Description: Highlights: • Lagrangian ocean analysis is a powerful way to analyse the output of ocean circulation models • We present a review of the Kinematic framework, available tools, and applications of Lagrangian ocean analysis • While there are unresolved questions, the framework is robust enough to be used widely in ocean modelling Abstract: Lagrangian analysis is a powerful way to analyse the output of ocean circulation models and other ocean velocity data such as from altimetry. In the Lagrangian approach, large sets of virtual particles are integrated within the three-dimensional, time-evolving velocity fields. Over several decades, a variety of tools and methods for this purpose have emerged. Here, we review the state of the art in the field of Lagrangian analysis of ocean velocity data, starting from a fundamental kinematic framework and with a focus on large-scale open ocean applications. Beyond the use of explicit velocity fields, we consider the influence of unresolved physics and dynamics on particle trajectories. We comprehensively list and discuss the tools currently available for tracking virtual particles. We then showcase some of the innovative applications of trajectory data, and conclude with some open questions and an outlook. The overall goal of this review paper is to reconcile some of the different techniques and methods in Lagrangian ocean analysis, while recognising the rich diversity of codes that have and continue to emerge, and the challenges of the coming age of petascale computing.

Description: We present a new surface-atmospheric dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently used in the framework of the Coordinated Ocean-ice Reference Experiments (COREs) and the Ocean Model Intercomparison Project (OMIP). A major improvement in JRA55-do is the refined horizontal grid spacing (∼ 55 km) and temporal interval (3 hr). The data production method for JRA55-do essentially follows that of the CORE dataset, whereby the surface fields from an atmospheric reanalysis are adjusted relative to reference datasets. To improve the adjustment method, we use high-quality products derived from satellites and from several other atmospheric reanalysis projects, as well as feedback on the CORE dataset from the ocean modelling community. Notably, the surface air temperature and specific humidity are adjusted using multi-reanalysis ensemble means. In JRA55-do, the downwelling radiative fluxes and precipitation, which are affected by an ambiguous cloud parameterisation employed in the atmospheric model used for the reanalysis, are based on the reanalysis products. This approach represents a notable change from the CORE dataset, which imported independent observational products. Consequently, the JRA55-do dataset is more self-contained than the CORE dataset, and thus can be continually updated in near real-time. The JRA55-do dataset extends from 1958 to the present, with updates expected at least annually. This paper details the adjustments to the original JRA-55 fields, the scientific rationale for these adjustments, and the evaluation of JRA55-do. The adjustments successfully corrected the biases in the original JRA-55 fields. The globally averaged features are similar between the JRA55-do and CORE datasets, implying that JRA55-do can suitably replace the CORE dataset for use in driving global ocean–sea-ice models.

Description: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958–2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (∼ 1∘) to eddy-resolving (∼ 0.1∘) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar, and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and the Antarctic Circumpolar Current – are significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve” most of these features, the improvements in temperature and salinity are inconsistent among the different model families, and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.

Description: We present a new framework for global ocean–sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean–sea-ice models (JRA55-do). We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean–ice Reference Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from 11 state-of-the-science global ocean–sea-ice models. In the present evaluation, multi-model ensemble means and spreads are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performance is assessed considering metrics commonly used by ocean modelers. Both OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more than 80 % of the time and region for most metrics, with the multi-model ensemble spread greatly exceeding the difference between the means of the two datasets. Many features, including some climatologically relevant ocean circulation indices, are very similar between OMIP-1 and OMIP-2 simulations, and yet we could also identify key qualitative improvements in transitioning from OMIP-1 to OMIP-2. For example, the sea surface temperatures of the OMIP-2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming slowdown in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that the last feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. These improvements represent a new capability of the OMIP-2 framework for evaluating process-level responses using simulation results. Regarding the sensitivity of individual models to the change in forcing, the models show well-ordered responses for the metrics that are directly forced, while they show less organized responses for those that require complex model adjustments. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean–sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases and limitations in the atmospheric forcing. In particular, further efforts are warranted to resolve remaining issues in OMIP-2 such as the warm bias in the upper layer, the mismatch between the observed and simulated variability of heat content and thermosteric sea level before 1990s, and the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets. Overall, the present assessment justifies our recommendation that future model development and analysis studies use the OMIP-2 framework.

Description: Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 45 (2018): 2744-2751, doi:10.1002/2017GL076574.

Description: Salinification has occurred in the South China Sea from late 2012 to the present, as shown by satellite Aquarius/Soil Moisture Active Passive data and Argo float data. This salinification follows a 20 year freshening trend that started in 1993. The salinification signal is strongest near the surface and extends downward under the seasonal thermocline to a depth of 150 m. The salinification occurs when the phase of the Pacific Decadal Oscillation switches from negative to positive. Diagnosis of the salinity budget suggests that an increasing net surface freshwater loss and the horizontal salt advection through the Luzon Strait driven by the South China Sea throughflow contributed to this ongoing salinification. In particular, a decrease in precipitation and enhanced Luzon Strait transport dominated the current intense salinification. Of particular interest is whether this salinification will continue until it reaches the previous maximum recorded in 1992.

