Yamada et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00397-1(2021) 8:11

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Evaluation of the contribution of tropical
cyclone seeds to changes in tropical
cyclone frequency due to global warmingin high-resolution multi-model ensemble
simulations
Yohei Yamada1* , Chihiro Kodama1, Masaki Satoh2, Masato Sugi3, Malcolm J. Roberts4, Ryo Mizuta3,
Akira T. Noda1, Tomoe Nasuno1, Masuo Nakano1 and Pier Luigi Vidale5

Abstract
Previous projections of the frequency of tropical cyclone genesis due to global warming, even in terms of sign ofthe change, depends on the chosen model simulation. Here, we systematically examine projected changes intropical cyclones using six global atmospheric models with medium-to-high horizontal resolutions included in thesixth phase of the Coupled Model Intercomparison Project/High-Resolution Model Intercomparison Project.
Changes in the frequency of tropical cyclone genesis could be broken down into the contributions from (i) thetropical cyclone seed, a depression having a closed contour of sea level pressure with a warm core and (ii) thesurvival rate, the ratio of the frequency of tropical cyclone genesis to that of tropical cyclone seeds. The multimodel ensemble mean indicates that tropical cyclone genesis frequencies are significantly decreased during theperiod 1990–2049, which is attributable to changes in tropical cyclone seeds. Analysis of the individual models
shows that although most models project a more or less decreasing trend in tropical cyclone genesis frequencies
and seeds, the survival rate also contributes to the result in some models. The present study indicates theusefulness of decomposition into the frequency of the tropical cyclone seeds and the survival rate to understandthe cause of uncertainty in projected frequencies of tropical cyclone genesis.
Keywords: Global warming, Tropical cyclone, Tropical cyclone seed, Survival rate, High-Resolution Model
Intercomparison Project1 Introduction
Many previous studies have examined changes in tropical cyclone (TC) activity, such as TC genesis frequency,
TC intensity, and precipitation associated with TCs, due
to global warming; these have been summarized byKnutson et al. (2010, 2019, 2020). However, the topic is
still controversial. Focusing on TC genesis frequency,* Correspondence: yoheiy@jamstec.go.jp
1
Japan Agency for Marine-Earth Science and Technology, 3173-25
Showa-machi, Yokohama, Kanagawa 236-0001, Japan
Full list of author information is available at the end of the article

most high-resolution atmospheric general circulation
models project a decrease in the frequency of the global
TC genesis due to global warming (Murakami et al.
2012, 2014; Oouchi et al. 2006; Roberts et al. 2015; Satohet al. 2015; Sugi et al. 2009, 2017; Sugi and Yoshimura
2012; Wehner et al. 2015; Yamada et al. 2010, 2017;
Yamada and Satoh 2013; Yoshida et al. 2017; Zhao et al.
2009). However, Bhatia et al. (2018) showed an increasein TC genesis frequency using a high-resolution atmosphere–ocean coupled general circulation model. Understanding the reasons for these discrepancies is necessary© The Author(s). 2021 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 the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence 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 this licence, visit http://creativecommons.org/licenses/by/4.0/.

(2021) 8:11

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in order to gain reliable future projections of TC
frequency.
Recently, some studies have noted the response of TC
seeds (incipient vortices of TCs) to global warming and
its relation to TC genesis frequency (Lee et al. 2020; Sugi
et al. 2020; Vecchi et al. 2019). Vecchi et al. (2019) investigated the reasons for the increase in TC genesis under
the warmer climate conditions projected by Bhatia et al.
(2018). Using the same model as Bhatia et al. (2018), i.e.,with nudged sea surface temperature (SST) experiments
and three horizontal resolutions (~ 200 km, ~ 50 km,
and ~ 25 km), they showed that global TC frequency responds to both an increase in TC seeds due to warming
and the less efficient development of these seeds intoTCs (survival rate; SR). In addition, the highest resolution model (~ 25 km) projected a more significant increase in TC seeds than the lower-resolution models.
Sugi et al. (2020) examined future change in TC seedfrequency using two high-resolution models: the 20-km
mesh of the Meteorological Research Institute/Japan
Meteorological Agency Atmospheric General Circulation
Model (MRI-AGCM3; Mizuta et al. 2012; Murakami
et al. 2012) and the 14-km mesh of the Nonhydrostatic
ICosahedral Atmospheric Model (NICAM; Satoh et al.
2008, 2014; Tomita and Satoh 2004; Kodama et al.
2015). Contrary to Vecchi et al. (2019), they showed aclear reduction in TC seed frequency, which contributes
to a decrease in the relatively weak TC frequency. Although changes in TC and TC seed frequencies showed
opposite signs between Vecchi et al. (2019) and Sugi
et al. (2020), both studies indicated that changes in TC
seed frequency under warmer climates possibly influencechanges in TC frequency. This discrepancy may be a result of the differing models, definitions of TC seeds,
and/or experimental designs between the two studies.
Variability in the response of TC seed frequency due toglobal warming provides potential clues as to discrepancies in future TC frequency projections among (i)models and (ii) horizontal resolutions, helping to reduce
uncertainties.
In order to evaluate differences in the response of TC
seed frequency and its relation to TC frequency acrossdifferent models and horizontal resolutions, it is necessary to examine the response of TC seed frequency to
global warming by utilizing the outputs of models with acommon setting and a specific definition of TC seed.
Haarsma et al. (2016) proposed the High-Resolution
Model Intercomparison Project (HighResMIP) under thesixth phase of the Coupled Model Intercomparison Project (CMIP6; Eyring et al. 2016). Eighteen models withmore than two types of horizontal resolution were included in HighResMIP (see Table A1 of Haarsma et al.
2016). Camargo (2013) examined TC activity using theCMIP5 dataset, showing that although resolution is not

the only contributor that determines model TC activity,
the models with the highest horizontal resolution reproduced the highest levels of global TC activity. Using
HighResMIP models, Roberts et al. (2019b) showed (i)increases in TC frequency and seasonal accumulated
cyclone energy and (ii) improvement in the representation of TC intensity and TC track with finer resolution
models. These findings indicate the advantage of HighResMIP for use in TC studies.
Using the HighResMIP dataset, we can examine thedifferences in the response of TC seed frequency and SR
between models under a common experimental protocol. The impact of horizontal resolution can also beassessed. In addition, we can discuss the contribution ofchanges in TC seed frequency and SR for each modeldue to global warming. We used six models, which aredescribed in Section 2. TCs and TC seeds were detectedwith an objective tracking algorithm named TempestExtremes (Ullrich and Zarzycki 2017; Zarzycki and Ullrich2017). In Section 2, we briefly describe the models, thetracking algorithm, and the definitions of TC seed andSR that were used in this study. We examine the trends
in three parameters (TC genesis frequency, TC seed frequency, and SR) in relation to (i) global warming and (ii)each other (Section 3). In Section 4, we discuss (i) thecontributions of TC seeds and SR to TC frequency and(ii) the impact of horizontal resolution on TC frequency,
TC seed frequency, and SR. The summary and concluding remarks are given in Section 5.

