The reappearance of a northeast Pacific marine heatwave (MHW) sounded alarms in late summer 2019 for a warming event on par with the 2013–2016 MHW known as The Blob. Despite these two events having similar magnitudes in surface warming, differences in seasonality and salinity distinguish their evolutions. We compare and contrast the ocean’s role in the evolution and persistence of the 2013–2016 and 2019–2020 MHWs using mapped temperature and salinity data from Argo floats. An unusual near-surface freshwater anomaly in the Gulf of Alaska during 2019 increased the stability of the water column, preventing the MHW from penetrating as deeply as the 2013–2016 event. This freshwater anomaly likely contributed to the intensification of the MHW by increasing the near-surface buoyancy. The gradual buildup of subsurface heat content throughout 2020 in the region suggests the potential for persistent ecological impacts.
Predictable internal climate variability on decadal timescales (2-10 years) is associated with large-scale oceanic processes, however these predictable signals may be masked by the noisy climate system. One approach to overcoming this problem is investigating state-dependent predictability - how differences in prediction skill depend on the initial state of the system. We present a machine learning approach to identify state-dependent predictability on decadal timescales in the Community Earth System Model version 2 by incorporating uncertainty estimates into a regression neural network. We leverage the network’s prediction of uncertainty to examine state dependent predictability in sea surface temperatures by focusing on predictions with the lowest uncertainty outputs. In particular, we study two regions of the global ocean - the North Atlantic and North Pacific - and find that skillful initial states identified by the neural network correspond to particular phases of Atlantic multi-decadal variability and the interdecadal Pacific oscillation.
El Niño Southern Oscillation (ENSO) is the leading mode of interannual climate variability, with large socioeconomical and environmental impacts. The main conceptual model for ENSO, the Recharge Oscillator (RO), considers two independent modes: the fast zonal tilt mode in phase with central-eastern Pacific Temperature (Te), and the slow recharge mode in phase quadrature. However, usual indices (western or equatorial sea level/thermocline depth h) do not orthogonally isolate the slow recharge mode, leaving it correlated with Te. Furthermore the optimal index is currently debated. Here, by objectively optimizing the RO equations fit to observations, we develop an improved recharge index. (1) Te-variability is regressed out, building h_ind statistically-independent from Te. Capturing the pure recharge, h_ind reconciles usual indices. (2) The optimum is equatorial plus southwestern Pacific h_ind_eq+sw (because of ENSO Ekman pumping meridional asymmetry). Using h_ind_eq+sw, the RO becomes more consistent with observations. h_ind_eq+sw is more relevant for ENSO operational diagnostics.
To capture the effects of mesoscale turbulent eddies, coarse-resolution Eulerian ocean models resort to tracer diffusion parameterizations. Likewise, the effect of eddy dispersion needs to be parameterized when computing Lagrangian pathways using coarse flow fields. Dispersion in Lagrangian simulations is traditionally parameterized by random walks, equivalent to diffusion in Eulerian models. Beyond random walks, there is a hierarchy of stochastic parameterizations, where stochastic perturbations are added to Lagrangian particle velocities, accelerations, or hyper-accelerations. These parameterizations are referred to as the 1st, 2nd and 3rd order ‘Markov models’ (Markov-N), respectively. Most previous studies investigate these parameterizations in two-dimensional setups, often restricted to the ocean surface. On the other hand, the few studies that investigated Lagrangian dispersion parameterizations in three dimensions, where dispersion is largely restricted to neutrally buoyant surfaces, have focused only on random walk (Markov-0) dispersion. Here, we present a three-dimensional isoneutral formulation of the Markov-1 model. We also implement an anisotropic, shear-dependent formulation of random walk dispersion, originally formulated as a Eulerian diffusion parameterization. Random walk dispersion and Markov-1 are compared using an idealized setup as well as more realistic coarse and coarsened (50 km) ocean model output. While random walk dispersion and Markov-1 produce similar particle distributions over time when using our ocean model output, Markov-1 yields Lagrangian trajectories that better resemble trajectories from eddy-resolving simulations. Markov-1 also yields a smaller spurious dianeutral flux.
