Journal
June 2016of the Meteorological Society of Japan, Vol.Y.94,
HARADA
No. 3, pp.et 269−302,
al.
2016
DOI:10.2151/jmsj.2016-015

269

The JRA-55 Reanalysis: Representation of Atmospheric Circulation
and Climate Variability
Yayoi HARADA, Hirotaka KAMAHORI, Chiaki KOBAYASHI, Hirokazu ENDO
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

Shinya KOBAYASHI, Yukinari OTA, Hirokatsu ONODA, Kazutoshi ONOGI,
Kengo MIYAOKA, and Kiyotoshi TAKAHASHIJapan Meteorological Agency, Tokyo, Japan(Manuscript received 15 July 2015, in final form 19 February 2016)

Abstract
This study investigates the quality of the Japanese 55-year Reanalysis (JRA-55), which is the second global
reanalysis constructed by the Japan Meteorological Agency (JMA), by comparing it with other reanalyses andobservational datasets. Improvements were found in the representation of atmospheric circulation on an isentropic
surface and in the consistency of momentum budget based on the mass-weighted isentropic zonal mean method.
The representation of climate variability in several regions was also examined. In the tropics, the frequencies ofhigh spatial correlations with precipitation, which were estimated using the Tropical Rainfall Measuring Mission
Multisatellite Precipitation Analysis, are clearly higher in JRA-55 than in JRA-25. The results indicate that JRA-55
generally improved the representations of phenomena on a wide range of space–time scales, such as equatorial
waves, and transient eddies in the storm track regions, compared with JRA-25 during the satellite era. Moreover,JRA-55 improved the temporal consistency compared with the older reanalyses throughout the reanalysis period.
In the stratosphere, we found larger discrepancies between reanalyses for the extra-tropical stratosphere during theSouthern Hemisphere (SH) winter. Comparisons with radiosonde temperature revealed that JRA-55 has a smaller
bias in temperature than the other reanalyses in the extra-tropical SH winter before 1979.
Some issues in JRA-55 were also identified. The amplitude of equatorial waves and Madden–Julian oscillation
in JRA-55 are weaker than in the other reanalyses. JRA-55 shows unrealistic strong cooling in South Americaand Australia, although the spatial distribution of the long-term temperature trends in JRA-55 is the closest to an
observational dataset of global historical surface temperature.
Keywords

1.

reanalysis; verification; climate variability; atmospheric circulation

Introduction

The Japan Meteorological Agency (JMA) has
conducted the second global atmospheric reanalysis,
Corresponding author: Yayoi Harada, MeteorologicalResearch Institute, 1-1 Nagamine, Tsukuba, Ibaraki
305-0052, Japan
E-mail: yharada@mri-jma.go.jp©2016, Meteorological Society of Japani.e., the Japanese 55-year Reanalysis (JRA-55). Thisis the first comprehensive reanalysis to cover the lasthalf-century since the European Centre for Medium-Range Weather Forecasts 45-year Reanalysis(ERA-40), and it is also the first to apply four-dimensional variational analysis (4D-Var) to this period.
Kobayashi et al. (2015), in the first comprehensive
report on JRA-55, outlined the general specifications and basic characteristics of this product. They

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Journal of the Meteorological Society of Japan

Vol. 94, No. 3

Table 1. Basic specifications of reanalyses used in this study.
Name

Organization

Reanalysis period

Resolution

Data assimilation method

JRA-55JRA-25
ERA-40ERA-Interim
NCEP/NCAR
NCEP-DOE2
CFSR
MERRA

JMA
JMA/CRIEPI
ECMWF
ECMWF
NCEP/NCAR
NCEP/DOE
NCEP
NASA GMAO

1958 to present
1979 to Jan. 2014
Sep. 1957 to Aug. 2002
1979 to present
1948 to present
1979 to present
1979 to present
1979 to present

TL319, L60 up to 0.1 hPa
T106, L40 up to 0.4 hPa
TL159, L60 up to 0.1 hPa
TL255, L60 up to 0.1 hPa
T62, L28 up to 3 hPa
T62, L28 up to 3 hPa
T382, L64 up to 0.266 hPa(2/3)°×0.5°, L72 up to 0.01 hPa

4D-Var
3D-Var
3D-Var
4D-Var
3D-Var
3D-Var
3D-Var (Coupled forecast system)
3D-Vardescribed the JRA-55 system configuration including
the data sources, quality control, data selection forobservations, data assimilation system (DAS), andforecast model. In addition, they described two major
improvements in quality over the Japanese 25-year
Reanalysis (JRA-25; Onogi et al. 2007) product. Oneof the improvements is the reduction of dry bias in the
Amazon basin, and another is the temporal consistency of temperature analysis.
The purpose of this study is to verify the representations of phenomena with various space–time scalesin specific vertical and horizontal regions of the global
atmosphere as well as those of atmospheric circulation and climate variability in JRA-55. We employ as
many observational datasets as possible for the verification of various phenomena represented in JRA-55
and conduct comparisons with other reanalyses. The
remainder of this manuscript is organized as follows.
In Section 2, we describe the data and methods usedin this investigation. Section 3 discusses improvements in the representation and consistency of theatmospheric flow. Sections 4, 5, and 6 focus on the
representations of climate variability in the tropics,
the extra-tropics, and the stratosphere, respectively.
Section 7 presents the global distributions of longterm trends in surface air temperature. Finally, asummary and conclusions are provided in Section 8.
As described by Kobayashi et al. (2015), 27 independent Japanese researchers participated in quality
evaluation prior to the release of the products. Theirevaluations revealed the improvement of JRA-55
in various aspects. For example, they evaluated thesignatures of naturally induced variability in the atmosphere (Mitchell et al. 2015), representation of thediurnal migrating tide in the stratosphere, extra-tropical cyclone activity, distribution of precipitation in
the tropics, and diurnal cycle over East Asia (Chenet al. 2014). Some of these evaluation studies areincluded in this paper.

2.

Data and methods

2.1 Reanalysis products for comparison
One of the purposes of this study is to clarify theadvantages of JRA-55 over JRA-25. The detailed
specifications of JRA-55 and JRA-25 were describedby Kobayashi et al. (2015) and Onogi et al. (2007),
respectively. In addition, we utilize products fromother operational forecast centers and research institutes; the leading centers being European Center for
Medium-range Weather Forecast (ECMWF), National
Centers for Environmental Prediction (NCEP),
National Aeronautics and Space Administration of
Global Modeling and Assimilation Office (NASA
GMAO), etc., as summarized in Table 1.
There are two major products of ECMWF; one is
the ERA-40 reanalysis, the other is ERA-Interim. The
ERA-40 reanalysis was generated with the three-dimensional variational (3D-Var) analysis version of theIntegrated Forecasting System that was operational
at ECMWF from June 2001 to January 2002 (Uppalaet al. 2005). ERA-Interim is based on the 12-hourly
4D-Var analysis and includes many updates from
ERA-40, such as variational bias correction to satellite
radiance (Dee et al. 2011).
The major product from NASA GMAO is theModern-Era Retrospective Analysis for Research andApplications (MERRA), which was generated withversion 5.2.0 of the Goddard Earth Observing System(GEOS-5) atmospheric model and 3D-Var based DAS(Rienecker et al. 2011).
We also use the NCEP/National Center for Atmospheric Research (NCAR) reanalysis (NCEP/NCAR
reanalysis; Kalnay et al. 1996; Kistler et al. 2001),
which covers more than 66 years since 1948, a
corrected version of the NCEP/NCAR reanalysis(NCEP-DOE reanalysis; Kanamitsu et al. 2002), anda new coupled global NCEP reanalysis called Climate
Forecast System Reanalysis (CFSR; Saha et al. 2010).

