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Prediction of Seasonal Tropical Cyclone Activity in the NUIST-CFS1.0 Forecast System


doi: 10.1007/s00376-023-2213-8

  • Prediction skill for the seasonal tropical cyclone (TC) activity in the Northern Hemisphere is investigated using the coupled climate forecast system (version 1.0) of Nanjing University of Information Science and Technology (NUIST-CFS1.0). This assessment is based on the seven-month (May to November) hindcasts consisting of nine ensemble members during 1982–2019. The predictions are compared with the Japanese 55-year Reanalysis and observed tropical storms in the Northern Hemisphere. The results show that the overall distributions of the TC genesis and track densities in model hindcasts agree well with the observations, although the seasonal mean TC frequency and accumulated cyclone energy (ACE) are underestimated in all basins due to the low resolution (T106) of the atmospheric component in the model. NUIST-CFS1.0 closely predicts the interannual variations of TC frequency and ACE in the North Atlantic (NA) and eastern North Pacific (ENP), which have a good relationship with indexes based on the sea surface temperature. In the western North Pacific (WNP), NUIST-CFS1.0 can closely capture ACE, which is significantly correlated with the El Niño–Southern Oscillation (ENSO), while it has difficulty forecasting the interannual variation of TC frequency in this area. When the WNP is further divided into eastern and western subregions, the model displays improved TC activity forecasting ability. Additionally, it is found that biases in predicted TC genesis locations lead to inaccurately represented TC–environment relationships, which may affect the capability of the model in reproducing the interannual variations of TC activity.
    摘要: 本文评估了南京信息工程大学耦合气候预报系统(1.0版,NUIST-CFS1.0)对1982-2019年5-11月北半球主要海域的热带气旋(TC)季节活动的预测技巧。评估发现NUIST-CFS1.0预测的热带气旋生成密度和路径密度的总体分布与观测有较好的一致性,但各海域的季节平均热带气旋生成频率和累积气旋能量(ACE)有所低估,这可能跟该耦合模式中大气模式部分的空间分辨率(T106)较低有关。此外,NUIST-CFS1.0能够很好的预测北大西洋(NA)和东北太平洋(ENP)热带气旋生成频率和累积气旋能量的年际变化,这主要是因为该海域的热带气旋活动与海温相关的指数有显著的相关关系。在西北太平洋(WNP),由于西北太平洋的累积气旋能量与厄尔尼诺-南方涛动(ENSO)有显著的相关性,模式可以很好的预测累积气旋能量的年际变化,但模式对西北太平洋 热带气旋生成频率年际变化的预测技巧很低。当西北太平洋被划分为东、西两部分子区域时,NUIST-CFS1.0显示出更好的热带气旋季节活动预测能力。研究还发现,预测热带气旋生成位置的偏差会导致热带气旋活动与环境因子的关系表述不准确,这可能会影响模式对热带气旋季节活动年际变化的预测能力。
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  • Figure 1.  Genesis densities as number density per season per unit area equivalent to a 4.5° × 4.5° box based on (a) IBTrACS (OBS) and (b) NUIST-CFS1.0 hindcasts during the MJJASON season of 1982–2019. The red box shows the main development regions (MDRs) in the WNP, ENP, and NA, which are defined as 5°–22.5°N, 110°–180°E in the WNP, 7.5°–15°N, 160°–80°W in the ENP, and 7.5°–22.5°N, 80°–20°W in the NA, respectively. The WNP is further divided into eastern and western parts by the longitude of 140°E.

    Figure 2.  Same as Fig. 1, but for TC track densities.

    Figure 3.  Climatological mean (a) SST (units: °C), (c) VWS (units: m s−1), (e) 850-hPa VOR (units: 10−5 s−1), and (g) 600-hPa RH (units: %) based on NUIST-CFS1.0 hindcasts for the MJJASON season of 1982–2019. The panels in the right column display the differences in the (b) SST, (d) VWS, (f) 850-hPa VOR, and (h) 600-hPa RH between model hindcasts and observations. Only significant differences are presented.

    Figure 4.  Hindcasts of MJJASON (left column) TC frequency and (right column) ACE (units: 104 kt2) in the (a, b) WNP, (c, d) WNP-E, and (e, f) WNP-W, respectively. Red lines indicate the observed time series, and black lines display the calibrated ensemble mean. Blue dots denote the calibration from individual ensemble members. Box-and-whisker plots display the 25th–75th and 10th–90th percentile ranges, respectively. Correlation coefficients between the observed time series and ensemble means are shown in each panel. Gray lines indicate the observed climatological means for each basin.

    Figure 5.  Same as Fig. 4, but for (a) Niño-3.4 and (b) EIO SSTA.

    Figure 6.  Composite differences of the (a) observed and (b) NUIST-CFS1.0-predicted 850-hPa wind (vector, units: m s−1,) and sea level pressure (shading, units: hPa) in warm and cold EIO years. Only significant sea level pressure differences are presented. Green vectors denote significant wind differences.

    Figure 7.  Same as Fig. 4, but for model hindcasts in the (a, b) ENP and (c, d) NA, respectively.

    Figure 8.  Same as Fig. 4, but for the predictions of (a) ENP SST_REL anomaly and (b) NA SST_REL anomaly.

    Figure 9.  Spatial distributions of the correlation coefficients between SST_REL and ENP TC frequency in (a) observations and (b) NUIST-CFS1.0. The dotted areas denote correlation coefficients are statistically significant at the 95% confidence level.

    Figure 10.  Regression coefficients of the MJJASON VWS (shading) onto the time series of Niño-3.4 index during 1982–2019 in (a) observations and (b) the NUIST-CFS1.0 hindcasts. The regression coefficients in (b) are the mean values of the coefficients over all nine members. The purple contours denote genesis densities in the NA.

    Figure 11.  Correlation coefficients of seasonal TC frequency and ACE between observations and the model hindcasts. The models with low horizontal resolutions (> 100 km) are marked in red, and those with high horizontal resolutions are marked in blue (~50–60 km) or green (~25 km). Bold markers indicate the correlation coefficients are statistically significant.

    Table 1.  Seasonal means of tropical cyclone (TC) frequency and accumulated cyclone energy (ACE) for the MJJASON season of 1982–2019 based on IBTrACS (OBS) and NUIST-CFS1.0 hindcasts.

    WNPENPNA
    TC frequency
    OBS22.3917.6612.61
    CFS11.335.075.37
    ACE (104 kt2)
    OBS187.36166.15124.81
    CFS36.5213.7916.31
    DownLoad: CSV

    Table 2.  Correlation coefficients (CC_Ms) and root-mean-square errors (RMSEs) of seasonal TC frequency (top row) and ACE (bottom row) between the observations and the calibrated ensemble means during 1982–2019. CC_I denotes the simple average of the correlation coefficients of seasonal TC frequency and ACE between observations and individual members. The standard deviations of the CC_Is are given in parentheses. STDV_O is one standard deviation of the observations. The SPRvERR indicates the ratio of the ensemble spread (averaged over all forecast years) to the RMSE. The values in bold indicate that the correlation coefficients are statistically significant at the 95% confidence level as examined by the one-tailed Student’s t-test.

