Persistent warm-eddy transport to Antarctic ice shelves driven by enhanced summer westerlies.

The offshore ocean heat supplied to the Antarctic continental shelves by warm eddies has the potential to greatly impact the melting rates of ice shelves and subsequent global sea level rise. While featured in modeling and some observational studies, the processes around how these warm eddies form and overcome the dynamic sub-surface barrier of the Antarctic Slope Front over the upper continental slope has not yet been clarified. Here we report on the detailed observations of persistent eddies carrying warm modified Circumpolar Deep Water (CDW) onto the continental shelf of Prydz Bay, East Antarctica, using subsurface mooring and hydrographic section data from 2013-2015. We show the warm-eddy transport is most active when the summer westerlies strengthen, which promotes the upwelling of CDW and initiates eddy formation and intrusions. Our study highlights the important role of warm eddies in the melting of Antarctica's ice shelves, both now and into the future.

Enhanced daytime atmospheric mercury in the marine boundary layer in the South Oceans.

Understanding the spatial and temporal variations of atmospheric mercury (Hg) in the marine boundary layer could advance our knowledge on ocean evasion of Hg. Here, we conducted continuous measurements of total gaseous mercury (TGM) in the marine boundary layer during a round-the-world cruise from August 2017 to May 2018. We observed the highest and lowest TGM concentrations in Southern Indian Ocean (1.29 ± 0.22 ng m-3) and Southern Atlantic Ocean (0.61 ± 0.28 ng m-3), respectively. During the daytime, enhanced TGM was observed with the diurnal amplitude difference reaching its maximum in the range of 0.30-0.37 ng m-3 in Southern Indian Ocean and Southern Ocean. The positive correlation between TGM (R2 = 0.68-0.92) and hourly solar radiation in each ocean suggested that the daytime enhanced TGM was likely driven by Hg photoreduction in seawater, after excluding the influence of other meteorological factors. The diurnal amplitude of TGM in the marine boundary layer might be impacted by the microbial productivity and the ratio of ultraviolet radiation. Our study highlights that ocean acts as a net TGM source during the daytime in the Southern Hemisphere and aqueous photoreduction process may play an important role in the biogeochemical cycling of Hg.

A quantitative analysis of marine heatwaves in response to rising sea surface temperature.

It has been proven that marine heatwaves (MHWs) have increased in frequency, duration, and intensity over the past few decades, and this trend will accelerate further under continued global warming. While more intense and frequent MHWs are an expected consequence of rising sea surface temperatures (SSTs) under continued global warming, it remains unclear to what degree per Celsius warming trend of SSTs contributes to the changes in the MHW metrics. Here, we focus on how the MHW metrics evolve with the SST warming trend by using an adaptive data analysis method based on observational datasets covering the past four decades. We find that the globally averaged increasing rates of the annual MHW frequency, duration, and maximum intensity are approximately 3.7 events, 7.5 days, and 2.2° Celsius per degree Celsius of SST rise, respectively. The increasing rates for the annual MHW days and the fraction of the spatial extents to the global ocean affected by MHWs are approximately 58.8 days and 13.9 % per degree Celsius of SST rise, respectively. Based on these observational-based increasing rates and the projected SST warming from the selected Coupled Model Intercomparison Project Phase 6 (CMIP6) models, the spatial distributions of changes in annual MHW days, frequency, and cumulative intensity are projected to exhibit 2-fold, 4-fold, and 6 to 8-fold increases under the three socioeconomic pathways (i.e., SSP126, SSP245, and SSP585), respectively. The globally averaged annual MHW days will increase to approximately 224.2 ± 26.9 days, and the largest changes are projected to occur in the northeast Pacific, the North Atlantic, the south Indian Oceans, and parts of the Southern Ocean, with approximately 14.8 ± 5.7 % of the global ocean reaching a permanent MHW state by the end of the twenty-first century under SSP585.

Arctic Ocean Amplification in a warming climate in CMIP6 models.

