Ocean Climate Model Research Articles

Fast spin-up of geochemical tracers in ocean circulation and climate models

Fast spin-up of geochemical tracers in ocean circulation and climate models

ICON‐O: The Ocean Component of the ICON Earth System Model—Global Simulation Characteristics and Local Telescoping Capability

AbstractWe describe the ocean general circulation model Icosahedral Nonhydrostatic Weather and Climate Model (ICON‐O) of the Max Planck Institute for Meteorology, which forms the ocean‐sea ice component of the Earth system model ICON‐ESM. ICON‐O relies on innovative structure‐preserving finite volume numerics. We demonstrate the fundamental ability of ICON‐O to simulate key features of global ocean dynamics at both uniform and non‐uniform resolution. Two experiments are analyzed and compared with observations, one with a nearly uniform and eddy‐rich resolution of ∼10 km and another with a telescoping configuration whose resolution varies smoothly from globally ∼80 to ∼10 km in a focal region in the North Atlantic. Our results show first, that ICON‐O on the nearly uniform grid simulates an ocean circulation that compares well with observations and second, that ICON‐O in its telescope configuration is capable of reproducing the dynamics in the focal region over decadal time scales at a fraction of the computational cost of the uniform‐grid simulation. The telescopic technique offers an alternative to the established regionalization approaches. It can be used either to resolve local circulation more accurately or to represent local scales that cannot be simulated globally while remaining within a global modeling framework.

FOCI-MOPS v1 – integration of marine biogeochemistry within the Flexible Ocean and Climate Infrastructure version 1 (FOCI 1) Earth system model

Abstract. The consideration of marine biogeochemistry is essential for simulating the carbon cycle in an Earth system model. Here we present the implementation and evaluation of a marine biogeochemical model, the Model of Oceanic Pelagic Stoichiometry (MOPS) in the Flexible Ocean and Climate Infrastructure (FOCI) climate model. FOCI-MOPS enables the simulation of marine biological processes, i.e. the marine carbon, nitrogen, and oxygen cycles with prescribed or prognostic atmospheric CO2 concentration. A series of experiments covering the historical period (1850–2014) were performed following the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP6 (Coupled Model Intercomparison Project 6) protocols. Overall, modelled biogeochemical tracer distributions and fluxes, transient evolution in surface air temperature, air–sea CO2 fluxes, and changes in ocean carbon and heat contents are in good agreement with observations. Modelled inorganic and organic tracer distributions are quantitatively evaluated by statistically derived metrics. Results of the FOCI-MOPS model, including sea surface temperature, surface pH, oxygen (100–600 m), nitrate (0–100 m), and primary production, are within the range of other CMIP6 model results. Overall, the evaluation of FOCI-MOPS indicates its suitability for Earth climate system simulations.

Enhanced Near-Inertial Waves and Turbulent Diapycnal Mixing Observed in a Cold- and Warm-Core Eddy in the Kuroshio Extension Region

Abstract Shipboard observations of upper-ocean current, temperature–salinity, and turbulent dissipation rate were used to study near-inertial waves (NIWs) and turbulent diapycnal mixing in a cold-core eddy (CE) and warm-core eddy (WE) in the Kuroshio Extension (KE) region. The two eddies shed from the KE were energetic, with the maximum velocity exceeding 1 m s−1 and relative vorticity magnitude as high as 0.6f. The mode regression method was proposed to extract NIWs from the shipboard ADCP velocities. The NIW amplitudes were 0.15 and 0.3 m s−1 in the CE and WE, respectively, and their constant phase lines were nearly slanted along the heaving isopycnals. In the WE, the NIWs were trapped in the negative vorticity core and amplified at the eddy base (at 350–650 m), which was consistent with the “inertial chimney” effect documented in existing literature. Outstanding NIWs in the background wavefield were also observed inside the positive vorticity core of the CE, despite their lower strength and shallower residence (above 350 m) compared to the counterparts in the WE. Particularly, the near-inertial kinetic energy efficiently propagated downward and amplified below the surface layer in both eddies, leading to an elevated turbulent dissipation rate of up to 10−7 W kg−1. In addition, bidirectional energy exchanges between the NIWs and mesoscale balanced flow occurred during NIWs’ downward propagation. The present study provides observational evidence for the enhanced downward NIW propagation by mesoscale eddies, which has significant implications for parameterizing the wind-driven diapycnal mixing in the eddying ocean. Significance Statement We provide observational evidence for the downward propagation of near-inertial waves enhanced by mesoscale eddies. This is significant because the down-taking of wind energy by the near-inertial waves is an important energy source for turbulent mixing in the interior ocean, which is essential to the shaping of ocean circulation and climate. The anticyclonic eddies are widely regarded as a conduit for the downward near-inertial energy propagation, while the cyclonic eddies activity influencing the near-inertial waves propagation lacks clear cognition. In this study, enhanced near-inertial waves and turbulent dissipation were observed inside both cyclonic and anticyclonic eddies in the Kuroshio Extension region, which has significant implications for improving the parameterization of turbulent mixing in ocean circulation and climate models.