    CC_MCC_IRMSESTDV_OSPRvERR
    TC frequency
    WNP0.240.13 (±0.11)4.543.711.32
    WNP-E0.610.45 (±0.06)3.794.341.11
    WNP-W0.410.22 (±0.19)3.493.471.35
    ACE (104 kt2)
    WNP0.650.41 (±0.08)42.7755.821.33
    WNP-E0.720.57 (±0.05)48.4259.511.20
    WNP-W0.230.11 (±0.13)19.0619.460.90
    DownLoad: CSV

    Table 3.  Correlation coefficients between large-scale environmental factors and TC frequency (top row) and ACE (bottom row) in the WNP, WNP-E, and WNP-W during the MJJASON season of 1982–2019. The correlation in the model is the mean value of the correlation in each member. The large-scale environmental indexes include 1) the Niño-3.4 index (5°S−5°N, 120°−170°W), 2) EIO SSTA averaged over 10°S−22.5°N, 75°−100°E (Zhan et al., 2011), 3) MDR SST, 4) MDR VWS, 5) MDR VOR at 850-hPa, and 6) MDR RH at 600-hPa. The values in bold indicate that the correlation coefficients are statistically significant at the 95% confidence level as examined by the one-tailed Student’s t-test. Model-produced correlations that are obviously different from the observations in terms of Fisher’s Z statistic are marked with an asterisk.

    WNPWNP-EWNP-W
    OBSCFSOBSCFSOBSCFS
    Niño3.40.010.250.450.54−0.55−0.41
    0.730.45*0.740.64−0.170.00
    EIO SSTA−0.57−0.25 −0.43−0.32−0.080.09
    −0.17−0.270.12−0.28−0.14−0.12
    MDR SST−0.20−0.21−0.400.02*0.370.12
    −0.36−0.24−0.240.06−0.06−0.21
    MDR VWS−0.26−0.02−0.50−0.320.12−0.27*
    −0.340.08*−0.46−0.260.180.05
    MDR VOR0.260.450.560.69−0.05−0.08
    0.780.610.790.760.260.29
    MDR RH0.000.26−0.050.34*0.630.34
    −0.310.19*0.050.280.330.20
    DownLoad: CSV

    Table 4.  Correlation coefficients between predicted and observed large-scale environmental factors during the MJJASON season of 1982–2019. The correlation skills are calculated based on ensemble-mean forecasts. The values in bold indicate that the correlation coefficients are statistically significant at the 95% confidence level as examined the one-tailed Student’s t test.

    WNPWNP-EWNP-WENPNA
    MDR SST0.790.740.770.880.77
    MDR VWS0.330.670.470.780.62
    MDR VOR0.810.840.680.080.45
    MDR RH0.640.610.630.180.23
    DownLoad: CSV

    Table 5.  Similar to Table 2, but for TC frequency over the ENP and NA.

    CC_MCC_IRMSESTDV_OSPRvERR
    TC frequency
    ENP0.580.39 ($ \pm $0.09)4.804.791.38
    NA0.540.41 ($ \pm $0.09)4.764.811.00
    ACE ($ {10}^{4}{\mathrm{k}\mathrm{t}}^{2} $)
    ENP0.700.48 ($ \pm $0.13)58.8782.511.28
    NA0.470.36 ($ \pm $0.09)65.7370.340.78
    DownLoad: CSV

    Table 6.  Similar to Table 3, but for the correlations in the ENP and NA. MDR SST, VWS, VOR, and RH are averaged over 7.5°–15°N, 160°–80°W in the ENP, and over 7.5°–22.5°N, 80°–20°W in the NA, respectively. SST_REL is defined as the difference in SST between the MDR of the ENP and NA and that in the global tropics (30°S–30°N).

    ENPNA
    OBSCFS1.0OBSCFS1.0
    Niño-3.40.460.51−0.32−0.56
    0.430.53−0.40−0.57
    SST_REL0.710.31*0.670.61
    0.680.33*0.640.61
    MDR SST0.520.410.650.46
    0.450.440.580.48
    MDR VWS−0.54−0.35−0.67−0.54
    −0.55−0.35−0.65−0.56
    MDR VOR0.100.120.430.56
    0.060.120.470.57
    MDR RH−0.050.160.330.56
    −0.100.160.370.52
    DownLoad: CSV

    Table 7.  List of forecast systems that predict interannual variations of seasonal TC activity: approximate horizontal resolution of the atmospheric component, predicted years, target months, and references.

    Forecast systemResolutionPredicted yearsTarget monthsReferences
    Met Office GloSea4~120 km1996–20096–11Camp et al., 2015
    Met Office GloSea5~60 km1996–20096–11 Camp et al., 2015
    1992–20136–11
    GFDL FLOR~50 km1980–20157–11Murakami et al., 2016
    GFDL HiFLOR~25 km1980–20157–11 Murakami et al., 2016
    Project MinervaT319, ~60 km1980–20115–11Manganello et al., 2016
    JMA/MRI-CGCM~180 km1979–20066–10Takaya et al., 2010
    DownLoad: CSV
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Manuscript received: 27 July 2022
Manuscript revised: 26 January 2023
Manuscript accepted: 31 January 2023
通讯作者: 陈斌, bchen63@163.com
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Prediction of Seasonal Tropical Cyclone Activity in the NUIST-CFS1.0 Forecast System

    Corresponding author: Jing-Jia LUO, jjluo@nuist.edu.cn
  • 1. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Institute for Climate and Application Research (ICAR), Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2. School of Atmospheric Sciences, and Key Laboratory of Mesoscale Severe Weather/Ministry of Education, Nanjing University, Nanjing 210023, China

Abstract: Prediction skill for the seasonal tropical cyclone (TC) activity in the Northern Hemisphere is investigated using the coupled climate forecast system (version 1.0) of Nanjing University of Information Science and Technology (NUIST-CFS1.0). This assessment is based on the seven-month (May to November) hindcasts consisting of nine ensemble members during 1982–2019. The predictions are compared with the Japanese 55-year Reanalysis and observed tropical storms in the Northern Hemisphere. The results show that the overall distributions of the TC genesis and track densities in model hindcasts agree well with the observations, although the seasonal mean TC frequency and accumulated cyclone energy (ACE) are underestimated in all basins due to the low resolution (T106) of the atmospheric component in the model. NUIST-CFS1.0 closely predicts the interannual variations of TC frequency and ACE in the North Atlantic (NA) and eastern North Pacific (ENP), which have a good relationship with indexes based on the sea surface temperature. In the western North Pacific (WNP), NUIST-CFS1.0 can closely capture ACE, which is significantly correlated with the El Niño–Southern Oscillation (ENSO), while it has difficulty forecasting the interannual variation of TC frequency in this area. When the WNP is further divided into eastern and western subregions, the model displays improved TC activity forecasting ability. Additionally, it is found that biases in predicted TC genesis locations lead to inaccurately represented TC–environment relationships, which may affect the capability of the model in reproducing the interannual variations of TC activity.

摘要: 本文评估了南京信息工程大学耦合气候预报系统(1.0版,NUIST-CFS1.0)对1982-2019年5-11月北半球主要海域的热带气旋(TC)季节活动的预测技巧。评估发现NUIST-CFS1.0预测的热带气旋生成密度和路径密度的总体分布与观测有较好的一致性,但各海域的季节平均热带气旋生成频率和累积气旋能量(ACE)有所低估,这可能跟该耦合模式中大气模式部分的空间分辨率(T106)较低有关。此外,NUIST-CFS1.0能够很好的预测北大西洋(NA)和东北太平洋(ENP)热带气旋生成频率和累积气旋能量的年际变化,这主要是因为该海域的热带气旋活动与海温相关的指数有显著的相关关系。在西北太平洋(WNP),由于西北太平洋的累积气旋能量与厄尔尼诺-南方涛动(ENSO)有显著的相关性,模式可以很好的预测累积气旋能量的年际变化,但模式对西北太平洋 热带气旋生成频率年际变化的预测技巧很低。当西北太平洋被划分为东、西两部分子区域时,NUIST-CFS1.0显示出更好的热带气旋季节活动预测能力。研究还发现,预测热带气旋生成位置的偏差会导致热带气旋活动与环境因子的关系表述不准确,这可能会影响模式对热带气旋季节活动年际变化的预测能力。

    • Tropical cyclones (TCs) are one of the most destructive natural hazards and can cause massive losses in many parts of the world during landfalls. Prediction of seasonal TC activity is rooted in the dependence of TC statistics on atmospheric and oceanic conditions. Favorable environment conditions, e.g., above-normal local sea surface temperature (SST) and low-level vorticity (VOR), as well as below-normal sea level pressure and vertical wind shear (VWS), increase the probability of TC genesis (Gray, 1979; Knaff, 1997; DeMaria et al., 2001; Nolan and Rappin, 2008; Kossin et al., 2010). The interannual variations in these seasonal atmospheric factors are related to the variations in the large-scale atmospheric circulations, which are strongly affected by the spatial patterns of SST anomalies, such as the El Niño–Southern Oscillation (ENSO).

