A document by Nathan Lenssen. Click on the document to view its contents.

This study observed seasonal trends and inferred drivers of CO2 biogeochemistry at the air-water interface of Lake Superior. Underway carbon dioxide partial pressure pCO2 was measured in surface water during 69 transects spanning ice free seasons of 2019-2022. These data comprise the first multiannual pCO2 time series in the Laurentian Great Lakes. Surface water pCO2 was closely tied to increasing atmospheric pCO2 by a 100 day CO2 equilibration timescale, while seasonal variability was controlled equally by thermal and biophysical drivers during the ice-free season. Comparison to previous modeling efforts indicates that Lake Superior surface pCO2 increased at a similar rate as the atmosphere over the preceding two decades. Spatial heterogeneity in CO2 dynamics was highlighted by a salinity-based delineation of “riverine” and “pelagic” regimes, each of which displayed a net CO2 influx over Julian days 100-300 on the order of 30 Gmol C. These findings refine previous estimates of Lake Superior C fluxes, support predictions of anthropogenic CO2 invasion, point to new observation strategies for large lakes, and highlight an urgent need for studies of changes to lacustrine C cycling.

The El Niño-Southern Oscillation (ENSO) phenomenon – the dominant source of climate variability on seasonal to multi-year timescales – is predictable a few seasons in advance. Forecast skill at longer multi-year timescales has been found in a few models and forecast systems, but the robustness of this predictability across models has not been firmly established owing to the cost of running dynamical model predictions at longer lead times. In this study, we use a massive collection of multi-model hindcasts performed using model analogs to show that multi-year ENSO predictability is robust across models and arises predominantly due to skillful prediction of multi-year La Niña events following strong El Niño events.

Initialized climate model simulations have proven skillful for near-term predictability of the key physical climate variables. By comparison, predictions of biogeochemical fields like ocean carbon flux, are still emerging. Initial studies indicate skillful predictions are possible for lead-times up to six years at global scale for some CMIP6 models. However, unlike core physical variables, biogeochemical variables are not directly initialized in existing decadal preciction systems, and extensive empirical parametrization of ocean-biogeochemistry in Earth System Models introduces a significant source of uncertainty. Here, we propose a new approach for improving the skill of decadal ocean carbon flux predictions using observationally-constrained statistical models, as alternatives to the ocean-biogeochemistry models. We use observations to train multi-linear and neural-network models to predict the ocean carbon flux. To account for observational uncertainties, we train using six different observational estimates of the flux. We then apply these trained statistical models using input predictors from the Canadian Earth System Model (CanESM5) decadal prediction system to produce new decadal predictions. Our hybrid GCM-statistical approach significantly improves prediction skill, relative to the raw CanESM5 hindcast predictions over 1990-2019. Our hybrid-model skill is also larger than that obtained by any available CMIP6 model. Using bias-corrected CanESM5 predictors, we make forecasts for ocean carbon flux over 2020-2029. Both statistical models predict increases in the ocean carbon flux larger than the changes predicted from CanESM5 forecasts. Our work highlights the ability to improve decadal ocean carbon flux predictions by using observationally-trained statistical models together with robust input predictors from GCM-based decadal predictions.

Seismic ocean thermometry uses sound waves generated by repeating earthquakes to measure temperature change in the deep ocean. In this study, waves generated by earthquakes along the Japan Trench and received at Wake Island are used to constrain temperature variations in the Kuroshio Extension region. This region is characterized by energetic mesoscale eddies and large decadal variability, posing a challenging sampling problem for conventional ocean observations. The seismic measurements are obtained from a hydrophone station off and a seismic station on Wake Island, with the seismic station's digital record reaching back to 1997. These measurements are combined in an inversion for the time and azimuth dependence of the range-averaged deep temperatures, revealing lateral and temporal variations due to Kuroshio Extension meanders, mesoscale eddies, and decadal water mass rearrangements. These results highlight the potential of seismic ocean thermometry for better constraining the variability and trends in deep-ocean temperatures. By overcoming the aliasing problem of point measurements, these measurements complement existing ship- and float-based hydrographic measurements.

Satellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly-known microstructural properties that vary strongly in time and space. A consequence is that surface-sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an “empirical state” method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical-empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This “empirical state” approach could be used to solve many other problems of earth system data assimilation.

