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The adjoint assimilation method has been applied to coupled ocean and sea ice models for sensitivity studies and Arctic state estimations. However, the accuracy of the adjoint model is degraded by simplifications of the adjoint of the sea ice model, especially the adjoint sea ice rheologies. As part of ongoing developments in coupled ocean and sea ice estimation systems, we incorporate and approximate the adjoint of viscous-plastic sea ice dynamics (adjoint-VP) and compare it with the adjoint of free-drift sea ice dynamics (adjoint-FD) through assimilation experiments. Using the adjoint-VP results in a further cost reduction of 7.9 % in comparison to adjoint-FD, with noticeable improvements in the ocean temperature over the open water and the intermediate layers of the Arctic Ocean. Adjoint-VP adjusts the model input more efficiently than adjoint-FD does by involving different sea ice retreat processes. For instance, adjoint-FD melts the sea ice up to 1.0 m in the marginal seas from May to June by overadjusting air temperature (>8 ∘C); adjoint-VP reproduces the sea ice retreat with smaller adjustments to the atmospheric state within their prior uncertainty range. These developments of the adjoint model here lay the foundation for further improving Arctic Ocean and sea ice estimations by comprehensively adjusting the initial conditions, atmospheric forcings, and parameters of the model.

Two high-resolution model simulations are used to investigate the spatiotemporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods of < 30 d, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume exchanges with the Pacific and Atlantic oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. The study confirms that satellite altimetric observations and Gravity Recovery and Climate Experiment (GRACE) could infer the total freshwater content changes in the Canadian Basin at periods longer than 1 year, but they are unable to depict the seasonal and subseasonal freshwater content changes. The increasing number of profiles seems to capture freshwater content changes since 2007, encouraging further data synthesis work with a more complicated interpolation method. Further, in situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

Abstract Sea level rise (SLR) is a long‐lasting consequence of climate change because global anthropogenic warming takes centuries to millennia to equilibrate for the deep ocean and ice sheets. SLR projections based on climate models support policy analysis, risk assessment and adaptation planning today, despite their large uncertainties. The central range of the SLR distribution is estimated by process‐based models. However, risk‐averse practitioners often require information about plausible future conditions that lie in the tails of the SLR distribution, which are poorly defined by existing models. Here, a community effort combining scientists and practitioners builds on a framework of discussing physical evidence to quantify high‐end global SLR for practitioners. The approach is complementary to the IPCC AR6 report and provides further physically plausible high‐end scenarios. High‐end estimates for the different SLR components are developed for two climate scenarios at two timescales. For global warming of +2°C in 2100 (RCP2.6/SSP1‐2.6) relative to pre‐industrial values our high‐end global SLR estimates are up to 0.9 m in 2100 and 2.5 m in 2300. Similarly, for a (RCP8.5/SSP5‐8.5), we estimate up to 1.6 m in 2100 and up to 10.4 m in 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long‐term benefits of mitigation. However, even a modest 2°C warming may cause multi‐meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high‐end assessments focused on instability mechanisms in Antarctica, while here we emphasize the importance of the timing of ice shelf collapse around Antarctica. This is highly uncertain due to low understanding of the driving processes. Hence both process understanding and emission scenario control high‐end SLR.

Abstract An approach to analyze high‐end sea level rise is presented to provide a conceptual framework for high‐end estimates as a function of time scale, thereby linking robust sea level science with stakeholder needs. Instead of developing and agreeing on a set of high‐end sea level rise numbers or using an expert consultation, our effort is focused on the essential task of providing a generic conceptual framework for such discussions and demonstrating its feasibility to address this problem. In contrast, information about high‐end sea level rise projections was derived previously either from a likely range emerging from the highest view of emissions in the Intergovernmental Panel on Climate Change assessment (currently the Representative Concentration Pathway 8.5 scenario) or from independent ad hoc studies and expert solicitations. Ideally, users need high‐end sea level information representing the upper tail of a single joint sea level frequency distribution, which considers all plausible yet unknown emission scenarios as well as involved physical mechanisms and natural variability of sea level, but this is not possible. In the absence of such information we propose a framework that would infer the required information from explicit conditional statements (lines of evidence) in combination with upper (plausible) physical bounds. This approach acknowledges the growing uncertainty in respective estimates with increasing time scale. It also allows consideration of the various levels of risk aversion of the diverse stakeholders who make coastal policy and adaptation decisions, while maintaining scientific rigor.