      ENSO is a major mode of climate variability associated with strong ocean–atmosphere interactions in the tropical Pacific and has been proven to be predictable on seasonal-to-interannual time scales (Luo et al., 2005a, 2008a; Kim et al., 2012; MacLachlan et al., 2015; Ham et al., 2019). The influences of ENSO on TC activity vary in different basins. Previous studies have revealed a negative correlation relationship between the Niño-3.4 index (an SST-based measure of ENSO) and the seasonal TC activity over the North Atlantic (NA) (Gray, 1984a, b; Gray et al., 1993; Landsea et al., 1999; Frank and Young, 2007). Some studies have found that there is little difference in eastern North Pacific (ENP) TC frequency between El Niño and non-El Niño years (e.g., Whitney and Hobgood, 1997; Collins and Mason, 2000). Others have demonstrated that the number of intense hurricanes increases during El Niño events (e.g., Gray and Sheaffer, 1991). By affecting VWS and upper-ocean heat content, ENSO can also modulate TC activity over the ENP (Camargo et al., 2007; Balaguru et al., 2013; Jin et al., 2014). In the western North Pacific (WNP), ENSO exerts an important and well-documented impact on the location of seasonal mean TC genesis (Chan, 1985; Chen et al., 1998; Chia and Ropelewski, 2002; Wang and Chan, 2002), but no significant linear relationship is found between TC frequency and the Niño-3.4 index (Wang and Chan, 2002; Camargo and Sobel, 2005; Chan, 2007). Additionally, ENSO-related atmospheric factors behave distinctively in modulating seasonal TC activity in different areas. In the NA and WNP, VWS and mid-troposphere relative humidity (RH) are the dominant factors affecting TC activity. While in the central North Pacific, low-level VOR anomalies play a key role (Camargo et al., 2007).

      Other tropical SST indexes are also found to be closely related to seasonal TC activity. For instance, the relative SST index (SST_REL), which is defined as the SST difference between the main development region (MDR; 7.5°–15°N, 160°–80°W in the ENP; 7.5°–22.5°N, 80°–20°W in the NA) and the global tropics (30°S–30°N), has been used to predict seasonal hurricane activity as well (Zhao et al., 2010; Vecchi et al., 2011). Zhan et al. (2011) pointed out that the eastern Indian Ocean (EIO) SST anomaly (SSTA) is a factor that modulates seasonal TC frequency over the WNP. Furthermore, Zhan et al. (2013) also found that the spring SST gradient between the southwestern Pacific and the western Pacific warm pool has a significant negative correlation with WNP TC activity.

      The significant correlation between tropical SST and TC activity makes it possible for climate models to predict or reproduce the interannual variations of TC activity. By using observational SST-forced atmospheric general circulation models (AGCMs), interannual variations of TC activity can be reproduced well, especially in the NA where local SST is essential in modulating TC activity (Zhao et al., 2009; Vecchi et al., 2011; Mei et al., 2019). A 25-km AGCM forced by persistent SSTAs was adopted for retrospective seasonal forecasts and has been proven to perform well in predicting TC frequency over the NA but relatively poorly over the North Pacific (Chen and Lin, 2011, 2013).

      In recent decades, coupled ocean–atmosphere general circulation models (CGCMs) have been widely applied in dynamical seasonal forecasts for TC activity, and some perform rather well in reproducing seasonal TC activities (e.g., Manganello et al., 2012, 2016; Kim et al., 2018). The coupled climate forecast system version 1.0 of Nanjing University of Information Science and Technology (NUIST-CFS1.0) is the first generation of the operational climate forecast system of Nanjing University of Information Science and Technology and is adapted from the previous global ocean–atmosphere coupled general circulation model named Scale Interaction Experiment-Frontier (SINTEX-F) (Luo et al., 2003, 2005a). Previous studies have shown that the SINTEX-F model can reduce equatorial SST biases with improved coupling physics and performs well in simulating the tropical climate (Luo et al., 2003, 2005b). This model can predict tropical climate variations (e.g., ENSO, Indian Ocean Dipole) well on seasonal-to-multiyear time scales (Luo et al., 2005a, 2007, 2008a, b). All El Niño and La Niña events during 1982–2001 are captured well, with the anomalous correlation coefficient exceeding 0.7 at lead times of up to 12 months (Luo et al., 2005a). The NUIST-CFS1.0 also has been proven to perform well in simulating the variations of tropical SST (He et al., 2020). This study aims to further assess the prediction skill for the interannual variations of seasonal TC activity in the Northern Hemisphere (NH) with the NUIST-CFS1.0 model.

      The remainder of this paper is organized as follows. Section 2 describes the prediction system, observational data, TC detection algorithm, and the methods for prediction skill evaluation. The climatology of predicted TCs and large-scale environmental factors are briefly introduced in section 3. Section 4 analyzes the seasonal forecast skill for basin-wide and regional TC activity. The NUIST-CFS1.0 and other forecast systems are compared in section 5. Finally, the conclusions are given in section 6.

    2.   Methodology
    • The NUIST-CFS1.0 adopts the European Centre Hamburg Atmospheric Model version 4 (ECHAM4), which has a spectral resolution of triangular truncation 106 (T106, ~1.1° × 1.1°) and 19 vertical levels, with the model top at 10 hPa, as its atmospheric component (Roeckner et al., 1996). The oceanic component is OPA8.2 (Madec et al., 1998) with 31 vertical levels in total and 19 levels in the top 400 m. The model resolution is a 2° Mercator mesh with an increased meridional resolution to 0.5° near the equator. These two components are directly coupled by the Ocean Atmosphere Sea Ice Soil (OASIS2.4; Valcke et al., 2000) coupler every two hours.

      Predictions are initialized with a coupled SST-nudging scheme. Model SSTs are strongly nudged toward the National Centers for Environmental Prediction (NCEP) SST daily observations (Reynolds et al., 2002). Nine ensemble members are generated by perturbing both the model coupling physics and initial conditions (see more details in Luo et al., 2005a, 2008a). Forecasts with lead times of up to 24 months are performed from the first day of each month during the period from 1982 to present (2023).

      In this study, the assessment of NUIST-CFS1.0 is performed for the prediction of TC activity over the NH only. It is based on the seven-month hindcasts initialized on 1 May during 1982–2019, as the seven months from May to November (MJJASON) cover the most active periods of TCs over the NH. Additionally, the results based on 6-h forecast outputs are displayed only for the regions of the WNP, ENP, and NA, as the prediction skill over the North Indian Ocean is found to be quite limited.

    • The datasets of observed TCs are obtained from the International Best Track Archive for Climate Stewardship (IBTrACS; Knapp et al., 2010, 2018) version v04r00, which provides the location and intensity of global TCs at an interval of 6 h. Only TCs with a lifetime maximum intensity greater than 35 kt (reaching the level of “tropical storm”) are discussed here.

