Eastern China suffered a record‐breaking heat wave throughout almost all of summer, during 2013. Recent studies found a discernible impact of anthropogenic forcing on this extreme heat wave event. In this study, we investigate the role of multidecadal variability (MDV) in regulating the likelihood of the 2013 heat wave event. Using the ensemble empirical mode decomposition (EEMD) method, we decomposed the heat wave index series into three components: (1) a non‐linear secular trend (ST), representing the long‐term anthropogenic warming; (2) MDV; and (3) the residual high‐frequency variability. For the period 1873–2013, the linear trend of heat wave strength (HW‐S, defined as the July–August mean temperatures) was 0.06 °C per decade, mainly because of the contribution of ST. MDV plays an important role in regulating changes over decades. ST and MDV together form a changing climate background (CB) for extreme events. The 2013 heat wave event would be almost impossible under the pre‐industrial CB but becomes likely under the long‐term warming background, with a return period of longer than 103 years. When the positive phase of MDV is added to the secular warming background (the current CB), the return period of a super‐heat wave such as the 2013 event becomes about 42 (12–103) years. The Atlantic Multidecadal Oscillation (AMO) shows significant correlation with the MDV component of the HW‐S in Shanghai. The correlation pattern between the AMO and MDV of July–August temperatures over the Northern Hemisphere is analysed to explain the AMO–Shanghai relationship. It is suggested that the likelihood of such an extreme event will increase with further long‐term climate warming, modified by low‐frequency oceanic variations such as the AMO.
Tropical cyclones (TCs) are natural disasters for coastal regions. TCs with maximum wind speeds higher than 32.7 m/s in the north-western Pacific are referred to as typhoons. Typhoons Sarika and Haima successively passed our moored observation array in the northern South China Sea in 2016. Based on the satellite data, the winds (clouds and rainfall) biased to the right (left) sides of the typhoon tracks. Sarika and Haima cooled the sea surface ~4 and ~2 °C and increased the salinity ~1.2 and ~0.6 psu, respectively. The maximum sea surface cooling occurred nearly one day after the two typhoons. Station 2 (S2) was on left side of Sarika’s track and right side of Haima’s track, which is studied because its data was complete. Strong near-inertial currents from the ocean surface toward the bottom were generated at S2, with a maximum mixed-layer speed of ~80 cm/s. The current spectrum also shows weak signal at twice the inertial frequency (2f). Sarika deepened the mixed layer, cooled the sea surface, but warmed the subsurface by ~1 °C. Haima subsequently pushed the subsurface warming anomaly into deeper ocean, causing a temperature increase of ~1.8 °C therein. Sarika and Haima successively increased the heat content anomaly upper than 160 m at S2 to ~50 and ~100 m°C, respectively. Model simulation of the two typhoons shows that mixing and horizontal advection caused surface ocean cooling, mixing and downwelling caused subsurface warming, while downwelling warmed the deeper ocean. It indicates that Sarika and Haima sequentially modulated warm water into deeper ocean and influenced internal ocean heat budget. Upper ocean salinity response was similar to temperature, except that rainfall refreshed sea surface and caused a successive salinity decrease of ~0.03 and ~0.1 psu during the two typhoons, changing the positive subsurface salinity anomaly to negative
Fine-scale parameterizations based on shear and stratification are widely used to study the intensity and spatial distribution of turbulent diapycnal mixing in the ocean. Two well-known fine-scale parameterizations, Gregg–Henyey–Polzin (GHP) parameterization and MacKinnon–Gregg (MG) parameterization, are assessed with the full-depth microstructure data obtained in the North Pacific. The GHP parameterization commonly used in the open ocean succeeds in reproducing the dissipation rates over smooth topography but fails to predict the turbulence over rough topography. Failure of GHP parameterization over rough topography is attributed to the deviation of internal wave spectrum from the Garrett–Munk (GM) spectrum. The internal wave field over rough topography is characterized by energetic intermediate-scale and small-scale internal waves that are not described well by the GM model. The MG parameterization that is widely used in coastal environments is found to be successful in reproducing the dissipation rates over both smooth and rough topographies. The efficacy of GHP and MG parameterizations in evaluating the dissipation rates has been assessed. The result indicates that MG parameterization predicts the magnitude and variability of the dissipation rates better than the GHP parameterization.
Measurements of turbulence in the deep ocean, particularly close to the bottom, are extremely sparse because of the difficulty and operational risk of obtaining deep profiles near the seafloor. A newly developed expendable instrument—the VMP-X (Vertical Microstructure Profiler–Expendable)—carries two microstructure shear probes to measure the fluctuations of vertical shear into the dissipation range and can profile down to a depth of 6000 m. Data from nine VMP-X profiles in the western Pacific Ocean near 11.6°N over rough topography display bottom-intensified turbulence with dissipation rates increasing by two factors of 10 to 4 W kg−1 within 200 m above the bottom. In contrast, over smooth topography in the southern South China Sea near 11°N, three profiles show that turbulence in the bottom boundary layer increases only slightly, with dissipation rates reaching 1 W kg−1. The eddy diffusivity over rough topography reached to 5 m2 s−1. The average diffusivity over all depths was 0.3 and 0.9 m2 s−1 for the tests in the southern South China Sea and in the western Pacific Ocean, respectively, and these values are much larger than previous estimates of less than ≈0.1 m2 s−1 for the main thermocline.
It is important to project the changes in extreme temperature in Eurasia, where more than two‐thirds of the world's population reside. Employing Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations and extreme temperature indices defined by the expert team on climate change detection and Indices, we firstly evaluate the performance of the CMIP6 models, and then project the spatial patterns of changes in extreme temperature in different periods under shared social‐economic Pathway scenarios and at different global warming levels. The results show that the performance of the CMIP6 models in simulating the indices of the coldest day (TXn), the coldest night (TNn), summer days (SU), tropical nights (TR) and frost days (FD) are good. Therefore, these five indices were selected for projection. Overall, TXn, TNn, SU and TR show an increasing trend and FD a decreasing trend, consistent with global warming in the future. The responses to global warming tend to be strongest in high latitudes for TXn and TNn, in high latitudes and high‐altitude areas for FD, and in some low‐latitude areas for SU and TR. At the local scale over Eurasia, where the change is larger than the regional median level, the changes in extreme temperature indices at 1.5°C of global warming above pre‐industrial levels are projected to be reduced by 30–55% and 55–85%, respectively, compared with the situation at 2.0°C and 3.0 warming. If global warming could be controlled to within 2.0°C, the changes in extreme temperature indices over Eurasia would be reduced by up to 60% compared with the situation at 3.0°C warming. Therefore, if global warming can be controlled to within a low warming target, the risk of extreme temperature change will be greatly reduced in these regions.
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