June 2016

Y. HARADA et al.

2.2 Observational datasets for verification
This section describes the observational datasets
used for verification.
Liebmann and Smith (1996) performed temporaland spatial interpolation of estimates of Outgoing
Longwave Radiation (OLR) using National Oceanicand Atmospheric Administration (NOAA) polar
orbiting satellites (Gruber and Krueger 1984), over aregular global grid, filled in the missing values at grid
points not covered by the satellites. We utilize thisinterpolated daily OLR (NOAA OLR) at a horizontal
resolution of 2.5° × 2.5° in this study.
Huffman et al. (2007) estimated precipitation at finescales (0.5° × 0.5° and 3 hourly) using the TropicalRainfall Measuring Mission (TRMM) Multisatellite
Precipitation Analysis (TMPA). In the TMPA framework, passive microwave data are collected fromvarious low-earth-orbit satellites such as the Microwave Imager on TRMM, Special Sensor Microwave
Imager (SSM/I) on Defense Meteorological Satellite Program satellites, Advanced Microwave Scanning Radiometer-Earth Observing System on the
Aqua satellite, and Advanced Microwave Sounding
Unit-B on the NOAA satellite series. Infrared data
and monthly gauge data are also used. This estimated
precipitation dataset is called TRMM3B42.
The best track for tropical cyclone verification wasprovided by Dr. M. Fiorino at the National Hurricane
Center for the JRA-55 project (Fiorino 2002).
The Global Precipitation Climatology Centre(GPCC) has been conducting a global analysis ofmonthly land surface precipitation based on in situ
rain-gauge data (Schneider et al. 2011). The full datareanalysis product (GPCC full ver. 6) is their mostaccurate product.
To verify the quality of sea level pressure (SLP)in reanalyses, we make use of an updated version of
the Hadley Centre’s monthly historical mean SLP(HadSLP2; Allan and Ansell 2006). HadSLP2 isconstructed by blending surface pressure data from
historical sources worldwide and from the International Comprehensive Ocean–Atmosphere Data Set(Worley et al. 2005).
The Earth Observing System Microwave Limb
Sounder (MLS), onboard the National Aeronautics
and Space Administration Aura satellite launched inJuly 2004 (Aura MLS), has been measuring temperatures, several atmospheric species, cloud ice, andgeopotential heights to provide information about theearth’s upper troposphere, stratosphere, and mesosphere (Waters et al. 2006). We calculate daily 2.5° ×
2.5° grid box-mean data using Aura MLS level 2271

data and reanalyses. Note that box-mean values fromreanalyses are used only when the corresponding
box-mean value from Aura MLS is present.
The Hadley Centre and the Climatic Research
Unit at the University of East Anglia (CRU) havecollaborated and provided the global temperature series. Morice et al. (2012) updated the series
to HadCRUT4, which is a combination of the newland air temperature CRUTEM4 (Jones et al. 2012)
and the sea surface temperature (SST) analysis
HadSST3 (Kennedy et al. 2011a, b). We make use of
HadCRUT4 to verify the long-term trends in surface
air temperature.
2.3 Methods
To examine datasets with different spatial resolutions, we bin the data into common grid cells witha horizontal resolution of 2.5° × 2.5° or 5° × 5° tocalculate the differences between the datasets. To
ensure comparability, the end of the reference period
was chosen to be 2001 when JRA-55 was comparedwith ERA-40.
To extract the various atmospheric waves or disturbances, we apply a Lanczos filter (Duchon 1979) tothe daily OLR and the daily zonal and meridional
wind components of the reanalysis products. We
investigate the activity of equatorial waves (Section
4.2) and synoptic variability in storm track regions at
mid-latitudes (Section 5.2).
3.
3.1

Representation and consistency of atmosphericflow

Improvements in the representation of
atmospheric flow
In the DAS of JRA-55, new radiation and advection schemes are included in the forecast model and
4D-Var with variational bias correction to satellite
radiances is implemented (Kobayashi et al. 2015).
Hence, it is expected that JRA-55 improves therepresentation of atmospheric flow and the temporalconsistency. We attempt to verify and visualize theimprovements in the atmospheric flow in the upper
troposphere. Previous studies have shown that Ertel
potential vorticity (PV) is conserved for adiabatic,
frictionless motion and that it acts as a tracer of theatmospheric flow on an isentropic surface (Hoskins
et al. 1985). Therefore, it is reasonable to verify the
representations of atmospheric flow and circulationin reanalyses using PV on the isentropic surface.
Figure 1 shows an example of the spatial PV distribution on a 360 K isentropic surface. Focusing on ahigh PV anomaly to the southwest of Japan, which

272

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means a higher value than that of its environmental
field, not deviation from the climatological mean, it
is apparent that the temporal consistency of JRA-55(Figs. 1a, c, e) is far superior to that of JRA-25 (Figs.
1b, d, f). Moreover, the scattered distribution of
PV is observable for JRA-25, while it is not seen in
JRA-55. In JRA-25, an Eulerian scheme and 3D-Var
were utilized. Eulerian schemes tend to have significant dispersion errors in fine-scaled humidity fields(Ritchie 1985). In addition, 3D-Var does not takeaccount of observational time accurately. These would
be related to the differences between the abovementioned analyses.
3.2 Consistency of zonal mean fields
In this subsection, we focus on the consistencyof the atmospheric flow in the zonal mean fields. To
verify the representation and consistency of atmospheric flow, we use the mass-weighted isentropic
zonal mean (MIM) method developed by Iwasaki(1989) (see also Iwasaki 1990, 1992, 1998; Tanaka
et al. 2004). The zonal mean primitive equation isused, and the vertical coordinate is defined by theisentropic zonal mean pressure. All variables representing the zonal mean field are zonally averaged
with normalized weights proportional to the air mass
between two isentropic surfaces:(∂pA(φ , θ , t ) * ≡ 1 ∫ A(λ , φ , θ , t )
2π
∂θ

(

∂p
d λ , (1)∂θ

where A represents a physical quantity, and λ, ϕ, θ, t,
and p are longitude, latitude, potential temperature,time, and pressure, respectively. The overbars andasterisks denote isentropic zonal mean and local mass
weight normalized by its zonal mean, respectively.
The zonal-mean zonal momentum equation andEliassen–Palm flux (EP flux) are represented asfollows:∂u* = − v* ∂u* cos φ − w*† ∂u* + f v*
∂ta cos φ∂φ∂ z†
+ ∇ • F + X *,a ρ0 cos φ(2)June 2016

Y. HARADA et al.


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F = ρ0 a cos φ −(u ′v ′)* , − (u ′w †′ ) *+1p ∂Φ  ,ρ0 ga cos φ∂λ p† ( )(3)

where a, f, u, v, w, and X are the earth’s radius, Coriolis parameter, zonal velocity, meridional velocity,
vertical velocity, and frictional forcing, respectively,
and F, ρ0, g, and Ф are EP flux, reference density,
gravitational acceleration, and geopotential, respectively. Daggers denote isentropic zonal means, andthe log-pressure coordinate for isentropic zonal meanpressure is given by Z† ≡ −H log(p†/p0), where H isscale height.
In the MIM framework, adiabatic wave motion,
which produces a Stokes drift, is separated fromdiabatic effects, and the MIM method enables analysis of a single hemispheric cell, such as the Brewer–
Dobson circulation (BDC) in the stratosphere and thetropospheric direct circulation. We apply the MIM
method to JRA-55 and JRA-25 model grid data andcalculate the regional average value of each termin Eq. (2). Using this method, the zonal-mean zonal
wind acceleration was found to balance the sum of
the forcing terms on the right side of the zonal meanequation (Eq. 2). However, in actual data assimilation, the analysis increment, which is defined as
analysis minus first guess (6-hourly forecast in thiscase), disturbs these terms, resulting in an imbalancein the momentum budget. Kobayashi et al. (2015)
showed that global mean of root-mean-square (RMS)
departures (or differences) from radiosonde temperatures decreased in JRA-55. In this study, we assess
whether the improved fit to observations contributes
to balancing the zonal momentum budget.
Figure 2 shows latitude–pressure cross-sections ofzonal-mean zonal wind and its RMS analysis increment averaged over Northern Hemisphere (NH)winters (December–February) from 1979/1980 to
2011/2012. While the features of the zonal-mean
zonal wind in JRA-55 (Fig. 2a) and JRA-25 (Fig.
2b) are generally consistent, the RMS analysis increments of the zonal-mean zonal wind in the extratropics for both the upper troposphere and the upper
stratosphere are greatly reduced in JRA-55 (Fig. 2c)compared with the corresponding values in JRA-25(Fig. 2d). Furthermore, focusing on the mass stream
function (Fig. 3), the analysis increments in the
troposphere in JRA-25 (Fig. 3d) are greatly reducedin JRA-55 (Fig. 3c). The reduction is related to thefact that the numerical weather prediction model inthe DAS of JRA-55 is better than that in JRA-25. In