A large strike-slip earthquake occurred in the Caribbean Sea on 28 January 2020. We inverted teleseismic P-waveforms from the earthquake to construct a finite-fault model by a new method of inversion that simultaneously resolves the spatiotemporal evolution of fault geometry and slip. The model showed almost unilateral rupture propagation westward from the epicenter along a 300 km section of the Oriente transform fault with two episodes of rupture at speeds exceeding the local shear-wave velocity. Our modeling indicated that the 2020 Caribbean earthquake rupture encountered a bend in the fault system associated with a bathymetric feature near the source region. The geometric complexity of the fault system triggered multiple rupture episodes and a complex rupture evolution. Our analysis of the earthquake revealed complexity of rupture process and fault geometry previously unrecognized for an oceanic transform fault that was thought to be part of a simple linear transform fault system.
Western boundary currents (WBCs) have intensified and become more eddying in recent decades due to the spin-up of the ocean gyres, resulting in warmer open ocean temperatures. However, relatively little is known of how WBC intensification will affect temperatures in adjacent continental shelf waters where societal impact is greatest. We use the well-observed East Australian Current (EAC) to investigate WBC warming impacts on shelf waters and show that temperature increases are non-uniform in shelf waters along the latitudinal extent of the EAC. Shelf waters poleward of 32°S, are warming more than twice as fast as those equatorward of 32°S. We show that non-uniform shelf temperature trends are driven by an increase in lateral heat advection poleward of the WBC separation, along Australia’s most populous coastline. The large scale nature of the process indicates that this is applicable to WBCs broadly, with far-reaching biological implications.
Marine heatwaves (MHWs) are extreme oceanic warm water events (above 90th percentile threshold) that significantly impact the marine environment. Several studies have recently explored the genesis and impacts of MHWs though they are least understood in the tropical Indian Ocean. Here we investigate the genesis and trend of MHWs in the Indian Ocean during 1982–2018 and their role in modulating the Indian monsoon. We find that the rapid warming in the Indian Ocean plays a critical role in increasing the number of MHWs. Meanwhile, the El Nino has a prominent influence on the occurrence of MHWs during the summer monsoon. The Indian Ocean warming and the El Nino variability have synergistically resulted in some of the strongest and long-lasting MHWs in the Indian Ocean. The western Indian Ocean (WIO) region experienced the largest increase in MHWs at a rate of 1.2–1.5 events per decade, followed by the north Bay of Bengal at a rate of 0.4–0.5 events per decade. Locally, the MHWs are induced by increased solar radiation, relaxation of winds, and reduced evaporative cooling. In the western Indian Ocean, the decreased winds further restrict the heat transport by ocean currents from the near-equatorial regions towards the north. Our analysis indicates that the MHWs in the western Indian Ocean and the north Bay of Bengal lead to a reduction in monsoon rainfall over the central Indian subcontinent. On the other hand, there is an enhancement of monsoon rainfall over southwest India due to the MHWs in the Bay of Bengal.
The biological oxygen (O2) saturation anomaly ΔO2/Ar is a tracer for net community production (NCP) in marine surface waters, with argon (Ar) normalization used to correct for physical effects on O2 supersaturation. Ship-board mass spectrometry has been used for ΔO2/Ar measurements, but this approach may not be accessible to many research groups. Here, we present a proof-of-concept for NCP estimates based on underway measurements of ΔO2/N2, which can be obtained from deployments of O2-Optodes and gas tension devices (GTD). We used a one-dimensional mixed layer model, validated against field observations, to evaluate divergence in ΔO2/Ar and ΔO/N2 resulting from differences in the sensitivity of Ar and nitrogen (N2) to various physical processes. Changes in sea surface temperature and responses in air-sea exchange most strongly decouple surface Ar and N2 with additional excess N2 associated with bubble-injection during high-wind conditions and vertical mixing in regions of elevated subsurface N2. In contrast, biological N2-fixation has a negligible contribution to the observed divergence between Ar and N2. Based on readily available environmental data, we present an approach to correct for Ar and N2 differences, yielding a new tracer, N2’, that is a near analog of Ar. We show that ΔO2/N2’ provides an excellent approximation to ΔO2/Ar, and that uncertainty and biases in ΔO2/N2’ are small relative to other errors in NCP calculations. Our results demonstrate the potential for ΔO2/N2’ measurements to expand NCP estimates from oceanographic research surveys, vessels of opportunity or autonomous surface vehicles.