Satellite sea ice concentrations (SICs), together with several ocean parameters, are assimilated into a regional Arctic coupled ocean–sea ice model covering the period of 2000–2008 using the adjoint method. There is substantial improvement in the representation of the SIC spatial distribution, in particular with respect to the position of the ice edge and to the concentrations in the central parts of the Arctic Ocean during summer months. Seasonal cycles of total Arctic sea ice area show an overall improvement. During summer months, values of sea ice extent (SIE) integrated over the model domain become underestimated compared to observations, but absolute differences of mean SIE to the data are reduced in nearly all months and years. Along with the SICs, the sea ice thickness fields also become closer to observations, providing added value by the assimilation. Very sparse ocean data in the Arctic, corresponding to a very small contribution to the cost function, prevent sizable improvements of assimilated ocean variables, with the exception of the sea surface temperature.

The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to investigate the spread in simulations of sea-level and ocean climate change in response to CO2 forcing by atmosphere–ocean general circulation models (AOGCMs). It is particularly motivated by the uncertainties in projections of ocean heat uptake, global-mean sea-level rise due to thermal expansion and the geographical patterns of sea-level change due to ocean density and circulation change. FAFMIP has three tier-1 experiments, in which prescribed surface flux perturbations of momentum, heat and freshwater respectively are applied to the ocean in separate AOGCM simulations. All other conditions are as in the pre-industrial control. The prescribed fields are typical of pattern and magnitude of changes in these fluxes projected by AOGCMs for doubled CO2 concentration. Five groups have tested the experimental design with existing AOGCMs. Their results show diversity in the pattern and magnitude of changes, with some common qualitative features. Heat and water flux perturbation cause the dipole in sea-level change in the North Atlantic, while momentum and heat flux perturbation cause the gradient across the Antarctic Circumpolar Current. The Atlantic meridional overturning circulation (AMOC) declines in response to the heat flux perturbation, and there is a strong positive feedback on this effect due to the consequent cooling of sea-surface temperature in the North Atlantic, which enhances the local heat input to the ocean. The momentum and water flux perturbations do not substantially affect the AMOC. Heat is taken up largely as a passive tracer in the Southern Ocean, which is the region of greatest heat input, while the weakening of the AMOC causes redistribution of heat towards lower latitudes. Future analysis of these and other phenomena with the wider range of CMIP6 FAFMIP AOGCMs will benefit from new diagnostics of temperature and salinity tendencies, which will enable investigation of the model spread in behaviour in terms of physical processes as formulated in the models.

Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the past two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm year−1, respectively (currently 0.6 and 1–2 mm year−1). Similarly, interannual global mean sea level variations (currently uncertain to 2–3 mm) need to be monitored with better accuracy. In this paper, we present various data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010–2013), using multi-mission satellite altimetry data over the 1993–2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, intercalibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL_cci products, based on tide gauges comparison and a sea level budget closure approach, as well as comparisons with ocean reanalyses and climate model outputs.

The impact of Greenland and Antarctic ice sheet mass loss on regional sea level is evaluated here under RCP 4.5 and RCP 8.5 scenarios for the period 2081–2099. To this end, estimates of associated fresh water sources are added to the Max Planck Institute for Meteorology’s Earth System Model ocean component and the dynamical impact is quantified in terms of the difference in sea level relative to previous phase 5 of the Coupled Model Intercomparison Project runs. Overall, the addition of these freshwater sources have only a small impact on regional sea level variations relative to the global mean (