Yamada et al. Progress in Earth and Planetary Science

2 Methods/experimental
2.1 Models and experimental design

In the present study, we used the six models included in
CMIP6 HighResMIP: Centre National de Recherches
Meteorologiques and Cerfacs for CMIP6 (CNRM-CM6;
Voldoire et al. 2019); EC-Earth3P (Haarsma et al. 2020);
the Hadley Centre Global Environment Model 3-Global
Coupled version 3.1 (HadGEM3-GC3.1; Roberts et al.
2019a); the Max Planck Institute for Meteorology Earth
System Model (MPI-ESM1.2; Gutjahr et al. 2019); MRIAGCM3 (Mizuta et al. 2012); and the latest stable version of NICAM modified for CMIP6 (NICAM16-S;
Kodama et al. 2020). We abbreviated them by combining
the model name and horizontal grid interval. For instance, CNRM156 means CNRM-CM6 with a horizontal
grid interval of 156 km. Other abbreviations and resolutions are listed in Table 1.
Haarsma et al. (2016) described the experimental design of the HighResMIP simulation, which consisted oftier 1, 2, and 3 simulations. Tiers 1 and 3 were atmosphere only simulations, whereas tier 2 was a coupled
ocean–atmosphere simulation. Vecchi et al. (2019)
showed the increase in TC seed due to the rise in SST(see their Fig. 14). Therefore, it is expected that a changeYamada et al. Progress in Earth and Planetary Science(2021) 8:11

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Table 1 Lists of models and horizontal grid intervals used in the present study
Model nameHorizontal grid interval at the equator

Abbreviation

Reference

CNRM-CM6

156 km

CNRM156

Voldoire et al. (2019)

55 km

CNRM055

EC-Earth3P

HadGEM3-GC3.1

MPI-ESM1.2

78 km

EC078

39 km

EC039

208 km

HG208

93 km

HG093

39 km

HG039

100 km

MPI10052 km

MPI052

MRI-AGCM3

60 km

MRI06020 km

MRI020NICAM.16-S

56 km

NCM05628 km

NCM02814 km

NCM014in TC seed frequency is related to that of SST (Vecchi
et al. 2019). We used the outputs of the tier 1 and 3 simulations but not tier 2 simulation which may complicatecomparison due to difference in SST response between
models. The tier 1 simulation spanned from 1950 to 2014,
in which SST and sea ice forcing were based on Hadley
Centre Sea Ice and SST data sets (HadISST2; Titchnerand Rayner 2014). The tier 3 simulation spanned from2015 to 2050, in which the change rate of SST and sea ice
forcing were derived from the ensemble mean of the
Coupled Model Intercomparison Project Phase 5 Representative Concentration Pathways 8.5 scenario. The interannual variability was derived from the observed historic
1950 to 2014 period (Haarsma et al. 2016). The methodology broadly followed that of Mizuta et al. (2008).
An exception to the simulation period was utilized for
NCM014. NCM014 is composed of three 11-year simulation periods: 1950–1960, 2000–2010, and 2040–2050.
Hence, we excluded NCM014 from the trend analysis
and took precautions when comparing between resultsof NCM14 and the other models. Although somemodels such as HadGEM3-GC3.1 have multiple ensemble members, we used a single ensemble member foreach model (r1i1p1f1) in the present study.
2.2 Tropical cyclone detection method

To detect TCs in each model, we used TempestExtremes(Ullrich and Zarzycki 2017; Zarzycki and Ullrich 2017).
TempestExtremes uses two steps to detect TCs: in thefirst step, the algorithm gathers TC candidates for eachtime step; in the second step, these candidates are connected between sequential time steps. In these steps, we
can flexibly select variables and their criteria. In thepresent study, we followed the configuration of Roberts

Haarsma et al. (2020)

Roberts et al. (2019a)

Gutjahr et al. (2019)

Mizuta et al. (2012)

Kodama et al. (2019)(2019) and Roberts et al. (2019b, 2020), in which the featured tracking variable is sea level pressure (SLP) on thenative analysis grid.
At the first step, the algorithm identifies candidates
based on local SLP minima with a warm core, which is defined as the geopotential height difference (250–500 hPa).
Simultaneously, these candidates must satisfy four criteria:(i) these candidates must have a closed contour of SLP
within 5.5° of each candidate, (ii) the SLP of a candidate
node must be 2 hPa lower than that on its closed contour,(iii) the candidate must have a closed contour of geopotential height difference (250–500 hPa) within 6.5° of thecandidate, and (iv) the geopotential height difference maximum within 1° of the candidate should be 6 m larger than
the geopotential height difference on the closed circle.
At the next step, TempestExtremes forms the paths ofthe candidates at different time steps. These paths must
include at least ten candidates while permitting a maximum gap size of three, which is the number of time steps
where no identification occurred. The TC lifetime must
be more than 60 h. In addition, the TC must form between 10° S–40° S or 10° N–40° N and must travel at least
8°. The distance between candidates must be less than 8°.
The criterion of topography excludes the path in which
candidates exist (i) more than ten timesteps over the topographic height of 1500 m and (ii) more than four time
steps over the topographic height over 10 m. These paths
are considered a storm trajectory. The first point in eachtrajectory is defined as the genesis of TC.
2.3 Definition of tropical cyclone seedIn the present study, we define the candidates that TempestExtremes detected at the first step as TC seeds. This
also includes TCs. The SR is defined asYamada et al. Progress in Earth and Planetary Science

SR ≡

N TC
;
N seed(2021) 8:11

ð1Þ

where NTC and Nseed indicate the annual frequencies
of TC genesis and seeds, respectively. It is important tonote that NTC is counted by the genesis number, while
Nseed is counted by the existence number (the total
number of candidates) within 40° S–40° N. In practice,some TC seeds will exist across a few time steps. These
consist of a few candidates. In the definition of Nseed inthis study, these candidates were not counted as one TC
seed but as multiple TC seeds. Therefore, the definitionof Nseed used in this study overestimates the number of
TC seeds.
Tory et al. (2018) evaluated a relationship between TC
and tropical depression (TD) which may develop into
TC by using ERA-I. They detected TC and TD with the
Okubo-Weiss-Zeta parameter TC detection scheme(Tory et al. 2013a, 2013b). The difference between TC
and TD is minimum time period in which the disturbance satisfies six thresholds (see Table 2 of Tory et al.
2018). The time period is 48 h for TC, and 24 h for TD.
As TD can be regard as TC seed, their method can avoid
the overestimation of Nseed. However, Tory et al. (2018)suggested that TD are difficult to define. The minimum
lifetime of TC seed seems to be also difficult to define.
In the present study, we simply define the candidates in
the first step of TempestExtremes as TC seed.