A suggested climate service for cyanobacteria blooms in the Baltic Sea – Comparing three monitoring methods

Dense blooms of filamentous cyanobacteria are recurrent phenomena in the Baltic Sea, with occasional negative effects on the surrounding ecosystem, as well as on tourism, human health, aquaculture, and fisheries. Establishing a climate service is therefore suggested; including multi-method observations of cyanobacteria biomass, biodiversity, and biogeography, in correspondence to biotic and abiotic factors. Three different approaches were compared for determination of spatial and temporal variability and trends of the blooms; 1) microscopy-based long-term data, 2) satellite remote sensing, and 3) phycocyanin fluorescence mounted on a merchant vessel. Firstly, microscopy-based data on cyanobacteria biomass from the period 2000–2020 showed that the toxin producing genus Nodularia and non-toxic Aphanizomenon both had summer means of 15 µg C L−1, while Dolichospermum was less dominant with a mean of 8 µg C L−1. Some years also the Kattegat was affected by cyanobacteria blooms, likely transported here by ocean currents. Secondly, the satellite remote sensing time series for the period 2002–2020 indicated that near surface blooms were most frequent in the Northern Baltic Proper and that near surface blooms have increased in the Bothnian Sea, starting later in the season than in the Baltic Proper. The largest extents (i.e., total area covered) were observed in 2005, 2008, and 2018. Thirdly, phycocyanin fluorescence from a flow through sensor mounted on a merchant vessel was used as a proxy for cyanobacteria biomass and correlated to cyanobacteria biomass estimated by microscopy. However, the satellite remote sensing data on surface accumulations showed little resemblance to the data on cyanobacteria biomass based on water sampling and microscopy, interpreted as an effect of methods. Sensors on satellites mainly detect surface accumulations of cyanobacteria while the microscopy data was based on samples 0–10 m, thereby comprising a larger community. Data from satellite remote sensing of cyanobacteria was correlated to the phycocyanin fluorescence indicating that similar bio-optical properties are observed. Finally, results from a downscaled ocean climate model (NEMONordic) were used to produce future scenarios for temperature and salinity, which directly affects cyanobacteria blooms in the Baltic Sea, supposedly by increasing in abundance and change in species composition. Short-term forecasts can be used together with observations for early warning of cyanobacteria blooms, and we suggest an internationally coordinated cyanobacteria observation and warning system for the Baltic Sea area.

Projected climate change impact on a coastal sea-As significant as all current pressures combined.

Climate change influences the ocean's physical and biogeochemical conditions, causing additional pressures on marine environments and ecosystems, now and in the future. Such changes occur in environments that already today suffer under pressures from, for example, eutrophication, pollution, shipping, and more. We demonstrate how to implement climate change into regional marine spatial planning by introducing data of future temperature, salinity, and sea ice cover from regional ocean climate model projections to an existing cumulative impact model. This makes it possible to assess climate change impact in relation to pre‐existing cumulative impact from current human activities. Results indicate that end‐of‐century projected climate change alone is a threat of the same magnitude as the combination of all current pressures to the marine environment. These findings give marine planners and policymakers forewarning on how future climate change may impact marine ecosystems, across space, emission scenarios, and in relation to other pressures.

