Progress in Earth andPlanetary Science

Shimabukuro et al.
Progress in Earth and Planetary Science(2023) 10:19
https://doi.org/10.1186/s40645-023-00547-1

Open Access

RESEARCH ARTICLE

Update of global maps of Alisov’s climateclassification
Ryu Shimabukuro1* , Tomohiko Tomita2 and Ken‑ichi Fukui3

Abstract
Proposed in 1954, Alisov’s climate classification (CC) focuses on climatic changes observed in January–July in largescale air mass zones and their fronts. Herein, data clustering by machine learning was applied to global reanalysis data
to quantitatively and objectively determine air mass zones, which were then used to classify the global climate. The
differences in air mass zones between two half-year seasons were used to determine climatic zones, which were thensubdivided into continental or maritime climatic regions or according to east–west climatic differences. This study
renews Alisov’s CC for the first time in almost 70 years and employs data-driven machine learning to establish a stand‑ard for causal CC based on air masses.
Keywords Alisov’s climate classification, Air mass, Front, Data clustering by machine learning
1 Introduction
Climate classification (CC) divides the Earth’s surface
into regions based on the similarity of climatic features.
The shift of climatology from the classical approach of
describing the characteristics of climatic elements to themodern approach of explaining the formation of climatic
phenomena is reflected in CC (Yazawa 1980). In otherwords, CC methods can be divided into two categories:
resultant CC based on classical climatology and causal
CC based on modern climatology.
With the development of synoptic meteorology andweather forecasting, modern climatology has been able
to reveal the physical processes behind and causal relationships among climatic phenomena. Bergeron (1930)
first introduced the concept of an “air mass,” which has
since been refined into air mass climatology (Fukui 1962).
*Correspondence:
Ryu Shimabukuro
ryujin.mabuya.1@gmail.com
1
Graduate School of Science and Technology, Kumamoto University,
Kumamoto, Japan
2
Faculty of Advanced Science and Technology, Kumamoto University,
Kumamoto, Japan
3
The Institute of Scientific and Industrial Research, Osaka University,
Osaka, Japan

An air mass is a large-scale atmospheric volume withuniform temperature and humidity that forms over vast
oceanic or continental surfaces until their properties
reach near-equilibrium. An air mass tends to form with alarge-scale stationary anticyclone. The boundary between
different air masses is defined as a transition zone or
front, where cyclonic disturbances such as extratropical
cyclones frequently occur, develop, and move eastward.
Because the weather and climate are inherent to an air
mass, an air mass can give comprehensive information
on a climate and the area covered by the climate (Fukui
1962). However, air mass climatology has two drawbacks(Yoshino 1978). First, determining the area of an air massin a globally applicable manner is difficult. A global classification tends to ignore local climatic features, but
a local classification would divide Earth’s surface into
many small areas. Second, the difficulty in determining
air masses can result in an arbitrary determination offronts. To overcome these two difficulties, an objective
and quantitative approach to determining air masses isrequired.
Figure 1 shows Alisov’s CC (ACC) (Alisov 1936,1954), which aims to understand global climatic features based on air masses. While the Köppen–Geiger
CC (KGCC) (Köppen 1918; Köppen and Geiger 1936)© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

is a representative resultant CC system based on differences in the vegetation landscape due to climate, the
ACC is a representative causal CC system based on themechanisms and physical processes caused by air mass
zones and their fronts. The mean positions of large-scale
air mass zones and fronts usually shift by season due toseasonal changes in general atmospheric circulations.
The north–south variations of air mass zones and fronts
can be used to divide climatic zones into two categories:
those that remain year round, and those that change seasonally. The ACC (1954) divides the global climate into
four air mass zones according to temperature (i.e., latitude): equatorial, tropical, polar, and Arctic/Antarctic.
Then, the differences between air masses in January andJuly are used to determine seven climatic zones. In thisstudy, the air mass zone was defined as the distributionof each air mass in the summer and winter seasons, andthe climatic zone was defined as the spatial extent of aclimate superimposed on a single map accounting forseasonal changes. However, the fronts at both latitudinal edges do not correspond to large precipitation lines,
which made Suzuki (1961) question whether the locations of the fronts were correctly determined. Wadachi(1997) argued that the ACC could not identify areas witha prevailing subtropical high. Despite some shortcomings, the ACC demonstrates the most crucial circulation

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processes in different climatic zones and may be used as abasis to explain global climatic types (Khlebnikova 2009).
Many causal CCs have the disadvantage of sometimes
not corresponding to actual climatic conditions (Nishina2019). The subtropical and polar climatic zones in the
ACC have been criticized for their low correspondencewith the vegetation landscape of arid and temperate climates in the KGCC (Khlebnikova 2009). Although the
KGCC is based on vegetation rather than the actual climate, it is widely used to explain agricultural and cultural
regions as one of the CC systems that best reflects realclimate differences (Nishina 2019). The latest revisions to
the KGCC (Kottek et al. 2006; Peel et al. 2007; Kriticos
et al. 2012; Beck et al. 2018) refer to terrestrial climatic
variables (i.e., monthly temperature and precipitation)
to approximate the vegetation distribution on land but
still exclude climatic zones over the ocean. Because the
KGCC is based on vegetation distribution, it is inherently
unable to classify the climate over oceans.
Alisov (1954) attempted to subdivide the seven major
climatic zones (Fig. 1) into 22 smaller regions using surface conditions such as continent vs. ocean, eastern vs.
western, and plains vs. mountains. However, he failed toreveal the figures with methodology behind such detailed
types (Mizukoshi and Yamashita 1985), which is probably because data with sufficiently long periods and highspatial resolution were unavailable in the 1950s. The