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2.4 Best track and reanalysis

We used the International Best Track Archive for Climate Stewardship (IBTrACS) version 04r00 (Knappet al. 2010, 2018) to assess TC frequency and geographical distribution. This dataset consisted of the “besttracks” provided by many agencies, covering global
storm activity. The dataset included not only TCs but
also other storms, i.e., disturbance, subtropical-, andextra-tropical cyclones. In our analysis, we choose TCsof which a 1-min maximum sustained wind speed exceeds 17.5 ms−1 in the IBTrACS. The wind speed wasbased on data reported by the national Hurricane Center(Jarvinen et al. 1984) and the Joint Typhoon Warning
Center (Chu et al. 2002). We used the IBTrACS between
1980 and 2014.
To the best of our knowledge, there are no available
global TC seeds datasets. Hurley and Boo (2014) presented the first global climatology of monsoon lowpressure systems based on the European Centre for
Medium-Range Weather Forecasts (ECMWF) Interim
Re-Analysis (ERA-I; Dee et al. 2011). These are regarded
as systems that may become TCs. Murakami (2014) and
Hodges et al. (2017) showed that the Japanese 55-year
reanalysis (JRA-55; Kobayashi et al. 2015; Harada et al.
2016) is the best in terms of its representation of TC frequency in state-of-the-art reanalysis data. For assessing
TC seed frequency and its geographical distribution, weused three reanalyses: ERA-I, JRA-55, and the fifth-

Table 2 Spearman’s rank correlations between TC genesis frequencies in HighResMIP multi models and in reanalyses, and IBTrACS.
Spearman’s rank correlations were calculated from 1980 to 2014. An asterisk indicates that a correlation is statistically significant at
the 90% confidence level (at least). P values are indicated in parentheses
Model/Analysis

Globe

Northern Hemisphere

Southern Hemisphere

CNRM156

0.030 (0.864)− 0.120 (0.494)

0.090 (0.609)

CNRM055

0.216 (0.214)

0.234 (0.175).111 (0.526)

EC078

0.162 (0.353)

0.087 (0.619)− 0.070 (0.690)

EC039− 0.007 (0.969)

0.059 (0.738)

0.018 (0.919)

MPI1000.181 (0.298)

0.103 (0.555)− 0.211 (0.224)

MPI052− 0.047 (0.790)− 0.168 (0.334)− 0.125 (0.474)

MRI0600.203 (0.242)

0.200 (0.249)− 0.161 (0.355)

MRI0200.099 (0.571)

0.102 (0.561)− 0.028 (0.874)

HG208

0.116 (0.507)

0.200 (0.250)

0.327 (0.055)

HG093− 0.063 (0.721)

0.104 (0.554)− 0.124 (0.476)

HG039

0.138 (0.429)

0.202 (0.245)

0.217 (0.211)

NCM0560.152 (0.384)

0.195 (0.262)− 0.065 (0.712)

NCM0280.236 (0.172)*0.290 (0.091)

0.099 (0.571)

MME mean

0.136 (0.436)

0.049 (0.780)− 0.051 (0.773)

MME median

0.131 (0.453)

0.114 (0.514)− 0.043 (0.807)

ERA-I*0.577 (< 0.001)*0.662 (< 0.001)− 0.017 (0.922)

JRA-55*0.535 (0.001)*0.492 (0.003)*0.466 (0.005)

ERA-5*0.333 (0.050)*0.501 (0.002)

0.158 (0.364)

(2021) 8:11

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generation ECMWF reanalysis (ERA-5: Copernicus Climate Change Service 2017). We applied TempestExtremes to ERA-I, JRA-55, and ERA-5 to count TC seeds
in the same manner as that for the HighResMIP simulation output.

5, and IBTrACS based on their mean values from 1990
to 2014 for the respective data. Table 2 shows the Spearman’s rank correlation of each model and reanalyses
with IBTrACS during the period in which available
period of IBTrACS overlapped tier 1 period; i.e., 1980–
2014. There were no significant positive correlations between the multi-model ensemble (MME) and IBTrACS
for TC genesis frequency in either hemisphere or the entire globe.
As for the individual models, some models showed
weak or moderate correlations, which appeared to increase with decreasing horizontal grid interval in somemodels (especially in the North Atlantic; Supplemental
table 1). However, we should not regard this impact ofhorizontal resolution as an improvement. Roberts et al.
(2019b) noted that a single member of a model is insufficient for evaluating the impact of horizontal resolution
on interannual variability. They suggested that at least
six members are required for robust basin-scalecorrelations.
JRA-55 showed a statistically significant correlation
with IBTrACS for the globe (correlation coefficient:00.54, p value: 0.001), for the Northern Hemisphere(correlation coefficient: 0.49, p value: 0.003), and for theSouthern Hemisphere (correlation coefficient: 0.47, pvalue: 0.005). ERA-I and ERA-5 also showed a statistically significant correlation with IBTrACS for the globe
and the Northern Hemisphere except for the Southern
Hemisphere. Supplemental table 1 lists correlation coefficients for individual ocean basins. JRA-55 has statistically significant correlation in all ocean basins except theSouth Atlantic, which are higher than those of the otherreanalyses except for the North Atlantic. JRA-55 well
captures the interannual variability of the observed TC
genesis frequency. As Murakami (2014) suggested, JRA55 properly represents the interannual variation of TC
frequency, which encourages the use of JRA-55 in thepresent study.
As for future changes in TC genesis frequency due toglobal warming, after 2039, approximately 75% of themodels projected a decrease in the global TC genesis
frequency compared with their mean value between
1990 and 2014 (Fig. 2a). Consistent with Roberts et al.
(2020), the decreasing trend was clear in the Southern
Hemisphere (Fig. 2c), whereas the trend in the Northern
Hemisphere was unclear (Fig. 2b).
Table 3 summarizes the trends in TC genesis frequency from 1990 to 2049 for the individual models. Except for MPI100, MPI052, and HG039, the models
projected a decreasing trend in global TC frequency.
These decreasing trends were statistically significant at
the 90% confidence level (at least) for CNRM055,
MRI060, MRI020, NCM056, and NCM028. The increasing trends in MPI100, MPI052, and HG039 were not

Yamada et al. Progress in Earth and Planetary Science

2.5 Statistical analysis

We employed the nonparametric Mann–Kendall test for
detecting time series trends in TC genesis frequency,
seed frequency, and SR. Spearman’s rank correlations
were calculated between TC genesis frequencies in the
HighResMIP simulations and IBTrACS, and between TC
seed frequencies in the HighResMIP simulations andJRA-55. We also calculated Spearman’s rank correlations
between three parameters: TC genesis frequency, seedfrequency, and SR. The responses of the three parameters to global warming were assessed with the Wilcoxon
rank-sum test. These statistical analyses were performed
using Python libraries (Oliphant, 2007; Millman andAivazis 2011; Hussain and Mahmud 2019).

3 Results
3.1 Tropical cyclone frequencies

Figure 1 shows the horizontal distributions of normalized
TC genesis density for IBTrACS, ERA-I, JRA-55, ERA-5,
and each model (non-normalized TC genesis densities areseen in Supplemental Fig. 1). Except in NCM014, this density was defined as the number of TCs per year generated ina 5° × 5° grid box between 1990 and 2014. In NCM014, theperiod spanned from 2000 to 2009. Each model represented
a broadly realistic geographical distribution compared with
IBTrACS (Fig. 1a), although most models showed fewer ormore annual TC genesis frequencies than that of the best
track. With the exception of NCM056 (Fig. 1p), the lowest
resolution version of each model underestimated global annual mean TC genesis frequency (Fig. 1e, g, i, k, and m). In
general, the frequencies in the higher-resolution versions
were increased (Fig. 1f, h, j, l, n, o, q, and r) compared withthe lower-resolution versions (Fig. 1e, g, i, k, m, and p),
which is consistent with the results of Roberts et al.
(2019b). With the settings used in this study, TempestExtremes showed little discrepancy in global annual mean
genesis frequency between CNRM055 and IBTrACS. This
may leave room for improvement in terms of tuning theTempestExtremes criteria to the other models, but thiswould complicate inter-model comparison and is outside
the scope of the present study.
Figure 2 shows the interannual variability of the normalized annual TC genesis frequency for IBTrACS,
ERA-I, JRA-55, ERA-5, and multi-models (except for
NCM014). Since global annual TC genesis frequencies
substantially varied between the models and their horizontal resolutions (Fig. 1), we normalized the annual TC
genesis frequencies of the models, ERA-I, JRA-55, ERA-Yamada et al. Progress in Earth and Planetary Science(2021) 8:11