Arctic near-surface air temperature warms much faster than the global average, a phenomenon known as Arctic Amplification. The change of the underlying Arctic Ocean could influence climate through its interaction with sea ice, atmosphere, and the global ocean, but it is less well understood. Here, we show that the upper 2000 m of the Arctic Ocean warms at 2.3 times the global mean rate within this depth range averaged over the 21st century in the Coupled Model Intercomparison Project Phase 6 Shared Socioeconomic Pathway 585 scenario. We call this phenomenon the "Arctic Ocean Amplification." The amplified Arctic Ocean warming can be attributed to a substantial increase in poleward ocean heat transport, which will continue outweighing sea surface heat loss in the future. Arctic Amplification of both the atmosphere and ocean indicates that the Arctic as a whole is one of Earth's regions most susceptible to climate change.

Spatiotemporal propagating decadal signal of ocean heat content and thermocline depth identified in the tropical Pacific.

Tropical Pacific decadal variability (TPDV) and its mechanisms are essential for understanding long-term variations in global climate. The spatiotemporal pattern of this decadal variation has yet to be clarified. Here, on the basis of observational data with the help of the adaptive data analysis method, we extracted and investigated the spatiotemporal evolution of the tropical Pacific decadal variability in upper ocean heat content (UOHC) and thermocline depth. The tropical decadal signal propagated eastward along the equator from the western Pacific to the eastern Pacific after the 1970s, with a speed of 4-5 cm s-1 yielding a decadal oscillation of approximately 11-13 years. This decadal variability of the thermocline fluctuations (UOHC) was proven to be closely correlated with western wind anomalies since the 1970s and may have been induced by the regime shift of the Pacific decadal oscillation. These peaks of decadal signals corresponded well with the strong El Niño-Southern Oscillation (ENSO) events, reflecting nonlinear rectification of ENSOs on TPDV. Moreover, the TPDV showed a modulating signal on moderate and weak ENSO events.

Seasonal to decadal spatiotemporal variations of the global ocean carbon sink.

The global ocean has absorbed approximately 30% of anthropogenic CO2  since the beginning of the industrial revolution. However, the spatiotemporal evolution of this important global carbon sink varies substantially on all timescales and has not yet been well evaluated. Here, based on a reconstructed observation-based product of surface ocean pCO2 and air-sea CO2  flux (the MPI-SOMFFN method), we investigated seasonal to decadal spatiotemporal variations of the ocean CO2  sink during the past three decades using an adaptive data analysis method. Two predominant variations are modulated annual cycles and decadal fluctuations, which account for approximately 46% and 25% of all extracted components, respectively. Although the whole summer to non-summer seasonal difference pattern is determined by the Southern Ocean, the non-summer CO2  sink at mid-latitudes in both hemispheres shows an increasing trend (a total increase of approximately 1.0 PgC during the period 1982-2019), while it is relatively stable in summer. On decadal timescales for the global ocean carbon sink, unlike the weakening decade (1990-1999) and the reinvigoration decade (2000-2009) in which the Southern Ocean plays the dominant role, the reinforcement decade (2010-2019) is mainly the result from the weakening source effect in the equatorial Pacific Ocean. Our results suggest that except for the Southern Ocean's role in the global ocean carbon sink, the strengthening non-summer's sink at mid-latitudes in both hemispheres and the decadal or longer timescales of equatorial Pacific Ocean dynamics should be fully considered in understanding the oceanic carbon cycle on a global scale.

Opening Fukushima floodgates: Modelling 137Cs impact in marine biota.

A numerical model was applied to simulate the transport of 137Cs released with the waters which were used to cool Fukushima reactors. These stored waters will be released to the Pacific Ocean according to Japanese government plans. The radionuclide transport model is Lagrangian and includes radionuclide interactions with sediments and an integrated dynamic foodweb model for biota uptake. Calculations made from a conservative approach indicate that expected concentrations in sediments and marine fish would be orders of magnitude below those detected after Fukushima accident and also lower than those resulting from global fallout background.

Transport and dispersion of tritium from the radioactive water of the Fukushima Daiichi nuclear plant.

Japan recently announced plans to discharge over 1.2 million tons of radioactive water from the Fukushima Daiichi Nuclear Power Plant (FDNPP) into the Pacific Ocean. The contaminated water can poses a threat to marine ecosystems and human health. To estimate the impact of the plan, here, we developed a three-dimensional global model to track the transport and dispersion of tritium released from the radioactive water of the FDNPP. The pollution scenarios for four release durations (1 month, 1 year, 5 years, and 10 years) were simulated. The simulation results showed that for the release in short-duration scenarios (1 month and 1 year), the peak plume with high tritium concentration shifted with the currents and finally reached the northeastern Pacific. For the long-duration scenarios (5 years and 10 years), the peak plume of the contaminated water was confined to coastal regions east of Japan.