Mesoscale eddies modulate the stratification, mixing and dissipation pathways, and tracer transport of oceanic flows over a wide range of spatiotemporal scales. The parameterization of buoyancy and momentum fluxes associated with mesoscale eddies thus presents an evolving challenge for ocean modelers, particularly as modern climate models approach eddy-permitting resolutions. Here we present a parameterization targeting such resolutions through the use of a subgrid mesoscale eddy kinetic energy budget (MEKE) framework. Our study presents two novel insights: (1) both the potential and kinetic energy effects of eddies may be parameterized via a kinetic energy backscatter, with no Gent-McWilliams along-isopycnal transport; (2) a dominant factor in ensuring a physically-accurate backscatter is the vertical structure of the parameterized momentum fluxes. We present simulations of 1/2$^\circ$ and 1/4$^\circ$ resolution idealized models with backscatter applied to the equivalent barotropic mode. Remarkably, the global kinetic and potential energies, isopycnal structure, and vertical energy partitioning show significantly improved agreement with a 1/32$^\circ$ reference solution. Our work provides guidance on how to parameterize mesoscale eddy effects in the challenging eddy-permitting regime.

It is well known that the motion of flexible vegetation leads to drag reduction in comparison to rigid vegetation. In this study, we use a numerical model to investigate how the detailed motion of kelp fronds in response to forcing by surface gravity waves can impact the drag exerted by the kelp on waves. We find that this motion can be characterized in terms of three dimensionless numbers: (1) the ratio of hydrodynamic drag to buoyancy, (2) the ratio of blade length to wave excursion, and (3) the Keulegan-Carpenter number, which measures the ratio of drag to inertial forces. We quantify drag reduction, and find that inertial forces can significantly impact the amplitude of kelp motion and amount of kelp drag reduction. Under certain wave conditions, inertial forces can cause kelp fronds to accelerate more quickly relative to the wave, which can lead to increased drag reduction and reduced wave energy dissipation. In some conditions, frond motion leads to drag augmentation in comparison to rigid fronds. Additionally, we discuss other features of kelp motion, such as the degree of asymmetry, and their relationship with enhanced drag reduction.

In this paper we analyse the material coherence of oceanic eddies sampled by ships during 9 oceanographic campaigns, 8 of which were conducted in the Atlantic Ocean (EUREC4A-OA, M124, MSM60, MSM74, M160, HM2016611, KB2017606, KB2017618) and one in the Indian Ocean (Physindien 2011). After reviewing previous definitions of coherence, we perform a relative error analysis of our data. To identify the eddy cores and assess the material coherence of the well-sampled eddies (19 out of 28 eddies in total), we use criteria based on active tracers (potential vorticity, temperature, salinity). The maximum tracer anomaly is often below the pycnocline (below the frequency stratification maximum). Therefore, some eddies are not considered to be materially coherent using only surface data, whereas they are when we study their three-dimensional structure. Two methods are then presented to extrapolate eddy volumes from a single ship section. The horizontal and vertical resolutions of the data are critical for this determination. Our results show that the outermost closed contour of the Brunt-Vaisala frequency is a good approximation for the materially coherent eddy core to determine the eddy volume.

A document by Ben Cala. Click on the document to view its contents.

Recent field observations suggest that the air-sea momentum flux (or the drag coefficient) is significantly reduced when the dominant wind-forced surface waves are misaligned from local wind. Such conditions may occur under rapidly changing strong winds (such as under tropical cyclones) or in coastal shallow waters where waves are refracted by bottom topography. A recent Large Eddy Simulation (LES) study also shows that the drag coefficient is reduced by a misaligned strongly forced wave train (with a small wave age of 1.37). In order to investigate more realistic field conditions, this study employs LES to examine the effect of a misaligned (up to 90o) surface wave train over a wide range of wave age up to 10.95. For all wave ages examined, the drag coefficient is reduced compared to the flat surface condition when the misalignment angle exceeds around 22.5-45o. The drag reduction may occur even if the form drag of the wave train is positive.

A document by Garrett Graham. Click on the document to view its contents.

A document by Shaokun Deng. Click on the document to view its contents.

A document by Ramin Familkhalili. Click on the document to view its contents.

Convective rolls contribute largely to the exchange of momentum, sensible heat and moisture in the boundary layer. They have been shown to reinforce air-sea interaction under strong wind conditions. This raises the question of how surface turbulent fluxes can, in turn, affect the rolls. Representing the air-sea exchanges during extreme wind conditions is a major challenge in weather prediction and can lead to large uncertainties in surface wind speed. The sensitivity of rolls to different representations of surface fluxes is investigated using Large Eddy Simulations. The study focuses on the Mediterranean windstorm Adrian, where convective rolls resulting from thermal and dynamical instabilities are responsible for the transport of strong winds to the surface. Considering sea spray in the parameterization of surface fluxes significantly influences roll morphology. Sea spray increases heat fluxes and favors convection. With this more pronounced thermal instability, the rolls are 30\% narrower and extend over a greater height, and the downward transport of momentum is intensified by 40\%, resulting in higher wind speeds at the surface. Convective rolls vanish within a few minutes in the absence of momentum fluxes, which maintain the wind shear necessary for their organization. They also quickly weaken without sensible heat fluxes, which feed the thermal instability required for their development, while latent heat fluxes play minor role. These findings emphasize the necessity of precisely representing the processes occurring at the air-sea interface, as they not only affect the thermodynamic surface conditions but also the vertical transport of momentum within the windstorm.