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Fig. 1 Horizontal distributions of normalized TC genesis density. Panels show TC genesis densities of best track, reanalyses, and HighResMIP
models: a IBTrACS, b ERA-I, c JRA-55, d ERA-5, e CNRM156, j CNRM055, g EC078, h EC039, i MPI100, j MPI052, k MRI060, l MRI020, m HG208, n
HG093, o HG039, p NCM056, q NCM028, and r NCM014. Analysis periods are from 1990 to 2014 for panels a–q, and from 2000 to 2009 for panelr. The numbers in parentheses are the annual mean TC genesis frequencies during the respective analysis period for best track, reanalyses, andeach model. Genesis density was defined as the number of TCs per year generated in a 5°×5° grid box. The genesis densities are normalized by
the respective annual mean TC genesis frequency

statistically significant. As a result, the MME mean andmedian showed a statistically significant decreasing trendwith at the 90% confidence level (at least).
Figure 3 shows the spatial distributions of fractionalchange in TC genesis density. Although the spatial distribution of future change differs between models, Fig. 3
represents some similar characteristics among models,
which might reflect their responses to a specific external
condition such as SST. For instance, in most models, TC
genesis frequency becomes more active near the west
coast of Africa, the west coast of North America, thenorth-west coast of Australia, and Madagascar. On theother hand, TC activity becomes inactive over the central South Pacific, the central Indian Ocean, and thewest side of the date line. The regions of decreasing TC

genesis negate those with increases, which leads to thedecreasing trend (Fig. 2).
3.2 TC seed frequencies

The time series of normalized annual TC seed frequency
is seen in Fig. 4. Inter-model variability in the annual TC
seed frequency appeared to be smaller than that of annual TC frequency (Fig. 2). This may be attributed to
the fact that ratio of variation of mean values in TC genesis frequency is larger than those of TC seed frequency
for all models. TC seed frequency in the MME median
had a significant positive correlation with that of JRA-55
over the Northern Hemisphere and a significantly negative correlation over the Southern Hemisphere. The TC
frequency in JRA-55 significantly correlated with that ofYamada et al. Progress in Earth and Planetary Science(2021) 8:11

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Fig. 2 Time series of normalized annual TC genesis frequency. Annual TC genesis frequency was normalized based on the mean value from 1990
to 2014 for respective data. Panels show time series of normalized annual TC genesis frequency for a the globe, b the Northern Hemisphere, andc the Southern Hemisphere, respectively. Black solid line indicates IBTrACS. Blue solid line shows the median value of models without NCM014.
Blue dotted line shows eleven years running mean of the median value of the models. Light blue colored area indicates a range between 5 and95 percentile values of the models. Deep blue colored area indicates a range between 25 and 75 percentile values of the models. Magenta line isa result from ERA-I. Orange line indicates JRA-55. Red line indicates ERA-5

Table 3 Trends in TC genesis, TC seed frequency, and survival
rate (SR). Trends were calculated from 1990 to 2049. An asterisk
indicates that a trend is statistically significant at the 90%
confidence level (at least) with the nonparametric Mann–Kendal
test. P values are indicated in parentheses
Model

TC genesis

Seed

SR

CNRM156− 0.09 (0.298)*− 0.22 (0.014)

0.08 (0.342)

CNRM055*− 0.21 (0.016)*− 0.32 (< 0.001)

0.10 (0.241)

EC078− 0.11 (0.234)− 0.09 (0.287)− 0.07 (0.403)

EC039− 0.10 (0.280)− 0.11 (0.207)− 0.04 (0.660)

MPI1000.01 (0.913)

0.12 (0.170)− 0.05 (0.583)

MPI0520.08 (0.397)− 0.10 (0.246)*0.19 (0.030)

MRI060*− 0.18 (0.045)*− 0.18 (0.046)

0.04 (0.619)

MRI020*− 0.19 (0.030)*− 0.32 (< 0.001)

0.14 (0.104)

HG208− 0.03 (0.725)− 0.02 (0.818)− 0.04 (0.642)

HG093− 0.14 (0.115)*− 0.18 (0.042)

0.01 (0.914)

HG039

0.04 (0.687)− 0.11 (0.235)*0.16 (0.065)

NCM056*− 0.27 (0.003)*− 0.28 (0.001)− 0.08 (0.355)

NCM028*− 0.28 (0.001)*− 0.26 (0.003)*− 0.24 (0.006)

MME mean*− 0.15 (0.093)*− 0.23 (0.010)

0.01 (0.944)

MME median*− 0.19 (0.034)*− 0.27 (0.003)

0.06 (0.536)

IBTrACS in the globe and both hemisphere (see Table 2).
Therefore, we calculated the correlation coefficients between each model and JRA-55 (Table 4). The annual
global TC seed frequencies of the MME median andmean did not correlate with those of JRA-55 as well as
did TC genesis frequency.
As for the individual models, correlation coefficients
between each model and JRA-55 are listed in Table 4.
NCM028 showed a moderate positive correlation with
JRA-55 across the globe (0.49) and in the Northern
Hemisphere (0.46), whereas CNRM055, MRI060, andNCM056 did so only in the Northern Hemisphere (0.35,
0.31, and 0.38, respectively). MPI100, MPI052, and
HG093 showed moderate negative correlations in theSouthern Hemisphere (− 0.46, − 0.51, and − 0.33, respectively). MPI100 also showed a moderate negative
correlation across the globe (− 0.38).
As for the trend in TC seed frequency, the mean andmedian values of MME showed statistically significant
decreasing trends at the 90% level (at least) across theglobe (Table 3). These decreasing trends were seen inboth hemispheres (Fig. 4). The decreasing trend in global TC seed frequency was consistent with that of TC
genesis frequency; i.e., the sign of the trends in TCs andseeds coincided with each other. As for the individualmodels, trends were consistent between TC and seed

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Fig. 3 Horizontal distributions of normalized future changes of TC genesis density. Panels show TC genesis densities of models: a CNRM156, b
CNRM055, c EC078, d EC039, e MPI100, f MPI052, g MRI060, h MRI020, i HG208, j HG093, k HG039, l NCM056, m NCM028, and p NCM014. Futurechange was defined as a difference between 25-year mean values of TC genesis density in the future climate (2025–2049) and the present-day
climate (1990–2014) except for NCM014. In the case of NCM014, the present-day climate spans 2000–2009, and the future climate spans 2040–
2049. Red colors indicate an increase in TC genesis density. Blue colors indicate a decrease in TC genesis density. Genesis density was defined as
the number of TCs per year generated in a 5°×5° grid box. The numbers in parentheses are changes in the global annual TC genesis frequency
for each model

frequency for all models except MPI052 and HG039. In
the cases of MPI052 and HG039, SR might overwhelm
the decrease in TC seed frequency.
To examine the consistency of the horizontal distribution between future fractional changes in the frequencies
of TC genesis and TC seeds, Fig. 5 shows the geographical distributions of future changes in TC seed frequency(Supplemental Figures 2 and 3 shows a TC existence
density in the present-day climate, and a normalized TC
existence density, respectively). The geographical distributions of the fractional change in TC seeds and genesis
were similar for each model (Figs. 3 and 5). However, insome regions, the change in TC seed frequency showed
opposite sign to that of TC genesis frequency (e.g., theEast China Sea). In general, the change in TC seed frequency was spatially noisier than that of TC genesis frequency, which is attributed to different definition ofdensity. In Fig. 3, only the TC genesis location isconsidered.
The behavior of TC seed frequency was similar to thatof TC genesis frequency in terms of time series andhorizontal distribution. This suggests that TC seed frequency contributed to TC genesis frequency.
3.3 Survival rate