273

the lower stratosphere, the BDC of JRA-55 (Fig. 3a)is slightly weaker than that of JRA-25 (Fig. 3b). For
example, the line of 0.61 × 1010 kg s−1 is observable
above the 50 hPa level in JRA-25, while it is seen
below the 50 hPa level in JRA-55. In reanalysis datasets, the BDC characteristics are dependent on thedata assimilation scheme and the forecast models used
to generate the first-guess field (Iwasaki et al. 2009).
The annual- and seasonal-mean upwelling of JRA-55
is weaker than that of JRA-25 and has very similarvalues to ERA-Interim at selected vertical levels (Fig.
4). Because the thermodynamic balance of JRA-55
is much better than that of JRA-25 (Kobayashi andIwasaki 2016), the BDC strength diagnosed using
JRA-55 is higher than that from JRA-25.
Figure 5 depicts the zonal momentum budgets
averaged over 45–60°N during the major sudden
stratospheric warming (SSW) event that occurred in
mid-January 2009 (Harada et al. 2010). SSW events
usually occur due to the upward propagation of planetary-scale wave packets from the troposphere, andthe convergence of these wave packets causes deceleration of zonal-mean zonal wind in the stratosphere.
The agreement between the zonal mean wind acceleration (the black lines in Fig. 5) and the sum of forcing
terms (the red lines in Fig. 5) indicates the consistency of the zonal momentum budget. In JRA-25
at the 5 hPa level, the sum of forcing terms does
not follow the zonal mean wind acceleration aroundthe peak of the zonal wind deceleration (Fig. 5d). In
contrast, in JRA-55, the sum of forcing terms closely
follows the zonal mean wind acceleration even at thepeak of the zonal wind deceleration (Fig. 5c). Moreover, at other pressure levels, JRA-55 shows good
agreement between the zonal wind acceleration andthe sum of forcing terms throughout the entire period.
This feature is common in other major SSW events(not shown).
As mentioned above, both the reduction of biases
in the general circulation fields and a more consistent zonal momentum budget are clearly observablein JRA-55. These results indicate improvement in the
representation of the global atmospheric circulation in
JRA-55.
4.

Climate variability in the tropics

4.1 Validation of precipitation in the tropics
Kobayashi et al. (2015) presented climatologies ofglobal precipitation distributions for several reanalyses and an observational dataset based on a monthly
mean timescale promoted by The Global Precipitation Climatology Project (GPCP, Adler et al. 2003).

274

Journal of the Meteorological Society of Japan

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In this section, we focus on the representation oftropical precipitation for shorter timescales against
TRMM3B42 (Huffman et al. 2007; see also Section
2.2). The forecast model used in JRA-55 implements
higher resolutions in both the horizontal and verticaldirections compared with that in JRA-25. Therefore,
it is expected that JRA-55 products have an improved
representation of the distribution of precipitation forsmaller and shorter space–time scales than typicalscales in monthly data.
Figure 6 shows the frequency of the spatial
correlation of the daily mean precipitation over thetropics with TRMM3B42 from 1998 to 2009. The
frequencies of high spatial correlations are clearlyhigher in JRA-55 (red diamonds) than in the NCEP/
NCAR reanalysis (right blue circles), JRA-25 (pinkdiamonds), CFSR (blue crosses), and ERA-Interim(green triangles) and are close to those in MERRA(purple crosses). This result indicates that JRA-55 has
higher quality in the representation of daily precipitation in the tropics. However, as mentioned by
Kobayashi et al. (2015), JRA-55 overestimates theprecipitation in the tropics compared with the observational precipitation dataset GPCP. Compared with
TRMM3B42, JRA-55 tends to overestimate the area–
mean precipitation rates for weak precipitation under
suppressed convection (Fig. 7a). The other reanalyses
used for comparison are similar to JRA-55 in thisJune 2016

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276

Journal of the Meteorological Society of Japan

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15
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0
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26
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06

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26

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February 2009

3101
06
February 2009

10
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5
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December 2008

Wind acceleration

06

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January 200926

(m s-1 day-1)
3101
06
February 2009

Wind acceleration(m s-1 day-1)

-5
11

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January 2009

26

3101
06
February 2009

Sum of forcing terms

0

06

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January 2009

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February 2009(m s-1 day-1)

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February 2009

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06

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February 2009

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January 200926

Sum of forcing terms

3101
06
February 2009

11

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21
January 2009

26

3101
06
February 2009

Sum of forcing terms

06

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21
January 2009

26

3101
06
February 2009

Wind acceleration

Sum of forcing terms

0
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06

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January 2009

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February 2009(l) JRA-25, 200 hPa

5Wind acceleration

Sum of forcing terms

11

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January 2009

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3101
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February 2009

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January 2009

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February 2009

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December 2008

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January 2009(j) JRA-25, 100 hPa

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December 2008(o) JRA-55, 500 hPa
Wind acceleration

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21
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December 2008(k) JRA-55, 200 hPa

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21
26
3101
December 2008

26

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Sum of forcing terms

0

06

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21
26
3101
December 2008(i) JRA-55, 100 hPa

5

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21
January 2009

Wind acceleration(m s-1 day-1)

10

Sum of forcing terms(m s-1 day-1)(m s-1 day-1)

510(m s-1 day-1)

26

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21
26
3101
December 2008

(m s-1 day-1)

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January 2009-5

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(m s-1 day-1)

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21
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3101
December 200811

Sum of forcing terms(f) JRA-25, 10 hPa
Sum of forcing terms(m s-1 day-1)(m s-1 day-1)

5

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10
5
0
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-10
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December 2008

Sum of forcing terms(e) JRA-55, 10 hPa
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5
0
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December 2008
10

Wind acceleration(d) JRA-25, 5 hPa

Wind acceleration(m s-1 day-1)(m s-1 day-1)(b) JRA-25, 2 hPa

Wind acceleration(m s-1 day-1)(a) JRA-55, 2 hPa

25
20
15
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5
0
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-10
-15
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-25
21
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3101
December 2008

Vol. 94, No. 3

506(p) JRA-25, 500 hPa
Wind acceleration

Sum of forcing terms

0
-5

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21
26
3101
December 2008

06

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January 2009

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February 2009

Fig. 5. Zonal momentum budgets averaged over 45–60°N during the major sudden stratospheric warming that
occurred in mid-January 2009. The black lines show zonal mean wind acceleration and the red lines show thesums of advection, Coriolis forcing, EP flux divergence, and frictional forcing terms.

regard, and all of the reanalyses overestimates precipitation when the TRMM3B42 precipitation is less than

4 mm day−1.

277

Y. HARADA et al.

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Fig. 6. Frequency of spatial correlation of daily mean precipitation with TRMM3B42 over the tropics (22°S–22°N)
from 1998 to 2009.

(b)
JRA-25 - TRMM 3B42[mm day-1]

JRA-55 - TRMM 3B42[mm day-1]

4.5
3.5
2.5
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2.5
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Fig. 7. Scatter diagrams of 22°S–22°N, 90–160°E averaged daily precipitation between TRMM 3B42 and thedifference between the reanalysis and TRMM3B42 during the NH warm season (May–September) from 2003 to
2009.

278

4.2

Journal of the Meteorological Society of Japan

Convectively coupled equatorial waves andMadden–Julian oscillation
The tropical or equatorial regions contain waves
with various space–time scales, such as convectively
coupled Kelvin waves, equatorial Rossby (ER) waves,and mixed Rossby–gravity (MRG) waves. In addition,
the Madden–Julian oscillation (MJO) is the dominant mode on an intra-seasonal timescale. Numerous
studies have been conducted using reanalysis data,
and recently Kim et al. (2014) evaluated the tropicalvariability of precipitation from five global reanalysis by comparison with an observational dataset.
Fujiwara et al. (2012) also investigated wave activity
in the tropical tropopause layer in seven reanalyses.
As discussed in these studies, the representations of
the equatorial waves and MJO are one of the mostimportant aspects of reanalysis quality. To verify
the representation of convectively coupled equatorial waves, we conducted a wavenumber–frequency
power spectrum analysis following Wheeler andKiladis (1999). They performed a wavenumber–frequency spectrum analysis for longitudes in thedomain 15°S–15°N using the daily NOAA OLR,
decomposed the data into symmetric and antisymmetric components, and isolated several statisticallysignificant spectral peaks. We apply this method to the
OLR estimated from several reanalyses and compare
the results of reanalyses with those of NOAA OLR.
Figure 8 shows the zonal wavenumber–frequency
power spectrum ratio of OLR to its background(defined as an average of smoothed symmetric andantisymmetric components) within 15°S–15°N for
the period from January 1981 to December 2001. For
the symmetric component (upper panels in Fig. 8)
comparison with NOAA OLR (Fig. 8h) shows thatthe reanalyses generally depict the MJO signal, butsome of them tend to be weaker than that of NOAA
OLR, while the MJO signals in JRA-55 (Fig. 8a) and
ERA-Interim (Fig. 8d) are stronger than in the otherreanalyses and are closer to those in the NOAA OLR
case (Fig. 8h). Moreover, the Kelvin wave signal in
JRA-55 is stronger than the others and is rather close
to that in the NOAA OLR case. On the other hand,the ER wave signal in JRA-55 is weaker than it is in
ERA-Interim (Fig. 8d), MERRA (Fig. 8e), and CFSR
(Fig. 8f), which are closer to the NOAA OLR case.
For the antisymmetric component (lower panels in
Fig. 8), although the MRG wave signals in the reanalyses are much weaker than they are in NOAA OLR(Fig. 8p), it is noteworthy that the MRG wave signal
in JRA-55 (Fig. 8i) is stronger than those in the otherreanalyses. Moreover, before 1978, the signals of theVol. 94, No. 3