Atlantic Water (AW) is the largest reservoir of heat in the Arctic Ocean, isolated from the surface and sea-ice by a strong halocline. In recent years AW shoaling and warming are thought to have had an increased influence on sea-ice in the Eurasian Basin. In this study we analyse 59000 profiles from across the Arctic from the 1970s to 2018 to obtain an observationally-based pan-Arctic picture of the AW layer, and to quantify temporal and spatial changes. The potential temperature maximum of the AW (the AW core) is found to be an easily detectable, and generally effective metric for assessments of AW properties, although temporal trends in AW core properties do not always reflect those of the entire AW layer. The AW core cools and freshens along the AW advection pathway as the AW loses heat and salt through vertical mixing at its upper bound, as well as via likely interaction with cascading shelf flows. In contrast to the Eurasian Basin, where the AW warms (by approximately 0.7°C between 2002 and 2018) in a pulse-like fashion and has an increased influence on upper ocean heat content, AW in the Canadian Basin cools (by approximately 0.1°C between 2008 and 2018) and becomes more isolated from the surface due to the intensification of the Beaufort Gyre. These opposing AW trends in the Eurasian and Canadian Basins of the Arctic over the last 40 years suggest that AW in these two regions may evolve differently over the coming decades.
A regional data-constrained coupled ocean-sea ice general circulation model and its adjoint are used to investigate mechanisms controlling the volume transport variability through Bering Strait during 2002 to 2013. Comprehensive time-resolved sensitivity maps of Bering Strait transport to atmospheric forcing can be accurately computed with the adjoint along the forward model trajectory to identify spatial and temporal scales most relevant to the strait's transport variability. The simulated Bering Strait transport anomaly is found to be controlled primarily by the wind stress on short time-scales of order 1 month. Spatial decomposition indicates that on monthly time-scales winds over the Bering and the combined Chukchi and East Siberian Seas are the most significant drivers. Continental shelf waves and coastally-trapped waves are suggested as the dominant mechanisms for propagating information from the far field to the strait. In years with transport extrema, eastward wind stress anomalies in the Arctic sector are found to be the dominant control, with correlation coefficient of 0.94. This implies that atmospheric variability over the Arctic plays a substantial role in determining Bering Strait flow variability. The near-linear response of the transport anomaly to wind stress allows for predictive skill at interannual time-scales, thus potentially enabling skillful prediction of changes at this important Pacific-Arctic gateway, provided that accurate measurements of surface winds in the Arctic can be obtained. The novelty of this work is the use of space and time-resolved adjoint-based sensitivity maps, which enable detailed dynamical, i.e. causal attribution of the impacts of different forcings.
