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Global ocean oxygen loss is projected to persist in the future, but Earth system models (ESMs) have not yet provided a consistent picture of how it will influence the largest oxygen minimum zone (OMZ) in the tropical Pacific. We examine the change in the Pacific OMZ volume in an ensemble of ESMs from the CMIP6 archive, considering a broad range of oxygen (O2) thresholds relevant to biogeochemical cycles and ecosystems (5–160 µmol/kg). Despite OMZ biases in the historical period of the simulations, the ESM ensemble projections consistently fall into three regimes across ESMs: an expansion of low oxygenated waters (+0.8 [0.6, 1.0] × 1016 m3/century for O2 ≤ 120 µmol/kg, ESM median and interquartile range); a slight contraction of the OMZ core although more uncertain across ESMs (−0.1 [−0.5, 0.0] × 1016 m3/century for O2 ≤ 20 µmol/kg); and at the transition from contraction to expansion regimes, a spatial redistribution but near‐zero change in the volume of hypoxic waters (0.0 [−0.3, +0.1] × 1016 m3/century for O2 ≤ 60 µmol/kg). Changes in circulation and biology dictate the shift from expansion to contraction. Specifically, reduced subtropical ventilation controls the expansion of low oxygenated waters, while a combination of circulation and biological changes explains the contraction of the core (likely changes in mixing, reduced intermediate ventilation and oxygen demand). Increased model complexity (e.g., ecosystem dynamics and equatorial circulation) likely stabilize the OMZ response, suggesting that future changes might lie in the lower bound of current projections. The expansion of low oxygenated waters which delimit the optimum habitat of numerous marine species would severely impact ecosystems and ecosystem services. Plain Language Summary Expansion of ocean areas with low oxygen concentrations threatens marine animals and could increase the production of greenhouse gases that warm the Earth. An essential question is how these low oxygen “blobs,” called oxygen minimum zones (OMZs), will evolve in the future. OMZs are difficult to simulate in climate models because they result from two strongly opposing processes: Physical supply of oxygen via the ocean circulation and oxygen consumption by biological respiration. Previous studies using older generations of models could not conclude whether the largest of these zones in the Pacific would grow or shrink in the future. We show that the Pacific OMZ will grow in response to climate change but that its core—where oxygen is lowest—will shrink. This expansion of the broad OMZ is caused by a weaker supply of oxygen rich waters by ocean circulation, whereas the contraction of the OMZ is influenced by a combination of changes in ocean circulation and biological activity. The expansion of the outer OMZ is likely bad news for the marine species that suffer in low oxygen conditions, and the people that depend on them (fishing and tourism). Key Points The Pacific oxygen minimum zone (OMZ) will expand but its core might contract under sustained anthropogenic forcing Non‐thermal changes (ocean circulation and biology) dictate the shift from core contraction to OMZ expansion The OMZ expansion would compress the habitat of marine species and impact ecosystems and ecosystem services

Coastal ecosystems release or absorb carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), but the net effects of these ecosystems on the radiative balance remain unknown. We compiled a dataset of observations from 738 sites from studies published between 1975 and 2020 to quantify CO2, CH4 and N2O fluxes in estuaries and coastal vegetation in ten global regions. We show that the CO2-equivalent (CO2e) uptake by coastal vegetation is decreased by 23–27% due to estuarine CO2e outgassing, resulting in a global median net sink of 391 or 444 TgCO2e yr−1 using the 20- or 100-year global warming potentials, respectively. Globally, total coastal CH4 and N2O emissions decrease the coastal CO2 sink by 9–20%. Southeast Asia, North America and Africa are critical regional hotspots of GHG sinks. Understanding these hotspots can guide our efforts to strengthen coastal CO2 uptake while effectively reducing CH4 and N2O emissions.The authors show that estuarine and coastal vegetation are collectively a greenhouse gas (GHG) sink for the atmosphere, but methane and nitrous oxide emissions counteract the carbon dioxide uptake. Critical coastal GHG sink hotspots are identified in Southeast Asia, North America and Africa.

Recent mass loss from ice sheets and ice shelves is now persistent and prolonged enough that it impacts downstream oceanographic conditions. To demonstrate this, we use an ensemble of coupled GISS‐E2.1‐G simulations forced with historical estimates of anomalous freshwater, in addition to other climate forcings, from 1990 through 2019. There are detectable differences in zonal‐mean sea surface temperatures (SST) and sea ice in the Southern Ocean, and in regional sea level around Antarctica and in the western North Atlantic. These impacts mostly improve the model's representation of historical changes, including reversing the forced trends in Antarctic sea ice. The changes in SST may have implications for estimates of the SST pattern effect on climate sensitivity and for cloud feedbacks. We conclude that the changes are sufficiently large that model groups should strive to include more accurate estimates of these drivers in all‐forcing historical simulations in future coupled model intercomparisons. Plain Language Summary Simulations of recent historical periods are a key test of climate model reliability and skill. These model simulations require an accounting of all the drivers of climate change. We show that the impact of historical changes in freshwater fluxes from ice sheets and ice shelves on the ocean (through changes in salinity and stratification) are detectable in sea surface temperature and sea ice trends, and help improve the match between the modeled climate changes and observations. We recommend that more accurate estimates of these drivers be included in all climate simulations that do not explicitly model ice sheets and ice shelves. Key Points The response to anomalous meltwater from ice sheets and shelves is large enough for it to be a forcing in historical climate simulations When the GISS model includes these drivers, Southern Ocean SST and sea ice trends better match observations Steric and dynamic impacts on regional sea level in parts of the North Atlantic and coastal Antarctica are significant