Description: Major State Research Development Program of China Grant Number: 2016YFC1402603; National Natural Science Foundation of China Grant Numbers: 41776025, 41476014, 41776026, 41676018; NOAA Climate Program Office MAPP Program Grant Number: NA15OAR4310088; NSF Physical Oceanography Program Grant Number: 1537136; National Science Foundation Grant Number: ICER‐1663704; Pearl River S&T Nova Program of Guangzhou; Open Project Program of State Key Laboratory of Tropical Oceanography Grant Number: LTOZZ1601

Description: 2018-09-05

Description: Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 123 (2018): 5808-5823, doi:10.1029/2018JC013895.

Description: Observations show that the westward transport of the Iceland‐Scotland overflow water (ISOW) through the Charlie‐Gibbs Fracture Zone (CGFZ) is highly variable. This study examines (a) where this variability comes from and (b) how it is related to the variability of ISOW transport at upstream locations in the Iceland Basin and other ISOW flow pathways. The analyses are based on a 35‐year 1/12° eddying Atlantic simulation that represents well the main features of the observed ISOW in the area of interest, in particular, the transport variability through the CGFZ. The results show that (a) the variability of the ISOW transport is closely correlated with that of the barotropic transports in the CGFZ associated with the meridional displacement of the North Atlantic Current front and is possibly induced by fluctuations of large‐scale zonal wind stress in the Western European Basin east of the CGFZ; (b) the variability of the ISOW transport is increased by a factor of 3 from the northern part of the Iceland Basin to the CGFZ region and transport time series at these two locations are not correlated, further suggesting that the variability at the CGFZ does not come from the upstream source; and (c) the variability of the ISOW transport at the CGFZ is strongly anticorrelated to that of the southward ISOW transport along the eastern flank of the Mid‐Atlantic Ridge, suggesting an out‐of‐phase covarying transport between these two ISOW pathways.

Description: Woods Hole Oceanographic Institution; National Oceanic and Atmospheric Administration Grant Number: NA15OAR4310088; U.S. National Science Foundation Grant Numbers: 1537136, OCE‐0926656

Description: 2019-02-20

Description: Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 121 (2016): 877–907, doi:10.1002/2015JC011290.

Description: Accelerating since the early 1990s, the Greenland Ice Sheet mass loss exerts a significant impact on thermohaline processes in the sub-Arctic seas. Surplus freshwater discharge from Greenland since the 1990s, comparable in volume to the amount of freshwater present during the Great Salinity Anomaly events, could spread and accumulate in the sub-Arctic seas, influencing convective processes there. However, hydrographic observations in the Labrador Sea and the Nordic Seas, where the Greenland freshening signal might be expected to propagate, do not show a persistent freshening in the upper ocean during last two decades. This raises the question of where the surplus Greenland freshwater has propagated. In order to investigate the fate, pathways, and propagation rate of Greenland meltwater in the sub-Arctic seas, several numerical experiments using a passive tracer to track the spreading of Greenland freshwater have been conducted as a part of the Forum for Arctic Ocean Modeling and Observational Synthesis effort. The models show that Greenland freshwater propagates and accumulates in the sub-Arctic seas, although the models disagree on the amount of tracer propagation into the convective regions. Results highlight the differences in simulated physical mechanisms at play in different models and underscore the continued importance of intercomparison studies. It is estimated that surplus Greenland freshwater flux should have caused a salinity decrease by 0.06–0.08 in the sub-Arctic seas in contradiction with the recently observed salinification (by 0.15–0.2) in the region. It is surmised that the increasing salinity of Atlantic Water has obscured the freshening signal.

Description: NSERC. Grant Numbers RGPIN 227438-09, RGPIN 04357 and RGPCC 433898; RFBR. Grant Number 13-05-00480, 14-05-00730, and 15-05-02457; NSF Grant Number: PLR-0804010, PLR-1313614, and PLR-1203720

Description: 2016-07-25

Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ocean Modelling 121 (2018): 49-75, doi:10.1016/j.ocemod.2017.11.008.

Description: Lagrangian analysis is a powerful way to analyse the output of ocean circulation models and other ocean velocity data such as from altimetry. In the Lagrangian approach, large sets of virtual particles are integrated within the three-dimensional, time-evolving velocity fields. Over several decades, a variety of tools and methods for this purpose have emerged. Here, we review the state of the art in the field of Lagrangian analysis of ocean velocity data, starting from a fundamental kinematic framework and with a focus on large-scale open ocean applications. Beyond the use of explicit velocity fields, we consider the influence of unresolved physics and dynamics on particle trajectories. We comprehensively list and discuss the tools currently available for tracking virtual particles. We then showcase some of the innovative applications of trajectory data, and conclude with some open questions and an outlook. The overall goal of this review paper is to reconcile some of the different techniques and methods in Lagrangian ocean analysis, while recognising the rich diversity of codes that have and continue to emerge, and the challenges of the coming age of petascale computing.

Description: EvS has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 715386). This research for PJW was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. Funding for HFD was provided by Grant No. DE-SC0012457 from the US Department of Energy. PB acknowledges support for this work from NERC grant NE/R011567/1. SFG is supported by NERC National Capability funding through the Extended Ellett Line Programme.