Fig. 1 Alisov’s seven climatic zones (Alisov 1954). 1: Equatorial zone, 2: Subequatorial zone, 3: Tropical zone, 4: Subtropical zone, 5: Polar zone, 6:
Subarctic zone, 7: Arctic/Antarctic zone

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

ACC, thus, does not consider east–west climatic differences in a climatic zone (e.g., Tokyo with a large amountof summer precipitation and Rome with the slight one
are included in the same climatic zone (Nishina 2019)).
Such subdivision is now possible with the presently available sophisticated data. Brunschweiler (1957) attempted
to divide air masses into continental and maritime types
and investigated the monthly occurrence frequency of
individual air masses based on analysis of daily data at
significant sites in the Northern Hemisphere. Then, he
used the monthly changes in air mass areas to redefine
the annual mean distribution of air masses in the Northern Hemisphere. Although Brunschweiler’s methodology
is clear, some researchers have questioned the rationale of using air mass frequencies of 80%, 50%, and 20%(Suzuki 1961). Oliver (1970) classified Australia in a similar manner using the annual prevailing air masses.
In recent years, weather observatories have been
exploited globally, and several studies have tried applying machine learning to clustering such data for global
CC (Mahlstein and Knutti 2010; Zscheischler et al. 2012;
Metzger et al. 2013; Zhang and Yan 2014; Rohli et al.
2015; Netzel and Stepinski 2016; Sathiaraj et al. 2019;
Gardner et al. 2020). Most of these studies focused on
reproducing or comparing their work with the revised
KGCC (Kottek et al. 2006; Peel et al. 2007). Rohli et al.
(2015) used global reanalysis data to extend the KGCC
over the whole Earth. Netzel and Stepinski (2016) showedthat an information-theoretic measure of clustering
called the V-measure (Rosenberg and Hirschberg 2007)
could be used to quantitatively assess the homogeneity
within a climatic type and differences between climatic
types. Sathiaraj et al. (2019) compared three clustering
techniques at their ability to identify climatic types in
the United States: K-means (MacQueen 1967), DBCAN(Ester et al. 1996), and BIRCH (Zhang et al. 1996). Other
studies have used clustering techniques to classify upperlevel air masses (Vrac et al. 2012; Pernin et al. 2016;
Watanabe et al. 2020). However, the above studies subjectively determined the number of clusters or air masses
for classification.
In this study, we applied a technique of data clustering by machine learning to global reanalysis data in an
attempt to objectively determine the global air mass distribution and develop a revised causal CC based on air
masses. Because reliable objective reanalysis data withhigh spatiotemporal resolution are now available for
Earth over a period of more than 40 years, it can be used
to revise the causal CC system to reflect actual climaticconditions more accurately. This will allow us to classify
the global climate in a data-driven manner by focusing
on climatic causes (i.e., air masses and fronts) instead ofobserved responses such as vegetation. Thus, this study

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renews the classical ACC for the first time in almost
70 years. We subdivide air mass zones into regions that
distinguish between land and ocean and that reflect
east–west differences. Objective estimation of the global
distribution of large-scale air masses, including over theoceans, will lead to better understanding of future climate change and will be helpful for determining appropriate mitigation and adaptation measures (Mahlstein
and Knutti 2010).
The remainder of this paper is organized as follows.
Section 2 explains the data and methodology. Section 3
presents the global distribution of air mass zones after
the optimal number of clusters is determined and compares the causal CC of this study with the conventional
ACC. Section 4 summarizes the findings of this study andbriefly discusses remaining issues.

2 Data and methodology
2.1 Data

We used Fifth-Generation ECMWF Atmospheric
Reanalysis of the Global Climate (ERA5) data (Hersbach et al. 2020) compiled by the European Centre for
Medium-Range Weather Forecasts (ECMWF). ERA5
data are available at high quality for more than 70 years
with a spatial resolution of 0.25° × 0.25° (Hersbach et al.
2020). The air mass analysis was based on monthly meandata for the temperature (T) and specific humidity (q )over the 40-year period of 1981–2020 at altitudes of 925–
775 hPa (i.e., seven pressure surfaces), which corresponds
to the lower troposphere. To correctly follow the definition of air mass, this study chose T and q , not employing
precipitation. Although the vertical extent of air masses
has not been clearly determined, we defined 775 hPa(i.e., about 2000 m) as the maximum altitude of air
masses in the lower troposphere. For instance, the Siberian High forms from wintertime cold air in the boundary layer as a typical continental high-pressure system,and the inversion layer often appears around an altitudeof 2000 m. The downward flow of the Hadley circulation
forms a subtropical high, and the trade wind inversion
forms around an altitude of 1000–2000 m. Referring tothese information, we set the altitude for an air mass tobe affected by the surface as 2000 m, although an atmospheric boundary layer typically has an altitude of around1500 m, which is lower than in the tropics.
To evaluate the locations of fronts, we employedmonthly mean surface precipitation data from the Global
Precipitation Climatology Project (GPCP) Version 2.3(Adler et al. 2018) on a 2.5° global grid for the 40-year
period of 1981–2020. To account for seasonal differences,a year was divided into the October–March (O–M) and
April–September (A–S) half-years.