When different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections. Plain Language Summary Extreme storm surges, rainfall or river discharge can cause flooding. When these events happen at the same time, even more severe flooding may follow. Climate change could affect the odds that drivers of flooding coincide, potentially leading to larger flood risk. However, few scientists have tried to compute such changes, using only a few different computer models of our climate. Here, we use a much larger set of climate models to compute how the odds that an extreme storm surge coincides with extreme precipitation could change in the future. We find that at the coasts of northwestern Europe, those odds will increase, whereas in southwestern Europe, they will mostly decrease. On average, the changes will be as large as 36%–49% of the current odds, depending on whether the concentration of greenhouse gases in the atmosphere will increase by a medium or a large amount. When we use smaller sets of climate models for our calculations, we get substantially different results in some cases. In conclusion, by using a larger set of climate models than previous studies, we have made more robust computations of how the odds that extreme storm surges and precipitation coincide will change in Europe. Key Points We project changes in the joint probability of storm surge and precipitation extremes based on a large ensemble of model simulations from the Coupled Model Intercomparison Project 6 The joint probability will increase in the northwest and decrease in the southwest of Europe, with an average absolute magnitude of 36%–49% Especially under lower emissions, often more than 5 or 6 climate model simulations are needed to draw robust conclusions on these changes

Because of the computational costs of computing storm surges with hydrodynamic models, projections of changes in extreme storm surges are often based on small ensembles of climate model simulations. This may be resolved by using data-driven storm-surge models instead, which are computationally much cheaper to apply than hydrodynamic models. However, the potential performance of data-driven models at predicting extreme storm surges, which are underrepresented in observations, is unclear because previous studies did not train their models to specifically predict the extremes. Here, we investigate the performance of neural networks at predicting extreme storm surges at 9 tide-gauge stations in Europe when trained with a cost-sensitive learning approach based on the density of the observed storm surges. We find that density-based weighting improves both the error and timing of predictions of exceedances of the 99th percentile made with Long-Short-Term-Memory (LSTM) models, with the optimal degree of weighting depending on the location. At most locations, the performance of the neural networks also improves by exploiting spatiotemporal patterns in the input data with a convolutional LSTM (ConvLSTM) layer. The neural networks generally outperform an existing multi-linear regression model, and at the majority of locations, the performance of especially the ConvLSTM models approximates that of the hydrodynamic Global Tide and Surge Model. While the neural networks still predominantly underestimate the highest extreme storm surges, we conclude that addressing the imbalance in the training data through density-based weighting helps to improve the performance of neural networks at predicting the extremes and forms a step forward towards their use for climate projections.

AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state‐of‐the‐art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multi‐depth levels of ocean data. We show that the ocean emulator—Samudra—which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remain stable, requiring further work. Plain Language Summary AI tools are extremely effective in making fast and accurate predictions on weather to seasonal timescales. Capturing decadal to centennial changes, which arise from ocean dynamics, remains an outstanding challenge. We built an advanced AI model called “Samudra” to simulate global ocean behavior. Samudra is trained on simulated data from a state‐of‐the‐art ocean climate model and predicts key ocean features such as sea surface height, currents, temperature, and salinity throughout the ocean's depth. Samudra can accurately recreate patterns in ocean variables, including year‐to‐year changes. It is stable over centuries and is 150 times faster than traditional ocean models. However, Samudra still faces challenges in balancing stability with accurately predicting the effects of external factors (like climate trends), and further improvements are needed to address this limitation. Key Points We develop a global, 3D, ocean autoregressive machine learning emulator for climate studies The emulator, based on a UNet architecture, is stable for centuries, producing accurate climatologies and variability of ocean variables The emulator training is robust to changes in seeds and initial conditions in the data

Oceanic tracers are transported by oceanic motions of all scales, but only the large-scale motions are resolved by the present-day Earth system models. In these models, the unresolved lateral sub-gridscale tracer transport is generally parameterized through diffusive closures with a scale-independent diffusion coefficient. However, evidence from observations and theory suggests that diffusivity varies spatially and is length-scale dependent. Here we provide new scale-dependent quantification of the global surface diffusivities. To this end we use a recently developed statistical inversion method, MicroInverse, to diagnose horizontal surface diffusivities from observed sea surface temperature and idealized model simulation. We compare the results to theoretical estimates of mixing by the large-scale shear and by the sub-gridscale velocity fluctuations. The diagnosed diffusivity magnitude peaks in the tropics and western boundary currents with minima in the subtropical gyres (~3000 and ~100 m 2 s −1 ) at ~40-km scale, respectively. Focusing on the 40–200-km length scale range, we find that the diffusivity magnitude scales with the length scale to a power n that is between 1.22 and 1.54 (90% confidence) in the tropics and also peaks at values above 1 in the boundary currents. In the midlatitudes we find that 0.58 < n < 0.87 (90% confidence). Comparison to the theory suggests that in regions with n > 1 the horizontal mixing is dominated by large-scale shear, whereas in regions where n < 1 the horizontal mixing is due to processes that are small compared to the 40–200-km length scale range considered in this study.

