Tropical cyclone genesis over the western North Pacific simulated by Coupled Model Intercomparison Project Phase 6 models
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Abstract: Threatening millions of people and causing billions of dollars in losses, tropical cyclones (TCs) are among the most severe natural hazards in the world, especially over the western North Pacific. However, the response of TCs to a warming or changing climate has been the subject of considerable research, often with conflicting results. In this study, the abilities of Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) models to simulate TC genesis are assessed through historical simulations. The results indicate that a systematic humidity bias persists in most CMIP6 models from corresponding CMIP Phase 5 models, which leads to an overestimation of climatological TC genesis. However, the annual cycle of TC genesis is well captured by CMIP6 models. The abilities of 25 models to simulate the geographical patterns of TC genesis vary significantly. In addition, seven models are identified as well simulated models, but seven models are identified as poorly simulated ones. A comparison of the environmental variables for TC genesis in the well-simulated group and the poorly simulated group identifies moisture in the mid-troposphere as a key factor in the realistic simulation of El Niño-Southern Oscillation (ENSO) impacts on TC genesis. In contrast with the observations, the poorly simulated group does not reproduce the suppressing effect of negative moisture anomalies on TC genesis in the northwestern region (20°–30°N, 120°–145°E) during El Niño years. Given the interaction between TC and ENSO, these results provide a guidance for future TC projections under climate change by CMIP6 models.
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Key words:
- CMIP6 /
- tropical cyclone /
- genesis potential index /
- relative humidity
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Figure 1. The observed and simulated climatological TC genesis during the period of 1980–2014 over the WNP. a. Results from IBTrACS. Black dots indicate TC genesis locations and colors represent TCGN in a regular grid with a horizontal resolution of 2.5°×2.5° (longitude×latitude). b. GPI derived using ERSSTv5 and NCEP/NCAR reanalysis. R represents the CPCC between the observation and simulation, which passes the two-tailed Student’s t-test at p = 0.01.
Figure 2. The observed and simulated annual cycle of TCGN in the period of 1980–2014 over the WNP. Red line represents the results from IBTrACS, and blue line represents the GPI derived using ERSSTv5 and NCEP/NCAR reanalysis. R is the Pearson linear correlation coefficient, which passes the two-tailed Student’s t-test at p = 0.01.
Figure 3. TC genesis during El Niño and La Niña years over the WNP. a. Results from IBTrACS. Red dots and blue dots indicate TC genesis locations in El Niño and La Niña years, respectively. b. Difference in the GPI (derived using ERSSTv5 and NCEP/NCAR reanalysis) between El Niño and La Niña years. Shaded areas represent the 99% confidence level according to the two-tailed Student’s t-test. Zonal gray line at 20°N and meridional line at 145°E divide the WNP into four subregions.
Figure 4. Cumulative distribution function of the longitude (a) and latitude (b) of TC genesis locations over the WNP. Blue and red lines represent the results of La Niña (LN) and El Niño (EN) years, respectively. The observed TC genesis is shown in solid lines and the observed GPIs (derived using ERSSTv5 and NCEP/NCAR reanalysis) are shown in dashed lines.
Figure 5. The simulated climatological GPIs over the WNP in CMIP6 models. a. Multi-model ensemble (MME) of 25 CMIP6 models and b–z. simulations in each of 25 CMIP6 models. The skill score of each model is shown on the upper-right corner from a to z.
Figure 7. The impacts of the
$ {H}_{600} $ bias on the GPI. a. Ensemble mean impact of 25 CMIP6 models and b–z. impacts for each of 25 CMIP6 models. The impacts shown by shaded areas is statistically significant at the 99% confidence level.
Figure 8. Annual cycle of the observed GPI (thick black line) and the simulated GPIs in 25 CMIP6 models. All GPIs are normalized by subtracting the corresponding climatological mean and dividing by the standard deviation. Numbers in the legend are the serial numbers corresponding to the 25 CMIP6 models in Table 1.
Figure 9. Differences in GPI between El Niño and La Niña years simulated in WMME (a) and PMME (b). Shaded areas represent the 99% confidence level according to the two-tailed Student’s t-test. Contours indicate the difference in the observed GPI (Fig. 3b) between El Niño and La Niña years.
Figure 10. Difference in GPI composites during El Niño and La Niña years caused by varying each variable in Eq. (1). Rows 1–4 show the contribution of low-level vorticity, vertical wind shear, humidity in the mid-troposphere, and potential intensity, respectively. Left column shows the results obtained with ERSSTv5 and NCEP/NCAR reanalysis. Middle column shows the results from the multi-model mean of the well-simulated group. Right column shows the results from the multi-model mean of the poorly simulated group. Shaded areas represent the 99% confidence level according to the two-tailed Student’s t-test. For each panel in the middle and right columns, the CPCCs between the panel and the corresponding observations (the panel on the left column) are shown (purple) in the upper-right corner. For example, the CPCC in b is the correlation coefficient between the pattern in b and the pattern in a.
