Long-term ocean temperature trend and marine heatwaves

Marine heatwaves(MHWs)can cause irreversible damage to marine ecosystems and livelihoods.Appropriate MHW characterization remains difficult,because the choice of a sea surface temperature(SST)temporal baseline strongly influences MHW identification.Following a recent work suggesting that there should be a communicating baseline for long-term ocean temperature trends(LTT)and MHWs,we provided an effective and quantitative solution to calculate LTT and MHWs simultaneously by using the ensemble empirical mode decomposition(EEMD)method.The long-term nonlinear trend of SST obtained by EEMD shows superiority over the traditional linear trend in that the data extension does not alter prior results.The MHWs identified from the detrended SST data exhibited low sensitivity to the baseline choice,demonstrating the robustness of our method.We also derived the total heat exposure(THE)by combining LTT and MHWs.The THE was sensitive to the fixed-period baseline choice,with a response to increasing SST that depended on the onset time of a perpetual MHW state(identified MHW days equal to the year length).Subtropical areas,the Indian Ocean,and part of the Southern Ocean were most sensitive to the long-term global warming trend.

Simulations and Projections of Winter Sea Ice in the Barents Sea by CMIP6 Climate Models

Dramatic changes in the sea ice characteristics in the Barents Sea have potential consequences for the weather and climate systems of mid-latitude continents,Arctic ecosystems,and fisheries,as well as Arctic maritime navigation.Simulations and projections of winter sea ice in the Barents Sea based on the latest 41 climate models from the Coupled Model Intercomparison Project Phase 6(CMIP6)are investigated in this study.Results show that most CMIP6 models overestimate winter sea ice in the Barents Sea and underestimate its decreasing trend.The discrepancy is mainly attributed to the simulation bias towards an overly weak ocean heat transport through the Barents Sea Opening and the underestimation of its increasing trend.The methods of observation-based model selection and emergent constraint were used to project future winter sea ice changes in the Barents Sea.Projections indicate that sea ice in the Barents Sea will continue to decline in a warming climate and that a winter ice-free Barents Sea will occur for the first time during 2042-2089 under the Shared Socioeconomic Pathway 585(SSP5-8.5).Even in the observation-based selected models,the sensitivity of winter sea ice in the Barents Sea to global warming is weaker than observed,indicating that a winter ice-free Barents Sea might occur earlier than projected by the CMIP6 simulations.

The response of the upper ocean to tropical cyclone Viyaru over the Bay of Bengal

Better forecast of tropical cyclone(TC) can help to reduce risk and enhance management. The TC forecast depends on the scientific understanding of oceanic processes, air-sea interaction and finally, the atmospheric process. The TC Viyaru is taken as an example, which is formed at the end of 11 May 2013 and sustains up to 17 May 2013 during pre-monsoon season. Argo data are used to investigate ocean response processes by comparing pre-and post-conditions of the TC. Eight oceanic parameters including the sea surface temperature(SST), the sea surface salinity(SSS), and the barrier layer thickness(BLT), the 26°C isotherm depth in the ocean(D26), the isothermal layer depth(ILD), the mixed layer depth(MLD), the tropical cyclone heat potential(TCHP) and the effective oceanic layer for cyclogenesis(EOLC) are chosen to evaluate the pre-and post-conditions of the TC along the track of Viyaru. The values of the SST, D26, BLT, TCHP and EOLC in the pre-cyclonic condition are higher than the post-cyclonic condition, while the SSS, ILD and MLD in the post-cyclonic condition are higher than the pre-cyclonic condition of the ocean due to strong cyclonic winds and subsurface upwelling. It is interesting that the strong intensity of the TC reduces less SST and vice versa. The satisfied real time Argo data is not available in the northern Bay of Bengal especially in the coastal region. A weather research and forecasting model is employed to hindcast the track of Viyaru, and the satellite data from the National Center Environmental Prediction are used to assess the hindcast.

