Kai Liu, Kang Xu, Tongxin Han, Congwen Zhu, Nina Li, Anboyu Guo, Xiaolu Huang. Evaluation and projection of marine heatwaves in the South China Sea: insights from CMIP6 multi-model ensemble[J]. Acta Oceanologica Sinica.
Citation:
Kai Liu, Kang Xu, Tongxin Han, Congwen Zhu, Nina Li, Anboyu Guo, Xiaolu Huang. Evaluation and projection of marine heatwaves in the South China Sea: insights from CMIP6 multi-model ensemble[J]. Acta Oceanologica Sinica.
Kai Liu, Kang Xu, Tongxin Han, Congwen Zhu, Nina Li, Anboyu Guo, Xiaolu Huang. Evaluation and projection of marine heatwaves in the South China Sea: insights from CMIP6 multi-model ensemble[J]. Acta Oceanologica Sinica.
Citation:
Kai Liu, Kang Xu, Tongxin Han, Congwen Zhu, Nina Li, Anboyu Guo, Xiaolu Huang. Evaluation and projection of marine heatwaves in the South China Sea: insights from CMIP6 multi-model ensemble[J]. Acta Oceanologica Sinica.
National Marine Environmental Forecasting Center, Beijing 100081, China
2.
State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
3.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
4.
National Meteorological Center, China Meteorological Administration, Beijing 100081, China
5.
Inner Mongolia Autonomous Region Meteorological Observatory, Huhhot 010051, China
Funds:
National Natural Science Foundation of China (42275024), the Key R&D Program of China (2022YFE0203500), the Guangdong Basic and Applied Basic Research Foundation (2023B1515020009, 2022B1212050003), the Youth Innovation Promotion Association CAS (2020340), the special fund of South China Sea Institute of Oceanology of the Chinese Academy of Sciences (SCSIO2023QY01), the Rising Star Foundation of the SCSIO (NHXX2018WL0201), and the Independent Research Project Program of LTO (LTOZZ2101).
This study evaluates the performance of 16 models sourced from the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating marine heatwaves (MHWs) in the South China Sea (SCS) during the historical period (1982−2014), and also investigates future changes in SCS MHWs based on simulations from three Shared Socioeconomic Pathway (SSP) scenarios (SSP126, SSP245, and SSP585) using CMIP6 models. Results demonstrate that the CMIP6 models perform well in simulating the spatial-temporal distribution and intensity of SCS MHWs, with their multi-model ensemble (MME) results showing the best performance. The reasonable agreement between the observations and CMIP6 MME reveals that the increasing trends of SCS MHWs are attributed to the warming sea surface temperature trend. Under various SSP scenarios, the year 2040 emerges as pivotal juncture for future shifts in SCS MHWs, marked by distinct variations in changing rate and amplitudes. This is characterized by an accelerated decrease in MHWs frequency and a notably heightened increase in mean intensity, duration, and total days after 2040. Furthermore, the projection results for SCS MHWs suggest that the spatial pattern of MHWs remains consistent across future periods. However, the intensity shows higher consistency only during the near-term period (2021−2050), while notable inconsistencies are observed during the medium-term (2041−2700) and long-term (2701−2100) periods under the three SSP scenarios. During the near-term period, the SCS MHWs are characterized by moderate and strong events with high frequencies and relatively shorter durations. In contrast, during the medium-term period, MHWs are also characterized by moderate and strong events, but with longer-lasting and more intense events under the SSP245 and SSP585 scenarios. However, in the long-term period, extreme MHWs become the dominant feature under the SSP585 scenario, indicating a substantial intensification of SCS MHWs, effectively establishing a near-permanent state.
Figure 1. Characteristics of the MHWs in the SCS during 1982–2014 from NOAA OISST data and the MME mean results based on the historical runs of the CMIP6 models. The spatial distributions of multiyear average (a) number of MHWs (MHWN; units: times), (b) MHWs intensity (MHWI; units: ℃/time), (c) MHWs duration (MHWD; units: days/time), and (d) annual total number of MHWs days (MHWT; units: days) in the SCS during 1982–2014. The solid gray grid lines in Figs 1a-d represent the center exceeding 2.7 times, 1.5 ℃/time, 12 days/time, and 33 days/year, respectively. Figs 1e-h are the same as Figs 1a-1d, but for CMIP6 MME results. The solid gray grid lines represent regions where the root mean squared error of the MME result is less than 20% relative to the observations.
Figure 2. Statistical metrics (MHWN, MHWI, MHWD, and MHWT) in the SCS MHWs based on the participating CMIP6 members versus the observations during the period of 1982–2014. COR is the pattern correlation coefficient, RSD is the ratio of standard deviation, $ {\text{RMSE}}^{\text{'}} $is the relative root mean squared error and IVS is the interannual variability skill score. See text for details.
Figure 3. Time series of the (a) MHWN (units: times), (b) MHWI (units: ℃/time), (c) MHWD (units: days/time), and (d) MHWT (units: days) averaged over the SCS (0°–25°N, 100°–125°E). The black (red) solid curves represent the results of the observations (the CMIP6 MME), and the black (red) dotted lines indicate the corresponding linear trends of the observations (the CMIP6 MME). The light red shadings denote the spread of 16 CMIP6 models included in the ensemble mean. The linear regression coefficients (Trend), correlation coefficients (R), and associated significance of p-values are shown on the top in each panel.
Figure 4. Inter-model relationships of the CMIP6 models between the SSTA trend and the trend of the SCS MHWs. Scatterplots of the SCS SSTA trend (units: ℃/decade) vs the SCS (a) MHWN trend (units: times/decade), (b) MHWI trend (units: (℃/time)/decade), (c) MHWD trend (units: (days/time)/decade), and (d) MHWT trend (units: days/decade). The colorful dots represent the results of 16 CMIP6 models, and the red star (black square) denotes the result of the observations (the CMIP6 MME). The black solid line denotes the best-fit line for the CMIP6 models based on linear regression, and the linear regression equation and its associated correlation coefficients, as well as the significance of p-values are shown on the left top of each panel.
Figure 5. Time series of the CMIP6 simulated SCS MHWs metrics from the historical and future (SSP) scenarios of global warming relative to 1985-2014. (a) MHWN (units: times), (b) MHWI (units: ℃/time), (c) MHWD (units: days/time), and (d) MHWT (units: days). The black, blue, orange, and red curves denote the MME results of the CMIP6 historical runs, SSP126, SSP245, and SSP585 scenarios, and the light black, blue, orange, and red shadings represent the corresponding spreads of CMIP6 models, respectively.
Figure 6. Boxplots of the CMIP6 projected SCS MHWs metrics including (a) MHWN (units: times), (b) MHWI (units: ℃/time), (c) MHWD (units: days/time), and (d) MHWT (units: days) during the near-term future (2021-2050), medium-term future (2041-2070) , and long-term future (2071-2100) for different levels of global warming. The blue, orange, and red boxplots represent the results under the SSP126, SSP245, and SSP585 scenarios, respectively. The box-and-whisker plots show the minimum value, 25th percentile, median value, 75th percentile and maximum value of each metrics of MHWs.
Figure 7. Pie charts showing the total days of the moderate, strong, severe, and extreme SCS MHWs in percentage during the (a) near-term future (2021-2050), (b) middle-term future (2041-2070) and (c) long-term future (2071-2100) under the SSP126 scenario. (d-f) and (g-i) same as (a-b), but for the SSP245 and SSP585 scenarios, respectively.
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