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

2.2 Methodology
2.2.1 Clustering process

K

1 925 hPa
1 925 hPa
∫ x dp /∫ dp,
g 775 hPa
g 775 hPa� an − ci �2 ,SSE =An air mass can be defined as a large-scale three-dimensional air volume with homogeneous temperature andhumidity. To identify an air mass, we took the verticalmean of an atmospheric variable in the lower tropospherefrom 925 to 775 hPa:x≡

Page 4 of 18(1)

where x is an atmospheric variable, the overbar (‾) indicates the vertical mean, g is the acceleration of gravity,and p is the atmospheric pressure. In this study, we substituted the air temperature T (K) and specific humidityq (g ­kg−1) into x . Equation (1) defines the mass-weighted
vertical mean of x . To remove small-scale disturbances,x was calculated for the O–M and A–S half-years (i.e.,
6-month means), as shown in Fig. 2. To align the scalesof the climatic variables ( T and q ), we applied z-scorenormalization (Raschka and Mirjalili 2020) to the global
horizontal data of both half-years using the means andstandard deviations.
To unify the clusters (i.e., air mass zones) identified in
the two half-years, the normalized data of both half-years
were simultaneously employed as input data. Multiplying
the number of features, number of grid points in the global
reanalysis data, and number of half-years resulted in input
data with an array size of 2 (T, q) × 1440 (longitudes) × 721(latitudes) × 2 (half-years) = 4,152,960. We then applied
K-means++ clustering (Arthur and Vassilvitskii 2007) tothe input data. Clustering is a statistical technique of grouping samples based on the similarity of features. K-meansclustering is a well-known non-hierarchical clustering technique (MacQueen 1967) that is widely used in industry andacademia. K-means clustering is suitable for analyzing big
data, because it requires less computation of distances thanhierarchical clustering techniques, in which close clusters
are merged successively. In addition, K-means++ clustering has been reported to be more effective and more consistent than conventional K-means clustering, where theclassification results depend on the position of the initialcentroid (Arthur and Vassilvitskii 2007; Raschka and Mirjalili 2020). Thus, we wed the K-means++ clustering technique to overcome the initial value dependency problem of
conventional non-hierarchical clustering techniques suchas K-means or K-medoids clustering.
K-means++ clustering creates clusters from the K
points located farthest each other as the initial centroid
position based on the probabilistic distribution of thesamples. Then, samples are clustered by iteratively minimizing the sum of squared errors (SSE), which is estimated as follows:i=1 an ∈Ci(2)where i is the number of a cluster from 1 to K , ci is thecentroid of i -th cluster (i.e., Ci), and an is a sample
belonging to Ci. The SSE is the sum of the squared Euclidean distances between an and ci for all clusters (i.e., thetotal variance of the clusters). A smaller SSE indicatesthat clusters are more compact in the feature space, andthe SSE can be used to quantitatively evaluate the clustering performance. To further reduce the dependency on
the initial position, we performed K-means++ clustering
20 times using different initial centroid positions, and theclustering results with the smallest SSE were selected as
the best.
One advantage of using K-means++ clustering rather
than hierarchical and density-based clustering techniques
is that the former retains the coordinates of the centroid’s
final position in feature space, which allows different
datasets (i.e., air temperature and specific humidity) to
be clustered using the same coordinates. We performed
K-means++ clustering by using the scikit‐learn Python
packages (Pedregosa et al. 2011).
2.2.2 Finding the appropriate number of clusters

Clustering techniques, including K-means++ clustering,
requires setting the number of clusters a priori. To determine the appropriate number of clusters, we utilized four
quantitative evaluation indices.
The first evaluation index was the Davies–Bouldin
index (DBI) (Davies and Bouldin 1979):
K

1
max
DBI =
i� =j
K
i=1




si + sj
,
di,j

(3)

where si is the average distance between all samples
belonging to a cluster Ci, and di,j =� ci − cj �2 is the L2
norm or Euclidean distance between the centroids of two
clusters Ci and Cj . The DBI represents the average similarity between a cluster Ci and its nearest cluster Cj fori, j = 1, . . . , K . A lower DBI indicates a greater distanceseparating the nearest cluster pair Ci and Cj . Thus, a small
DBI indicates that the number of clusters is appropriate.
The second evaluation index was the silhouette coefficient (SC) (Rousseeuw 1987), calculated for a single sample an:SCn =en − fn,
max en , fn

(4)

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

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Fig. 2 Vertical mean distributions (925–775 hPa) of the temperature (K; contour) and specific humidity (g k­ g−1 ; shading) in the (a) O–M half-year
and (b) A–S half-year for 1981–2020. The contour interval is 5 K. The scale of shading is shown at the bottom, and the warm and cold colors indicate
relatively high and low specific humidity, respectively

where fn is the average distance of sample an to all othersamples in the same cluster, and en is the average distanceof sample an to samples in the next nearest cluster. The
SCn value varies between –1 and 1, with a value near 1
indicating that the clustering result is satisfactory on thesample an. The average of SCn for an in the entire dataset,

SC = N1Nn=1

SCn (N is the number of all samples), can beused to evaluate the overall performance of the clusteringresults.
The third evaluation index was the Cailnski–Harabasz
index (CHI) (Caliński and Harabasz 1974):

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

trace(BK )N−K,×trace(WK )
K−1

(5)