The poleward enhanced Arctic Ocean cooling machine in a warming climate.

As a cooling machine of the Arctic Ocean, the Barents Sea releases most of the incoming ocean heat originating from the North Atlantic. The related air-sea heat exchange plays a crucial role in both regulating the climate and determining the deep circulation in the Arctic Ocean and beyond. It was reported that the cooling efficiency of this cooling machine has decreased significantly. In this study, we find that the overall cooling efficiency did not really drop: When the cooling efficiency decreased in the southern Barents Sea, it increased in the northern Barents and Kara Seas, indicating that the cooling machine has expanded poleward. According to climate model projections, it is very likely that the cooling machine will continue to expand to the Kara Sea and then to the Arctic Basin in a warming climate. As a result, the Arctic Atlantification will be enhanced and pushed poleward in the future.

A model for the spread of infectious diseases compatible with case data.

For epidemics such as COVID-19, with a significant population having asymptomatic, untested infection, model predictions are often not compatible with data reported only for the cases confirmed by laboratory tests. Additionally, most compartmental models have instantaneous recovery from infection, contrary to observation. Tuning such models with observed data to obtain the unknown infection rate is an ill-posed problem. Here, we derive from the first principle an epidemiological model with delay between the newly infected (N) and recovered (R) populations. To overcome the challenge of incompatibility between model and case data, we solve for the ratios of the observed quantities and show that log(N(t)/R(t)) should follow a straight line. This simple prediction tool is accurate in hindcasts verified using data for China and Italy. In traditional epidemiology, an epidemic wanes when much of the population is infected so that 'herd immunity' is achieved. For a highly contagious and deadly disease, herd immunity is not a feasible goal without human intervention or vaccines. Even before the availability of vaccines, the epidemic was suppressed with social measures in China and South Korea with much less than 5% of the population infected. Effects of social behaviour should be and are incorporated in our model.

The causality from solar irradiation to ocean heat content detected via multi-scale Liang-Kleeman information flow.

Solar irradiation is the primary driving force for the Earth's climate system. However, we are still short of powerful tools to study the variability of the Earth's climate due to the solar activity. Here we apply the Liang-Kleeman information flow to quantify the causality from Total Solar Irradiance (TSI) to the global ocean heat content anomaly (OHCA). It reveals that the information flow from TSI to OHCA varies in both time and space. We adapt the method into a multi-scale version which describes the variation of information flow on different timescales. In different ocean basins, the significant information flow from TSI to OHCA varies on different timescales, which could be several decades, much longer than the timescale of the correlation revealed by wavelet coherence. Then we calculate the information flow from TSI to the first three expansion coefficients of the OHCA Empirical Orthogonal Functions. The results indicate that TSI is a part cause of the El Niño-Southern Oscillation (ENSO) and global warming, especially in the 1970s. In the recent 40 years, the contribution of TSI to the variation of the OHCA becomes less significant probably due to the increasing influence of human activity on the climate system.

Centuries of monthly and 3-hourly global ocean wave data for past, present, and future climate research.

Ocean surface waves are essential to navigation safety, coastal activities, and climate systems. Numerical simulations are still the primary methods used in wave climate research, especially in future climate change scenarios. Recently, First Institute of Oceanography-Earth System Model version 2.0 (FIO-ESM v2.0), a global climate model coupled with an ocean wave model, was carried out the Coupled Model Intercomparison Project phase 6 (CMIP6) experiments. Here, we present the global monthly-mean and 3-hourly instantaneous wave parameter dataset from the FIO-ESM v2.0 CMIP6 experiments, including 700-year piControl, 165-year historical, three 86-year future scenarios (ssp125, ssp245, and ssp585, respectively), and two 150-year climate sensitive experiments (1pctCO2 and abrupt-4xCO2) simulations. Historical results show that the model can capture the basic wave climate features under climate change. These unique centuries of global wave data are from a fully coupled system and can provide the community with a vital long-term data source for scientific and engineering applications, such as wave climate research, wave-related process studies and parameterizations, as well as coastal and near-shore industry designs.

Anisotropy of the sea surface height wavenumber spectrum from altimeter observations.

