninsula. ERA5
exhibits the opposite biases in comparison with ERA-Interim in the three subregions in
JJA. A cooling trend occurs in ERA5 and ERA-Interim over East Antarctica, and a warm-
ing trend occurs over the Antarctic Peninsula with the exception of DJF. We conclude that
ERA5 performs well in Antarctica, but it is necessary to correct the biases to improve the
reanalysis. Despite the bias present in ERA5, in Antarctica, with sparse in situ observa-
tions, ERA5 is the most up-to-date reanalysis model and can play an important role as an
effective tool to study climate change.
Author Contributions: Conceptualization, J.Z. and A.X.; methodology, Y.W. (Yetang Wang); soft-
ware, J.Z. and B.X.; validation, X.Q. and Y.W. (Yetang Wang); formal analysis, J.Z.; resources, J.Z.;
data curation, Y.W. (Yicheng Wang) and B.X.; writing—original draft preparation, J.Z.; writing—
review and editing, A.X. and X.Q.; visualization, J.Z.; supervision, A.X.; project administration, A.X.
All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by National Natural Science Foundation of China, grant number
41671073 and 41476164.
Figure 11.
Trends in annual mean temperature from RECONCFSR and ERA5 during the period 1979–2012. The gray shaded
areas with trends significant at the 95% confidence level.
5. Conclusions
Based on the monthly near-surface air temperature from 41 weather stations in Antarc-
tica, compared with that of ERA-Interim, the performance of ERA5 has been assessed in
all of Antarctica and the three subregions namely East Antarctica, West Antarctica, and
the Antarctic Peninsula. The variability in annual and seasonal mean temperature can be
reproduced by ERA5, although bias occurs. Over the whole of Antarctica, ERA5 presents
a cold bias with the exception of JJA, while ERA-Interim shows a cold bias in all annual
and seasonal means. The two reanalyses exhibit lower bias at coastal stations in all cases,
and the lowest correlation occurs in DJF. For all stations, the correlations between ERA5
and monthly observations are higher than 0.95, indicating a high linear relationship and
good performance, and the difference between ERA5 and ERA-Interim is lower than 0.01.
The significant correlations of ERA5 are higher than 0.80 at most stations in annual and
seasonal mean temperatures. Generally, ERA5 has the highest linearity in SON, with
significant correlation coefficients higher than 0.90, and the lowest linear relationship is
shown in DJF. There are regional differences in ERA5 capacity, with high correlations in
West Antarctica and the Antarctic Peninsula. Compared with ERA5, ERA-Interim has a
slightly higher linear relationship with observations in the Antarctic Peninsula. Warm bias
in ERA5 prevails for the stations located in the interior of the Antarctica, and the biases for
coastal stations are irregular. ERA5 shows a warm bias in East Antarctica except in JJA, a
cold bias in West Antarctica and a warm bias on the Antarctic Peninsula. ERA5 exhibits the
opposite biases in comparison with ERA-Interim in the three subregions in JJA. A cooling
trend occurs in ERA5 and ERA-Interim over East Antarctica, and a warming trend occurs
over the Antarctic Peninsula with the exception of DJF. We conclude that ERA5 performs
well in Antarctica, but it is necessary to correct the biases to improve the reanalysis. Despite
the bias present in ERA5, in Antarctica, with sparse in situ observations, ERA5 is the most
up-to-date reanalysis model and can play an important role as an effective tool to study
climate change.
Author Contributions:
Conceptualization, J.Z. and A.X.; methodology, Y.W. (Yetang Wang); software,
J.Z. and B.X.; validation, X.Q. and Y.W. (Yetang Wang); formal analysis, J.Z.; resources, J.Z.; data
curation, Y.W. (Yicheng Wang) and B.X.; writing—original draft preparation, J.Z.; writing—review
and editing, A.X. and X.Q.; visualization, J.Z.; supervision, A.X.; project administration, A.X. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by National Natural Science Foundation of China, grant number
41671073 and 41476164.

Atmosphere 2021,12, 217 21 of 23
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The ERA5 data presented in this study are openly available at https://
cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset, ERA-Interim data presented in this
study are openly available at https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=
sfc/, and observations are openly available at https://legacy.bas.ac.uk/met/READER/surface
(accessed on 2 February 2021).
Acknowledgments:
We thank the European Center for Medium-Range Weather Forecasts and
Scientific Committee on Antarctic Research for providing data to improve the paper.
Conflicts of Interest:
The authors declared that they have no conflict of interest to this work. We
declare that we have no financial and personal relationships with other people or organizations that
can inappropriately influence our work.
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