      Atmospheric variables (6-h wind at 850 hPa and 200 hPa, RH) are provided by the Japanese 55-year Reanalysis (Kobayashi et al., 2015). Observed SSTs are obtained from the daily Optimum Interpolation Sea Surface Temperature (OISST) product released by the National Oceanic and Atmospheric Administration (NOAA) (Huang et al., 2021).

    • Based on the 6-h model outputs, TC-like vortexes are identified through an objective feature-tracking method similar to that used in Bengtsson et al. (2007) and Manganello et al. (2012, 2016). The detailed steps are as follows.

      Step 1: Vortex Detection (detecting all the vortex tracks).

      1) Relative VOR is averaged over the levels of 850 hPa, 700 hPa, and 600 hPa.

      2) A low-pass filter is used to remove small-scale waves (with a magnitude less than 103 km in terms of the horizontal scale) in the vertically averaged VOR.

      3) Vortexes are detected as local maxima in the averaged relative VOR field with values greater than 5$ \text{×} $10−6 s−1.

      4) Two vortexes are considered as the same one if the distance of their centers (the location of the local maximum VOR) within six hours is less than 3°.

      Step 2: TC Identification (separating TCs from other synoptic cyclone systems).

      1) For intensity threshold, the maximum surface (10-m) wind speed ($ {V}_{\mathrm{m}\mathrm{a}\mathrm{x}} $) should be greater than 12.8 m s−1.

      2) To meet a warm core condition, the VOR difference between 850 hPa and 250 hPa should be larger than zero.

      3) To ensure a coherent vertical structure, there must be a VOR maximum at each level between 850 hPa and 250 hPa near the TC center.

      4) Criteria 1–3 must be achieved for at least four consecutive time steps (i.e., 24 h).

      5) Cyclogenesis (first identification of a TC) should occur in the tropical band between 0°–20°N over land and 0°–30°N over oceans.

      The basin of a TC is assigned based on the location where the TC reaches its maximum intensity. To evaluate the simulation performance or prediction skill for TCs by global climate models, various criteria have been employed to identify TCs and to determine the cutoff between tropical storms and tropical depressions (e.g., Camargo et al., 2005; Mei et al., 2019). Manganello et al. (2012) pointed out that there are essentially two ways to obtain the detection thresholds for TCs in simulation: either the model TC identification criteria closely match observed TCs, or calibration of TC counts against observations needs to be performed to obtain the preferred detection thresholds.

      In this study, we test both ways by choosing intensity thresholds of 12.8 m s−1 and 9.5 m s−1, respectively. The criterion of 12.8 m s−1 is used to make the intensities of the chosen TCs closely match the observed ones. Note that the threshold of surface wind speed is adjusted for model resolution based on Walsh et al. (2007, see their Fig. 2). With this criterion, the seasonal mean TC frequency and accumulated cyclone energy (ACE) are systematically underestimated in all basins (discussed later in section 3). With the criterion of 9.5 m s−1, the selected TC counts in all three basins are comparable to observations, while the predicted spatial distribution of TC genesis density is quite different from observations (figures not shown). Meanwhile, the predicted location of maximum concentration of TC genesis in model hindcasts extends more eastward than what is seen in observations over the WNP. In addition, the TC activity over the Gulf of Mexico and the east coast of the United States is not well-represented. As discussed in later paragraphs, an accurate representation of TC genesis location is essential in predicting the relationship between interannual variations of seasonal TC activity and environmental factors. Accordingly, an accurate TC genesis location is also an important factor that should be considered when determining TC identification criteria. Therefore, the criterion of 12.8 m s−1 is chosen here, for which predicted TC genesis location is closer to observations. Note that prediction skills for different surface wind speed thresholds are found to be similar.

    • To evaluate the skill of NUIST-CFS1.0 in predicting TC activity over the NH, the correlation coefficients and root-mean-square errors (RMSEs) between the predicted and observed TC frequency and ACE during the MJJASON season of 1982–2019 are calculated. ACE is an integrated measure of TC activity and is calculated by squaring the maximum surface wind speed at each time interval along a track and then summing it up over all tracks in a season (see Bell et al., 2000). RMSE is computed after calibrating the model predictions to remove the systematic bias in the predicted ensemble-mean quantities. The calibration means the predicted TC frequency and ACE for each ensemble member are scaled by the ratio between the observed and predicted ensemble-mean values during 1982–2019.

      The ratio of the ensemble spread to the RMSE (SPRvERR; e.g., Buizza et al., 2005) is used to estimate the statistical reliability of model forecasts. The ensemble spread is computed as the square root of average ensemble variance over all forecast years (Fortin et al., 2014). In a perfectly reliable ensemble forecast when the forecast uncertainty is fully accounted for, the SPRvERR equals one.

    3.   TC climatology
    • As shown in Table 1, the seasonal mean TC frequency and ACE are systematically underestimated in all three basins. As seen in many other studies (e.g., Camargo et al., 2005; Manganello et al., 2012), the intensity of TCs in the low-resolution model is weaker than in observations. The coarse resolution of NUIST-CFS1.0 is one of the main reasons for the lower ACE. The TC frequency and ACE over the WNP predicted by NUIST-CFS1.0 are the largest among all the three basins, which is consistent with observations. The predicted TC frequency during the MJJASON season is similar between the ENP and the NA, whereas five more TCs are observed over the ENP than the NA.

      WNPENPNA
      TC frequency
      OBS22.3917.6612.61
      CFS11.335.075.37
      ACE (104 kt2)
      OBS187.36166.15124.81
      CFS36.5213.7916.31

      Table 1.  Seasonal means of tropical cyclone (TC) frequency and accumulated cyclone energy (ACE) for the MJJASON season of 1982–2019 based on IBTrACS (OBS) and NUIST-CFS1.0 hindcasts.

      The densities of cyclogenesis in IBTrACS and NUIST-CFS1.0 hindcasts are shown in Fig. 1. Cyclogenesis location in the model is defined as the point where the vortex with a $ {V}_{\mathrm{m}\mathrm{a}\mathrm{x}} $ reaching 12.8 m s−1 or above is first identified, while the observed genesis is defined as the first record in IBTrACS. As shown in Fig. 1, the most active and concentrated area of cyclogenesis is located in the ENP in both observations and the hindcasts. The area of intense cyclogenesis in the WNP in the hindcasts extends more eastward. In addition, the model can reproduce the maximum cyclogenesis in the South China Sea well but with a more southward location. The largest differences appear in the NA, where the cyclogenesis in the hindcasts is found to be far away from the west coast of Africa. Moreover, the predicted TC activity decreases over the Gulf of Mexico and the east coast of the United States compared with IBTrACS.

      Figure 1.  Genesis densities as number density per season per unit area equivalent to a 4.5° × 4.5° box based on (a) IBTrACS (OBS) and (b) NUIST-CFS1.0 hindcasts during the MJJASON season of 1982–2019. The red box shows the main development regions (MDRs) in the WNP, ENP, and NA, which are defined as 5°–22.5°N, 110°–180°E in the WNP, 7.5°–15°N, 160°–80°W in the ENP, and 7.5°–22.5°N, 80°–20°W in the NA, respectively. The WNP is further divided into eastern and western parts by the longitude of 140°E.

      As shown in Fig. 2, the track densities in the WNP, ENP, and NA in hindcasts are underestimated. The track density areas of the three basins in the model are smaller than those in the observations. In the WNP, there is a local maximum to the east of the Philippines in both the observations and the hindcasts. However, the model fails to reproduce the secondary maximum over the South China Sea. The main area of track density along the east coast of the United States in the NA is also absent in the model. This may be attributed to the underestimated TC genesis frequency over the western part of the WNP (WNP-W) and the northern part of the NA in the model (Fig. 1).