We considered the ratio of TC frequency to TC seed frequency as the SR (Eq. 1). The SR varied interannually aswell as TC genesis and its seed frequencies (data notshown). Table 3 summarizes the trend in the SR for eachmodel. The SR showed statistically significant increasing
trends at the 90% confidence level (at least) for MPI052
and HG039, and a decreasing trend for NCM028. The
other models did not show statistically significant trends.
The trends in SR showed the opposite sign to those of
TC genesis frequency in CNRM156, CNRM055,
MPI100, MRI060, MRI020, HG093, and HG039, although the trends between them showed the same signin EC078, EC039, MPI052, HG208, NCM056, andNCM028. These results suggest that the change in TC
frequency is associated with both TC seed frequency
and SR, with the dominant factor depending on themodel. In the following subsection, we quantify theYamada et al. Progress in Earth and Planetary Science(2021) 8:11

Page 9 of 17

Fig. 4 Time series of normalized annual TC seed frequency. Annual TC seed frequency was normalized based on the mean value from 1990 to2014 for respective data. Panels show time series of normalized annual TC genesis frequency for a the globe, b the Northern Hemisphere, and c
the Southern Hemisphere, respectively. Blue solid line shows the median value of models without NCM014. Blue dotted line shows eleven years
running mean of the median value. Light blue colored area indicates a range between 5 and 95 percentile values of models. Deep blue coloredarea indicates a range between 25 and 75 percentile values of models. Magenta line is a result from ERA-I. Orange line indicates JRA-55. Red line
indicates ERA-5

Table 4 Spearman’s rank correlations between TC seed frequencies in HighResMIP multi models and in JRA-55. Spearman’s rankcorrelations were calculated from 1980 to 2014. An asterisk indicates that a correlation is statistically significant at the 90%
confidence level (at least). P values are indicated in parentheses
Model

Globe

Northern Hemisphere

Southern Hemisphere

CNRM156

0.145 (0.406)

0.133 (0.445)− 0.024 (0.891)

CNRM055

0.233 (0.178)*0.345 (0.042)

0.038 (0.828)

EC078− 0.029 (0.869)

0.070 (0.689)− 0.164 (0.346)

EC039− 0.054 (0.756)− 0.021 (0.906)− 0.167 (0.338)

MPI100*− 0.381 (0.024)− 0.033 (0.852)*− 0.457 (0.006)

MPI052− 0.258 (0.135)

0.014 (0.937)*− 0.506 (0.002)

MRI0600.190 (0.274)*0.309 (0.071)− 0.130 (0.455)

MRI0200.065 (0.711)

0.231 (0.182)− 0.117 (0.502)

HG208

0.225 (0.194)

0.264 (0.125)

0.192 (0.268)

HG093− 0.063 (0.717)

0.197 (0.257)*− 0.328 (0.055)

HG039

0.111 (0.527)

0.263 (0.126)− 0.154 (0.377)

NCM0560.243 (0.159)*0.379 (0.025)

0.132 (0.451)

NCM028*0.490 (0.003)*0.456 (0.006)

0.259 (0.133)

MME mean0.015 (0.934)

0.221 (0.202)*− 0.326 (0.056)

MME median

0.069 (0.694)*0.333 (0.050)*− 0.303 (0.077)

Yamada et al. Progress in Earth and Planetary Science(2021) 8:11

Page 10 of 17

Fig. 5 Horizontal distributions of future change in TC seed existence density. Existence density was defined as the number of TC seeds per year
existed in a 5°×5° grid box. Future change was defined as a difference between 25-year mean values of TC genesis density in the future climate(2025–2049) and the present-day climate (1990–2014). Red colors indicate an increase in TC seed existence density due to global warming. Bluecolors indicate a decrease in TC seed existence density. Panels show future changes in TC seed existence density of HighResMIP models: a
CNRM156, b CNRM055, c EC078, d EC039, e MPI100, f MPI052, g MRI060, h MRI020, i NICAM056, and j NICAM028. The numbers in parentheses
are changes in the global annual TC seed frequency for each model

relationships between TC genesis frequency, TC seedfrequency, and SR.
3.4 Relationships between each parameter

To quantify the relationships between annual TC genesis
frequency, seed frequency, and SR, Table 5 lists the correlation coefficients between these three parametersfrom 1950 to 2049. As for TC genesis and its seed frequency, CNRM-CM6, EC-Earth3P, MRI-AGCM3,
HadGEM3-GC3.1, and NICAM.16-S showed strong, statistically significant correlations (greater than 0.7) at the
99.9% confidence level, whereas MPI-ESM1.2 showed
only a moderate correlation (0.65). Correlations for single models were almost unchanged for different horizontal resolutions. In the reanalyses, the correlation
coefficients were strong (0.87 for JRA-55 and 0.86 for
ERA-I), and moderate (0.65 for ERA-I). In addition, TC
genesis frequency in IBTrACS showed a moderate correlation with TC seed frequency which is substitution
from JRA-55. These findings suggest that TC seed frequency strongly affects TC genesis frequency.

In terms of TC genesis frequency and SR, EC078,
MPI-ESM1.2, HG208, NCM028, and NCM014 showedstrong, statistically significant correlations (greaterthan 0.7) at the 99.9% confidence level. CNRM156,
EC039, MRI-AGCM3, HG093, HG039, and NICA
M056 showed moderate correlations (0.4–0.7) at the
99.0% confidence level (at least). CNRM055 showed aweak correlation (0.33). Differing from TC seed, thecorrelation of SR with TC frequency in CNRM-CM6
varied substantially between horizontal resolutions. In
the reanalyses, JRA-55 showed a weak correlation(0.37), whereas ERA-I and ERA-5 showed a moderatecorrelation (0.65 and 0.43). In IBTrACS, there was amoderate correlation (0.47). Most models and reanalyses showed that TC seed frequency exhibited a stronger relation to TC genesis than to SR (except for
MPI-ESM1.2 and HG208).
On the other hand, TC seed frequency and SR show
generally weaker correlations compared with other pairsof parameters. This indicates that SR may be independent of TC seed frequency.