equatorial waves and the MJO in JRA-55 (Fig. 9a) aremuch stronger than those in ERA-40 and the NCEP/
NCAR reanalysis (Figs. 9b, c). This result indicatesthat the quality of JRA-55 is consistent throughout its
reanalysis period.
One noteworthy issue is related to the variability
in equatorial waves and the MJO. JRA-55 underestimates the background power spectrum of OLR(not shown). Therefore, the amplitudes of equatorial
waves and the MJO in JRA-55 are weaker than they
are in the other reanalyses during the whole reanalysis period. Figure 10 shows the longitudinal distribution of 20–100-day band-pass-filtered OLR variability averaged over 3°S–3°N for the period from1980 to 2001. In particular, the amplitude of the MJO
is very weak in the Indian Ocean and the western
Pacific. This feature is commonly observed for equatorial waves (not shown). Yokoi (2015) compared
three reanalyses (JRA-55, JRA-25, and ERA-Interim)with column water vapor (CWV) and precipitation
retrieved from SSM/I, focusing on intra-seasonal variability in the tropics, and noted that while the forecast model used for JRA-55 is able to simulate eastward propagation of such CWV anomalies, it tends toweaken their amplitudes due to the excessively weak
cloud–radiative feedback in the forecast model.
4.3

Representation of position and intensity oftropical cyclones
To represent tropical cyclones (TCs) reasonably
well in data-sparse areas such as the tropical oceans,
JRA-55 assimilates tropical cyclone wind retrievals(TCRs, Fiorino 2002) which are derived from best
track data to mimic radiosonde observations, as usedin JRA-25 (Fiorino 2002). Kobayashi et al. (2015)
found that JRA-55 represents around 95 % of the 6hour global TC frequencies observed for the period
from the 1950s to the 1980s and 85–90 % in the
2000s. On the other hand, ERA-Interim, which does
not assimilate artificial TC information such as TCRs,
detects 60 % in the 1980s and 75–80 % in the 2000s.
The differences in the TC detection rates of JRA-55
and ERA-Interim demonstrate the usefulness of TCRs
in TC representation. Murakami (2014) evaluatedand compared the reproducibility of TCs in JRA-55,
JRA-25, ERA-40, ERA-Interim, CFSR, and MERRA
and found that JRA-55 was the best of the six reanalyses in terms of the skill of the spatial and temporal
distribution of TC frequency. Here, we evaluate TC
representation in JRA-55 in terms of position andintensity by comparison with the best track as well aswith JRA-25 and ERA-Interim.

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from January 1981 to December 2001 for (a, i) JRA-55, (b, j) JRA-25, (c, k) ERA-40, (d, l) ERA-Interim, (e,m) MERRA, (f, n) CFSR, (g, o) NCEP/NCAR reanalysis, and (h, p) NOAA OLR. Upper (lower) panels show
symmetric (antisymmetric) components.

Figures 11 and 12 show the geographical distribution and zonal mean climatology, respectively, ofthe root mean square errors (RMSEs) of the differ-ences between the TC center positions reproduced in
JRA-55, JRA-25, and ERA-Interim against the best
track. Here, climatology is defined as the average

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Fig. 9. As in Fig. 8, but from January 1958 to December 1978 for (a, d) JRA-55, (b, e) ERA-40, and (c, f) the
NCEP/NCAR reanalysis.

20

JRA-55JRA-25
ERA-40ERA-Interim

16
12

MERRA
CFSR
NCEP/NCAR
NOAA

8
4
0

0

60E

180
120E
Longitude

120W

60W

Fig. 10. Longitudinal distribution of 20–100-day band-pass-filtered OLR variability averaged over 3°S–3°N from1980 to 2001.

for 1981–2010, and the zonal mean includes only
non-missing grid points. Schenkel and Hart (2012)

evaluated the mean difference between the TC positions in the reanalyses and the best track. Because

June 2016

Y. HARADA et al.

(a)

281

(b)

(c)

Fig. 11. Geographical distribution of the RMSE of TC position (km) with respect to the best track TC position in (a)JRA-55, (b) JRA-25, and (c) ERA-Interim averaged from 1981 to 2010.

TC Position RMSE

Position RMSE (km)

300

200

JRA-55JRA-25
ERA-interim100

060 50 40 30 20 10 0 -10 -20 -30 -40 -50 -60
latitudeFig. 12. Zonal mean of the RSME of TC positiondifference for JRA-55, JRA-25, and ERA-Interim.

averaging weight is larger for TCs with the larger
position error in the case of the RMSE, the magnitudeof the TC position error tend to be highlighted in thelarge position error area for the RSME than for themean distance.
In Fig. 11, the RMSE is smaller than 100 km in
most areas in both JRA-55 and JRA-25. The grid
spacing of the JRA-55 and JRA-25 products usedin this study is 1.25°; hence, the RMSEs for the TC
positions in JRA-55 and JRA-25 are less than the grid
spacing in most areas. Thus, the TC position errors
in JRA-55 and JRA-25 are negligible compared withthe grid resolution, except at higher latitudes. TCs
move faster at high latitudes than at low latitudes,
meaning that the RSME is larger. Indeed, in the threereanalyses, large RMSEs are evident in the northeast
sectors of the western North Pacific and the North
Atlantic basins where most TCs undergo transitions to
extra-tropical cyclones and move with higher speeds.
In ERA-Interim, the RMSE distribution is qualitatively similar to those in JRA-55 and JRA-25. In
the extra-tropics of the western North Pacific and theNorth Atlantic, the magnitudes of the RMSE at mostgrid points are smaller than the 150 km grid spacingof the ERA-Interim product used in this study. The
RMSEs in the northeast sector of the basins are muchlarger than the grid spacing, with values greaterthan 300 km at some grid points. On the other hand,
RMSEs larger than the grid spacing are evident in thetropics and the SH (Figs. 11c, 12, respectively). The
larger RMSE in ERA-Interim is thought to be related
to the geographical distribution of the observational
data. In the NH extra-tropics, more accurate TC representation is achieved due to the dense distribution of282

Journal of the Meteorological Society of Japan
D

E

F

G

Vol. 94, No. 3

Best Track

Fig. 13. Geographical distributions of ACE (m2 s−2) in (a) JRA-55, (b) JRA-25, (c) ERA-Interim, and (d) best
track. Note that different color scales are utilized for reanalyses and the best track.

ACE
JRA-55JRA-25
ERA-interim
Best Track

300

2000

1500

200

1000

100

500

0

ACE for BST (m 2 /s2 )400
ACE for RA (m2 /s2 )observational data, and a smaller position error fromthe best track is also expected. In the tropics and SH,
however, the representation of TCs is relatively poorand their position errors also become large due tothe scarcity of observations. In JRA-55 and JRA-25,
comparable TC position errors are evident in bothhemispheres due to the utilization of TCRs, evenin areas with sparse observations. In JRA-25 and
ERA-Interim, there are high RMSE spikes around
45°S. This error consists of only one grid point
containing a small number of TCs; therefore, we canconsider that these are exceptional cases. In TC analysis and modeling studies using reanalysis products,
the exact TC position is often required, and large position errors are a serious problem. Schenkel and Hart(2012) showed the superiority of the TC position error
in JRA-25 and CFSR, which introduce artificial TC
manipulation, compared with those in other reanalyses without TC manipulation. Our results with less
TC position error in JRA-55 support their results.
We evaluate the TC intensity as another aspectof TC representation. Here, we utilize accumulated cyclone energy (ACE) (Bell et al. 2000) as
the measure of TC intensity, which is defined as thesimple accumulation of the TC maximum wind speed
squared in a 6-hour interval, which indicates the wind

0
60

45

30

15

0
-15
latitude

-30

-45

-60

Fig. 14. Zonal mean ACEs from JRA-55, JRA-25,
ERA-Interim, and the best track.

energy due to TCs. The ACE is measured in units of
kt2 in its original form, but we use units of m2 s–2.
Figures 13 and 14 show the geographical distribution and zonal mean of ACE for JRA-55, JRA-25,
ERA-Interim, and the best track. Note that in thesefigures the scale used for the reanalyses is different
from that used for the best track because of the large
differences between their ACEs. It should be notedthat the TC intensities in reanalyses are generally
weak compared with those observed due to a coarse