      Figure 2.  Same as Fig. 1, but for TC track densities.

      It is well recognized that a reliable prediction of TC activity depends on a realistic representation of tropical SST and the associated atmospheric conditions (Gray, 1979; Knaff, 1997; DeMaria et al., 2001; Emanuel, 2007). The spatial distributions of climatological mean environmental factors (SST, VWS, low-level VOR, and mid-level RH) by model hindcasts are shown in Fig. 3. The highest mean SST and the lowest mean VWS (Figs. 3a, c) provide favorable conditions for TC genesis in the WNP. Figure 3b displays that the SST biases are noticeable in the tropical Pacific and Atlantic, which is common in the current generation of general circulation models (Wang et al., 2014; Xu et al., 2014a, b; Hsu et al., 2019). Large positive biases are found over the eastern tropical Atlantic and Pacific with magnitudes of up to 2°C. Cold SST biases appear in the northwestern Atlantic and Pacific, with weaker magnitudes. Hsu et al. (2019) proposed that the combination of the Pacific SST cold bias and Pacific SST warm bias would induce an eastward shift of WNP TC genesis. The strong VWS related to the east side of the tropical upper-tropospheric trough in the central North Pacific suppresses TC genesis (Figs. 3c, d; Kelley and Mock 1982; Fitzpatrick et al., 1995; Wu et al., 2015). The negative VWS biases in the eastern part of the WNP (WNP-E) may also contribute to the eastward shift of the mean location of TC genesis (Fig. 1). In the NA, the cold SST biases and positive VWS biases may lead to less TC genesis in the Gulf of Mexico and along the east coast of the United States (Fig. 1; Figs. 3b, d). It is noteworthy that the criterion of cyclogenesis occurring within 0°–30°N over oceans excludes TCs generated north of 30°N near the east coast of the United States. The westward shift of TC genesis in the NA MDR may be related to the warmer climatological mean SST, weaker VWS, and larger low-level VOR around 40°W longitude in the model (Figs. 3b, d, f). A large portion of ENP TCs form from easterly waves coming across the Central American mountainous region (Avila et al., 2003; Franklin et al., 2003), which are always poorly represented in low-resolution models (Martin and Thorncroft, 2015). It is likely that the model’s low resolution contributes to the deficient prediction of TC frequency over the ENP by the NUIST-CFS1.0 (Table 1).

      Figure 3.  Climatological mean (a) SST (units: °C), (c) VWS (units: m s−1), (e) 850-hPa VOR (units: 10−5 s−1), and (g) 600-hPa RH (units: %) based on NUIST-CFS1.0 hindcasts for the MJJASON season of 1982–2019. The panels in the right column display the differences in the (b) SST, (d) VWS, (f) 850-hPa VOR, and (h) 600-hPa RH between model hindcasts and observations. Only significant differences are presented.

    4.   Seasonal forecast skill for TC activity
    • NUIST-CFS1.0 performs well in forecasting ACE in the WNP. The correlation coefficient of the seasonal ACE between the observations and the calibrated ensemble means during 1982–2019 is 0.65 (Table 2 and Fig. 4b). However, the performance of NUIST-CFS1.0 in predicting TC frequency over the entire WNP is relatively poor, with the correlation coefficient being only 0.24 (Table 2 and Fig. 4a). In 1992, 2013, and 2014, the observed TC frequency is outside of the 10th–90th percentile range of ensemble hindcasts. However, the hindcasts can still successfully capture some extreme cases, such as those in 1998 and 2010 (Fig. 4a). The RMSE for the calibrated TC frequency is larger than one standard deviation of the observed TC counts (Table 2), indicating that the prediction error for TC frequency is quite large in the WNP. The correlation coefficients of seasonal TC activity (including TC frequency and ACE) between the observations and individual ensemble members of NUIST-CFS1.0 (Table 2) are also calculated. It is found that the correlation coefficients for individual members are generally lower than those of the ensemble means, suggesting that the ensemble forecast approach is helpful in improving the prediction of seasonal TC activity over the WNP.

      Figure 4.  Hindcasts of MJJASON (left column) TC frequency and (right column) ACE (units: 104 kt2) in the (a, b) WNP, (c, d) WNP-E, and (e, f) WNP-W, respectively. Red lines indicate the observed time series, and black lines display the calibrated ensemble mean. Blue dots denote the calibration from individual ensemble members. Box-and-whisker plots display the 25th–75th and 10th–90th percentile ranges, respectively. Correlation coefficients between the observed time series and ensemble means are shown in each panel. Gray lines indicate the observed climatological means for each basin.

      CC_MCC_IRMSESTDV_OSPRvERR
      TC frequency
      WNP0.240.13 (±0.11)4.543.711.32
      WNP-E0.610.45 (±0.06)3.794.341.11
      WNP-W0.410.22 (±0.19)3.493.471.35
      ACE (104 kt2)
      WNP0.650.41 (±0.08)42.7755.821.33
      WNP-E0.720.57 (±0.05)48.4259.511.20
      WNP-W0.230.11 (±0.13)19.0619.460.90

      Table 2.  Correlation coefficients (CC_Ms) and root-mean-square errors (RMSEs) of seasonal TC frequency (top row) and ACE (bottom row) between the observations and the calibrated ensemble means during 1982–2019. CC_I denotes the simple average of the correlation coefficients of seasonal TC frequency and ACE between observations and individual members. The standard deviations of the CC_Is are given in parentheses. STDV_O is one standard deviation of the observations. The SPRvERR indicates the ratio of the ensemble spread (averaged over all forecast years) to the RMSE. The values in bold indicate that the correlation coefficients are statistically significant at the 95% confidence level as examined by the one-tailed Student’s t-test.

      ACE is controlled by not only TC frequency but also TC intensity and lifetime. TCs in El Niño years tend to be more intense and long-lived than those in La Niña years (Chia and Ropelewski, 2002; Camargo and Sobel, 2005; Chen et al., 2006), as indicated by the large correlation coefficient between ACE and the Niño-3.4 index (Table 3; the correlation coefficient is 0.73 in observations). The hindcasts can reproduce the positive relationship between ACE and the Niño-3.4 index but with the correlation coefficient (0.45) being much lower than that seen in the observations. The NUIST-CFS1.0 seasonal hindcast skill for the Niño-3.4 index is very high with the correlation coefficient being 0.83 (Fig. 5a). Wang and Chan (2002) proposed that the equatorial central and eastern Pacific warming during El Niño years tends to induce pronounced equatorial westerly anomalies in the western Pacific. Large meridional shears associated with the equatorial westerly anomalies increase low-level relative VOR, which would promote TC genesis, especially in the southeastern part of the WNP. The hindcasts capture this relationship well, i.e., the interannual variations of the seasonal ACE in model predictions are positively related to the average low-level relative VOR (Table 3). However, the negative relationship between MDR VWS and ACE is not captured by NUIST-CFS1.0.