Ocean ventilation translates atmospheric forcing into the ocean interior. The Southern Ocean is an important ventilation site for heat and carbon and is likely to influence the outcome of anthropogenic climate change. We conduct an extensive backwards-in-time trajectory experiment to identify spatial and temporal patterns of ventilation. Temporally, almost all ventilation occurs between August and November. Spatially, ‘hotspots’ of ventilation account for 60% of open-ocean ventilation on a 30 year timescale; the remaining 40% ventilates in a circumpolar pattern. The densest waters ventilate on the Antarctic shelf, primarily near the Antarctic Peninsula (40%) and the west Ross sea (20%); the remaining 40% is distributed across East Antarctica. Shelf-ventilated waters experience significant densification outside of the mixed layer.

Intense atmosphere-ocean-ice interactions in the Ross Sea play a vital role in global overturning circulation by supplying saline and dense shelf waters. Since the 1960s, freshening of the Ross Sea shelf water has led to a decline in Antarctic Bottom Water formation. Since the early 2010s, however, the salinity of the western Ross Sea has rebounded. This study adopts an ocean-sea ice model to investigate the causes of this salinity rebound. Model-based salinity budget analysis indicates that the salinity rebound was driven by increased brine rejection from sea ice formation, triggered by the nearly equal effects of local anomalous winds and surface heat flux. The local divergent wind anomalies promoted local sea ice formation by creating a thin ice area, while a cooling heat flux anomaly decreased the surface temperature, increasing sea ice production. This highlights the importance of understanding local climate variability in projecting future dense shelf water change.

The concentration of chlorophyll-a (CHL) is an important proxy for autotrophic biomass and primary production in the ocean. Quantifying trends and variability in CHL are essential to understanding how marine ecosystems are affected by climate change. Previous analyses have focused on assessing trends in CHL mean, but little is known about observed changes in CHL extremes and variance. Here we apply a quantile regression model to detect trends in CHL distribution over the period of 1997-2022 for several quantiles. We find that the magnitude of trends in upper quantiles of global CHL (>90th) are larger than those in lower quantiles (≤50th) and in the mean, suggesting a growing asymmetry in CHL distribution. On a regional scale, trends in different quantiles are statistically significant at high latitude, equatorial, and oligotrophic regions. Assessing changes in CHL distribution has potential to yield a more comprehensive understanding of climate change impacts on CHL.

Phytoplankton stoichiometry modulates the interaction between carbon, nitrogen and phosphorus cycles, yet most biogeochemical models represent phytoplankton C:N:P as constants. This simplification has been linked to Earth System Model (ESM) biases and potential misrepresentation of biogeochemical responses to climate change. Here we integrate key elements of the Adaptive Trait Optimization Model (ATOM) for phytoplankton stoichiometry with the Carbon, Ocean Biogeochemistry and Lower Trophics (COBALT) ocean biogeochemical model. Within a series of global ocean-ice-ecosystem retrospective simulations, ATOM-COBALT reproduced observations of particulate organic matter N:P, and compared to static N:P, exhibited reduced phytoplankton P-limitation, enhanced N-fixation, and increased low-latitude export, leading to improved consistency with observations. Two mechanisms together drove these patterns: the growth hypothesis and frugal P-utilization during scarcity. The addition of translation compensation- differential temperature dependencies of photosynthetic relative to biosynthetic processes- led to relatively modest strengthening of N:P variations and biogeochemical responses relative to growth-plus-frugality. Comparison of the multi-mechanism model herein against frugality-only models suggest that both can capture observed N:P patterns and produce qualitatively similar biogeochemical effects. There are, however, quantitative response differences and different responses across N:P mechanisms are expected under climate change- with the growth rate mechanism adding a distinct biogeochemical footprint in highly-productive low-latitude regions. These results suggest that variable phytoplankton N:P makes some biogeochemical processes resilient to environmental changes, and support using dynamic N:P formulations with the ocean biogeochemical component of next generation of ESMs.