A model for the transportation and distribution of jellyfish Rhopilema esculentum for stock enhancement in the Liaodong Bay, China

A numerical model for jellyfish Rhopilema esculentum stock enhancement is developed for the first time. The model is based on an operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas and uses a Lagrangian particle-tracking scheme to track the trajectories of released jellyfish. The Jellyfish are modeled as particles with diel vertical migration and are passively drifted by the current and dispersion due to the sub-grid processes. A comparison between the simulation and survey results demonstrate that the model can capture the primary distribution patterns of the released jellyfish. The model results show that the ocean current and indirect wind impact are the main drivers controlling the jellyfish transport. A connectivity matrix between the release sites and fishing grounds indicates the top of the bay is better than the eastern and western coasts for jellyfish fishing. The matrix also shows that only 45% and 27% of the jellyfish released from Wafangdian(WFD) can enter the fishing ground in 2008 and 2010; thus, the site near WFD is not an advisable location for jellyfish release. A Lagrangian probability density function based on a nine-year tracing experiment validates the results and further provides a "climatology" distribution of the released jellyfish.Several experiments are conducted to examine the sensitivity of the model to random walk schemes and to release conditions. The model requires a random walk but is insensitive to the random walk scheme. The experiments with different habitat depths show that if the jellyfish are fixed on the bottom of the water, most of them will be transported to the center, or even out of the bay, by the bottom circulation.

China published ocean forecasting system for the 21st-Century Maritime Silk Road on December 10, 2018

1 The unique ocean forecasting system (OFS) based on FIO-COM The OFS is based on the surface wave-tide-circulation coupled ocean model developed by the First Institute of Oceanography (FIO-COM), Ministry of Natural Resources, China. The half-century challenge that ocean circulation models must address is that the forecasting/simulated sea surface temperature overheats while the sub-surface temperature is too cold, especially during the summer. Qiao et al.(2004, 2010, 2016) found that the non-breaking surface wave can generate turbulence through wave-turbulence interaction, and they developed the wave-induced mixing theory, which has been confirmed by observations, laboratory experiments and model numerical simulations. As validated by ocean circulation models from various research groups, including Geophysical Fluid Dynamics Laboratory (GFDL) in the US (Fan and Griffies, 2014), Uppsala University of Sweden (Wu et al., 2015), Laboratoire d’Etudes en Geophysique et Oceanographie Spatiale (LEGOS) in France (Malek and Babanin, 2014), Budapest University of Technology and Economics in Hungary (Peter and Kramer, 2016) and the Ocean University of China (Lin et al., 2006), the non-breaking surface waveinduced vertical mixing (Bv) can always dramatically improve the simulation capacity of various ocean circulation models. The First Institute of Oceanography (FIO) research group found that tidal-induced mixing plays a key role in the formation of coastal upwelling, in the bottom mixed layer and in areas with sea mounts (Lv et al., 2006, 2008). With the above breakthroughs, the first surface wavetide-circulation coupled model of FIO-COM was developed in 2013. It was adopted to produce a reanalysis dataset for the period of January 2014 to April 2016, and it has been used for the operational OFS since May 2016. A highly efficient parallel scheme was designed to use the full capacity of Taihu Light with 10 649 600 CPU cores (Qiao et al., 2016), which earned a finalist nomination for the international Association for Computing Machinery (ACM) Gordon Bell Prize.

Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.

FIO-ESM v2.0 CORE2-forced experiment for the CMIP6 Ocean Model Intercomparison Project

We introduced the Coupled Model Intercomparison Project Phase 6(CMIP6)Ocean Model Intercomparison Project CORE2-forced(OMIP-1)experiment by using the First Institute of Oceanography Earth System Model version 2.0(FIO-ESM v2.0),and comprehensively evaluated the simulation results.Unlike other OMIP models,FIO-ESM v2.0 includes a coupled ocean surface wave component model that takes into account non-breaking surface wave-induced vertical mixing in the ocean and effect of surface wave Stokes drift on air-sea momentum and heat fluxes in the climate system.A sub-layer sea surface temperature(SST)diurnal cycle parameterization was also employed to take into account effect of SST diurnal cycle on air-sea heat fluxes to improve simulations of air-sea interactions.Evaluations show that mean values and long-term trends of significant wave height were adequately reproduced in the FIO-ESM v2.0 OMIP-1 simulations,and there is a reasonable fit between the SST diurnal cycle obtained from in situ observations and that parameterized by FIO-ESM v2.0.Evaluations of model drift,temperature,salinity,mixed layer depth,and the Atlantic Meridional Overturning Circulation show that the model performs well in the FIO-ESM v2.0 OMIP-1 simulation.However,the summer sea ice extent of the Arctic and Antarctic is underestimated.