      WNPWNP-EWNP-W
      OBSCFSOBSCFSOBSCFS
      Niño3.40.010.250.450.54−0.55−0.41
      0.730.45*0.740.64−0.170.00
      EIO SSTA−0.57−0.25 −0.43−0.32−0.080.09
      −0.17−0.270.12−0.28−0.14−0.12
      MDR SST−0.20−0.21−0.400.02*0.370.12
      −0.36−0.24−0.240.06−0.06−0.21
      MDR VWS−0.26−0.02−0.50−0.320.12−0.27*
      −0.340.08*−0.46−0.260.180.05
      MDR VOR0.260.450.560.69−0.05−0.08
      0.780.610.790.760.260.29
      MDR RH0.000.26−0.050.34*0.630.34
      −0.310.19*0.050.280.330.20

      Table 3.  Correlation coefficients between large-scale environmental factors and TC frequency (top row) and ACE (bottom row) in the WNP, WNP-E, and WNP-W during the MJJASON season of 1982–2019. The correlation in the model is the mean value of the correlation in each member. The large-scale environmental indexes include 1) the Niño-3.4 index (5°S−5°N, 120°−170°W), 2) EIO SSTA averaged over 10°S−22.5°N, 75°−100°E (Zhan et al., 2011), 3) MDR SST, 4) MDR VWS, 5) MDR VOR at 850-hPa, and 6) MDR RH at 600-hPa. The values in bold indicate that the correlation coefficients are statistically significant at the 95% confidence level as examined by the one-tailed Student’s t-test. Model-produced correlations that are obviously different from the observations in terms of Fisher’s Z statistic are marked with an asterisk.

      Figure 5.  Same as Fig. 4, but for (a) Niño-3.4 and (b) EIO SSTA.

      The interannual variations of WNP TC frequency are hard to capture in many prediction systems, especially in those low-resolution ones (e.g., Camargo et al., 2005; Camp et al., 2015). Previous studies (Ramage and Hori, 1981; Lander, 1994; Wang and Chan, 2002; Chen et al., 2006) have shown that annual TC frequency over the WNP has no close relationship with the Niño-3.4 index, with the correlation coefficient reaching only 0.01 in the observations (Table 3). In NUIST-CFS1.0 hindcasts, the average correlation coefficient between TC frequency and the Niño-3.4 index over all nine ensemble members is 0.25, which is slightly larger than that in the observations. In the entire WNP, the interannual variations of seasonal TC frequency are not closely correlated to the changes of any environment variable, as indicated by the low correlation coefficients of the observed TC frequency with the dynamic and thermodynamic factors averaged over the WNP MDR. The correlation of the predicted variables is not significantly different from that of the observed variables (Table 3). The interannual variations of seasonal mean SST and low-level VOR averaged over the respective MDR of each basin are reproduced well in the model, and the correlations between the ensemble means and the observations are around 0.80 (Table 4). As one of the dominant factors modulating the variations of WNP TC activity associated with ENSO (Camargo et al., 2007), the prediction skill for locally averaged VWS over the entire WNP is relatively low (with the correlation coefficient being 0.33).

      WNPWNP-EWNP-WENPNA
      MDR SST0.790.740.770.880.77
      MDR VWS0.330.670.470.780.62
      MDR VOR0.810.840.680.080.45
      MDR RH0.640.610.630.180.23

      Table 4.  Correlation coefficients between predicted and observed large-scale environmental factors during the MJJASON season of 1982–2019. The correlation skills are calculated based on ensemble-mean forecasts. The values in bold indicate that the correlation coefficients are statistically significant at the 95% confidence level as examined the one-tailed Student’s t test.

      Zhan et al. (2011) demonstrated that EIO SSTA is one of the factors that modulate seasonal WNP TC frequency. In the observations, the correlation coefficient between interannual variations of the seasonal mean EIO SSTAs and TC genesis frequency during 1982–2019 is 0.57 (Table 3). The prediction skill for the EIO SSTA index is rather good, with the correlation coefficient reaching 0.61 (Fig. 5b). However, the relationship between EIO SSTA and seasonal TC frequency in NUIST-CFS1.0 is not significant (the average correlation coefficient of individual members is 0.25).

      The years with EIO SSTA greater (less) than one standard deviation in both the observations and each member of NUIST-CFS1.0 are selected as strong warm (cold) EIO years. Figure 6 shows the differences of the observed and predicted 850-hPa wind and sea level pressure between the warm and cold EIO years, respectively. It is found that the lower troposphere is dominated by an anomalous anticyclonic circulation over the WNP. In the model, the anomalous equatorial easterlies over the South China Sea and WNP driven by the anomalous convective heating over the EIO are more obvious. Compared with observations, the anomalous anticyclonic circulation in the model does not cover the main WNP TC genesis region and it is located further northward and eastward (Fig. 1b). Therefore, the relationship between EIO SSTA and seasonal TC frequency in model hindcasts is weak. This may be one of the reasons for the poor performance of NUIST-CFS1.0 in forecasting annual variations of TC frequency.

      Figure 6.  Composite differences of the (a) observed and (b) NUIST-CFS1.0-predicted 850-hPa wind (vector, units: m s−1,) and sea level pressure (shading, units: hPa) in warm and cold EIO years. Only significant sea level pressure differences are presented. Green vectors denote significant wind differences.

    • Since TC frequency over the WNP shows distinct regional features (Wang and Chan, 2002; Kim et al., 2010b) and the predictability sources of TC genesis in individual WNP subregions may differ from each other (Kim et al., 2010a; Lu et al., 2010), the performance in predicting TC activity in subregions over the WNP is assessed. Here, the longitude of 140°E is used to divide the WNP into the WNP-E and WNP-W, following Wang and Chan (2002). The ensemble mean of the hindcasts skillfully predicts the interannual variations of the seasonal TC frequency over the WNP subregions (Figs. 4c, e; Table 2). The correlation coefficients are 0.61 and 0.41 for the WNP-E and WNP-W, respectively. The model performs better for WNP-E ACE, with the correlation coefficient reaching 0.72, and relatively worse (0.23) for WNP-W ACE. NUIST-CFS1.0 can realistically reproduce most of the observed environment–TC correlations (Table 3) and capture the interannual variation characteristics of large-scale environmental indexes well (Table 4).

      As shown in Table 3, observed TC frequency over the WNP-E (WNP-W) is positively (negatively) correlated with the Niño-3.4 index, indicating great impacts from ENSO. During El Niño (La Niña) years, TC formation is enhanced (weakened) in the WNP-E (WNP-W) (Wang and Chan, 2002; Wang et al., 2019). Over the open ocean, TCs that form over the WNP-E tend to be longer and stronger. The seasonal variation of ACE in the WNP-E is closely related to the Niño-3.4 index, with the correlation coefficient being 0.74 in the observations and 0.64 in the model. The Niño-3.4 index could explain ~55% of observed WNP-E ACE variations. As suggested by Wang and Chan (2002), the enhanced TC formation in the WNP-E is attributed to the increase in low-level VOR generated by El Niño-induced equatorial westerlies. Observed WNP-E ACE also has a close relationship with 850-hPa VOR, as shown in Table 3. The good performance in predicting interannual variations of TC activity over the WNP-E by NUIST-CFS1.0 is attributed to the high skill for forecasting the Niño-3.4 index (Fig. 5a). However, in the WNP-W, the relationship between ACE and the Niño-3.4 index is not significant in either the observations or the model hindcasts, and thereby, interannual variations of ACE in this region are hard to capture in NUIST-CFS1.0.

      Apart from ENSO, the SSTA in the EIO also exerts statistically significant impacts on TC frequency over the WNP-E (Table 3). The correlation coefficient between WNP-E TC frequency and EIO SSTA is −0.43 in observations, while it is not significant in model hindcasts. However, the difference in correlation coefficients between model hindcasts and observations is not obvious. In addition, MDR SST, VWS, and low-level VOR are all significantly related to the interannual variation of seasonal TC frequency over the WNP-E. However, in the WNP-W, the local environmental factors are not significantly related to TC frequency, except for mid-level RH. Therefore, the interannual variations of TC activity over the WNP-W is harder to capture compared with those over the WNP-E. Moreover, it is found that the standard deviations of correlation coefficients between individual ensemble members and the SPRvERR are quite large over the WNP-W compared to those over the WNP-E (Table 2). In the WNP-E, the SPRvERR of predicted TC frequency is closer to one (after calibration), indicating the model uncertainty is well accounted for and the model predictions of TC frequency are more reliable. In the WNP-W, the ensemble spread is much larger than the RMSE (the SPRvERR equals 1.3 for predicted TC frequency) and the standard deviations of correlation coefficients are comparable to the average correlation coefficients. This indicates that there is an over-dispersion of TC frequency over the WNP-W in the model.