Coastal wetlands play a critical role in maintaining the health of our planet by providing essential ecosystem services such as flood control, water purification, and critical habitat for a vast variety of species. However, their vulnerability to climate change and sea-level rise poses a significant threat to these services. Therefore, to provide long-term protection against erosion and sea-level rise, a shoreline restoration project was designed in coastal North Carolina (US) to use dredged sediments and rebuild the historic footprint of an eroded shoreline marsh adjacent to a regional airport's runway. To evaluate the potential benefits of this restoration project, the Sea Level Affecting Marsh Model (SLAMM) was employed. The developed model was run at a high spatial resolution (1m cell size) to investigate the effects of sea-level rise on the wetland communities and estimate the potential benefits of using dredged sediment to increase surface elevation. The results of the SLAMM model indicated that the restoration project offers substantial benefits in terms of shoreline marsh persistence through 2050, under all sea-level rise scenarios. This finding is significant because it shows that the restoration project can provide immediate benefits and help sustain the coastal wetlands in the face of sea-level rise. However, the benefits of the restoration project start to diminish after 2050, and differences among marsh areas in the restored and unrestored scenario decrease with increasing rates of sea-level rise. Therefore, it is essential to develop adaptive management strategies to ensure the long-term persistence of coastal wetlands and their ecosystem services. Overall, this study shows that the beneficial use of dredged sediments as a nature-based solution can effectively sustain coastal habitats threatened by sea-level rise and erosion.

The Congolese upwelling system (CUS), located along the West African coast north of the Congo River, is one of the most productive and least studied systems in the Gulf of Guinea. The Sea Surface Temperature minimum in the CUS occurs in austral winter, when the winds are weak and not particularly favorable to coastal upwelling. Here, for the first time, we use a high-resolution regional ocean model to identify the key atmospheric and oceanic processes that control the seasonal evolution of the mixed-layer temperature in a 1°-wide coastal band from 6°S to 4°S. The model is in good agreement with observations on seasonal timescales, and in particular reproduces the signature of the surface upwelling during the austral winter, the shallow mixed-layer due to salinity stratification, and the signature of coastal wave propagation. The analysis of the mixed-layer heat budget reveals a competition between warming by air-sea fluxes, dominated by the solar flux throughout the year, and cooling by vertical mixing at the base of the mixed-layer, as other tendency terms remain weak. The seasonal cooling is induced by vertical mixing, but is not controlled by the local wind. A subsurface analysis shows that remotely-forced coastal trapped waves raise the thermocline from April to August, which strengthens the vertical temperature gradient at the base of the mixed-layer and leads to the mixing-induced seasonal cooling in the Congolese upwelling system.

The Surface Ocean CO2 Atlas (SOCAT) of CO2 fugacity (fCO2) observations is a key resource supporting annual assessments of CO2 uptake by the ocean and its side effects on the marine ecosystem. SOCAT data are usually released with a lag of up to 1.5 years which hampers timely quantification of recent variations of carbon fluxes between the Earth System components, not only with the ocean. This study uses a statistical ensemble approach to analyse fCO2 with a latency of one month only based on the previous SOCAT release and a series of predictors. A retrospective prediction for the years 2021-2022 is made to test the model skill, followed by the generation of fCO2 and fluxes from January to August in 2023. Results indicate a modest degradation of the model skill in prediction mode and open the possibility to provide robust information about marine carbonate system variables with low latency.

A document by Shelley Stall. Click on the document to view its contents.

Marine extreme events such as marine heatwaves, ocean acidity extremes and low oxygen extremes can pose a substantial threat to marine organisms and ecosystems. Such extremes might be particularly detrimental (i) when they occur compounded in more than one stressor, and (ii) when the extremes extend substantially across the water column, restricting the habitable space for marine organisms. Here, we use daily output from a hindcast simulation (1961-2020) from the ocean component of the Community Earth System Model (CESM) to characterise such column-compound extreme events (CCX), employing a relative threshold approach to identify the extremes and requiring them to extend vertically over at least 50m. The diagnosed CCXs are prevalent, occupying worldwide in the 1960s about 1% of the volume contained within the top 300m. Over the duration of our simulation, CCXs become more intense, last longer, and occupy more volume, driven by the trends in ocean warming and ocean acidification. For example, the triple CCX have expanded 24-fold, now last 3-times longer, and have become 6-times more intense since the early 1960s. Removing this effect with a moving baseline permits us to better understand the key characteristics of the CCXs. They last typically about 10 to 30 days and predominantly occur in the tropics and the high latitudes, regions of high potential biological vulnerability. Overall, the CCXs fall into 16 clusters, reflecting different patterns and drivers. Triple CCX are largely confined to the tropics and the North Pacific, and tend to be associated with the El Nino-Southern Oscillation.