It is well established that small scale cross-density (diapycnal) turbulent mixing induced by breaking of overturns in the interior of the ocean plays a significant role in sustaining the deep ocean circulation and in regulation of tracer budgets such as those of heat, carbon and nutrients. There has been significant progress in the fluid mechanical understanding of the physics of breaking internal waves. Connection of the microphysics of such turbulence to the larger scale dynamics, however, is significantly underdeveloped. We offer a hybrid theoretical-statistical approach, informed by observations, to make such a link. By doing so, we define a bulk flux coefficient, $\Gamma_B$, which represents the partitioning of energy available to an ‘ocean box’ (such as a grid cell of a coarse resolution climate model), from winds, tides, and other sources, into mixing and dissipation. $\Gamma_B$ depends on both the statistical distribution of turbulent patches and the flux coefficient associated with individual patches, $\Gamma_i$. We rely on recent parameterizations of ~$\Gamma_i$~ and the seeming universal characteristics of statistics of turbulent patches, to infer $\Gamma_B$, which is the essential quantity for representation of turbulent diffusivity in climate models. By applying our approach to climatology and global tidal estimates, we show that on a basin scale, energetic mixing zones exhibit moderately efficient mixing that induces significant vertical density fluxes, while quiet zones (with small background turbulence levels), although highly efficient in mixing, exhibit minimal vertical fluxes. The transition between the less energetic to more energetic zones marks regions of intense upwelling and downwelling of deep waters. We suggest that such upwelling and downwelling may be stronger than previously estimated, which in turn has direct implications for the closure of the deep branch of the ocean meridional overturning circulation as well as for the associated tracer budgets.
This study examines causes of the double silica maximum in the deep interior Northeast Pacific Basin using a stochastic Lagrangian tracer model based on steady-state advective fields and diapycnal diffusion established by a hydrographic inverse method that conserves potential vorticity and salinity. Lateral diffusion, unresolved by the inverse model, is adjusted for overall agreement with radiocarbon distribution. The double silica maximum in vertical profiles arises from an eastern-intensified single-maximum in the North Pacific Deep Water along the northern domain boundary (originating in the western Pacific), and a strong subarctic bottom source supplying silica to Upper Circumpolar Deep Water density surfaces that successively intersect the seafloor over a broad area east of 150°W, associated geostrophically with southward flow. The existence of the double silica maximum requires weak diapycnal transport in the deep interior, with broader implications for the conceptual picture of meridional overturning circulation in the North Pacific.
The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of the existence of dynamical coherent regions, THOR identifies these and their link to distinct currents and mechanisms such as the formation regions of deep water masses, and the location of the Gulf Stream and North Atlantic Current. Beyond a black box approach, THOR is engineered to elucidate its source of predictive skill rooted in physical understanding. A labeled dataset is engineered using an explicitly interpretable equation transform and k-means application to model data, allowing theoretical inference. A multilayer perceptron is then trained, explaining its skill using a combination of layerwise relevance propagation and theory. With abrupt CO2 quadrupling, the circulation weakens due to a shift in deep water formation regions, a northward shift of the Gulf stream and an eastwards shift in the North Atlantic Current. If CO2 is increased 1% yearly, similar but weaker patterns emerge influenced by natural variability. THOR is scalable and applicable to a range of models using only the ocean depth, dynamic sea level and wind stress, and could accelerate the analysis and dissemination of climate model data. THOR constitutes a step towards trustworthy machine learning called for within oceanography and beyond.
Soluble and total trace metals were measured in 4 size fractionated aerosol samples collected over the tropical eastern Atlantic Ocean. In samples that were dominated by Saharan dust, the size distributions of total iron, aluminium, titanium, manganese, cobalt and thorium were very similar to one another and to the size distributions of soluble manganese, cobalt and thorium. Finer particle sizes (< ~3 µm) showed enhanced soluble concentrations of iron, aluminium and titanium, possibly as a result of interactions with acidic sulfate aerosol during atmospheric transport. The difference in fine particle solubility between these two groups of elements might be related to the hyperbolic increase in the fractional solubility of iron, and a number of other elements, during the atmospheric transport of Saharan dust, which is not observed for manganese and its associated elements. In comparison to elements whose solubility varies during atmospheric transport, the stability of thorium fractional solubility should reduce uncertainties in the use of dissolved concentrations of this element in seawater as a proxy for dust deposition, although this topic requires further work.