    • NUIST-CFS1.0 shows good performance in predicting the interannual variations of seasonal mean TC frequency over the ENP and NA (Table 5 and Figs. 7a, c). The correlation coefficients between observed and ensemble mean TC frequency are 0.58 and 0.54 in the ENP and NA, respectively. NUIST-CFS1.0 predictions of TC frequency over the NA are relatively more reliable, with the SPRvERR being one. Additionally, the multidecadal variations are captured well, especially in the ENP. Before 1994 and after 2012, TC genesis frequency over the ENP is above the climatological mean, while TCs occur less frequently during 1995–2011. The correlation coefficient of ACE in the ENP reaches 0.70 (Fig. 7b), indicating good performance by the model. The model also performs well in predicting NA ACE (Fig. 7d). The RMSEs for calibrated TC frequency in the ENP and NA are comparable to one standard deviation of the observed TC counts (Table 5). For ACE, the RMSE is less than one standard deviation of the observations, which means the prediction errors for ENP and NA ACE are acceptable. The correlation coefficients of individual members are also smaller than those of the ensemble means (Table 5).

      CC_MCC_IRMSESTDV_OSPRvERR
      TC frequency
      ENP0.580.39 ($ \pm $0.09)4.804.791.38
      NA0.540.41 ($ \pm $0.09)4.764.811.00
      ACE ($ {10}^{4}{\mathrm{k}\mathrm{t}}^{2} $)
      ENP0.700.48 ($ \pm $0.13)58.8782.511.28
      NA0.470.36 ($ \pm $0.09)65.7370.340.78

      Table 5.  Similar to Table 2, but for TC frequency over the ENP and NA.

      Figure 7.  Same as Fig. 4, but for model hindcasts in the (a, b) ENP and (c, d) NA, respectively.

      Seasonal TC activity over the ENP and NA is modulated by climate variabilities like ENSO (Camargo et al., 2010; Zhao et al., 2010). In addition, SST_REL is also found to be useful in predicting seasonal TC activity (see also Zhao et al., 2010; Vecchi et al., 2011). As shown in Table 6, the interannual variations of TC frequency and ACE in the ENP are significantly related to SST-based indexes (Niño-3.4 index, ENP SST_REL, and MDR SST) and MDR VWS. It is found that the observed correlations of TC frequency and ACE in the ENP with the corresponding climatic indexes are reproduced well by NUIST-CFS1.0. In particular, the hindcast skill for ENP MDR SST and VWS is fairly high (Table 4). As proposed by Zhao et al. (2010), the quality of the prediction of seasonal TC activity in a coupled atmosphere–ocean model depends largely on the model’s ability to predict the evolution of SST_REL. In NUIST-CFS1.0, SST_REL in the ENP is well forecasted, with the correlation coefficient between the predicted and observed values reaching 0.79 (Fig. 8a). However, the relationship between TC activity and ENP SST_REL in the model is significantly underestimated compared to observations. Figure 9 shows the correlation between the relative SST anomaly and ENP TC frequency in observations and the hindcasts. It is found that in observations, the strongest correlations are located near the western coast of Mexico and the ENP MDR region (Fig. 9a). In the model, the maximum correlations appear in the region of 10°S–30°N, 180°–100°W (Fig. 9b). Therefore, ENP SST_REL is not a major predictor for annual TC counts in the model.

      ENPNA
      OBSCFS1.0OBSCFS1.0
      Niño-3.40.460.51−0.32−0.56
      0.430.53−0.40−0.57
      SST_REL0.710.31*0.670.61
      0.680.33*0.640.61
      MDR SST0.520.410.650.46
      0.450.440.580.48
      MDR VWS−0.54−0.35−0.67−0.54
      −0.55−0.35−0.65−0.56
      MDR VOR0.100.120.430.56
      0.060.120.470.57
      MDR RH−0.050.160.330.56
      −0.100.160.370.52

      Table 6.  Similar to Table 3, but for the correlations in the ENP and NA. MDR SST, VWS, VOR, and RH are averaged over 7.5°–15°N, 160°–80°W in the ENP, and over 7.5°–22.5°N, 80°–20°W in the NA, respectively. SST_REL is defined as the difference in SST between the MDR of the ENP and NA and that in the global tropics (30°S–30°N).

      Figure 8.  Same as Fig. 4, but for the predictions of (a) ENP SST_REL anomaly and (b) NA SST_REL anomaly.

      Figure 9.  Spatial distributions of the correlation coefficients between SST_REL and ENP TC frequency in (a) observations and (b) NUIST-CFS1.0. The dotted areas denote correlation coefficients are statistically significant at the 95% confidence level.

      In the NA, the observed correlations between TC activity and corresponding climatic indexes are reproduced well in model hindcasts (Tables 4, 6, and Fig. 8b). In the model, TC frequency over the NA is significantly related to the Niño-3.4 index (the correlation coefficient is −0.56), while the correlation is not significant in observations (Table 6). As discussed in Wang and Lee (2009), TC activity over the NA is out of phase with that over the ENP, which is closely related to the factors of VWS and convective instability. In observations, the correlation coefficient of TC frequency between these two basins is −0.47. In the model, this out-of-phase relationship is well represented. The mean correlation coefficient of predicted TC frequency between the NA and ENP is −0.75, larger than that in observations. Figure 10 shows the regression of VWS onto the Niño-3.4 index and NA genesis density in observations and the model hindcasts. The spatial patterns of regressed VWS are quite similar between observations and the hindcasts. However, there is an absence of cyclogenesis over the Gulf of Mexico and the east coast of the United States in the hindcasts where VWS is negatively correlated with the Niño-3.4 index. TC genesis appears in the region where VWS is positively correlated with the Niño-3.4 index in the hindcasts. This could explain why the correlation between TC frequency and the ENSO index is much larger in NUIST-CFS1.0 than in observations.

      Figure 10.  Regression coefficients of the MJJASON VWS (shading) onto the time series of Niño-3.4 index during 1982–2019 in (a) observations and (b) the NUIST-CFS1.0 hindcasts. The regression coefficients in (b) are the mean values of the coefficients over all nine members. The purple contours denote genesis densities in the NA.

    5.   Discussion
    • The good performance in predicting tropical circulation and tropical SST-based variations such as ENSO, EIO SSTA, and SST_REL in NUIST CFS1.0 lays a foundation for the skillful prediction of TC activity variations. The model also captures the observed relationships between environmental factors and TC activity well. However, the model’s performance in predicting interannual variations of TC frequency and ACE in the NH can be further improved. As discussed in section 4.1, the predictability sources of TC activity vary between different subregions of the WNP. In the WNP-E, the SST-related indexes, such as the Niño-3.4 index and EIO SSTA, are identified as good predictors for TC frequency variations. However, in the WNP-W, the relationship between the Niño-3.4 index and TC frequency is not that significant. The eastward shift in the location of TC genesis in NUIST-CFS1.0 causes a higher correlation coefficient between the ENSO index and WNP TC frequency than that in observations. The results in section 4.2 also show that the decrease in TC activity over the Gulf of Mexico and the east coast of the United States in the model leads to a more significant relationship between NA TC frequency and the Niño-3.4 index than what is seen in observations. Therefore, the deviation of predicted TC genesis location may lead to the inaccurate TC–environment relationship, which is essential to achieving good prediction skill for interannual variations of TC activity.