Yamada et al. Progress in Earth and Planetary Science(2021) 8:11

Table 5 Spearman’s rank correlations between TC genesis
frequency, TC seed frequency, and survival rate (SR) in eachmodel and reanalysis
Model/Analysis

Seed vs. TC

SR vs. TC

Seed vs. SR

CNRM156*0.80 (< 0.001)*0.64 (< 0.001)

0.08 (0.428)

CNRM055*0.79 (< 0.001)*0.33 (0.001)*− 0.24 (0.015)

EC078*0.79 (< 0.001)*0.75 (< 0.001)*0.24 (0.015)

EC039*0.83 (< 0.001)*0.63 (< 0.001)

0.14 (0.152)

MPI100*0.65 (< 0.001)*0.84 (< 0.001)*0.18 (0.080)

MPI052*0.65 (< 0.001)*0.75 (< 0.001)

0.03 (0.734)

MRI060*0.81 (< 0.001)*0.48 (< 0.001)− 0.08 (0.414)

MRI020*0.77 (< 0.001)*0.43 (< 0.001)*− 0.17 (0.083)

HG208*0.75 (< 0.001)*0.80 (< 0.001)*0.25 (0.011)

HG093*0.78 (< 0.001)*0.67 (< 0.001)

0.12 (0.247)

HG039*0.80 (< 0.001)*0.60 (< 0.001)

0.06 (0.553)

NCM056*0.91 (< 0.001)*0.69 (< 0.001)*0.35 (< 0.001)

NCM028*0.80 (< 0.001)*0.76 (< 0.001)*0.26 (0.008)

NCM014*0.91 (< 0.001)*0.74 (< 0.001)*0.42 (0.019)

ERA-I*0.86 (< 0.001)*0.65 (< 0.001)*0.29 (0.093)

JRA-55*0.87 (< 0.001)*0.37 (0.030)− 0.08 (0.664)

ERA-5*0.65 (< 0.001)*0.43 (< 0.001)*− 0.34 (0.044)

IBTrACS*0.51 (0.002)-0.08 (0.659)*− 0.88 (< 0.001)

Spearman’s rank correlations between the three parameters (TC, seed, and SR)
were calculated from 1950 to 2049 for models except for NCM014, and from1980 to 2014 for JRA-55, ERA-I, and IBTrACS. The TC seed of IBTrACS was
substituted by that of JRA-55. The correlation in NCM014 was calculated
during the periods for which the data are available: 1950–1960, 2000–2009,
and 2040–2049. An asterisk indicates that a correlation is statisticallysignificant at the 90% confidence level (at least). P values are indicatedin parentheses

4 Discussion
In the present study, we examined the responses of threeparameters (TC genesis frequency, TC seed frequency,
and SR) to global warming, as well as their interrelationships, using the output of the HighResMIP
multi-model simulation. TC genesis frequency appeared
to be associated with TC seed frequency and SR. Here,
we discuss contributions of TC seed frequency and SR
to a change in TC genesis frequency due to global
warming, as well as the influences of model type andhorizontal grid interval on the three parameters.
4.1 Contributions of TC seed and survival rate to futurechanges in TC genesis frequency

To quantify the contributions of TC seed and SR, we defined the present-day climate as the period from 1990 to2014 (the last 25 years of the tier 1 experiment) and thefuture climate as the period from 2025 to 2049 (the last
25 years of the tier 3 experiment). We calculated theircontributions to the change in TC genesis frequency between two climate conditions for each model and horizontal resolution. In NCM014, we regard 2000–2009 asPage 11 of 17

the present-day climate, and 2040–2049 as the futureclimate. In this study, we simply define the relationshipbetween TC genesis frequency (NTC), TC seed frequency(Nseed), and SR as that shown in Eq. 1. Considering future changes in TC genesis frequency due to global
warming, Eq 1 is rewritten as follows:
ΔN TC ΔN seed ΔSR¼
þþ Residual;
N TC
N seedSRð2Þwhere △ indicates difference in each parameter between the future climate and the present-day climate.
The denominators are the mean values in the presentday climate for each parameter. Each term in Eq. 1 indicates fractional changes in the three parameters due to.
global warming: TC frequency ( ΔN TC), seed freN TC.
.
quency (ΔN seed), SR (ΔSR), and residual.
SR
N seedTable 6 lists the fractional changes for each model aswell as the mean values of TC (number year−1), TC seed(number year−1), and SR (%) the present-day climate.
Similar to the trends described in Section 3, thesechanges varied among the models. Even in individualmodels, the amplitude or sign of change varied among
horizontal resolutions and was not systematic. Statistically significant decreases were detected in (i) TC genesis frequency for CNRM055, MRI020, NCM056,
NCM028, and NCM014; (ii) TC seed frequency for
CNRM156, CNRM055, MRI060, MRI020, NCM056,
NCM028, and NCM014; and (iii) SR for only NCM028.
Figure 6 visually presents these fractional changes. Theseplots show that the contributions of seed frequency and
SR to TC frequency varied among the models and theirresolutions. In CNRM-CM6 and MRI-AGCM3, a decrease in seed frequency overwhelmed the increase in
SR, with the impact of the decrease in seed becoming
conspicuous at finer resolutions. In NICAM.16-S, bothseed frequency and SR simultaneously played a role indecreasing TC frequency, with the magnitude of theircontribution varying with horizontal resolution. In ECEarth3P, MPI-ESM1.2, and HadGEM3-GC3.1, the signand magnitude of each parameter nonlinearly differed
between horizontal resolutions. As these three models
did not show significant changes in each parameter, it isunclear whether change in seed frequency or SR (orboth) controlled changes in TC frequency. Based on the
results from CNRM-CM6, MRI-AGCM3, and NICA
M.16-S, the decrease in seed frequency appeared to be amain contributor to the decrease in TC frequency.
Vecchi et al. (2019) and Sugi et al. (2020) reported opposite signs for changes in seed frequency when using
different definitions for the seeds and different models.
Our results also show different signs between models
and horizontal resolutions in spite of using a specificYamada et al. Progress in Earth and Planetary Science(2021) 8:11

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Table 6 Fractional changes in TC genesis frequency, TC seed frequency, and SR
Model
CNRM156

TC

Seed

SR

Resi

△ [%]

Pmean

△[%]

Pmean

△[%]

Pmean

△[%]

− 6.2740.8*− 8.441034.72.16

3.9− 0.01

CNRM055− 7.5783.5*− 9.662725.52.08

3.1− 0.01EC078− 13.1221.0− 8.10394.7− 5.00

5.3

0.02

EC039

− 4.5235.4− 5.41

575.0

1.41

6.1

0.52

MPI100− 1.57

10.2

3.68

519.9

− 4.81

1.9

0.45

MPI052

5.05

11.9− 5.15

756.6

9.40

1.6

− 0.80

MRI060− 5.11

57.1*− 5.992192.50.73

2.6− 0.15

MRI020*− 7.12

93.9*− 10.04

4238.5

3.16

2.2

0.23

HG208

− 3.3227.7

0.40

1062.5− 4.652.6− 0.93

HG093− 4.67105.4− 4.454242.6− 0.402.5− 0.19HG039− 0.13150.9− 1.476409.1

1.44

2.4

0.11

NCM056*− 6.33

213.6*− 5.08

10108.6− 1.372.1− 0.12

NCM028*− 5.13274.6*− 2.29

15982.6*− 2.911.7− 0.08267.8*− 4.37

16598.2− 1.801.6− 0.01

NCM14*− 6.16

MME mean*− 4.72*− 4.560.04− 0.20

MME median*− 5.32*− 4.120.20− 1.40Fractional changes (△ [%]) were calculated based on the ratio of differences between mean values of the future climate (2025–2049) and the present-day climate(1990–2014) to the mean value of the present-day climate (Pmean). In NCM014, we regard 2000–2009 as the present-day climate, and 2040–2049 as the futureclimate. Residual (Resi) was calculated based on Eq. 2. MME mean and median indicates change ratio of TC genesis frequency, TC seed frequency, and survival
rate calculated from the multi models except NCM014, which were normalized based on the mean value from 1990 to 2014. Positive values indicate an increasein the respective variable due to global warming, whereas negative values denote a decrease. Statistical significance of future changes was determined with the
Wilcoxon rank-sum test. An asterisk indicates that a future change is statistically significant at the 90% confidence level (at least)

Fig. 6 Change ratio of TC genesis frequency, TC seed frequency, and survival rate. Horizontal axis indicates models. Change ratio was calculated
based on the ratio of differences between mean values of the future climate (2025–2049) and the present-day climate (1990–2014) to the meanvalue of the present-day climate. In NCM014, we regard 2000–2009 as the present-day climate, and 2040–2049 as the future climate. Residual(Resi) was calculated based on Eq. 2. MME mean and median indicates change ratio of TC genesis frequency, TC seed frequency, and survival rate
calculated from the multi models except NCM014, which were normalized based on the mean value from 1990 to 2014. Deep colored boxes
indicate that its change is statistically significant at least 90% level with the Wilcoxon rank-sum test

(2021) 8:11

Page 13 of 17

definition of the seed. Although the most models (12models) showed a decrease in seed frequency, two lowresolution models (MPI100 and HG208) showed an increase. Inter-model differences in TC seed change mightplay a key role in understanding variability in future TC
projections among models.

damping of the divergent component of horizontal flow,
showing a continuous increase in global TC frequency
with increasing strength of the divergence damping.