In this paper, the zonal and meridional sea surface height (SSH) wavenumber spectra are systematically calculated using along-track and gridded altimeter products, and the slopes of the SSH wavenumber spectra over the mesoscale band, which is defined by the characteristic length scale of mesoscale signals, are estimated. The results show that the homogeneous spectral slopes calculated from the along-track and gridded altimeter datasets have a similar spatial pattern, but the spectral slopes from gridded altimeter data are generally steeper than that from the along-track data with an averaged difference of 1.5. Significant differences are found between the zonal and meridional spectra, which suggest that SSH wavenumber spectra are indeed anisotropic. Furthermore, the anisotropy exhibits strong regional contrast: in the equatorial region, the zonal spectrum is steeper than its corresponding meridional spectrum, while in the eastward-flowing high EKE regions the meridional spectrum is steeper than its zonal counterpart. The anisotropy of SSH wavenumber spectral slopes implies that EKE distributes anisotropically in different directions, and this distribution is closely associated with the generation and nonlinear evolution of mesoscale movements.

Data concerning statistical relation between obliquity and Dansgaard-Oeschger events.

Data presented are related to the research article entitled "Using Holo-Hilbert spectral analysis to quantify the modulation of Dansgaard-Oeschger events by obliquity" (J. Deng et al., 2018). The datasets in Deng et al. (2018) are analyzed on the foundation of ensemble empirical mode decomposition (EEMD) (Z.H. Wu and N.E. Huang, 2009), and reveal more occurrences of Dansgaard-Oeschger (DO) events in the decreasing phase of obliquity. Here, we report the number of significant high Shannon entropy (SE) (C.E. Shannon and W. Weaver, 1949) of 95% significance level of DO events in the increasing and decreasing phases of obliquity, respectively. First, the proxy time series are filtered by EEMD to obtain DO events. Then, the time-varying SE of DO modes are calculated on the basis of principle of histogram. The 95% significance level is evaluated through surrogate data (T. Schreiber and A. Schmitz, 1996). Finally, a comparison between the numbers of SE values that are larger than 95% significance level in the increasing and decreasing phases of obliquity, respectively, is reported.

Modelling oil trajectories and potentially contaminated areas from the Sanchi oil spill.

Oil spills are major threats to marine ecosystems. Here, we establish a three-dimensional oil spill model to simulate and project the short- and long-term trajectories of oil slicks and oil-contaminated water that leaked from the Sanchi wreckage. The pollution probability in surrounding areas for the period up to 180 days after the Sanchi sank is statistically analysed. The short-term simulations are consistent with synchronous SAR images and observational reports. The potentially polluted areas depend on the properties of the released oil. The coastal areas most likely to be affected by the bunker oil are located in the Ryukyu Island Chain, Tsushima Strait, on the south and east coasts of Japan. Approximately 50% to 70% of oil particles remain in the ocean and mainly expand along the Ryukyu Island Chain and the region southeast of the Sanchi wreck. Subsurface oil-contaminated water is likely to enter the Sea of Japan along the Tsushima Strait. Due to the rapid evaporation rate of condensate oil, the potentially polluted area is confined to regions within a 100 × 100 km area around the location of the shipwreck, and the contaminated region is closely associated with the surface wind.

Spatiotemporal evolution of the chlorophyll a trend in the North Atlantic Ocean.

Analyses of the chlorophyll a concentration (chla) from satellite ocean color products have suggested the decadal-scale variability of chla linked to the climate change. The decadal-scale variability in chla is both spatially and temporally non-uniform. We need to understand the spatiotemporal evolution of chla in decadal or multi-decadal timescales to better evaluate its linkage to climate variability. Here, the spatiotemporal evolution of the chla trend in the North Atlantic Ocean for the period 1997-2016 is analyzed using the multidimensional ensemble empirical mode decomposition method. We find that this variable trend signal of chla shows a dipole pattern between the subpolar gyre and along the Gulf Stream path, and propagation along the opposite direction of the North Atlantic Current. This propagation signal has an overlapping variability of approximately twenty years. Our findings suggest that the spatiotemporal evolution of chla during the two most recent decades is part of the multidecadal variations and possibly regulated by the changes of Atlantic Meridional Overturning Circulation, whereas the mechanisms of such evolution patterns still need to be explored.

Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.

In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.