Seagrass Thermal Limits and Vulnerability to Future Warming

Seagrasses have experienced major losses globally mostly attributed to human impacts. Recently they are also associated with marine heat waves. The paucity of information on seagrass mortality thermal thresholds prevents the assessment of the risk of seagrass loss under marine heat waves. We conducted a synthesis of reported empirically- or experimentally-determined seagrass upper thermal limits (Tlimit) and tested the hypothesis that they increase with increasing local annual temperature. We found that Tlimit increases 0.42± 0.07°C per°C increase in in situ annual temperature (R2 = 0.52). By combining modelled seagrass Tlimit across global coastal areas with current and projected thermal regimes derived from an ocean reanalysis and global climate models (GCMs), we assessed the proximity of extant seagrass meadows to their Tlimit and the time required for Tlimit to be met under high (RCP8.5) and moderate (RCP4.5) emission scenarios of greenhouse gases. Seagrass meadows worldwide showed a modal difference of 5°C between present Tmax and seagrass Tlimit. This difference was lower than 3°C at the southern Red Sea, the Arabian Gulf, the Gulf of Mexico, revealing these are the areas most in risk of warming-derived seagrass die-off, and up to 24°C at high latitude regions. Seagrasses could meet their Tlimit regularly in summer within 50-60 years or 100 years under, respectively, RCP8.5 or RCP4.5 scenarios for the areas most at risk, to more than 200 years for the Arctic under both scenarios. This study shows that implementation of the goals under the Paris Agreement would safeguard much of global seagrass from heat-derived mass mortality and identifies regions where actions to remove local anthropogenic stresses would be particularly relevant to meet the Target 10 of the Aichi Targets of the Convention of the Biological Diversity.

Using Machine Learning at scale in numerical simulations with SmartSim: An application to ocean climate modeling

We demonstrate the first climate-scale, numerical ocean simulations improved through distributed, online inference of Deep Neural Networks (DNN) using SmartSim. SmartSim is a library dedicated to enabling online analysis and Machine Learning (ML) for high performance, numerical simulations. In this paper, we detail the SmartSim architecture and provide benchmarks including online inference with a shared ML model, EKE-ResNet, on heterogeneous HPC systems. We demonstrate the capability of SmartSim by using it to run a 12-member ensemble of global-scale, high-resolution ocean simulations, each spanning 19 compute nodes, all communicating with the same ML architecture at each simulation timestep. In total, 970 billion inferences are collectively served by running the ensemble for a total of 120 simulated years. The inferences are used to predict the oceanic eddy kinetic energy (EKE), which is a variable that is used to tune different turbulence closures in the model and thus directly affects the simulation. The root-mean-square of the error in EKE (as compared to an eddy-resolving simulation) is 20% lower when using the ML-prediction than the previous state of the art. This demonstration is an example of how machine learning methods can be integrated into traditional numerical simulations, replace prognostic equations, and preserve overall simulation stability without significantly affecting the time to solution.

Comparative computational performance of two different techniques for calculation of the sea surface height in a climate ocean model

A modern trend in computational ocean modelling is based on an implicit calculation of the sea surface height, whereby a diagnostic equation is solved. In deriving this equation, in the INMCM5 climate model a time-stepping scheme used, where the stage of adjustment of the Coriolis force to the barotropic gradient is modeled separately, thereby leading to a two-dimensional system of equations with three independent variables. In this paper, an approach to derivation of the sea surface height equation in the climate ocean model is proposed, which allows a reduction of the independent variables from three to one. The computational efficiency of both methods is compared using an analytical solution for the equatorial baroclinic Kelvin wave as a test.