Yamada et al. Progress in Earth and Planetary Science

4.2 Dependency on model and its resolution

As seen in the previous subsection, our results showed
little systematic change in the response of TC frequency,
seed frequency, and SR to global warming in relation tohorizontal resolution. This may be associated with theimpact of horizontal resolution on the interannual variability of TC frequency, as shown in Roberts et al.
(2019b). However, Roberts et al. (2019b) showed that
TC frequency is substantially increased even for a single
model with a reduced horizontal grid interval. Similarly,
Fig. 1 and Table 6 show systematic increases in TC frequency for a single model with finer horizontal resolution. We discuss the dependencies of the threeparameters on model type and horizontal resolution.
In Fig. 7, each panel shows the 25-year mean TC genesis frequency, seed frequency, and SR in the presentday climate (1990–2014), which are sorted by the horizontal resolution of each model. In NCM014, the period
is from 2000 to 2009. The horizontal grid interval appears to be contributing to the difference in TC genesis
frequency between resolutions for each model, but not
to the difference between models (Fig. 7a). For instance,
the higher-resolution version of EC-Earth3P (EC039)
shows larger TC frequency than does its lowerresolution version (EC078). However, the TC genesis
frequency of EC039 is less than those of HG093,
MRI060, NCM056, and CNRM055, in which the horizontal grid intervals are larger than that of EC039.
A similar characteristic was seen in the TC seed frequency for inter-model differences and inter-horizontal
resolutions for each model (Fig. 7b). On the other hand,
the SR was higher in the lower-resolution versions of themodels than it was in the higher-resolution versions (except for EC-Earth3P (Fig. 6c)). The three parameterslinearly depended on the model’s horizontal resolution.
TC genesis frequency and TC seed frequency were increased with finer resolution for all the models, whereas
SR showed a decreasing tendency (except for in ECEarth3P).
Summarizing Fig. 7, we can see the systematic increasein TC seeds for a single model with finer horizontal
resolution. Models in which more seeds occur generate
more TC. Inter-model differences in TC seeds might berelated to model configuration, dynamical core, andphysical process such as cumulus convection (Murakami
et al. 2012; Reed et al. 2015; Zhao et al. 2012). For instance, Zhao et al. (2012) investigated the impact of

5 Summary and concluding remarks
In the present study, we investigated future changes in
TC seed frequency and its relation to TC frequency
using a HighResMIP multi-model simulation. TempestExtremes was utilized for detecting TCs and TC seeds
in each model. We estimated the SR, which defined theratio of TC genesis frequency to TC seed frequency.
The MME showed a statistically significant decreasing
trend in TC genesis frequency and TC seed frequency
from 1990 to 2049. The SR did not show a significant
trend. As for the individual models, models with a significant decreasing trend in TC frequency also showedsignificant decreasing trends in TC seed frequency(Table 3). Annual TC and seed frequency in the models
and three reanalyses strongly correlated with each other(except for MPI-ESM1.2 and ERA-5) (Table 5). In
addition, TC genesis frequency in IBTrACS was moderately correlated with the TC seeds detected in JRA-55(0.41). The correlation coefficients between annual TC
frequency and SR were significant and were weaker thanthose between TC and seed frequencies, except for MPIESM1.2. The horizontal distributions of change in TC
genesis frequency were similar to those in seed frequency for each model (Figs. 3 and 5). These results indicate that TC frequency is possibly constrained by TC
seed, SR, and their balance. This is broadly consistent
with the findings of Vecchi et al. (2019).
We compared the contributions of changes in TC seedfrequency and SR to changes in TC genesis frequency
between models and their horizontal resolutions (Fig. 6
and Table 6). In CNRM-CM6, MRI-AGCM3, and NICA
M.16-S, the signs of the contributions of both parameters differed between models but were unchanged between horizontal resolutions. In CNRM-CM6 and MRIAGCM3, the decrease in TC seed overwhelmed the increase in SR, contributing to the decrease in TC frequency. In NICAM.16-S, the decreases in both factors
contributed to the decrease in TC frequency. In ECEarth3P, MPI-ESM1.2, and HadGEM3-GC3.1, the signsof the contributions differed between not only themodels but also their horizontal resolutions. Thesemodels did not show significant changes in TC frequency, seed frequency, and SR between the present-day
and future climates (Fig. 6 and Table 6). Although thecontributions of changes in TC seed and SR differed between models and their horizontal resolutions, the response of TC frequency to global warming is attributed
to the response of TC seed and SR. These differencespossibly induced the differences in the future projectionof TC frequency across previous studies. Therefore, it isYamada et al. Progress in Earth and Planetary Science(2021) 8:11

Page 14 of 17

Fig. 7 Scatter plots of TC genesis frequency, TC seed frequency, and SR versus horizontal resolution. Panels show a annual mean TC
genesis frequency, b annual mean TC seed frequency, and c annual mean SR, which are sorted by horizontal grid interval (x-axis). Annual
mean value is calculated by averaging a variable from 1990 to 2014. In NCM014, annual mean value is calculated by averaging a variablefrom 2000 to 2009

important to understand differences in the behavior of
TC seeds in terms of climate change across models.
As well as the TC tracking algorithm and its criterion,
the definition of TC seed is critical to elucidate the roleof future TC seed changes in TC frequency change. Although the definition may be incomplete, a specific definition should be applied to all models to reducecomplexity. In the present study, we simply defined candidates in the first step of TempestExtremes as TC
seeds, i.e., disturbances having a closed contour of SLP
with warm core (see Section 2.2). This definition may induce uncertainty of conclusion in the present study. We
investigated the influence of (i) the geopotential requirement of third and fourth criteria in the first step of TempestExtremes, and (ii) duration of TC seed following

Tory et al. (2018). In the both cases, TC seed significantly correlates with TC genesis frequency even if thedefinition of TC seed is modified. However, these modifications affected the magnitude of contribution of TC
seed and SR to future change in TC genesis frequency.
Recently, Camargo et al. (2020) demonstrated thatthe mean state of models does not have a consistent
relationship with the mean TC activity in models byusing thirty models, and suggested that understanding of TC seeds in addition to the large-scale environmental impact on TC genesis may provide
information on understanding of model TC climatological biases. A more accurate definition is required
to increase the robustness of the assessment of the
impact of TC seed frequency on TC frequency. In

Yamada et al. Progress in Earth and Planetary Science(2021) 8:11

addition, we did not discover any reason for why TC
seed frequency changes with global warming, whichmight be related to the responses of the Madden–Julian Oscillation, easterly wave, and monsoon troughto global warming. These topics are candidates for
future work.