CHI =

where WK =K 


i=1 an ∈Ci(an − ci )(an − ci ) is a variancematrix within clusters, and BK =tKni (ci − co )(ci − co )t

i=1

is a variance matrix between clusters (ni is the number ofsamples belonging to cluster Ci and co is the centroid of
all samples). The superscript t denotes transposition. A
larger value for CHI indicates a denser sample distribution in each cluster and greater distance between clusters. In other words, a large CHI indicates that the
number of clusters is appropriate.
The fourth evaluation index was the Bayesian information criterion (BIC) (Schwarz 1978), which is used
to select models from a finite set of models. Under theassumption that the samples in cluster Ci follow a Gaussian distribution, BIC can be defined as follows:

BIC =

K 


log L(θi ; an ∈ Ci ) −

i=1


D
log ni ,2(6)where L is the likelihood function of the model, θi is theset of parameters of the likelihood function, and D is thenumber of parameters of the model with K components.
The second term represents a penalty term imposed toprevent model overfitting due to an increase in the number of parameters. Note that we calculated BIC with the
opposite sign of the usual definition. Thus, a larger value
for the BIC indicates a better model prediction of thesample data and true unknown distribution.
2.2.3 Sea–land contrast and east–west climatic differences

Figures 2 and 3 show the distributions of the temperatureand specific humidity in the lower troposphere and theiranomalies from the zonal means in the O–M and A–S
half-years, respectively. According to the ACC, latitudinaldifferences in the downward surface solar radiation flux
determine the global surface temperature. Therefore, thenumber of latitudinal air mass zones may be increased bysimply increasing the number of clusters (Fig. 2). Thus,
we also considered anomalies from the zonal (0°–360° E)
mean at each latitude to clarify the sea–land or east–west
distribution of air masses (Fig. 3). In addition, z-scorenormalization shown in Sect. 2.2.1 is applied to global
anomalies in the mean data of the O–M and A–S halfyears. The normalized anomalies were used to classify air
mass zones into two for both half-years to represent dry
and moist air masses.

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3 Results and discussion
3.1 Determination of number of clusters

Figure 4 shows the values of the evaluation indices forthe number of clusters. The DBI (blue) significantly
decreased as the number of clusters was increased fromtwo to four and reached its minimum at four, after which
it rapidly increased with more clusters. This suggeststhat four clusters are optimal. The SC (red) decreased
as the number of clusters increased from two to six, andit increased from six to eight and decreased again after
eight. The decreasing trend was partially mitigated fromthree to four. Therefore, we judged that four clusters wereapplicable.
The CHI (green) and BIC (orange) both increased
as the number of clusters was increased from two toten. Both indices rapidly increased in value from two
to four clusters but increased gradually from four toten clusters. Based on these results, we determined
the statistically optimal number of clusters to be four,which indicates that the global climate can be divided
into four air mass zones. Thus, Alisov’s four air mass
zones from the 1950s are supported from a data-driven
perspective using high-quality global reanalysis data.
3.2 Global distributions of air mass zones

Figure 5 shows the global distributions of the four air
mass zones in the O–M and A–S half-years from low tohigh latitudes: tropical (red), subtropical (yellow), polar(light blue), and Arctic/Antarctic (purple). The tropical and subtropical air mass zones correspond to theequatorial and tropical air mass zones in the ACC (notshown). The seasonal changes in the general atmospheric circulations caused the air mass zones to movetoward the poles in the summer hemisphere and toward
the equator in the winter hemisphere (Figs. 5a and b).
This indicates that the north–south seasonal shift was
accurately captured. The Arctic air mass zone wasshown to dominate from the North Pole to northeastern Siberia and Canada in the O–M half-year (Fig. 5a),but it disappeared in the A–S half-year (Fig. 5b).
We then investigated the sensitivity of the air mass
zone distribution to differences in altitude by comparing the distributions estimated using the verticalmeans over 925–775 hPa and 925–850 hPa. The overall
distribution of the four air mass zones showed almost
no changes. Using the 925–850 hPa mean somewhat
expanded the distribution of the tropical air mass zone
over the ocean and shrank the distribution of the subtropical air mass zone (not shown). We attributed thisto the reduced influence of trade wind inversion in andaround subtropical anticyclones. Thus, the air mass
zone distribution seemed to be robust against differences in the altitude range for the lower troposphere.

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

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Fig. 3 Distributions of temperature (K; contour) and specific humidity (g ­kg−1; shading) anomalies from the zonal means in the (a) O–M half-year
and (b) A–S half-year. The contour interval is 2 K . The warm solid and cold dashed contours indicate relatively high and low temperatures,
respectively. The scale of shading is shown at the bottom, and the red and blue colors indicate positive and negative anomalies, respectively of thespecific humidity

The white contours in Fig. 5 indicate areas where
the half-year mean precipitation was greater than
3 mm ­day−1. The large rain bands or fronts in the midlatitudes are located near the boundary between thesubtropical (yellow) and polar (light blue) air mass zones
in the Northern Hemisphere during the O–M half-year(Fig. 5a) and near the boundary between tropical (red)and subtropical (yellow) air mass zones in the Northern Hemisphere during the A–S half-year (Fig. 5b). The
Atlantic polar front in boreal winter (Fig. 5a) is a transition zone with high baroclinicity where a subtropical air mass of the Azores high meets a polar air mass