Numerical wave models are used for a wide range of applications, from the global ocean to coastal scales. Here we report on significant improvements compared to the previous hindcast by Rascle and Ardhuin (2013). This result was obtained by updating forcing fields, adjusting the spectral discretization and retuning wind wave growth and swell dissipation parameters. Most of the performance analysis is done using significant wave heights (Hs) from the recent re-calibrated and denoised satellite altimeter data set provided by the European Space Agency Climate Change Initiative (ESA-CCI), with additional verification using spectral buoy data. We find that, for the year 2011, using wind fields from the recent ERA5 reanalysis provides lower scatter against satellite H s data compared to historical ECMWF operational analyses, but still yields a low bias on wave heights that can be mitigated by re-scaling wind speeds larger than 20 m/s. Alternative blended wind products can provide more accurate forcing in some regions, but were not retained because of larger errors elsewhere. We use the shape of the probability density function of H s around 2 m to fine tune the swell dissipation parameterization. The updated model hindcast appears to be generally more accurate than the previous version, and can be more accurate than the ERA5 H s estimates, in particular in strong current regions and for Hs greater than 7 m.
Seasonal prediction is one important element in a seamless prediction chain between weather forecast and climate projections. After several years of common development in collaboration with Universität Hamburg and Max Planck Institute for Meteorology, the Deutscher Wetterdienst performs operational seasonal forecasts since 2016 with the German Climate Forecast System, now in Version 2 (GCFS2.0). Here, the configuration of previous system GCFS1.0 and the current GCFS2.0 are described and the performance of the two systems is compared over the common hindcast period of 1990-2014. In GCFS2.0, the forecast skill is improved compared to GCFS1.0 during boreal winter, especially for the Northern Hemisphere where the Pearson correlation has doubled for the North Atlantic Oscillation index. During boreal summer, overall a similar performance of GCFS2.0 in comparison to GCFS1.0 is assessed. Future developments for climate forecasts need a stronger focus on the performance of seasonal dependent processes in a model system.
In this study, a storm surge model of the semi-enclosed Tokyo Bay was constructed to investigate its hydrodynamic response to major typhoon parameters, such as the point of landfall, approach angle, forward speed, size, and intensity. The typhoon simulation was validated for Typhoon Lan in 2017, and 31 hypothetical storm surge scenarios were generated to establish the sensitivity of peak surge height to the variation in typhoon parameters. The maximum storm surge height in the upper bay adjacent to the Tokyo Metropolitan Area was found to be highly sensitive to the forward speed and size of the passing typhoon. However, the importance of these parameters in disaster risk reduction has been largely overlooked by researchers and disaster managers. It was also determined that of the many hypothetical typhoon tracks evaluated, the slow passage of a large and intense typhoon transiting parallel to the longitudinal axis of Tokyo Bay, making landfall 25 km southwest, is most likely to cause a hazardous storm surge scenario in the upper-bay area. The results of this study are expected to be useful to disaster managers for advanced preparation against destructive storm surges.
Iron is a key micronutrient controlling phytoplankton growth in vast regions of the global ocean. Despite its importance, uncertainties remain high regarding external iron source fluxes and internal cycling on a global scale. In this study, we used a global dissolved iron dataset, including GEOTRACES measurements, to constrain source and scavenging fluxes in the marine iron component of a global ocean biogeochemical model. Our model simulations tested three key uncertainties: source inputs of atmospheric soluble iron deposition (varying from 1.4–3.4 Gmol/yr), reductive sedimentary iron release (14–117 Gmol/yr), and compared a variable ligand parameterization to a constant distribution. In each simulation, scavenging rates were tuned to reproduce the observed global mean iron inventory for consistency. The variable ligand parameterization improved the global model-data misfit the most, suggesting that heterotrophic bacteria are an important source of ligands to the ocean. Model simulations containing high source fluxes of atmospheric soluble iron deposition (3.4 Gmol/yr) and reductive sedimentary iron release (114 Gmol/yr) further improved the model most notably in the surface ocean. High scavenging rates were then required to maintain the iron inventory resulting in relatively short surface and global ocean residence times of 0.83 and 7.5 years, respectively. The model simulates a tight spatial coupling between source inputs and scavenging rates, which may be too strong due to underrepresented ligands near source inputs, contributing to large uncertainties when constraining individual fluxes with dissolved iron concentrations. Model biases remain high and are discussed to help improve global marine iron cycle models.