June 2016(a)JRA-55(b)JRA-25

60°N

60°N

30°N

30°N

0°

0°

30°S

30°S

60°S

60°S
0°

283

Y. HARADA et al.

60°E(c)ERA-40

120°E

180°

120°W

60°W

0°

60°E

120°E

(d)ERA-Interim

180°

120°W

60°W

0.90

60°N

60°N

0.85

30°N

30°N

0.80

0°

0°

0.75

30°S

30°S

0.70

60°S

60°S
0°

60°E

(e)MERRA

120°E

180°

120°W

60°W

0.65
0°

60°E

(f)CFSR

120°E

180°

120°W

60°W

0.60
0.55

60°N

60°N

0.50

30°N

30°N

0.45

0°

0°

0.40

30°S

30°S

0.35

60°S

60°S
0°

60°E

120°E

180°

120°W

60°W

0°

60°E

120°E

180°

120°W

60°W

(g)NCEP/NCAR

0.30
0°

60°E

120°E

180°

120°W

60°W

0.25

60°N
30°N
0°
30°S
60°S

Fig. 15. Temporal correlation coefficients of monthly mean precipitation anomaly with GPCC for (a) JRA-55, (b)JRA-25, (c) ERA-40, (d) ERA-Interim, (e) MERRA, (f) CFSR, and (g) NCEP/NCAR reanalysis from 1980 to
2001. Temporal correlation coefficients are shown only for grid boxes in which rain gauge stations are present
within corresponding areas for more than 80 % of the analyzed time period.

model resolution that does not resolve the inner structures of TCs. Figure 13 shows the qualitatively similar
geographical distributions of the ACEs of the threereanalyses and the best track. The relatively large
ACEs are located to the east of the Philippines and to
the west of Mexico for the North Pacific, and in thearea around Florida for the North Atlantic. In the SH,
there is a zonal ACE maximum along 15–20°S. In thezonal mean shown in Fig. 14, the overall latitudinalvariation of ACEs in the reanalyses is similar to thatin the best track. Of the three reanalyses, the largest
ACE is observed in JRA-55, and the second largest
in JRA-25, in the NH. The ratios of ACE north of
30°N to their maximum values in three reanalyses are
relatively large compared with that of the best track;
this implies that rapid intensification of TCs moving
toward high latitudes is not properly analyzed. Further
study regarding the reason for such TC behavior

284[mm day-1]

Journal of the Meteorological Society of Japan

8
7
6
5
4

Vol. 94, No. 3

JRA-55JRA-25
GPCC

3
2
1
1980

1982

1984

1986

1988

1990

[mm day-1]

Year

8
7
6
5
4

JRA-55JRA-25
GPCC

3
2
1
1992

1994

1996

1998

2000

Year
Fig. 16.

Time series of monthly mean precipitation averaged over 30–40°N, 80–100°W.

would be necessary. Schenkel and Hart (2012)
reported a northward shift in the TC maximum windspeed distributions in some reanalyses, including
JRA-25 and ERA-Interim, compared with that of thebest track. The relatively large ACE north of 30°N is
considered to reflect a similar tendency in the locationof the maximum wind speed found in the reanalyses.
This topic needs to be improved in future reanalyses.
5.

Climate variability in the extra-tropics

5.1 Precipitation over land
As described in Section 4.1, Kobayashi et al.
(2015) compared global precipitation with the GPCP
produced using both satellite data and surface rain
gauge data. However, over land, the reliability ofsatellite-derived precipitation data is lower than it isover the ocean (Nicholson et al. 2003a, b). Therefore,
in this section, the performance and representation of
JRA-55 for precipitation over extra-tropical land aredescribed using GPCC data (Schneider et al. 2011; seealso Section 2.2).
Figure 15 shows the distribution of the temporalcorrelation coefficient between the reanalyses andmonthly mean precipitation obtained using GPCC
data from 1980 to 2001. Note that temporal correlation coefficients are calculated only for grid boxes inwhich the rain gauge stations are present within thecorresponding area for more than 80 % of the period.
Areas of low correlation commonly appear in thetropics, for example, Central Africa, and the Amazon.
However, these panels also indicate that the reanalyses yield more accurate precipitation data over landJune 2016

285

Y. HARADA et al.

(a)JRA-55(b)ERA-40

60°N

60°N

30°N

30°N

0°

0°

30°S

30°S

60°S

60°S
0°

60°E

120°E

180°

120°W

60°W

0°

60°E

120°E

180°

120°W

60°W

(c)NCEP/NCAR

0°

60°E

120°E

180°

120°W

60°W

60°N
30°N
0°
30°S

0.90
0.85
0.80
0.75
0.70
0.65
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25

60°S

Fig. 17.

As in Fig. 15, but from 1958 to 1979 for (a) JRA-55, (b) ERA-40, and (c) NCEP/NCAR reanalysis.

in the NH extra-tropics. Furthermore, JRA-55 (Fig.
15a) is superior to JRA-25 (Fig. 15b), ERA-40 (Fig.
15c), MERRA (Fig. 15e), and CFSR (Fig. 15f) inregions such as Europe, Russia, southeastern North
America, and around Brazil, and is approximately
comparable to ERA-Interim (Fig. 15d); in particular, improvements in southeastern North America
and around Brazil in JRA-55 from JRA-25 are noteworthy. The exclusion of surface pressure observations over the Amazon basin in JRA-55 is related
to the improvements around Brazil, as described in
Section 7.2 of Kobayashi et al. (2015).
Figure 16 shows time series of monthly mean
precipitation data averaged over 30–40°N, 80–100°W,
corresponding to southeastern North America.
JRA-25 tends to overestimate the precipitation inthis area during the warm season, resulting in a lower
temporal correlation with the GPCC data. The precipitation estimated by JRA-55 is satisfactorily consistent
with the GPCC results.
Figure 17 illustrates the distribution of the temporalcorrelation coefficient between the reanalyses andmonthly mean precipitation obtained using GPCC
data from 1958 to 1979. JRA-55 exhibits higher
correlation coefficients over Eurasia and North
America compared with those of ERA-40 and the
NCEP/NCAR reanalysis. This result indicates that the
representation of precipitation over extra-tropical landin JRA-55 is of high quality throughout the reanalysis

period.
5.2 Comparison of storm tracks
In the extra-tropics, synoptic-scale cyclones play
an important role in providing the momentum flux
to sustain mid-latitude large-scale circulation. We
refer to synoptic-scale cyclones as transient eddies, incontrast to stationary Rossby waves. Transient eddies
develop along SST fronts, where the main regions of
the tracks of extra-tropical disturbances are formed(Nakamura et al. 2008, Sampe et al. 2010). Compoet al. (2011) compared the synoptic variabilities inthe storm track regions and found weaker variabilities
where observations were sparse. Here, we describe
the representation of transient eddies in the extratropics in JRA-55, focusing on the effect of changing
the observing system on the synoptic variability in thestorm track regions in JRA-55.
Figure 18 shows 2–8-day band-pass-filtered transient eddy kinetic energies (KEs) at the 300 hPa
level during the NH cold season (November–March)from 1980/1981 to 2000/2001. Two regions ofmaximum KE are observable in the North Pacificand the North Atlantic, and all reanalyses succeed inqualitatively representing these regions. However,
the North Pacific KEs in JRA-25, ERA-40, and the
NCEP/NCAR reanalyses are clearly lower than thosein ERA-Interim. The KE in JRA-55 is also slightly
lower than that in ERA-Interim but it is stronger than

286

Journal of the Meteorological Society of Japan(b)JRA-25 30°W
0°

0°

12
0°E

0°
18

150°E

(e)MERRA30°W
0°(f)CFSR
60

30
°E

120°W
60°E
0°
18

15

150°E

60
°W

12

0°

0°E

18

150°E

150
145
140
135
130
125
120
115
110
105
100
95
90
85
80
75
70
65
60
55
50

°E
90

15
0°W

60°E

120°W

°W

30

90

°E

0°

15
0°W

120°W
60°E
0°W

60°E

(g)NCEP/NCAR
30°W

°W
90

150°E

°W

°E

12
0°E

°

0
18

°E

60

90

°E

90

°E

90
12
0

30°W

0°

°W

°W
90

°W

90

30
°E

°W

120°W

60

0°
18

150°E

15
0°W

0°

[m2 s-2]
120°W

120°W
60°E
15
0°W

120°W
60°E
°W

60°E

15
0

°E
12
0°E

0°
18

150°E

(d)ERA-Interim
30°W

°W

°E
90

90

°E
90
12
0°E

60

°W
90

30
°E

°W

°W

°W

30

(c)ERA-40 30°W

60

90

90

°E

°W

15
0°W

60

30
°E

0°

30
°E

(a)JRA-55 30°W

Vol. 94, No. 3

12
0

0°
18

°E

150°E

Fig. 18. Band-pass-filtered 2–8 day transient eddy KEs at 300 hPa level during NH cold season (November–March) from 1980/1981 to 2000/2001 for (a) JRA-55, (b) JRA-25, (c) ERA-40, (d) ERA-Interim, (e) MERRA, (f)CFSR, and (g) NCEP/NCAR reanalysis.