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Fig. 4 Evaluation indices used to quantitatively determine the optimal number of clusters: DBI (blue), SC (red), CHI (green), and BIC (orange). All
indices are dimensionlessextending southward. The high baroclinicity induces thedevelopment of extratropical cyclones, whose eastward
movement brings precipitation over Europe. Thus, theclustering results seem to capture intense mid-latitude
rain bands as fronts with baroclinic instability. Meanwhile, the subtropical front in the north Pacific crosses
the Yangtze River basin and Japanese archipelago inboreal summer and represents the Meiyu–Baiu front,
which brings significant precipitation to East Asia
(Fig. 5b). In other words, the Meiyu–Baiu front is captured as a pronounced subtropical front between thetropical (red) and subtropical (yellow) air mass zones,
which is consistent with Ninomiya’s results (1984). Thus,
the clustering results accurately captured frontal precipitation between air mass zones in the mid- and highlatitudes. The heavy precipitation in the tropics and weak
precipitation around 60°S seem unrelated to the boundaries between air mass zones.
Figure 6 shows the sample statistics of the four air mass
zones (i.e., clusters) in each half-year. In Fig. 6a, the centroids of the clusters (black stars) are almost equidistant,which ensures good clustering. The distribution pattern
may reflect the Clausius–Clapeyron relation at the leftedge line, where the amount of water vapor increases
exponentially with increasing temperature. The centroidof each cluster is located near the left edge of the feature space because the samples were concentrated near

the edge, although colors appear equal. The subtropical
air mass zone (yellow) had the largest area in the feature space, which may reflect the large temperature andhumidity ranges of this zone. Figure 6b shows the sample means (error bars: standard deviations) of T and q
for each air mass zone in the Northern Hemisphere.
The fronts can be characterized according to differences
between the atmospheric characteristics of two adjacent
air mass zones. Because the latitude of each air mass zone
shifts north or south according to the season, T and q ofeach zone did not differ significantly between half-years.
The tropical and subtropical air mass zones had a relatively small temperature difference of less than 7 K but
a large humidity difference of about 5.4 g ­kg−1. The polar
air mass zone had a lower temperature and humidity
than the subtropical air mass zone, and the temperatureand humidity differences were about 16 K and 2.7 g ­kg−1,
respectively. These results suggest that the subtropicalfront is characterized by a large humidity gradient while
the polar front is characterized by both temperature andhumidity fronts. The Arctic air mass zone had lower temperature and humidity than the polar air mass zone in the
O–M half-year with temperature and humidity differences of about 14 K and 1.5 g ­kg−1, respectively. Thesefeatures were almost the same in the Southern Hemisphere (not shown).

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Fig. 5 Global distributions of four air mass zones (shading) and surface precipitation greater than 3 mm ­day−1 (white contour) in the (a) O–M
half-year and (b) A–S half-year. The contour interval is 3 mm ­day−1. The colors are explained at the bottom. Blank areas indicate regions where thealtitude was greater than 775 hPa (about 2000 m). T: Tropical, S: Subtropical, P: Polar, A: Arctic/Antarctic

3.3 Global distributions of climatic zones and comparison
with classical ACC​

Figure 7 shows a world map of the climatic zones, which
was created by overlapping the air mass zones of theO–M and A–S half-years (Fig. 5), and represents therenewed ACC. The hatched areas indicate regions where
the air mass zones alternate between half-years. Table 1

summarizes the features of the climatic zones. The symbols used in Fig. 7 and Table 1 are discussed below. Notethat the seasonal differences between the two hemispheres during a half-year were considered.
The global climate can be classified into four stablezones that are dominated by the same air mass zones
throughout the year (TT, SS, PP, AA) and three zones

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Fig. 6 (a) Distribution and centroid in feature space of the global air mass zones, and (b) the mean values (error bars: standard deviations) of thetemperature and specific humidity in each air mass zone for the Northern Hemisphere (0°–90° N) in the O–M and A–S half-years

with a one-class change each half-year (ST, PS, AP).
This is similar to the classical ACC (Fig. 1). In addition,some regions have two-class changes (AS, PT) such as
the eastern Siberia (ultramarine), central North America (black), Yangtze River basin (black), and westernJapan (black) (Fig. 7). These regions experience significant changes in climate throughout the year.
The most significant difference between the renewed
ACC (Fig. 7) and classical ACC (Fig. 1) is the large widthof the tropical climatic zone (TT) in the renewed ACC,
which is the equatorial climatic zone in the classicalShimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

ACC. Alisov (1954) probably estimated the location of
the inter-tropical convergence zone empirically because
meteorological data, especially humidity, were scarce atthat time, particularly over oceans. In addition, Alisov(1954) seemed to have arbitrarily determined the locations of fronts using only the surface temperature.
Figure 8 shows the global distributions of the four air
mass zones in January and July estimated only by thetemperature like Alisov (1954). The distributions of the
Arctic/Antarctic air mass zones are similar to those in
Fig. 5 except for the Antarctic zone in January. In addition, the width of the tropical air mass zone increased
markedly from January to July. In July (Fig. 8b), the northern edge of the tropical air mass zone reached about 60°
N over Eurasia and North America, and the polar air
mass zone disappeared in the high latitudes of the Northern Hemisphere.
The white contours in Fig. 8 indicate areas with precipitation of greater than 3 mm ­day−1. Although themonthly mean precipitation in January and July was
greater than the seasonal means of the O–M and A–S
half-years (Fig. 5), the distributions of the monthly andseasonal means did not show significant differences forthe cold and warm periods. Figure 8b shows a misalignment between the subtropical front and intense rain

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Table 1 Nine climatic zones
SymbolClimatic zones