Figure 11. Differences in SST (a, b), SAT (c, d) and CP (e, f) between El Niño and La Niña years simulated in WMME (a, c, e) and PMME (b, d, f). Shaded areas represent the 99% confidence level according to the two-tailed Student’s t-test. Contours indicate the differences in the SST from ERSSTv5, SAT from NCEP/NCAR reanalysis, and CP from ERA5 between El Niño and La Niña years. The CPCCs between the simulations and the observations are shown (purple) in the upper-right corner.
Table 1. List of the 25 CMIP6 models employed in this study
No. Model Institute ID Resolution (number of grids, lon×lat) Atmosphere Ocean 1 ACCESS-CM2 CSIRO-ARCCSS 192×144 360×300 2 ACCESS-ESM1-5 CSIRO 192×145 360×300 3 BCC-CSM2-MR BCC 320×160 360×232 4 BCC-ESM1 BCC 128×64 360×232 5 CAMS-CSM1-0 CAMS 320×160 360×200 6 CanESM5 CCCma 128×64 361×290 7 CAS-ESM2-0 CAS 256×128 362×196 8 CESM2 NCAR 288×192 320×384 9 CESM2-FV2 NCAR 144×96 320×384 10 CESM2-WACCM NCAR 288×192 320×384 11 CESM2-WACCM-FV2 NCAR 144×96 320×384 12 CMCC-CM2-SR5 CMCC 288×192 362×292 13 FGOALS-g3 CAS 180×80 360×218 14 FIO-ESM-2-0 FIO-QLNM 192×288 320×384 15 GISS-E2-1-G NASA-GISS 144×90 360×180 16 GISS-E2-1-H NASA-GISS 144×90 360×180 17 MCM-UA-1-0 UA 96×80 192×80 18 MIROC6 MIROC 256×128 360×256 19 MPI-ESM1-2-HR MPI-M DWD DKRZ 384×192 802×404 20 MRI-ESM2-0 MRI 320×160 360×364 21 NESM3 NUIST 192×96 384×362 22 NorESM2-LM NCC 144×96 360×384 23 NorESM2-MM NCC 288×192 360×384 24 SAM0-UNICON SNU 288×192 320×384 25 TaiESM1 AS-RCEC 288×192 320×384 Note: CSIRO-ARCCSS: Commonwealth Scientific and Industrial Research Organisation-Australian Research Council Centre of Excellence for Climate System Science; CSIRO: Commonwealth Scientific and Industrial Research Organisation; BCC: Beijing Climate Center; CAMS: Chinese Academy of Meteorological Sciences; CCCma: Canadian Centre for Climate Modelling and Analysis; CAS: Chinese Academy of Sciences; NCAR: National Center for Atmospheric Research; CMCC: Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici; FIO-QLNM: First Institute of Oceanography-Ministry of Natural Resources, Qingdao National Laboratory for Marine Science and Technology; NASA-GISS: National Aeronautics and Space Administration-Goddard Institute for Space Studies; UA: University of Arizona-Department of Geosciences; MIROC: Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, RIKEN Center for Computational Science; MPI-M DWD DKRZ: Max Planck Institute for Meteorology, German Meteorological Service, German Climate Computing Center; MRI: Meteorological Research Institute; NUIST: Nanjing University of Information Science and Technology; NCC: Norwegian Climate Centre; SNU: Seoul National University; AS-RCEC: Academia Sinica-Research Center for Environmental Changes. 
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Table 2. Centered pattern correlation coefficients (CPCCs) of 25 CMIP6 models with the observed GPIs
Evaluation Model CPCC Well-simulated TaiESM1 0.94 ACCESS-CM2 0.93 FIO-ESM-2-0 0.89 BCC-CSM2-MR 0.88 CESM2-WACCM 0.86 CMCC-CM2-SR5 0.85 NorESM2-LM 0.85 Moderately simulated CESM2 0.83 BCC-ESM1 0.83 CAMS-CSM1-0 0.82 FGOALS-g3 0.81 NorESM2-MM 0.80 SAM0-UNICON 0.79 CESM2-WACCM-FV2 0.79 CESM2-FV2 0.77 ACCESS-ESM1-5 0.77 MIROC6 0.75 CAS-ESM2-0 0.72 Poorly simulated MRI-ESM2-0 0.71 MPI-ESM1-2-HR 0.71 GISS-E2-1-H 0.70 GISS-E2-1-G 0.69 CanESM5 0.65 MCM-UA-1-0 0.51 NESM3 0.48 Note: All CPCCs depict statistical significance based on a two-tailed Student’s t-test (>99%).
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Table 3. R2 difference among CPCCs of WMME, MME, and PMME.
WMME MME PMME WMME (0.93) 0 0.03 0.35 MME (0.91) −0.03 0 0.31 PMME (0.72) −0.35 −0.31 0 Note: Values in bold font were applied to statistically significant differences (two-tailed, p<0.01) based on the Hotelling’s T-squared test and Steiger’s Z-test.
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