    • The skill of NUIST-CFS1.0 in predicting seasonal TC activity is compared with the skill of other forecast systems built based on an ocean–atmosphere coupled model. These forecast systems are developed by the UK Met Office (Camp et al., 2015), Geophysical Fluid Dynamics Laboratory (GFDL) (Murakami et al., 2016), European Centre for Medium-Range Weather Forecasts (ECMWF) (Manganello et al., 2016), and Japan Meteorological Agency (JMA) (Takaya et al., 2010). The information for predicted seasonal TC activity in each forecast system is summarized in Table 7, and the correlation coefficients are presented in Fig. 11.

      Forecast systemResolutionPredicted yearsTarget monthsReferences
      Met Office GloSea4~120 km1996–20096–11Camp et al., 2015
      Met Office GloSea5~60 km1996–20096–11 Camp et al., 2015
      1992–20136–11
      GFDL FLOR~50 km1980–20157–11Murakami et al., 2016
      GFDL HiFLOR~25 km1980–20157–11 Murakami et al., 2016
      Project MinervaT319, ~60 km1980–20115–11Manganello et al., 2016
      JMA/MRI-CGCM~180 km1979–20066–10Takaya et al., 2010

      Table 7.  List of forecast systems that predict interannual variations of seasonal TC activity: approximate horizontal resolution of the atmospheric component, predicted years, target months, and references.

      Figure 11.  Correlation coefficients of seasonal TC frequency and ACE between observations and the model hindcasts. The models with low horizontal resolutions (> 100 km) are marked in red, and those with high horizontal resolutions are marked in blue (~50–60 km) or green (~25 km). Bold markers indicate the correlation coefficients are statistically significant.

      Similar to NUIST-CFS1.0, the interannual variation of seasonal TC frequency in the WNP is difficult to capture in the models with low horizontal resolutions (>100 km), such as the UK Met Office Global Seasonal Forecast System version 4 (GloSea4) and the JMA/Meteorological Research Institute (MRI) CGCM (Fig. 11a). For high-resolution models, the UK Met Office Global Seasonal Forecast System version 5 (GloSea5) and the experimental coupled prediction system based on ECMWF system 4 (Project Minerva) can predict the interannual variations of WNP TC frequency well. However, the correlation coefficients in the GFDL 50-km-resolution Forecast-oriented Low Ocean Resolution model (FLOR) and its 25-km-resolution version (HiFLOR) are not significant (0.13 for FLOR and 0.28 for HiFLOR). The skill in predicting seasonal ACE in the WNP for NUIST-CFS1.0 is comparable to the skill for the forecast systems listed in Table 7 (Fig. 11b). All the models can capture interannual variations of seasonal WNP ACE well, with the correlation coefficient for GloSea5 being the highest (>0.8).

      The correlation coefficients for ENP and NA TC frequency in NUIST-CFS1.0 are above 0.5, which are higher than those in GloSea4 (Fig. 11a). With a similar horizontal resolution, GloSea4 cannot capture the interannual variations of seasonal TC frequency over the ENP and NA. The skill in reproducing ENP and NA TC frequency for NUIST-CFS1.0 is comparable to the skill for the high-resolution models (GloSea5, FLOR, and HiFLOR). It is noteworthy that the correlation coefficients for ENP TC frequency during 1996–2009 from GloSea5 and the NA TC numbers in the T319 experiments of Minerva are not significant. NUIST-CFS1.0 also shows a relatively higher skill in predicting the seasonal ACE index over the ENP among the forecast systems listed here (Fig. 11b). The correlation coefficient for NA ACE in NUIST-CFS1.0 is higher than that in GloSea4 and is comparable to that in GloSea5 and Minerva. Additionally, FLOR and HiFLOR show better skill for NA ACE. In general, the prediction skill for TC frequency and ACE from NUIST-CFS1.0 is comparable to that in the other forecast systems.

    6.   Conclusions
    • This study assesses the skill in predicting seasonal TC activity in the NH during the MJJASON season of 1982–2019 for NUIST-CFS1.0 ensemble hindcasts. The overall distributions of predicted TC genesis and track densities are similar to observations. However, seasonal mean TC frequency and ACE are systematically underestimated in all basins due to the low resolution of the model. The location of the maximum concentration of TC genesis over the WNP in model hindcasts extends slightly further to the east. In the NA, the main center of cyclogenesis in model hindcasts is located far away from the west coast of Africa. There is a decrease in TC activity over the Gulf of Mexico and along the east coast of the United States in model hindcasts.

      NUIST CFS1.0 performs well in predicting the tropical climate. The interannual variations of tropical SST-based variables (such as ENSO, EIO SSTA, and SST_REL) are well captured, and the correlations between TC activity and corresponding climate indexes are well reproduced. NUIST CFS1.0 can skillfully predict the interannual variations in seasonal TC activity, especially in the NA and ENP. However, ENP SST_REL is not a major factor contributing to the annual TC count in the model. In the NA, TC frequency is significantly related to the Niño-3.4 index in the model hindcasts, while this relationship is not significant in observations. The difference may be caused by the absence of cyclogenesis in the Gulf of Mexico and along the east coast of the United States in the model hindcasts where VWS is negatively correlated with the Niño-3.4 index.

      The prediction of the interannual variations of seasonal TC frequency over the entire WNP is still challenging. NUIST-CFS1.0 shows low skill in reproducing the interannual variations of TC frequency in the WNP. Due to the weak relationship between the variations of TC frequency and the environmental variables, the model can hardly capture the interannual variations in seasonal TC frequency. In this study, EIO SSTA is the only factor that is significantly related to the observed seasonal WNP TC frequency. The skill in predicting EIO SSTA is fairly good, with the correlation coefficient skill reaching 0.64. However, the relationship between the EIO SSTA-based index and TC frequency in model hindcasts is not significant. The good performance for WNP ACE in NUIST-CFS1.0 may be attributed to the high hindcast skill for the ENSO index and associated influence. The performance in predicting the interannual variations of TC activity can be highly improved when the WNP is divided into the two subregions of WNP-E and WNP-W. The high skill in predicting the Niño-3.4 index by NUIST-CFS1.0 plays an important role in the good prediction of TC activity over the WNP-E. The prediction skill for TC activity over the WNP-W is relatively lower than for that over the WNP-E. As for the complex synoptic environment in East Asia, the internal variability in annual TC frequency may play a more important role in the WNP-W.

      The relationships between the environment and TC activity vary in different subregions of a basin. It is important to improve the simulation/prediction skill for TC genesis climatology, which is essential to the model capability for capturing the interannual variations of TC activity.

      Acknowledgements. This research was supported in part by the National Key Research and Development Program of China (Grant No. 2020YFA0608000) and the Nature Science Foundation of China (Grant Nos. 42005002, 42030605, and 42105003). We acknowledge the High-Performance Computing Center of Nanjing University of Information Science and Technology for its support of this work.

      Data availability statement. The IBTrACS data can be obtained from https://www.ncei.noaa.gov/products/international-best-track-archive?name=ib-v4-access. The JRA55 reanalysis data are available at https://rda.ucar.edu/datasets/ds628.1/. The OISST data can be downloaded from https://www.ncei.noaa.gov/products/optimum-interpolation-sst. The NUIST-CFS1.0 prediction dataset on which this paper is based is too large to be retained or publicly archived with available resources. Documentation and methods used to support this study are available from Jing-jia LUO (jingjia_luo@hotmail.com) at Nanjing University of Information Science and Technology, Nanjing, China. The TC dataset and model variables used in the paper are uploaded at https://pan.baidu.com/s/1dMLctISZ5AXFSh9sYxbe6g?pwd=P023.

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