the KEs in the other reanalyses. Moreover, during
the NH cold seasons from 1958/1959 to 1979/1980,
the KE in ERA-40 is quite weak compared with its
values after 1980/1981 (Fig. 19). Although the KE in
JRA-55 is also weaker, it is much stronger than thosein the ERA-40 and NCEP/NCAR reanalyses. These
results indicate that JRA-55 is not much affected bychanges in the observation system on the representation of storm tracks. Furthermore, JRA-55 improves
the temporal consistency.
In order to assess the representations of storm

tracks, we conducted an investigation using bandpass-filtered daily mean OLR. The development oftransient eddies is closely related to the distributionof developed clouds. Hence, it is necessary to assess
not only the KEs of transient eddies but also the
distributions of developed clouds that correspond toareas of lower OLR in the mid-latitudes. Figure 20
shows the frequencies of the spatial correlations of
2–8 day band-pass-filtered daily mean OLRs overthe storm track regions with NOAA OLR during
the NH cold seasons from 1980/1981 to 2000/2001.

287

Y. HARADA et al.

30°W

0°
30
°E

15

120°W

15
0°W

120°W

0°W

60°E

60°E

150°E

0°
18

°W

0°E

90

12

°W

°E

0°
18

60

90

150°E

0°

°W

°E
90

°E
90
12

30°W

60

°W
90

°W
90

30
°E

°W

120°W

60

30
°E

0°

0°E

(C)NCEP/NCAR(b)ERA-40
30°W

15
0°W

(a)JRA-55

60°E

June 2016

12
0°E

150°E

0°
18

[m2 s-2]
50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150

Fig. 19. As in Fig. 18, but during the NH cold season from 1959/1960 to 1979/1980 for (a) JRA-55, (b) ERA-40,
and (c) NCEP/NCAR reanalysis.

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Atlantic and (b) the North Pacific storm track regions, respectively.

288

Journal of the Meteorological Society of Japan)UHTXHQF\

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Fig. 21. As in Fig. 20, but for SH storm track region (30–60°S, entire zonal) during the SH cold season (May–
September) from 1980 to 2001.

Clearly, high spatial correlation coefficients in boththe North Atlantic and the North Pacific are more
frequent in JRA-55 (red diamonds), CFSR (bluecrosses), and ERA-Interim (green triangles) than in
MERRA (purple crosses), ERA-40 (right green triangles), and JRA-25 (pink diamonds). In particular, lowspatial correlation coefficients in the North Atlantic
storm track region are less frequent in JRA-55 thanin JRA-25 (Fig. 20a), indicating that JRA-55 greatly
improves the representation of each transient eddy
from JRA-25 in this region. In the SH cold season(Fig. 21), the qualities of ERA-Interim and CFSR are
outstanding, while JRA-55 is not as good as these tworeanalyses. However, it clearly improves upon the
representation of each transient eddy from JRA-25, asin the North Atlantic.
5.3 Siberian HighIn the NH winter, the Siberian High develops innortheastern Eurasia in association with the accumulation of an extremely cold air mass. Cold air outbreaks
from the Siberian High greatly influence the climatesof East Asia and Europe, and even that of Southeast
Asia. Here we focus on SLP over Eurasia in boreal
winter, which is used to measure the intensity of theSiberian High.
Figure 22 shows the distributions of SLP during
the NH winter (December–February) from 1980/1981
to 2000/2001 in the observational dataset HadSLP2(Allan and Ansell 2006) and in that subtracted fromeach of the reanalysis products. The SLP in northeastern Eurasia is the highest in the NH (Fig. 22a),
and all reanalyses have a distinct negative bias innortheastern Eurasia (Figs. 29b–h). Clearly, JRA-55(Fig. 22b) reduces the negative bias in this region
compared with JRA-25 (Fig. 22c). Figure 23 showstime series of NH winter mean SLP averaged over
40–60°N, 80–120°E, corresponding to the area of
the Siberian High. The long-term trend of JRA-55(red line in Fig. 23) is not very different from thatof HadSLP2. On the other hand, ERA-40 exhibits
much higher SLP values before 1979 than during thesubsequent period, resulting in a marked negative
trend for the whole period. Although reanalyses are
generally consistent after 1980, except for MERRA,
which yields assignable negative bias in this region
throughout the period, differences from HadSLP2 areobservable. In addition, JRA-25 exhibits lower values
in the 1980s. JRA-25 has larger SLP analysis increments during 1979–1988 compared with the period
after 1989 (Fig. 24). We excluded large negative
biased surface pressure observations from Mongolian
stations, which resulted in a successful reduction of
inappropriate analysis increments.
6.

Climate variability in the stratosphere

6.1 Stratospheric temperature
Kobayashi et al. (2015) compared monthly
temperature anomalies from reanalyses with independent observational datasets for the layers fromthe lower stratosphere to the top of the stratosphere.
In this section, we discuss the features on a regional
basis. Figure 25 shows time series of regionallyaveraged 30-hPa-level temperature deviations fromthe climatological mean for the period from 1981 to
2001. In the extra-tropical stratosphere during the NH
winter (Fig. 25a), the inter-annual variability of thereanalyses is approximately consistent. In the tropics

June 2016

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Y. HARADA et al.
[hPa]

15
0°W
60
°W

15
0°W

120°W

30
°E

120°W
120°W
30°W

60
°W

120°W
15
0°W

120°W

30
°E

0°
18

°W
90

60°E

150°E

°E
90

0°
18

15
0°W

120°W

30
°E

60°E

15
0°W

0°
°W
90

150°E

60
°W

9.5
8.5
7.5
6.5
5.5
4.5
3.5
2.5
1.5
0.5
-0.5
-1.5
-2.5
-3.5
-4.5
-5.5
-6.5
-7.5
-8.5
-9.5

(h)NCEP/NCAR - HadSLP2

60
°W

°E
90
12
0°E

30°W

°W
90

30°W

0°
18

°E
90
12
0°E

0°
18

(g)CFSR - HadSLP2
0°

30
°E
0°

°W
90

150°E

150°E

(f)MERRA - HadSLP2

60
°W

°E
90
12
0°E

[hPa]

15
0°W

15
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30°W

60
°W

60°E

120°W

30
°E

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90

12
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(e)ERA-Interim - HadSLP2

0°
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(d)ERA-40 - HadSLP2
0°

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0°E

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(c)JRA-25 - HadSLP2

0°

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°W
90

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90

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12
0°E

1038
1036
1034
1032
1030
1028
1026
1024
1022
1020
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1016
1014
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1010
1008
1006
1004
1002
1000
998

60°E

60
°W

30
°E

0°

30°W

60°E

(a)HadSLP2

12
0°E

150°E

0°
18

Fig. 22. Distributions of (a) SLP (hPa) from HadSLP2 and (b–h) that subtracted from each reanalysis productduring NH winter (December–February) from 1980/1981 to 2000/2001. Gray shadings indicate areas with altitudes exceeding 3000 m.

290

Journal of the Meteorological Society of Japan

Vol. 94, No. 3

1036
(hPa)

1034
1032
1030
1028
1026

JRA-55
ERA-40
NCEP/NCAR
HadSLP2

1024
1022
1960
Fig. 23.

1965

JRA-25ERA-Interim
MERRA
CFSR

1970

1975

1980

1985

1990

Year (Dec. - Feb. mean)

1995

2000

2005

2010

Time series of NH winter (December–February) mean SLP (hPa) averaged over 40–60°N, 80–120°E.

1
(hPa)
0
-1
JRA-55JRA-25

-2
-3
1980

1985

1990
1995
2000
Year (Dec. - Feb. mean)

2005

Fig. 24. Time series of winter (December–February) mean SLP analysis increments (hPa) averaged over 45–55°N,
90–105°E.