Winter / Summer

Behavior

TT

TropicalTropical / TropicalstableST

Quasi-tropical

Subtropical / Tropical

one-class changeSS

Subtropical

Subtropical / Subtropi‑calstablePS

Temperate

Polar / Subtropical

one-class change

PP

Polar

Polar / PolarstableAP

Subarctic

Arctic / Polar

one-class changeAA

Arctic /Antarctic Arctic / Arctic

stableAS

Subarctic

Arctic / Subtropicaltwo-class changePT

Temperate

Polar / Tropicaltwo-class change

Nine climatic zones based on the dominant air mass zones during the winterand summer half-years in each hemisphere and their relationship with the
behavior of the air mass zones

bands of greater than 6 mm ­day−1 in July and a generally
poor representation of the subtropical to temperate climates. This suggests that the humidity gradient is morecritical than the temperature gradient for precipitationfrom the Meiyu–Baiu front. Overall, the renewed ACC(Fig. 7) better explains climatic phenomena in the Northern Hemisphere based on the concepts of air masses andFig. 7 Global climatic zones obtained by overlapping the air mass zones of the winter and summer half-years in each hemisphere (Fig. 5; see
Table 1 for symbols). The hatched areas indicate the regions where the air mass zones alternate with the season. The blank areas mean the same as
in Fig. 5

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

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Fig. 8 Global distributions of four air mass zones (shading) and surface precipitation greater than 3 mm ­day−1 (white contour) in (a) January and(b) July. The contour interval is 3 mm ­day−1. The blank areas mean the same as in Fig. 5. T: Tropical, S: Subtropical, P: Polar, A: Arctic/Antarctic

fronts than the classical ACC (Fig. 8) because the former
considers both the temperature and specific humidity
while the latter only considers the temperature.
3.4 Subdivision of climatic zones

We compared the renewed ACC (Fig. 7) with the latest
KGCC (Beck et al. 2018) in deserts for reference. The

comparison showed that two deserts in central Eurasia
belonged not to the subtropical climatic zone (SS) witharid characteristics but the temperate climatic zone (PS)with polar frontal precipitation. According to the latest KGCC (Beck et al. 2018), the desert climate can bedivided into two categories: hot (BWh) and cold (BWk)with mean annual temperatures above and below 18 °C,

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

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Fig. 9 Regions with dry and moist air masses (shading) obtained by subdividing each air mass zone (Fig. 5) into two clusters, and surface
precipitation greater than 3 mm ­day−1 (white contour) in the (a) O–M half-year and (b) A–S half-year. The contour interval is 3 mm ­day−1. The blankareas mean the same as in Fig. 5. Td: Tropical dry, Tm: Tropical moist, Sd: Subtropical dry, Sm: Subtropical moist, Pd: Polar dry, Pm: Polar moist, Ad:
Arctic/Antarctic dry, Am: Arctic/Antarctic moist

respectively. The KGCC classifies the desert climate incentral Eurasia as BWk because of the low half-year temperature in the winter (Fig. 2a) and the Somali regionof Africa as BWh. However, the renewed ACC classifies
the later region as belonging to the tropical zone (TT)with humid climatic features (Fig. 7). It is difficult for the

renewed ACC to explain the desert formation mechanism of this region as the subtropical air mass zone withdry atmospheric characteristics. Thus, we subdivided theclimatic zones in Fig. 7 into climatic regions to discuss
this issue in more detail.

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

Table 2 Characteristics of each dry and moist air mass
SymbolTypes of air mass

T ONDJFM

q ONDJFM T AMJJAS

q AMJJAS

Td

Tropical dry

290.799.68

291.01

9.96

Tm

Tropical moist

290.5910.72

290.82

10.80

Sd

Subtropical dry

288.934.64

291.48

4.83

Sm

Subtropical moist

282.525.26

282.05

5.31

Pd

Polar dry

268.462.27

268.92

2.52

Pm

Polar moist

268.542.42

269.35

2.61

AdArctic dry

253.390.57

243.370.24

AmArctic moist

254.810.67

250.01

0.48

Mean temperature T (K) and specific humidity q (g k­ g−1) in each dry and moist
air mass for the O–M and A–S half-years

Figure 9 shows the distributions of the air mass zones
in Fig. 5 when further subdivided into dry (subscript

Page 14 of 18d) and moist (subscript m) air masses. The air masses
were classified as dry or moist considering the global
anomaly distribution (Fig. 3) and mean values of T and
q . First, four air mass zones were prepared, each consisting only of grid points classified into the same air mass
zone (uniform values of T and q ) between two half-years(see Sect. 3.2). Then, each air mass zone was divided into
two air masses using anomalies of T and q (Fig. 3) atthose grid points, which resulted in 4 × 2 = 8 air masses.
Table 2 summarizes the mean values of T (K) and q (g­kg−1) of each air mass. The air masses were labeled as
dry and moist, although dry and moist air masses areusually equivalent to continental and maritime ones,
respectively. Figure 9 shows that, in the tropics, intense
precipitation occurred mostly in the moist air mass (Tm)and at its boundary with the dry air mass (Td). In theFig. 10 (a) Global climatic regions obtained by overlapping the air mass distributions of the winter and summer half-years in each hemisphere(Fig. 9; see Table 3 for the symbols). The hatched areas indicate regions where the air masses alternate with the half-year. The white contours
indicate the boundaries of climatic zones (Fig. 7). The blank areas mean the same as in Fig. 5