The effect of experiment conditioning on estimates of human influence on extreme weather

Many methods and climate/weather modelling tools have been used over the past decade for assessment of the role of anthropogenic emissions in recent specific weather events (“event attribution”). Differences in the methods and models often correspond to differences in the characterisation, or conditioning, of an observed extreme event within a model, and this might be expected to affect any attribution statement. In practice, however, it may not always be feasible or practical to use the most appropriate method for the question at hand, or to use multiple methods so as to arrive at a generic conclusion. This is especially true given the growing interest in making rapid assessments of extreme events within operational forecast centres. How transferable are conclusions across methods, hence allowing the substitution of one method or modelling tool for another? In this paper we investigate differences in event attribution conclusions across a wide range of experiment designs, running from free-running simulations of atmosphere–ocean climate models, through to weather forecasts constrained to reproduce the nature of the event quite closely. Across a number of recent extreme weather events over Aotearoa New Zealand, we find no systematic differences in conclusions across the various experiment setups. This surprising result offers hope that attribution statements may be transferable across methods, because errors that arise when transferring results across methods are overshadowed by other errors and uncertainties given current technology.

Oceanographic regional climate projections for the Baltic Sea until 2100

Abstract. The Baltic Sea, located in northern Europe, is a semi-enclosed, shallow and tideless sea with seasonal sea-ice cover in its northern sub-basins. Its long water residence time contributes to oxygen depletion in the bottom water of its southern sub-basins. In this study, recently performed scenario simulations for the Baltic Sea including marine biogeochemistry were analysed and compared with earlier published projections. Specifically, dynamical downscaling using a regionally coupled atmosphere–ocean climate model was used to regionalise four global Earth system models. However, as the regional climate model does not include components representing terrestrial and marine biogeochemistry, an additional catchment and a coupled physical–biogeochemical model for the Baltic Sea were included. The scenario simulations take the impact of various global sea level rise scenarios into account. According to the projections, compared to the present climate, higher water temperatures, a shallower mixed layer with a sharper thermocline during summer, less sea-ice cover and greater mixing in the northern Baltic Sea during winter can be expected. Both the frequency and the duration of marine heat waves will increase significantly, in particular in the coastal zone of the southern Baltic Sea (except in regions with frequent upwellings). Nonetheless, due to the uncertainties in the projections regarding regional winds, the water cycle and the global sea level rise, robust and statistically significant salinity changes could not be identified. The impact of a changing climate on biogeochemical cycling is predicted to be considerable but still smaller than that of plausible nutrient input changes. Implementing the proposed Baltic Sea Action Plan, a nutrient input abatement plan for the entire catchment area, would result in a significantly improved ecological status of the Baltic Sea, including reductions in the size of the hypoxic area also in a future climate, which in turn would increase the resilience of the Baltic Sea against anticipated climate change. While our findings regarding changes in heat-cycle variables mainly confirm earlier scenario simulations, they differ substantially from earlier projections of salinity and biogeochemical cycles, due to differences in experimental setups and in input scenarios for bioavailable nutrients.

Bio-Inspired Ocean Exploration

Substantial efforts have been made to expand our knowledge of the physics, biology, chemistry, and geography of the ocean using state-of-the-art measurement tools. With new global projects and technological advances, the collaborative efforts of the Ocean Decade (2021–2030) are well on the way to revolutionizing our knowledge of ocean sciences and sustainability. Yet even today, over three-quarters of the seafloor is still unmapped, more than 90% of marine life still awaits discovery and classification, and the number of ocean sensors required to study global phenomena at sufficient temporal and spatial resolutions is seemingly intractable. To address this challenge, new approaches such as bio-inspired robotics can expand our existing toolbox and bridge this knowledge gap. The concept of biology-inspired engineering has emerged as a powerful tool to complement traditional engineering approaches to technology development. For example, specific swimming features of jellyfish and fish have been applied to a variety of fields, from vehicular propulsion to wind energy to medical diagnostics. In particular, jellyfish are advantageous model organisms because of their energy efficiency, with the lowest known cost of transport compared to other animals, as well as their ubiquity and survivability in various ocean environments. In this article we highlight the evolution of research into jellyfish-inspired robotic constructs and their potential applications in ocean exploration. After initial projects using entirely engineered materials (i.e., jellyfish-​inspired submarine propellers) and tissue engineering methods (i.e., rat cardiac cells seeded on flexible films), recent work to integrate microelectronic systems onto live jellyfish demonstrates that their swimming speeds can be increased (up to three times compared to their baselines) and their energy efficiency can be improved (up to four times compared to their baselines). This shows promise for the robotic control of jellyfish in real-world oceanic environments, where the animals are already distributed globally. Future work can improve the maneuverability of these bio-hybrid jellyfish robots, incorporate miniaturized sensors to profile regions of interest, and ultimately deploy swarms of these low-power, low-cost robots to obtain high-resolution data and improve ocean climate models. The synergy of bio-inspired technologies with existing ocean measurement tools holds promise to push the frontiers of ocean exploration and stewardship.