Recent projections indicate that the range of the annual sea‐level cycle (ASLC) may increase, affecting flood risk, groundwater, and ecosystems. However, existing projections have several limitations, such as their exclusion of the inverse‐barometer effect, their uncertainty due to internal variability, and/or their regional focus. Furthermore, the historical ASLC in the climate models used for these projections was not extensively evaluated. Here, we address these limitations using a large ensemble of simulations from the Coupled Model Intercomparison Project 6. Compared to observations, we find that four climate models perform particularly poorly. Excluding these, our multi‐model median projections for 2071–2100 indicate an average increase in the ASLC range at tide‐gauge locations of 7.9% under a high emissions scenario. Changes under lower scenarios are similar spatially but have a smaller magnitude. Regions with above‐average changes include Northwestern Europe, the Mediterranean, Asia, and the North Pacific. In several regions, the inverse‐barometer effect substantially contributes to the total changes. The median projected shifts in the peak month of the ASLC are mostly small, but like for the projected range changes, larger changes cannot be excluded given the substantial uncertainties. Finally, we find that the ASLC changes translate to differences between seasonal and annual mean sea‐level changes of up to 5.4 cm under the highest emissions scenario. Consequently, sea‐level projections that exclude seasonal changes under‐ or overestimate total sea‐level change in specific seasons, sometimes by more than 8%. This will influence projections of hazards and impacts tied to specific seasons. Plain Language Summary The average sea level has an annual cycle, just like temperature. In this paper, we use simulations of climate models to estimate how the annual cycle of sea level will change in the future due to climate change, in a more comprehensive way than previous studies. We find that at most places, the annual sea‐level cycle will get larger, especially if we will not substantially reduce our emissions of greenhouse gases. This also means that on the long term, the average sea level will rise faster in some seasons than in others, which is an effect that previous studies did not yet explicitly consider at a global scale. In several regions, not only changes in the ocean, but also changes in atmospheric pressure are responsible. The changes in the annual sea‐level cycle that we project may affect flood risk, groundwater flow, and the health of ecosystems. Key Points The range of the annual sea‐level cycle may substantially change, especially in Europe, the Mediterranean, Asia, and the North Pacific In several regions, the inverse‐barometer effect is at least as an important driver of these changes as ocean dynamic sea‐level change Sea‐level projections based on annual means under‐ or overestimate sea‐level change in specific seasons, sometimes by more than 8%

The subtropical ocean, exposed to evaporation excess over precipitation, is characterized by regional sea surface salinity maxima (SSS-max). Ocean circulation and mixing processes inject freshwater, establishing a quasi-steady state, though imbalances across the time spectrum result in periods of increasing and decreasing SSS-max. The integrated effect of the array of atmospheric and oceanic forces governing sea surface salinity is shaped by the specific regional ocean basin configuration as well as their coupling to the global ocean system, resulting in SSS-max patterns and locations that display marked differences between the subtropical regimes. We provide a brief description of the SSS-max characteristics of the five subtropical regimes and present aspects of their regional settings that may account for their dissimilarities.

The global ocean is losing oxygen with warming. Observations and Earth system model projections, however, suggest that this global ocean deoxygenation does not equate to a simple and systematic expansion of tropical oxygen minimum zones (OMZs). Previous studies have focused on the Pacific Ocean; they showed that the outer OMZ deoxygenates and expands as oxygen supply by advective transport weakens, the OMZ core oxygenates and contracts due to a shift in the composition of the source waters supplied by slow mixing, and in between these two regimes oxygen is redistributed with little effect on OMZ volume. Here, we examine the OMZ response to warming in the Indian Ocean using an ensemble of Earth system model high-emissions scenario experiments from the Coupled Model Intercomparison Project Phase 6. We find a similar expansion–redistribution–contraction response but show that the unique ocean circulation pathways of the Indian Ocean lead to far more prominent OMZ contraction and redistribution regimes than in the Pacific Ocean. As a result, only the outermost volumes (oxygen>180 µmol kg−1) expand. The Indian Ocean experiences a broad oxygenation in the southwest driven by a reduction in waters supplied by the Indonesian Throughflow in favor of high-oxygen waters supplied from the southern Indian Ocean gyre. Models also project a strong localized deoxygenation in the northern Arabian Sea due to the rapid warming and shoaling of marginal sea outflows (Red Sea and Persian Gulf) and increases in local stratification with warming. We extend the existing conceptual framework used to explain the Pacific OMZ response to interpret the response in the Indian Ocean.