Next generation of Bluelink ocean reanalysis with multiscale data assimilation: BRAN2020

BRAN2020 (2020 version of the Bluelink ReANalysis) is an ocean reanalysis that combines observations with an eddy-resolving, near-global ocean general circulation model to produce a four-dimensional estimate of the ocean state. The data assimilation system employed is ensemble optimal interpolation, implemented with a new multiscale approach that constrains the broad-scale ocean properties and the mesoscale circulation in two steps. There is a separation in the scales that are corrected in the two steps: the high-resolution step corrects the mesoscale dynamics in the same way as previous versions of BRAN, while the extra coarse step is effective at correcting biases that develop at large scales. The reanalysis currently spans January 1993 to December 2019 and assimilates observations of in situ temperature and salinity, as well as of satellite sea-level anomaly and sea surface temperature. BRAN2020 is planned to be updated to within months of real time after this initial release, until an updated version of BRAN is available. Reanalysed fields from BRAN2020 generally show much closer agreement to observations than all previous versions with misfits between reanalysed and observed fields reduced by over 30 % for some variables, for subsurface temperature and salinity in particular. The BRAN2020 dataset is comprised of daily averaged fields of temperature, salinity, velocity, mixed-layer depth and sea level. Reanalysed fields realistically represent all of the major current systems within 75∘ S and 75∘ N, excluding processes relating to sea ice but including boundary currents, equatorial circulation, Southern Ocean variability and mesoscale eddies. BRAN2020 is publicly available at https://doi.org/10.25914/6009627c7af03 (Chamberlain et al., 2021b) and is intended for use by the research community.

Ocean Response to Super-Typhoon Haiyan

Present paper studies the ocean response to super-typhoon Haiyan based on satellite and Argo float data. First, we show the satellite-based surface wind and sea surface temperature during super-typhoon Haiyan, and evaluate the widely-used atmospheric and oceanic analysis-or-reanalysis datasets. Second, we investigate the signals of Argo float, and find the daily-sampling Argo floats capture the phenomena of both vertical-mixing-induced mixed-layer extension and nonlocal subsurface upwelling. Accordingly, the comparisons between Argo float and ocean reanalysis reveal that, the typhoon-induced upwelling in the ocean reanalysis needs to be further improved, meanwhile, the salinity profiles prior to typhoon arrival are significantly biased.

The North Equatorial Countercurrent East of the Dateline, Its Variations, and Its Relationship to the El Niño Event

Using forty years (1978–2017) of Ocean Reanalysis System 4 (ORAS4) dataset, the purpose of this study is to investigate the fluctuation of the North Equatorial Countercurrent (NECC) to the east of the dateline in relation to the presence of three kinds of El Niño events. From spring (MAM) through summer (JJA), we found that the NECC was stronger during the Eastern Pacific El Niño (EP El Niño) and the MIX El Niño than during the Central Pacific El Niño (CP El Niño). When it comes to winter (DJF), on the other hand, the NECC was stronger during the CP and MIX El Niño and weaker during the EP El Niño. This NECC variability was affected by the fluctuations of thermocline depth near the equatorial Pacific. Moreover, we also found that the seasonal southward shift of the NECC occurred between winter and spring, but the shift was absent during the CP and MIX El Niño events. This meridional shift was strongly affected by the local wind stress.

An Assessment of Recently Released High-Degree Global Geopotential Models Based on Heterogeneous Geodetic and Ocean Data

The development of the global geopotential model (GGM) broadens its applications in ocean science, which emphasizes the importance for model assessment. We assess the recently released high-degree GGMs over the South China Sea through heterogeneous geodetic observations and synthetic/ocean reanalysis data. The comparisons with a high resolution (∼3 km) airborne gravimetric survey over the Paracel Islands show that XGM2019e_2159 has relatively high quality, where the standard deviation (SD) of the misfits against the airborne gravity data is ∼3.1 mGal. However, the comparisons with local airborne/shipborne gravity data hardly discriminate the qualities of other GGMs that have or truncated to the same expansion degree. Whereas, the comparisons with the synthetic/ocean reanalysis data demonstrate that the qualities of the values derived from different GGMs are not identical, and the ones derived from XGM2019e_2159 have better performances. The SD of the misfits between the mean dynamic topography (MDT) derived from XGM2019e_2159 and the ocean data is 2.5 cm; and this value changes to 7.1 cm/s (6.8 cm/s) when the associated zonal (meridian) geostrophic velocities are assessed. In contrast, the values derived from the other GGMs show deteriorated qualities compared to those derived from XGM2019e_2159. In particular, the contents computed from the widely used EGM2008 have relatively poor qualities, which is reduced by 3.9 cm when the MDT is assessed, and by 4.0 cm/s (5.5 cm/s) when the zonal (meridian) velocities are assessed, compared to the results derived from XGM2019e_2159. The results suggest that the choice of a GGM in oceanographic study is crucial, especially over coastal zones. Moreover, the synthetic/ocean data sets may be served as additional data sources for global/regional gravity field assessment, which are useful in regions that lack of high-quality geodetic data.