Local Sea-Level Rise Caused by Climate Change in the Northwest Pacific Marginal Seas Using Dynamical Downscaling

Global climate models (GCMs) have limited capacity in simulating spatially non-uniform sea-level rise owing to their coarse resolutions and absence of tides in the marginal seas. Here, regional ocean climate models (RCMs) that consider tides were used to address these limitations in the Northwest Pacific marginal seas through dynamical downscaling. Four GCMs that drive the RCMs were selected based on a performance evaluation along the RCM boundaries, and the latter were validated by comparing historical results with observations. High-resolution (1/20°) RCMs were used to project non-uniform changes in the sea-level under intermediate (RCP 4.5) and high-end emissions (RCP 8.5) scenarios from 2006 to 2100. The predicted local sea-level rise was higher in the East/Japan Sea (EJS), where the currents and eddy motions were active. The tidal amplitude changes in response to sea-level rise were significant in the shallow areas of the Yellow Sea (YS). Dynamically downscaled simulations enabled the determination of practical sea-level rise (PSLR), including changes in tidal amplitude and natural variability. Under RCP 8.5 scenario, the maximum PSLR was ∼85 cm in the YS and East China Sea (ECS), and ∼78 cm in the EJS. The contribution of natural sea-level variability changes in the EJS was greater than that in the YS and ECS, whereas changes in the tidal contribution were higher in the YS and ECS. Accordingly, high-resolution RCMs provided spatially different PSLR estimates, indicating the importance of improving model resolution for local sea-level projections in marginal seas.

Performance of the Adriatic Sea and Coast (AdriSC) climate component – a COAWST V3.3-based one-way coupled atmosphere–ocean modelling suite: ocean results

Abstract. In this study, the Adriatic Sea and Coast (AdriSC) kilometre-scale atmosphere–ocean climate model covering the Adriatic Sea and northern Ionian Sea is presented. The AdriSC ocean results of a 31-year-long (i.e. 1987–2017) climate simulation, derived with the Regional Ocean Modeling System (ROMS) 3 km and 1 km models, are evaluated with respect to a comprehensive collection of remote sensing and in situ observational data. In general, it is found that the AdriSC model is capable of reproducing the observed sea surface properties, daily temperatures and salinities, and the hourly ocean currents with good accuracy. In particular, the AdriSC ROMS 3 km model demonstrates skill in reproducing the main variabilities of the sea surface height and the sea surface temperature, despite a persistent negative bias within the Adriatic Sea. Furthermore, the AdriSC ROMS 1 km model is found to be more capable of reproducing the observed thermohaline and dynamical properties than the AdriSC ROMS 3 km model. For the temperature and salinity, better results are obtained in the deeper parts than in the shallow shelf and coastal parts, particularly for the surface layer of the Adriatic Sea. The AdriSC ROMS 1 km model is also found to perform well in reproducing the seasonal thermohaline properties of the water masses over the entire Adriatic–Ionian domain. The evaluation of the modelled ocean currents revealed better results at locations along the eastern coast and especially the northeastern shelf than in the middle eastern coastal area and the deepest part of the Adriatic Sea. Finally, the AdriSC climate component is found to be a more suitable modelling framework to study the dense water formation and long-term thermohaline circulation of the Adriatic–Ionian basin than the available Mediterranean regional climate models.