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

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Table 3 Twenty-seven climatic regions and their correspondence to the KGCC​
SymbolClimatic regions (Winter / Summer)

Landscape (cf. Beck et al. 2018)

TdTd

Tropical year-round dry (Td / Td)

Deserts: BWh

TmTm

Tropical year-round moist (Tm / Tm)

Tropical rainforests: Af, (Aw, Am)

TdTm

Tropical summer-moist (Td / Tm)

Savannas: Aw, (BSh)TmTd

Tropical summer-dry (Tm / Td)

SdTd

Quasi-tropical year-round dry (Sd / Td)

Steppes: BSh, (BWh)SmTm

Quasi-tropical year-round moist (Sm / Tm)

Evergreen forest: Cfa, Cwa

SdTm

Quasi-tropical summer-moist (Sd / Tm)

Aw, (BSh, Cwa)

SmTd

Quasi-tropical summer-dry (Sm / Td)

SdSd

Subtropical year-round dry (Sd / Sd)

Hot-Deserts: BWh

SmSm

Subtropical year-round moist (Sm / Sm)

Mediterranean: Csa, Csb, (BWh, BSh)

SdSm

Subtropical summer-moist (Sd / Sm)

Bwh

SmSd

Subtropical summer-dry (Sm / Sd)

BWh, (BWk, BSk)

PdSd

Temperate year-round dry (Pd / Sd)

Cold-Deserts: BWk

PmSm

Temperate year-round moist (Pm / Sm)

Broadleaf, Mixed forests: Cfb, Dfb, (Dfc)

PdSm

Temperate summer-moist (Pd / Sm)

Coniferous forests: Dfa, Dfb, (DWa)

PmSd

Temperate summer-dry (Pm / Sd)

BWk

PdTm

Temperate summer-wet (Pd / Tm)

Cfa, Dfa, (DWa)

PdPd

Polar year-round dry (Pd / Pd)

PmPm

Polar year-round moist (Pm / Pm)

PdPm

Polar summer-moist (Pd / Pm)

Dfc

PmPd

Polar summer-dry (Pm / Pd)

Dfc

AdPd

Subarctic year-round dry (Ad / Pd)

Taigas, Tundras: Dfc, ET
Dsb

AmPm

Subarctic year-round moist (Am / Pm)

AdPm

Subarctic summer-moist (Ad / Pm)

Dfd

AmPd

Arctic (Am / Pd)

ET, (Dfc)

AdSm

Subarctic summer-warm (Ad / Sm)

Dwb, Dwc

AmAm

Antarctic (Am / Am)EFNames and symbols of each climatic region and the dominant air masses during the winter and summer half-years in each hemisphere (in parentheses in the middle
column) and their correspondence to the KGCC (right column; Beck et al. 2018).

mid-latitudes, the subtropical dry air mass (Sd) extended
throughout the year over the Sahara and Namib deserts
in Africa, the Arabian Peninsula, and dry regions along
the western coasts of North and South America. The subtropical dry air mass had a higher temperature and lower
humidity of about 9 K and 0.5 g ­kg−1, respectively, than
the subtropical moist air mass during the A–S half-year(see Sd and Sm in Table 2). Thus, the subtropical dry
air mass accurately represented the center of subtropical anticyclones with extremely hot and dry characteristics, which are a significant factor contributing to desert
formation.
Figure 10 shows the overlapping seasonal changes of
the air mass distributions in Fig. 9. The hatched areas
indicate regions where air masses alternated between
the winter and summer half-years in each hemisphere.
Table 3 summarizes the name and symbol of climatic
regions, the types of air masses that dominated each halfyear for each hemisphere, and their correspondence tothe vegetation landscape of the latest KGCC (Beck et al.

2018). A climatic region is expressed by four letters (e.g.,
XxYy). The first two and last two letters indicate the air
masses that dominate the winter and summer half-years,
respectively. The capital X and Y can be replaced by T, S,
P, or A, and the small x and y can be replaced by d or m.
Each stable zone (i.e., XX in Fig. 7) except for Antarctica was divided into four regions: where a dry or moist
air mass dominates all year round (XdXd and XmXm),
and where a dry/moist air mass dominates in the winter and a moist/dry air mass dominates in the summer(XdXm and XmXd) (see Table 3). Seasonal one-classchange zones (XY) (Fig. 7) were divided into four regions:where dry and moist air masses within the same air mass
zone alternated seasonally (XdYd, XmYm), and where
dry and moist air masses within adjacent air mass zones
alternate seasonally (XdYm, XmYd). The temperate andsubarctic climatic zones included seasonal two-classchange regions (PdTm and AdSm), which resulted in five
types of climatic regions (see Table 3).

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

We focused on the Somali and central Eurasia regions
where the renewed ACC ran into a problem and foundthat these regions were covered by dry air masses
throughout the year (Fig. 9). The Somali region is covered by a tropical dry air mass all year, which forms a
dry region (TdTd) in the tropical climatic zone. The
central Eurasia region is covered by a subtropical dry
air mass centered in the Sahara that expands northeastward during the A–S half-year (Fig. 9b) and by a polar
dry air mass in the polar air mass zone (Table 2) during the O–M half-year (Fig. 9a). This explains why two
locally arid climatic regions (PdSd) formed in the temperate climatic zone (Fig. 10).
In contrast to the classical ACC, the climatic regionsin Fig. 10 better represent east–west climatic differences,
especially at the mid- to high latitudes, such as the western and eastern coasts of the continental landmasses
in the Northern Hemisphere. Our climatic regions
also showed good correspondence with the vegetation
landscape of the KGCC (Table 3). This means that the
renewed ACC connects, through observations, purely
meteorological concept of air masses to the global distribution of vegetation, and thus to the delineation of ecoregions. The renewed ACC can be used to gain insights
into the climatic distribution over oceans, which is notclassified by the KGCC in principle (Kottek et al. 2006;
Peel et al. 2007; Kriticos et al. 2012; Beck et al. 2018).