(Fig. 25b), the reanalyses are also comparable except
for JRA-25, which exhibits an artificial jump in 1998.
Kobayashi et al. (2015) reported that the forecast
model used in JRA-25 had a significant cold bias in
the lower stratosphere, causing difficulty in correcting
the bias due to satellite radiances and resulting insome discontinuity in the analysis in response tochanges in observing systems or bias correction
methods for satellite radiances. JRA-55 succeeded in
reducing such discontinuous changes considerably. At
high latitudes in the stratosphere during the SH winter(Fig. 25c), larger discrepancies between the reanalyses are observable before 1979. The temperature
anomalies of ERA-40 are much colder than those of
JRA-55 in this period.
Figure 26 compares the reanalyses with radiosonde
observations that passed quality control checks in the
JRA-55 system at the 30 hPa level over New Zealandin the SH winter (June–August) 1963. We selected
the observational points, which are the southernmost
available radiosonde observations. ERA-40 seems
to have a cold bias at this level. Uppala et al. (2005)
pointed out a pronounced (~10 K) cold bias in winterand springtime analyses in the Antarctic lower stratosphere in the early years; this is evident from comparison with later ERA-40 analyses and other climatologies. The result above is consistent with this finding.
Figure 27 illustrates the results of a comparisonof retrieved temperatures from Aura MLS (Waters
et al. 2006; see also Section 2.2). The details of thecomparison method are described in Section 2.3. In
the upper stratosphere, JRA-25 has a cold bias around
5 hPa in the NH and a warm bias just above this level(Fig. 27c). Although JRA-55 has reduced biases in
the lower stratosphere compared with JRA-25 (Fig.
27b), it has a large warm bias in the lower meso-

June 2016

291

Y. HARADA et al.

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Fig. 25. Time series of 30-hPa-level temperature deviations (K) from climatological mean from 1981 to 2001,
averaged over (a) 60–90°N, NH winter (December–February), (b) 22°S–22°N annual mean, and (c) 60–90°S, SH
winter (June–August).

sphere (above 1 hPa) except in the polar region of theNH. MERRA does not have any large biases except
around the stratopause and above, and ERA-Interim
has a smaller bias profile than the other reanalyses
generally. These features indicate that these reanalyses
improve the representation of stratospheric temperature, although large uncertainties still exist in andaround the stratopause and above.

6.2

Sudden Stratospheric warming in the Northern
Hemisphere
SSW is a phenomenon with a hemispheric spatial
scale but a short timescale in the winter stratosphere,causing the polar stratospheric temperature to increaseby more than 25 K within a week. To represent SSW
events more precisely, it is important to make use ofas many upper-air observations as possible. However,during the pre-satellite era (before 1979), radiosondes

292

Journal of the Meteorological Society of Japan
235
230

Reanalysis (K)

225
220
215
210
205
200

NCEP/NCAR
JRA-55
ERA-40

195
190

210

215

220

225

Radiosonde (K)

230

235

Fig. 26. Scatter diagram showing radiosondetemperatures at 30 hPa from stations at Invercargill (New Zealand) and Campbell Island (New
Zealand), and values at the analysis grid points
nearest to these locations, during the SH winter(June–August) 1963. Only quality-controlled
radiosonde observations are plotted.

were the only upper-air observing system and radiosonde observations were sparse in the polar region,
making it difficult to represent SSW events during thisperiod.
Figure 28 shows time series of temperature at
30 hPa averaged over 60–90°N in the NH winter of
1962/1963. In January 1963, a wave-2-type major
SSW occurred in the NH (Finger and Teweles 1964).
The rapid increase in temperature is observable in thesecond half of January 1963, with the temperaturepeaking at the beginning of February 1963. At thispeak, ERA-40 yields the highest temperature among
the three reanalyses. JRA-55 is a few degrees lower
than ERA-40 at the peak but is comparable in otherperiods. On the other hand, the NCEP/NCAR reanalysis is not consistent with ERA-40 and JRA-55. It
tends to be warmer before the SSW and colder thanthe others around the peak of the warming.
Figure 29 compares the reanalyses with radiosonde
observations at the 30 hPa level in Canada (red starand diamond in Fig. 30a mark the locations of radiosonde observations). Evidently, ERA-40 fits the radiosonde observations well over the entire temperature
range, while JRA-55 has a cold bias for temperatures above 220 K. The NCEP/NCAR reanalysis also
seems to have a cold bias for temperatures above 220

Vol. 94, No. 3

K and a warm bias below 210 K. These features may
be related to the weak amplitude of planetary waves
with zonal wavenumber 2 in the numerical prediction
model.
Figure 30 shows the 30 hPa geopotential heightsof the reanalyses on 31 January 1963 at 00 UTC. At
the peak of the warming, two ridges develop over the
North Atlantic and the Kamchatka Peninsula, and thepolar vortex splits. One of the polar vortices shifts
over Novaya Zemlya. JRA-55 and ERA-40 represent
these features and resemble each other. On the other
hand, in the NCEP/NCAR reanalysis, the development of the two ridges is weaker and the polar vortex
over Novaya Zemlya is stronger than it is in the otherreanalyses. These results suggest that the representation of SSW events in these reanalyses contains
uncertainty during the pre-satellite era.
6.3 Equatorial stratosphere
The quasi-biennial oscillation (QBO) is a quasi-periodic oscillation of the zonal wind between easterly
and westerly flow and is a robust phenomenon in
the equatorial lower stratosphere (Ebdon 1960). The
stratospheric semi-annual oscillation (SSAO) is dominant in the equatorial upper stratosphere (Reed 1965;
Jones and Pyle 1984).
Figure 31 shows time–pressure cross-sections of
the zonal-mean zonal wind component averaged over5°S–5°N for the period from 1958 to 1978. JRA-55,
ERA-40, and the NCEP/NCAR reanalysis well represent the general features of the QBO and SSAO.
However, the scatter diagrams in Fig. 32 show that thecorrespondence between the reanalyses and the radiosonde observations before 1978 are not comparable
to those after 1979 (not shown). In particular, thedifferences between the reanalyses and the radiosonde
observations in the easterly phase at the 10 hPa and
30 hPa levels are very large. In addition, the reanalyses exhibit an easterly bias when radiosonde observations indicate a weak westerly phase, except for
JRA-55 at the 70 hPa level. Such cases occur during
westerly acceleration phase (eastward wind shear)(Fig. 33a). Westerly accelerations in the reanalyses
are only about half of those in the radiosonde observations (Fig. 33b). Kawatani et al. (2010) pointed
out that eastward equatorial waves including Kelvinwaves contribute up to 25–50 % to driving the QBO
in the eastward wind shear (westerly acceleration
phase). The forecast model used in JRA-55 does not
simulate QBO spontaneously (Kobayashi et al. 2014)
because it does not use a non-orographic gravity wave
drag parameterization. Therefore, the weak westerlyJune 2016

293

Y. HARADA et al.
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temperature bias between reanalyses and Aura MLS during the NH winter (December–February) from 2005/2006
to 2011/2012.

Journal of the Meteorological Society of Japan
235

260

230

250

225

240
Reanaysis (K)Temperature (K)

294

220
215
210
205

NCEP/NCAR
ERA-40200

JRA-55195

1-Dec 16-Dec 31-Dec 15-Jan 30-Jan 14-Feb 1-Mar 16-Mar 31-Mar

1962

1963
Date

Fig. 28. Time series of temperature at 30 hPa
averaged over 60–90°N in the NH winter of
1962/1963.

acceleration in the reanalyses may have been causedby the lack of non-orographic gravity wave dragparameterization and the weaker amplitude of equatorial waves before 1978, as described in Section 4.2.
This issue requires further investigation.
7.

Long-term trends in surface air temperature

Kobayashi et al. (2015) compared globally averaged land-surface air temperature anomalies fromthe Climate Research Unit (CRU) temperature database (CRUTEM4, Jones et al. 2012) with those of
the NCEP/NCAR reanalyses, ERA-40, JRA-25, andJRA-55 and showed that the low-frequency variabilityof land-surface temperature anomalies in JRA-55 isreasonably similar to that in CRUTEM4 except fora difference of less than 0.1 K in warming after the
1990s. To further evaluate the surface air temperature accuracy of JRA-55, Fig. 34 compares the global
distributions of long-term temperature trends in
HadCRUT4 (Morice et al. 2012, see also Section 2.2),
ERA-40, JRA-55, and the NCEP/NCAR reanalysis
for the period from 1958 to 2001. The spatial distribution of the long-term temperature trend in JRA-55
seems to be the closest to that in HadCRUT4. To
confirm this result quantitatively, the global meansof the RMSE between the HadCRUT4 and reanalyses over land areas are calculated, and we foundthat the RMSE for JRA-55 is 0.0989 K decade−1 andthat it is smaller than the values for ERA-40 (0.129 K

Vol. 94, No. 3

230
220
210
200
190
180

NCEP/NCAR
JRA-55
ERA-40180 190 200 210 220 230 240 250 260
Radiosonde (K)Fig. 29. Scatter diagram showing radiosondetemperatures at 30 hPa from stations at Norman
Wells (Canada) and Resolute (Canada), andvalues at the analysis grid points nearest tothese locations in the NH winter (December–
February) of 1962/1963. Only quality-controlled
radiosonde observations are plotted. Red starand diamond in Fig. 30a mark the locations ofNorman Wells and Resolute, respectively.