Delayed-Mode Quality Control of Oxygen, Nitrate, and pH Data on SOCCOM Biogeochemical Profiling Floats

The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project has deployed 194 profiling floats equipped with biogeochemical (BGC) sensors, making it one of the largest contributors to global BGC-Argo. Post-deployment quality control (QC) of float-based oxygen, nitrate, and pH data is a crucial step in the processing and dissemination of such data, as in situ chemical sensors remain in early stages of development. In situ calibration of chemical sensors on profiling floats using atmospheric reanalysis and empirical algorithms can bring accuracy to within 3 μmol O2 kg–1, 0.5 μmol NO3– kg–1, and 0.007 pH units. Routine QC efforts utilizing these methods can be conducted manually through visual inspection of data to assess sensor drifts and offsets, but more automated processes are preferred to support the growing number of BGC floats and reduce subjectivity among delayed-mode operators. Here we present a methodology and accompanying software designed to easily visualize float data against select reference datasets and assess QC adjustments within a quantitative framework. The software is intended for global use and has been used successfully in the post-deployment calibration and QC of over 250 BGC floats, including all floats within the SOCCOM array. Results from validation of the proposed methodology are also presented which help to verify the quality of the data adjustments through time.

Adaptive Wavelet Methods for Earth Systems Modelling

This paper reviews how dynamically adaptive wavelet methods can be designed to simulate atmosphere and ocean dynamics in both flat and spherical geometries. We highlight the special features that these models must have in order to be valid for climate modelling applications. These include exact mass conservation and various mimetic properties that ensure the solutions remain physically realistic, even in the under-resolved conditions typical of climate models. Particular attention is paid to the implementation of complex topography in adaptive models. Using wavetrisk as an example, we explain in detail how to build a semi-realistic global atmosphere or ocean model of interest to the geophysical community. We end with a discussion of the challenges that remain to developing a realistic dynamically adaptive atmosphere or ocean climate models. These include scale-aware subgrid scale parameterizations of physical processes, such as clouds. Although we focus on adaptive wavelet methods, many of the topics we discuss are relevant for adaptive mesh refinement (AMR).

Full‐Depth Scalings for Isopycnal Eddy Mixing Across Continental Slopes Under Upwelling‐Favorable Winds

AbstractMesoscale eddy mixing profoundly modulates the ocean tracer budgets, and is typically parameterized via the isopycnal eddy diffusivity in ocean climate models. However, relatively little is known about the magnitude/structure of isopycnal eddy diffusivity across continental slopes, which hinders the understanding and prediction of shelf‐open ocean exchanges. In this study, we quantify the isopycnal eddy diffusivity in a suite of eddy‐resolving, process‐oriented simulations of mesoscale turbulence over continental slopes under upwelling‐favorable winds, a configuration that commonly arises around the margins of subtropical gyres. Cross‐shore eddy diffusivity is found to be suppressed in the upper open ocean occupied by strong alongshore flows, but enhanced at depths where alongshore flows are weakened, a finding that is consistent with the enhancement of eddy mixing near the steering level. Over continental slopes, eddy diffusivity also strengthens at mid‐depths, but almost vanishes near the seafloor. To theoretically constrain the simulated eddy fluxes, we examine the scaling of eddy diffusivity proposed by Ferrari and Nikurashin (2010, https://doi.org/10.1175/2010JPO4278.1), which accounts for the suppression of eddy mixing induced by the relative propagation of eddies to the mean flow. We show that, apart from the mean‐flow suppression effect, the eddy anisotropy effect induced by steep topography shapes both the horizontal and vertical structures of cross‐shore eddy diffusivity. Finally, we propose prospective closures of the eddy propagation speed and eddy anisotropy effect over continental slopes using the large‐scale flow and bathymetric quantities. This work offers a basis upon which a “slope‐aware” parameterization of mesoscale eddy mixing can be developed.