4 Conclusions
For many years, one of the most critical issues in air mass
climatology has been developing an objective method
to determine the air mass distribution. In this study, weapplied K-means++ clustering to global reanalysis data
to quantitatively and objectively determine the boundaries between air masses. Then, we subdivided air mass
zones into regions based on continental or maritime climates and east–west climatic differences, and we considered the half-year changes in air mass zones. Thus, we
renewed the classical ACC for the first time in almost
70 years.
We began by questioning whether the global climate
can be divided into four air mass zones as Alisov didin the 1950s. Our statistical evaluation of four indices(DBI, SC, CHI, and BIC) confirmed that the division of
the global climate into four air mass zones is suitable(Fig. 4). We then applied K-means++ clustering to classify the global climate for the O–M and A–S half-years(Fig. 5). The clustering results showed consistency withrain bands along fronts that were probably induced bybaroclinic instability in the mid-latitudes. Alisov (1954)
devised seven climatic zones (Fig. 1) based on the January–July alternation of air mass zones. By considering
the half-year alternation of air mass zones (Fig. 5), we

Page 16 of 18

also obtained nine climatic zones (Fig. 7, Table 1). Thesezones can be used to diagnose changes in large-scaleatmospheric circulations in relation to the seasonal shiftof air mass zones.
The seasonal distributions of the air mass zones wereused to establish four stable climatic zones (TT, SS, PP,AA), three one-class change climatic zones (ST, PS, AP),and two two-class change climatic zones (AS, PT) (Fig. 7).
These climatic zones can explain climatic phenomenain the Northern Hemisphere more realistically than theoriginal ACC because they consider both the temperature and humidity rather than just the temperature.
Finally, we subdivided the climatic zones into climatic
regions to reflect differences between the ocean andcontinent and the east and west coasts. The 27 climatic
regions established in this study (Fig. 10, Table 3) accomplish the subdivision of climatic zones left undone by
Alisov (1954) and his successors. Our climatic regions
can also be used to gain insight into the climatic distribution over oceans, which cannot be done by the KGCC.
The renewed ACC improves the correspondence between
mid-latitude climatic features and vegetation landscape,which was one of the issues of the original ACC. Thus,
the renewed ACC represents a significant advance in
air mass climatology. The data-driven machine learning
approach can be used to establish a standard for causal
CC based on air masses. Note that we did not define theclimate at high altitudes higher than 2000 m (e.g., theTibetan Plateau, Antarctica, and Greenland) in this study.
One of the advantages of using the renewed ACC
is to refine the climate change research. The classical
ACC cannot diagnose the effects of global warming on
air mass distributions. The renewed ACC may be applicable to visualizing climate change projected by global
climate models (Netzel and Stepinski 2016). We will beable to trace the meridional shift of climatological fronts
associated with climate change, which can assist withcarrying out the measures recommended by the Intergovernmental Panel on Climate Change and revive air
mass climatology.
Abbreviations
CC	Climate classification
ACC​	Alisov’s climate classification
KGCC​	Köppen-Geiger climate classification
ERA5	Fifth-generation ECMWF atmospheric reanalysis of the global
climate
ECMWF	European Centre for Medium-Range Weather Forecasts
GPCP	Global Precipitation Climatology Project
SSE	Sum of squared errors
DBI	Davies–Bouldin index
SC	Silhouette coefficient
CHI	Cailnski–Harabasz index
BIC	Bayesian information criterion

Shimabukuro et al. Progress in Earth and Planetary Science(2023) 10:19

Acknowledgements
We acknowledge Dr. Yamaura of RIKEN Center for Computational Science andtwo anonymous reviewers for their constructive comments. We would also
like to thank Enago (www.​enago.​jp) for the English language review.
Author contributions
RS and TT proposed the fundamental idea, conceived and designed the study.
RS carried out the air mass analysis. KF supported about the interpretation ofclustering technique, particularly about the indices for determining the num‑
ber of clusters. TT collaborated with the corresponding author in the construc‑tion of manuscript. All authors read and approved the final manuscript.
Funding
This work was supported in part by "Dynamic Alliance for Open Innovation
Bridging Human, Environment and Materials" from the Ministry of Education,
Culture, Sports, Science and Technology of Japan (MEXT).
Availability of data and material
The ERA5 reanalysis dataset used in this study are available via Copernicus Cli‑
mate Change Service Climate Data Store (CDS), https://​cds.​clima​te.​coper​nicus.​
eu/. The GPCP Version 2.3 monthly dataset used in this study are available
via NOAA National Centers for Environmental Information (NCEI) webserver,
https://​www.​ncei.​noaa.​gov/​data/​global-​preci​pitat​ion-​clima​tology-​proje​ct-​
gpcp-​month​ly/​access/.

Declarations
Competing interests
The authors declare that they have no competing interest.
Received: 15 December 2022 Accepted: 19 March 2023

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