The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018

Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships,while limited by the capability of climate models.A bias correction methodology in this study was proposed and performed on raw products from two climate models,the First Institute Oceanography Earth System Model(FIOESM)and the National Centers for Environmental Prediction(NCEP)Climate Forecast System(CFS),to improve 60 days predictions for Arctic sea ice.Both models were initialized on July 1,August 1,and September 1 in 2018.A 60-day forecast was conducted as a part of the official sea ice service,especially for the ninth Chinese National Arctic Research Expedition(CHINARE)and the China Ocean Shipping(Group)Company(COSCO)Northeast Passage voyages during the summer of 2018.The results indicated that raw products from FIOESM underestimated sea ice concentration(SIC)overall,with a mean bias of SIC up to 30%.Bias correction resulted in a 27%improvement in the Root Mean Square Error(RMSE)of SIC and a 10%improvement in the Integrated Ice Edge Error(IIEE)of sea ice edge(SIE).For the CFS,the SIE overestimation in the marginal ice zone was the dominant features of raw products.Bias correction provided a 7%reduction in the RMSE of SIC and a 17%reduction in the IIEE of SIE.In terms of sea ice extent,FIOESM projected a reasonable minimum time and amount in mid-September;however,CFS failed to project both.Additional comparison with subseasonal to seasonal(S2S)models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases.

Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation

The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.

Geostrophic Spirals Generated by the Horizontal Diffusion of Vortex Stretching in the Yellow Sea

Horizontal velocity spirals with a clockwise rotation(downward looking) rate of 1.7?m^(-1), on average, were observed in the western and northern Yellow Sea from December 2006 to February 2007. With the observed thermal wind relation,the beta-spiral theory was used to explain the dynamics of spirals. It was found that the horizontal diffusion of geostrophic vortex stretching is likely to be a major mechanism for generating geostrophic spirals. Vertical advection associated with surface/bottom Ekman pumping and topography-induced upwelling is too weak to support these spirals. Strong wind stirring and large heat loss in wintertime lead to weak stratification and diminish the effects of vertical advection. The cooling effect and vertical diffusion are offset by an overwhelming contribution of horizontal diffusion in connection with vortex stretching. The Richardson number-dependent vertical eddy diffusivity reaches a magnitude of 10^(-4) m^2 s^(-1) on average. An eddy diffusivity of 2870 m^2 s^(-1) is required for dynamic balance by estimating the residual term. This obtained value of 10-4 m^2 s^(-1) is in good agreement with the estimation in terms of observed eddy activities. The suppressed and unsuppressed diffusivities in the observation region are 2752 and 2881 m^2 s^(-1), respectively, which supports a closed budget for velocity rotation.

Noise reduction of acoustic Doppler velocimeter data based on Kalman filtering and autoregressive moving average models

Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of Kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.

Introduction of the special issue on the 10th WESTPAC International Scientific Conference

This special issue is the scientific legacy of the 10th WESTPAC International Scientific Conference, which was sponsored by State Oceanic Administration of China and the Intergovernmental Oceanographic Commission of UNESCO, and organized by the IOC Sub- Commission for the West Pacific (WESTPAC) and the First Institute of Oceanography of China, which was held in Qingdao, China during 17–20 April 2017 with more than 700 participants from 21 countries. During the conference, the international scientific steering committee announced the special issue of Acta Oceanologica Sinica for building a scientific legacy. Forty-five manuscripts were submitted with 20 papers accepted for this special issue.