The Irish Atlantic CoCliME case study configuration, validation and application of a downscaled ROMS ocean climate model off SW Ireland

This ocean numerical modelling study is focused on the shelf waters off southwest Ireland. Outputs from the model are used to provide scientists and policy makers with climate change information related to oceanic conditions that influence harmful algal blooms in the region. Four simulations were developed to include a 20-year hindcast simulation (1997–2016), a 31-year historical simulation (1975–2005), and two 30-year future climate model projections (2006–2035) for the representative carbon pathways (RCP) 4.5 and 8.5 scenarios. We evaluated the hindcast model skill by comparing the simulation outputs with measured observational data and calculated statistics such as the bias, and the root mean square error (RMSE). The observations consisted of satellite sea surface temperature and, CTD temperature and salinity profile data collected in Irish waters. The sea surface temperature RMSEs between the 20-year hindcast simulation and the satellite data were ca. 0.50°C. A minimum RMSE of ca. 0.20°C was recorded in December 2015. The basin-averaged bias (difference) and RMSE for near bottom temperature between the RCP 4.5 and the historical simulation was 0.14°C and 0.27°C respectively. The sea surface temperature anomaly pattern for the RCP 8.5 shows warming across the whole model domain. The sea surface salinity and near bottom salinity climate simulation anomaly maps indicate a general freshening on the southwest Irish shelf. A change of ca. 0.2 sea surface salinity and near bottom salinity was observed. The RCP 8.5 simulation shows the highest current velocities when compared to other simulations. The Irish coastal current pattern under the RCP 8.5 scenario appears very intense and well defined with a velocity > 20 [cm/s].

Buoyancy and Wind Driven Changes in Subantarctic Mode Water During 2004–2019

AbstractThe Subantarctic Mode Water (SAMW) is a thick low‐stratified subsurface water mass located north of the Antarctic Circumpolar Current, and it is important for heat uptake. How the SAMW has changed and what drives that change remain to be quantified. By synthesizing observations, eddy‐resolving ocean model hindcasts and climate model simulations, we show that the SAMW has become warmer, fresher, lighter, and weaker in recent decades. As revealed by partially coupled model experiments, the SAMW core has remained at a constant depth since 2004 due to a balance between the enhanced wind stress curl, which deepens the SAMW core, and the buoyancy gain, which shallows the core. The lightening and weakening of the SAMW mainly result from the increased surface buoyancy gain due to climate warming, while the enhanced westerlies play a secondary role in slowing this trend. As greenhouse warming continues, the increased stratification will keep weakening the SAMW.

Generating a High-Resolution Time-Series Ocean Surface Net Radiation Product by Downscaling J-OFURO3

The ocean surface net radiation ( $R_{n}$ ) characterizing ocean surface radiation budget is a key variable in ocean climate modeling and analysis. In this study, a downscaling scheme was developed to generate a high-resolution (0.05°) time-series (2002–2013) daily ocean surface $R_{n}$ from the third-generation Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO3) at 0.25° based on the Advanced Very-High-Resolution Radiometer (AVHRR) top-of-atmosphere (TOA) observations (AVH021C) and other ancillary information (Clearness Index and cloud mask). This downscaling scheme includes the statistical downscaling models and the residual correction post-processing. A series of angle-dependent downscaling statistical models were established between the daily ocean surface $R_{n}$ in J-OFURO3 and the AVHRR TOA data, and then, the residual correction was conducted to the model estimates $R_{n}$ _AVHRR_est to obtain the final downscaled data set $R_{n}$ _AVHRR. Validation against the measurements from 57 moored buoy sites in six ocean observing networks shows the high accuracy of the downscaled estimates $R_{n}$ _AVHRR_est with a $R^{2}$ of 0.88, RMSE of 23.44 $\text{W}\cdot \text{m}^{-2}$ , and bias of $-0.14\,\,\text{W}\cdot \text{m}^{-2}$ under all-sky condition. The results of the spatio-temporal analysis in $R_{n}$ _AVHRR and intercomparison with Cloud and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) products also indicated that the superior of the $R_{n}$ _AVHRR with more detailed information especially in the hot spot regions, such as central tropical Pacific (warming pool), Atlantic and Equatorial Eastern Indian Ocean (EIO).