Background: Climate change, in particular the exposure to heat, impacts on human health and can trigger diseases. Pregnant people are considered a vulnerable group given the physiological changes during pregnancy and the potentially long-lasting consequences for the offspring. Evidence published to date on higher risk of pregnancy complications upon heat stress exposure are from geographical areas with high ambient temperatures. Studies from geographic regions with temperate climates are sparse; however, these areas are critical since individuals may be less equipped to adapt to heat stress. This study addresses a significant gap in knowledge due to the temperature increase documented globally., Methods: Birth data of singleton pregnancies (n = 42,905) from a tertiary care centre in Hamburg, Germany, between 1999 and 2021 were retrospectively obtained and matched with climate data from the warmer season (March to September) provided by the adjacent federal meteorological station of the German National Meteorological Service to calculate the relative risk of heat-associated preterm birth. Heat events were defined by ascending temperature percentiles in combination with humidity over exposure periods of up to 5 days. Further, ultrasound data documented in a longitudinal prospective pregnancy cohort study (n = 612) since 2012 were used to identify pathophysiological causes of heat-induced preterm birth., Findings: Both extreme heat and prolonged periods of heat exposure increased the relative risk of preterm birth (RR: 1.59; 95% CI: 1.01-2.43; p = 0.045; RR: 1.20; 95% CI: 1.02-1.40; p = 0.025). We identified a critical period of heat exposure during gestational ages 34-37 weeks that resulted in increased risk of late preterm birth (RR: 1.67; 95% CI: 1.14-1.43; p = 0.009). Pregnancies with a female fetus were more prone to heat stress-associated preterm birth. We found heat exposure was associated with altered vascular resistance within the uterine artery., Interpretation: Heat stress caused by high ambient temperatures increases the risk of preterm birth in a geographical region with temperate climate. Prenatal routine care should be revised in such regions to provide active surveillance for women at risk., Funding: Found in acknowledgements., Competing Interests: Declaration of interests All authors declare no potential conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Global sea level rise (SLR) associated with a warming climate exerts significant stress on coastal societies and low-lying island regions. The rates of coastal SLR observed in the past few decades, however, have large spatial and temporal differences from the global mean, which to a large part have been attributed to basin-scale climate modes. In this paper, we review our current state of knowledge about climate modes' impacts on coastal sea level variability from interannual-to-multidecadal timescales. Relevant climate modes, their impacts and associated driving mechanisms through both remote and local processes are elaborated separately for the Pacific, Indian and Atlantic Oceans. This paper also identifies major issues and challenges for future research on climate modes' impacts on coastal sea level. Understanding the effects of climate modes is essential for skillful near-term predictions and reliable uncertainty quantifications for future projections of coastal SLR., (© The Author(s) 2019.)

Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth's climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modes and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this paper, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR., (© The Author(s) 2016.)

Vertical mixing is an important factor in determining the temperature, sharpness and depth of the equatorial Pacific thermocline, which are critical to the development of El Ninõ and Southern Oscillation (ENSO). Yet, properties, dynamical causes and large-scale impacts of vertical mixing in the thermocline are much less understood than that nearer the surface. Here, based on Argo float and the Tropical Ocean and Atmosphere (TAO) mooring measurements, we identify a large number of thermocline mixing events occurring down to the lower half of the thermocline and the lower flank of the Equatorial Undercurrent (EUC), in particular in summer to winter. The deep-reaching mixing events occur more often and much deeper during periods with tropical instability waves (TIWs) than those without and under La Niña than under El Niño conditions. We demonstrate that the mixing events are caused by lower Richardson numbers resulting from shear of both TIWs and the EUC.

Ocean data assimilation brings together observations with known dynamics encapsulated in a circulation model to describe the time-varying ocean circulation. Its applications are manifold, ranging from marine and ecosystem forecasting to climate prediction and studies of the carbon cycle. Here, we address only climate applications, which range from improving our understanding of ocean circulation to estimating initial or boundary conditions and model parameters for ocean and climate forecasts. Because of differences in underlying methodologies, data assimilation products must be used judiciously and selected according to the specific purpose, as not all related inferences would be equally reliable. Further advances are expected from improved models and methods for estimating and representing error information in data assimilation systems. Ultimately, data assimilation into coupled climate system components is needed to support ocean and climate services. However, maintaining the infrastructure and expertise for sustained data assimilation remains challenging.

Regional sea level changes can deviate substantially from those of the global mean, can vary on a broad range of timescales, and in some regions can even lead to a reversal of long-term global mean sea level trends. The underlying causes are associated with dynamic variations in the ocean circulation as part of climate modes of variability and with an isostatic adjustment of Earth's crust to past and ongoing changes in polar ice masses and continental water storage. Relative to the coastline, sea level is also affected by processes such as earthquakes and anthropogenically induced subsidence. Present-day regional sea level changes appear to be caused primarily by natural climate variability. However, the imprint of anthropogenic effects on regional sea level-whether due to changes in the atmospheric forcing or to mass variations in the system-will grow with time as climate change progresses, and toward the end of the twenty-first century, regional sea level patterns will be a superposition of climate variability modes and natural and anthropogenically induced static sea level patterns. Attribution and predictions of ongoing and future sea level changes require an expanded and sustained climate observing system.