6 Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s40645-020-00397-1.
Additional file 1: Supplemental Figure 1. Horizontal distributions ofannual TC genesis density. Panels show TC genesis densities of best track,reanalyses, and HighResMIP models: (a) IBTrACS, (b) ERA-I, (c) JRA-55, (d)ERA-5, (e) CNRM156, (f) CNRM055, (g) EC078, (h) EC039, (i) MPI100, (j)MPI052, (k) MRI060, (l) MRI020, (m) HG208, (n) HG093, (o) HG039, (p)NCM056, (q) NCM028, and (r) NCM014. Analysis periods are from 1990 to2014 for panels a–q, and from 2000 to 2009 for panel r. The numbers inparentheses are the annual mean TC genesis frequencies during the respective analysis period for best track, reanalyses and each model. Genesis density was defined as the number of TCs per year generated in a
5°×5° grid box.
Additional file 2: Supplemental Figure 2. Horizontal distributions ofNormalized TC seed existence density. Panels show TC genesis densities
of reanalyses and HighResMIP models: (a) ERA-I, (b) JRA-55, (c) ERA-5, (d)CNRM156, (e) CNRM055, (f) EC078, (g) EC039, (h) MPI100, (i) MPI052, (j)MRI060, (k) MRI020, (l) HG208, (m) HG093, (n) HG039, (o) NCM056, (p)NCM028, and (q) NCM014. Analysis periods are from 1990 to 2014 forpanels a–p, and from 2000 to 2009 for panel q. The numbers in parentheses are the annual mean TC seed existence frequencies during the respective analysis period for reanalyses and each model. Existence density
was defined as the number of TC seeds per year existed in a 5°×5° gridbox. The existence densities are normalized by the respective annual
mean TC seed existence frequency.
Additional file 3: Supplemental Figure 3. Horizontal distributions ofAnnual TC seed existence density. Panels show TC genesis densities ofreanalyses and HighResMIP models: (a) ERA-I, (b) JRA-55, (c) ERA-5, (d)CNRM156, (e) CNRM055, (f) EC078, (g) EC039, (h) MPI100, (i) MPI052, (j)MRI060, (k) MRI020, (l) HG208, (m) HG093, (n) HG039, (o) NCM056, (p)NCM028, and (q) NCM014. Analysis periods are from 1990 to 2014 forpanels a–p, and from 2000 to 2009 for panel q. The numbers in parentheses are the annual mean TC seed existence frequencies during the respective analysis period for reanalyses and each model. Existence density
was defined as the number of TC seeds per year existed in a 5°×5° gridbox.

Page 15 of 17

with a horizontal grid interval of 28 km; NCM056: NICAM with a horizontal
grid interval of 56 km; SLP: Sea level pressure; SR: Survival rate; SST: Sea
surface temperature; TC: Tropical cyclone; TD: Tropical depression
Acknowledgments
JRA-55 data from the JMA, and ERA-5 and ERA-I data from the ECMWF. The
HighResMIP simulations of NICAM.16-S and MRI-AGCM3 were performed on
the Earth Simulator at the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and some of them on the K computer (proposal number
hp150287, hp160230, hp170234, hp180182, and hp190152). The authors acknowledge Dr. Ishi and anonymous reviewers for their constructive comments, and Editorial office for their supports. We used Gnuplot and GrADS
for drawing figures and python for statistical analysis. YY thanks Enago for
the English language review.
Authors’ contributions
YY analyzed the simulations and drafted the manuscript. CK, MSa, MSu, MJR,
RM, ATN, TN, MN, and PLV helped in the interpretation and collaborated
with the corresponding author in the construction of the manuscript. All coauthors participated in discussions of the results and commented on the original manuscript. MJR led CMIP6 HighResMIP and conducted HadGEM3GC3.1. CK conducted the simulations of NICAM.16-S. RM conducted the simulations of MRI-AGCM3. All authors read and approved the final manuscript.
Funding
This study was supported by the Environment Research and Technology
Development Fund (2RF-1701) of the Environmental Restoration andConservation Agency of Japan (ERCA); the Integrated Research Program for
Advancing Climate Models (TOUGOU program: JPMXD0717935457,
JPMXD0717935561), and the FLAGSHIP2020 project within the priority study4(proposal number hp150287, hp160230, hp170234, hp180182, andhp190152), of the Ministry of Education, Culture, Sports, Science andTechnology (MEXT) of Japan; and the PRIMAVERA project, funded by theEuropean Union's Horizon 2020 program under Grant Agreement no.
641727.
Availability of data and materials
The HighResMIP datasets analyzed during the current study are (and will be)available in the Earth System Grid Federation repository, [https://esgf-node.
llnl.gov/search/cmip6/]. Tropical cyclone tracks as calculated by theTempestExtremes algorithm are (and will be) available in the Centre forEnvironmental Data Analysis repository, [https://catalogue.ceda.ac.uk/uuid/43
8268b75fed4f27988dc02f8a1d756d].
Competing interests
The authors declare that they have no competing interests.
Author details
Japan Agency for Marine-Earth Science and Technology, 3173-25
Showa-machi, Yokohama, Kanagawa 236-0001, Japan. 2Atmosphere andOcean Research Institute, The University of Tokyo, 5-1-5 Kashiwanoha,
Kashiwa, Chiba 277-8564, Japan. 3Meteorological Research Institute, Japan
Meteorological Agency, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan.
4
Met Office, Exeter EX1 3PB, UK. 5National Centre for Atmospheric Science,
Department of Meteorology, University of Reading, Reading RG6 6BB, UK.
1

Additional file 4.
Abbreviations
CMIP6: The sixth phase of the Coupled Model Intercomparison Project;
CNRM-CM6: Centre National de Recherches Meteorologiques and Cerfacs for
CMIP6; CNRM055: CNRM-CM6 with a horizontal grid interval of 55 km;
CNRM156: CNRM-CM6 with a horizontal grid interval of 156 km; EC039: ECEarth3P with a horizontal grid interval of 78 km; EC078: EC-Earth3P with ahorizontal grid interval of 78 km; ECMWF: The European Centre for MediumRange Weather Forecasts; ERA-I: The ECMWF Interim Re-Analysis; ERA-5: The
fifth-generation ECMWF reanalysis; HighResMIP: High Resolution Model
Intercomparison Project; IBTrACS: International Best Track Archive for Climate
Stewardship; JRA55: Japanese 55-year Reanalysis; MME: Multi model
ensemble; MPI-ESM1.2: Max Planck Institute for Meteorology Earth System
Model; MPI052: MPI-ESM1.2 with a horizontal grid interval of 52 km;
MPI100: MPI-ESM1.2 with a horizontal grid interval of 100 km; MRIAGCM3: Meteorological Research Institute/Japan Meteorological Agency
Atmospheric General Circulation Model; MRI020: MRI-AGCM3 with ahorizontal grid interval of 20 km; MRI060: MRI-AGCM3 with a horizontal gridinterval of 60 km; NICAM: Nonhydrostatic ICosahedral Atmospheric Model;
NICAM.16-S: The latest stable version of NICAM modified for CMIP6;
NCM014: NICAM with a horizontal grid interval of 14 km; NCM028: NICAM

Received: 31 March 2020 Accepted: 2 December 2020

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