The mechanism of the banded structure of drifting macroalgae in the Yellow Sea

At the end of May 2008,a massive bloom of macroalgae occurred in the western Yellow Sea off China and lasted for nearly two months,and annual blooms have occurred since then on. During bloom period,the surface-drifting macroalgae have showed an interesting pattern dominated by a banded structure,and the distance between neighboring bands ranged from hundreds of meters to about 6 km with a peak at 1–1.5 km,which is an order of higher than the scale of Langmuir circulation of 50–100 m. In order to explain this new phenomenon,ocean current data obtained from a Doppler current profiler off Qingdao was used to implement stability analysis. By numerically solving the resulting differential Orr-Sommerfeld equation,the secondary circulation induced from the instability of the Emkan current was found to fit well with the observed spatial scale of the surface-drifting macroalgae’s banded structure. As the wind driven Emkan current exist universally in the global ocean,it is reasonable to conclude that the banded structure with kilometers distance between adjoining bands is ubiquitous. We found a new circulation in the upper ocean which is important for exchange of energy,materials and gas between the upper ocean and subsurface layer.

Link between the Barents Oscillation and Recent Boreal Winter Cooling over the Asian Midlatitudes

The link between boreal winter cooling over the midlatitudes of Asia and the Barents Oscillation(BO) since the late 1980s is discussed in this study, based on five datasets. Results indicate that there is a large-scale boreal winter cooling during 1990–2015 over the Asian midlatitudes, and that it is a part of the decadal oscillations of long-term surface air temperature(SAT)anomalies. The SAT anomalies over the Asian midlatitudes are significantly correlated with the BO in boreal winter. When the BO is in its positive phase, anomalously high sea level pressure over the Barents region, with a clockwise wind anomaly,causes cold air from the high latitudes to move over the midlatitudes of Asia, resulting in anomalous cold conditions in that region. Therefore, the recent increasing trend of the BO has contributed to recent winter cooling over the Asian midlatitudes.

Evidence of Upwelling along Peninsular Malaysia during Southwest Monsoon

Upwelling off the east coast of Peninsular Malaysia (PM) was detected from recent cruise data collected during the southwest monsoon. Thermocline lifting was observed at 104?E from a number of parallel transects. To confirm the presence of upwelling, satellite remote sensing data were used, and numerical model experiments were conducted. A cooler sea-surface temperature along the coast was spotted from both in-situ and satellite data while upward movement from the model agreed with field data. The southwesterly wind that blows along PM from June to September is believed to be the important mechanism that contributed to this upwelling through an Ekman dynamics process.

Observation and analysis of the influence of wind waves on air-sea momentum fluxes

Due to the long-standing lack of understanding the role of wind waves on wind stress at moderate to high wind speeds,a high-frequency turbulence observation system is used in this study to obtain air-sea momentum flux data under pure wind wave conditions based on the tower-based marine meteorological observation platform in the southern Bohai Sea.Moreover,the modulation of wind waves on wind stress under wind speeds greater than 10 m s^(–1)is analyzed.The results indicated that the wind wave states caused by winds from the northwest and northeast are different under the influence of cold air,resulting in different wind stresses and drag coefficients.The wind stress increases with an increasing wind speed,reaching its maximum value when the northwest wind is nearly 20 m s^(–1),while the extreme value of the drag coefficient is basically the same when the northwest wind speed is the maximum and the northeast wind wave significant wave height is the maximum.The drag coefficient increases with an increasing wind speed within the range of 10–15 m s^(–1),reaching saturation at 15 m s^(–1).The critical wind speed is smaller than other observed results.Further analysis showed that wind-induced turbulent stress deviates from the observed values,and the degree of deviation depends on the wind speed and wave state,with a greater deviation caused by strong winds and waves.The wave-induced stress can correct the negative deviation between wind-induced turbulent stress and the observed value,and the drag coefficient calculated based on the modified wind stress tends to be close to the observed value overall.