Rapid Sea-Level Rise in the Southern-Hemisphere Subtropical Oceans

The subtropical oceans between 35°-20°S in the Southern Hemisphere (SH) have exhibited prevailingly rapid sea-level rise (SLR) rates since the mid-20th century, amplifying damages of coastal hazards and exerting increasing threats to South America, Africa, and Australia. Yet, mechanisms of the observed SLR have not been firmly established, and its representation in climate models has not been examined. By analyzing observational sea-level estimates, ocean reanalysis products, and ocean model hindcasts, we show that the steric SLR of the SH subtropical oceans between 35°-20°S is faster than the global mean rate by 18.2%±9.9% during 1958-2014. However, present climate models—the fundamental bases for future climate projections—generally fail to reproduce this feature. Further analysis suggests that the rapid SLR in the SH subtropical oceans is primarily attributable to the persistent upward trend of the Southern Annular Mode (SAM). Physically, this trend in SAM leads to the strengthening of the SH subtropical highs, with the strongest signatures observed in the southern Indian Ocean. These changes in atmospheric circulation promote regional SLR in the SH subtropics by driving upper-ocean convergence. Climate models show systematic biases in the simulated structure and trend magnitude of SAM and significantly underestimate the enhancement of subtropical highs. These biases lead to the inability of models to correctly simulate the observed subtropical SLR. This work highlights the paramount necessity of reducing model biases to provide reliable regional sea-level projections.

Salinity Interdecadal Variability in the Western Equatorial Pacific and Its Effects During 1942-2018

Ocean reanalysis products are used to examine salinity variability and its relationships with temperature in the western equatorial Pacific during 1942-2018. An ensemble empirical mode decomposition (EEMD) method is adopted to separate salinity and temperature signals at different time scales; a focus is placed on interdecadal component in this study. Pronounced interdecadal variations in salinity are seen in the western equatorial Pacific, which exhibits persistent and transitional phases in association with temperature. A surface freshening is accompanied by a surface warming during the 1980s-1990s, but saltening and cooling in the 2000s, with interdecadal shifts occurring around the late 1970s, late 1990s, and in 2016-2018, respectively. Determined by anomaly signs of temperature and salinity, their combined effects can be density-compensated or density-uncompensated, acting to produce density variability that is suppressed or enhanced, respectively. The effects are phase- and region dependent. In the subsurface layers at 200m, where salinity and temperature anomalies are nearly of the same sign during interdecadal evolution, their effects are mostly density-compensated. The situation is more complicated in the surface layer. Variations in SSS and SST during the persistent phases tend to be of opposite sign with their density-uncompensated effects, acting to enhance density anomalies; but they can be of the same sign during the transitional periods, with density-compensated salinity effects. Examples are given for relationships among these fields which exhibit phase differences in anomaly transitions in the late 1990s in the western equatorial Pacific; salinity anomalies are seen to cause a delay in phase transition of density anomalies. Furthermore, their relative contributions to interdecadal variabilities of density and stratification are quantified. The consequences for salinity effects are also discussed with their feedbacks on local SST.

Next generation of Bluelink ocean reanalysis with multiscale data assimilation: BRAN2020

BRAN2020 is an ocean reanalysis that combines ocean observations with an eddy-resolving, near-global ocean general circulation model, to produce four-dimensional estimates of the ocean state. The data assimilation system employed is ensemble optimal interpolation, implemented with a new multiscale approach that constrains the broad-scale ocean properties and the mesoscale circulation in two steps. The reanalysis spans January 1993 to December 2019, and assimilates observations of in situ temperature and salinity, as well as satellite sea-level anomaly and sea surface temperature. Reanalysed fields from BRAN2020 generally show much closer agreement to observations than all previous versions with mis-fits between reanalysed and observed fields reduced by over 30 % for some variables. The BRAN2020 dataset is comprised of daily-averaged fields of temperature, salinity, velocity, mixed-layer depth, and sea-level. Reanalysed fields realistically represent all of the major current systems within 75° S and 75° N, excluding processes relating to sea ice, but including boundary currents, equatorial circulation, Southern Ocean variability, and mesoscale eddies. BRAN2020 is publicly-available at https://doi.org/10.25914/6009627c7af03 (Chamberlain et al., 2021b) and is intended for use by the research community.

International Quality-Controlled Ocean Database (IQuOD) v0.1: The Temperature Uncertainty Specification

Ocean temperature observations are crucial for a host of climate research and forecasting activities, such as climate monitoring, ocean reanalysis and state estimation, seasonal-to-decadal forecasts, and ocean forecasting. For all of these applications, it is crucial to understand the uncertainty attached to each of the observations, accounting for changes in instrument technology and observing practices over time. Here, we describe the rationale behind the uncertainty specification provided for all in situ ocean temperature observations in the International Quality-controlled Ocean Database (IQuOD) v0.1, a value-added data product served alongside the World Ocean Database (WOD). We collected information from manufacturer specifications and other publications, providing the end user with uncertainty estimates based mainly on instrument type, along with extant auxiliary information such as calibration and collection method. The provision of a consistent set of observation uncertainties will provide a more complete understanding of historical ocean observations used to examine the changing environment. Moving forward, IQuOD will continue to work with the ocean observation, data assimilation and ocean climate communities to further refine uncertainty quantification. We encourage submissions of metadata and information about historical practices to the IQuOD project and WOD.