decade−1) and the NCEP/NCAR reanalysis (0.151 K
decade−1). The NCEP/NCAR reanalysis shows negative trends in the Sahara, around the Tibetan Plateau,
and in Australia and western North America, which
are inconsistent with HadCRUT4. In ERA-40, thenegative trends in South America and Australia arelarge. JRA-55 also shows poor agreement with observations in southeastern Australia.
Figure 35 shows time series of SH winter meansurface temperature (K) averaged over 20–35°S,
140–155°E, corresponding to southeastern Australia.
Before 1965, the analysis and 6-hour forecast (background) are consistent. However, after the 1970s, thedifference between the analysis and the 6-hour forecast gradually increases, reaching about 1 K after the
1990s. We suppose that the difference is related to thechange in the number of stations observing the surface
air temperature in southeastern Australia. In Australia,
the number of surface observations was less than 10
before 1965, which was not sufficient to correct thewarm bias in background fields that originated in theforecast model used in JRA-55. As the number ofsurface observation stations has increased over timeand reached 80 after 1978, the observations haveJune 2016

295

Y. HARADA et al.
[gpm](c) NCEP/NCAR(b) ERA-40
0°W
15

60

90°E

°E

°E

90°E
90°W

12

60

°E

90°E
90°W

30

°E
30

°W

°W

30

0°

60

°W

°W
°E
30

60

60
30
°W

15
0°E

0°E
12

12
0°W

0°W

15
0°E
12

0°E

12

90°W

180°

15
0°E

0°W

0°E

0°W

180°

15

12

180°

0°W

15

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30

°W

0°

24360
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23280
23160
23040
22920
22800
22680
22560
22440
22320
22200
22080
21960

60

(a) JRA-55

0°Fig. 30. Geopotential heights at 30 hPa (gpm) on 31 January 1963 at 0000 UTC in the NH for (a) JRA-55, (b)ERA-40, and (c) NCEP/NCAR reanalysis. Red star and diamond in (a) mark locations of Norman Wells andResolute, respectively.

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to 1978 for (a) JRA-55, (b) ERA-40, and (c) NCEP/NCAR reanalysis.

296

Journal of the Meteorological Society of Japan
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June 2016

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Y. HARADA et al.

(a) 30-hPa Zonal wind (m s-1)

10
0
-10

Radiosonde
JRA-55
ERA-40
NCEP/NCAR

-20
-30
-40

25
20
15
10
5
0
-5
-10
-15

1966

1967

1968

Year

1969

1970

(b) 30-hPa Zonal wind acceleration (m s-1 month-1)

1966

1967

1968

Year

1969

1971
Radiosonde
JRA-55
ERA-40
NCEP/NCAR

1970

1971

Fig. 33. Time series of (a) 30 hPa zonal wind component (m s−1) and (b) its acceleration (m s−1 month−1) forreanalyses and radiosonde observations from 1966 to 1971. Reanalyses are averaged over 0–2.5°N, 100–105°E,
and radiosonde observations are from Singapore.

begun to have a greater impact on the analysis. Consequently, they may have caused an artificial negative
trend in this region.
8.

Summary and conclusions

We have reported on the quality of JRA-55 fromvarious viewpoints. Firstly, we showed improvement in the representation of atmospheric circulation on an isentropic surface and consistency in thezonal mean field. In particular, we found that JRA-55
significantly improved the consistency of the zonal
momentum budget based on the MIM method even in
the stratosphere when major SSW events occur. Next,
we examined the representation of atmospheric variability in several regions.
In the tropics, we found that the frequencies ofhigh spatial correlations with TRMM3B42 are clearlyhigher in JRA-55 than in the NCEP/NCAR reanalysis,
JRA-25, CFSR, and ERA-Interim and are close to
those in MERRA. Moreover, from the results of thewavenumber–frequency power spectrum analysis, thesignals of equatorial waves and the MJO in JRA-55
are found to be much stronger than those in ERA-40
before 1978.
In the extra-tropics, we found that JRA-55
reduced the overestimation of precipitation in southeastern North America, where it was overestimatedby JRA-25 in the NH warm season. Moreover, weestimated the activity of transient eddies from the
2–8-day band-pass-filtered transient eddy KE at the
300 hPa level and found that the activity of transient
eddies in JRA-55 is much stronger than those in
ERA-40 and the NCEP/NCAR reanalysis during the
NH cold season before 1979/1980. We also examined the frequency of spatial correlation of 2–8 dayband-pass-filtered daily mean OLR over the storm
track regions with NOAA OLR during the NH coldseason and found that JRA-55, CFSR, and ERA-Interim more frequently had high spatial correlation
coefficients than MERRA, ERA-40, and JRA-25 did

298

Journal of the Meteorological Society of Japan

Vol. 94, No. 3

Fig. 34. Global distributions of long-term surface air temperature trends (K decade−1) from 1958 to 2001. Reanalysis data were re-gridded to 5° × 5° resolution. Trend values for HadCRUT4 were calculated only for grid boxesin which data are available for more than 80 % of investigated time period.

in the North Atlantic storm track region. This result
indicates that JRA-55 significantly improved therepresentation of each transient eddy compared with
JRA-25 in this region. In the SH cold season, JRA-55
is clearly superior to JRA-25. In addition, JRA-55
reduced the negative bias of SLP in northeastern
Eurasia and improved the SLP long-term trends inthe region where the Siberian High develops in boreal
winter.
In the stratosphere, we found larger discrepancies
between reanalyses for the extra-tropical stratosphere
during the extra-tropical SH winter. Comparisons withradiosonde temperature revealed that JRA-55 has asmaller bias in temperature than the other reanalyses
in the extra-tropical SH winter before 1979.
In summary, JRA-55 generally yielded improved
representations over a range of space–time scales

compared with JRA-25 during the satellite era, andit improved the temporal consistency compared with
ERA-40 and the NCEP/NCAR reanalysis throughout
the reanalysis period. We suppose that these improvements result from major updates in the JMA operational numerical weather prediction system, such asincreased resolution, the introduction of new radiation and advection schemes to the forecast model, andadaption of 4D-Var with variational bias correction tosatellite radiances (Kobayashi et al. 2015).
We also found some issues in JRA-55. The amplitudes of equatorial waves and the MJO in JRA-55
are lower than those in ERA-Interim and JRA-25. In
particular, the amplitude of the MJO is very low in
the Indian Ocean and the western Pacific. For tropical cyclones, rapid intensification of TCs moving
toward high latitudes is not properly analyzed. In theJune 2016

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Y. HARADA et al.

291

Surface Temp. (K)JRA-55 analysis

JRA-55 6-hourly forecast

290
289
288
287
286

1960

1965

1970

1975

1980

1985

1990

Year (Jun. - Aug. mean)

1995

2000

2005

2010

Fig. 35. Time series of surface temperature (K) averaged over 20–35°S, 140–155°E during the SH winter (June–
August) from 1958 to 2012. Black and blue lines represent analysis and 6 -hourly forecast, respectively.

stratosphere, JRA-55 has a large, warm bias in thelower mesosphere, except in the polar region in thewinter hemisphere. We also found that large uncertainties still exist around the stratopause and abovein JRA-55, ERA-Interim, and MERRA. Moreover,in the equatorial stratosphere, the difference between
reanalyses and radiosonde observations in the easterly phase at the 10 hPa and 30 hPa levels is verylarge, and JRA-55, ERA-40, and the NCEP/NCAR
reanalysis exhibit an easterly bias when radiosonde
observations indicate a weak westerly phase of the
QBO before 1978. Such cases occur in the westerly
acceleration phase of the QBO, and westerly accelerations in reanalyses are only about half of those in theradiosonde observations. We also found that JRA-55
and ERA-40 show unrealistic strong cooling in SouthAmerica and Australia, although the spatial distribution of the long-term temperature trend in JRA-55 is
the closest to HadCRUT4. To deal with these issues,
it is essential to reduce the forecast model biases usedin the DAS and to improve the parameterizations ofphysical processes. In the future, we will conduct
further investigations to clarify the improvements in
JRA-55 and will provide additional information on
JRA-55 quality.
Acknowledgments
Verification of the JRA-55 product was supported
by many JMA and MRI staff members. We would liketo thank all these individuals for providing us with
their invaluable help and advice. In addition, 27 independent researchers participated in quality evaluation
prior to the release of the product. We sincerely appreciate their invaluable contributions.

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