Seasonal variations in wave-induced stress over global ocean based on China France Oceanography Satellite

Due to the scarcity of simultaneous observations on global-scale wind and wave spectra,there has been limited research on the characteristics of global wave-induced stress and wind stress with wave effects using observed wave spectra,particularly their seasonal variations.The China France Oceanography Satellite(CFOSAT)for the first time can simultaneously observe global sea surface wind and wave spectra,providing a solid data basis for investigating this difficult issue.In this study,the seasonal characteristics of global sea surface wave-induced stress and wind stress were analyzed by combining one-year simultaneous wind and wave observations from CFOSAT with a wave boundary layer model.Waveinduced stress was divided into wind-wave-induced stress and swell-induced stress based on different wave forms.The results showed that the wave-induced stress presented a significant inverse correlation with swell index.A higher swell index corresponded to a larger proportion of swell-induced stress,resulting in a decrease in wind stress,and vice versa,wind-wave-induced stress was dominant,resulting in an increase in wind stress.From spring to winter in the Northern Hemisphere(NH),wind-wave-induced stress predominated in the westerly belt of the Southern Hemisphere(SH),while swell-induced stress predominated near the equator.Further analysis revealed that the seasonal variation in wind-waveinduced stress in the SH was not significant,however,wind-wave-induced stress during the boreal summer was significantly lower than that in other seasons.The absolute value of swell-induced stress in the SH showed a trend of decrease and then increase from spring to winter.The percentage of increase or decrease in wind stress after considering the waveinduced stress showed a roughly symmetrical pattern between the NH and SH during the spring and autumn seasons,while the summer and winter seasons showed an asymmetrical feature.Wave-induced stress significantly modulated wind stress,resulting in zonal mean variations by up to±30%.This finding further highlights the important modulation of surface waves on wind stress at the global scale.

Development of Climate and Earth System Models in China: Past Achievements and New CMIP6 Results

The Earth–Climate System Model(ECSM)is an important platform for multi-disciplinary and multi-sphere integration research,and its development is at the frontier of international geosciences,especially in the field of global change.The research and development(R&D)of ECSM in China began in the 1980 s and have achieved great progress.In China,ECSMs are now mainly developed at the Chinese Academy of Sciences,ministries,and universities.Following a brief review of the development history of Chinese ECSMs,this paper summarized the technical characteristics of nine Chinese ECSMs participating in the Coupled Model Intercomparison Project Phase 6 and preliminarily assessed the basic performances of four Chinese models in simulating the global climate and the climate in East Asia.The projected changes of global precipitation and surface air temperature and the associated relationship with the equilibrium climate sensitivity under four shared socioeconomic path scenarios were also discussed.Finally,combined with the international situation,from the perspective of further improvement,eight directions were proposed for the future development of Chinese ECSMs.

A data driven time-dependent transmission rate for tracking an epidemic:a case study of 2019-nCoV

Ever since the first report of the discovery of an unknown novel coronavirus that had caused pneumonia in Wuhan,China,had filed to WHO by China,on December 31,2019,the government had instituted drastic measures to contain the outbreak of the epidemic.To keep transparency,the China's National Health Commission reports confirmed,suspected,recovered and death case numbers daily,yet the epidemiological community is still calling for more information.Specifically,they want to have the critical"Basic Reproduction,Number"Ro.According to Jones[1]and van den Driessche and Watmough[2],Ro,is defined as the expected number of secondary cases produced by a single(typical)infection in a completely susceptible population.

Could CMIP6 climate models reproduce the early-2000s global warming slowdown?

The unexpected global warming slowdown during 1998–2013 challenges the existing scientific understanding of global temperature change mechanisms,and thus the simulation and prediction ability of state-of-the-art climate models since most models participating in phase 5 of the Coupled Model Intercomparison Project(CMIP5)cannot simulate it.Here,we examine whether the new-generation climate models in CMIP6 can reproduce the recent global warming slowdown,and further evaluate their capacities for simulating key-scale natural variabilities which are the most likely causes of the slowdown.The results show that although the CMIP6 models present some encouraging improvements when compared with CMIP5,most of them still fail to reproduce the warming slowdown.They considerably overestimate the warming rate observed in 1998–2013,exhibiting an obvious warming acceleration rather than the observed deceleration.This is probably associated with their deficiencies in simulating the distinct temperature change signals from the human-induced long-term warming trend and/or the three crucial natural variabilities at interannual,interdecadal,and multidecadal scales.In contrast,the 4 models that can successfully reproduce the slowdown show relatively high skills in simulating the long-term warming trend and the three keyscale natural variabilities.Our work may provide important insight for the simulation and prediction of near-term climate changes.