Search

Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea‐surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100‐km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large‐scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14‐km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28‐km regional refinement and uniform 111‐km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4‐standard‐deviation anomaly in the seasonal‐mean NAO for 2°C SST anomalies. The lower‐resolution simulations show a weaker response with a different spatial structure. The enhanced large‐scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient‐eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher‐resolution models and that regional refinement in key regions offers a potential pathway to improve multi‐year regional climate predictions based on midlatitude SSTs. Plain Language Summary: Variations in the ocean surface temperature (SST) influence the atmospheric circulation and thus climate over land. Canonical understanding is that tropical SSTs are more important than SSTs in midlatitudes. However, this understanding is based on climate models that don't resolve processes at scales less than 100 km. Here, we show that by increasing the atmospheric model resolution to resolve features on smaller scales, such as weather fronts, we find a larger atmospheric circulation response to midlatitude SST anomalies in the North Atlantic. North Atlantic SST anomalies can be predicted multiple years in advance, and a larger atmospheric circulation response to these predictable SST anomalies therefore implies increased predictability of climate over the surrounding land regions. Key Points: There is a large circulation response to idealized Gulf Stream sea‐surface temperature (SST) anomalies in an atmospheric model with 14‐km regional grid refinementThis response is weaker or absent in simulations with 28‐km or coarser resolution, which do not fully resolve mesoscale frontal processesTransient‐eddy fluxes of heat and momentum are modified as fronts pass over warm SSTs, leading to a large‐scale circulation response [ABSTRACT FROM AUTHOR]

Accelerated progress in climate modeling is urgently needed for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet climatically important, such as turbulence and cloud formation. These processes will not be explicitly resolvable for the foreseeable future, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary artificial intelligence (AI)-based methods to model subgrid-scale processes. This strategy employs AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. In addition, increasing the resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distribution. However, currently feasible horizontal resolutions are limited to O(10km) because higher resolutions would impede the creation of the ensembles that are needed for model calibration and uncertainty quantification, for sampling atmospheric and oceanic internal variability, and for broadly exploring and quantifying climate risks. By synergizing decades of scientific development with advanced AI techniques, our approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. [ABSTRACT FROM AUTHOR]

Antarctic sea ice has exhibited significant variability over the satellite record, including a period of prolonged and gradual expansion, as well as a period of sudden decline. A number of mechanisms have been proposed to explain this variability, but how each mechanism manifests spatially and temporally remains poorly understood. Here, we use a statistical method called low-frequency component analysis to analyze the spatiotemporal structure of observed Antarctic sea ice concentration variability. The identified patterns reveal distinct modes of low-frequency sea ice variability. The leading mode, which accounts for the large-scale, gradual expansion of sea ice, is associated with the Interdecadal Pacific Oscillation and resembles the observed sea surface temperature trend pattern that climate models have trouble reproducing. The second mode is associated with the central Pacific El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode and accounts for most of the sea ice variability in the Ross Sea. The third mode is associated with the eastern Pacific ENSO and Amundsen Sea Low and accounts for most of the pan-Antarctic sea ice variability and almost all of the sea ice variability in the Weddell Sea. The third mode is also related to periods of abrupt Antarctic sea ice decline that are associated with a weakening of the circumpolar westerlies, which favors surface warming through a shoaling of the ocean mixed layer and decreased northward Ekman heat transport. Broadly, these results suggest that climate model biases in long-term Antarctic sea ice and large-scale sea surface temperature trends are related to each other and that eastern Pacific ENSO variability is a key ingredient for abrupt Antarctic sea ice changes. [ABSTRACT FROM AUTHOR]

Accelerating progress in climate modeling is urgent for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet are climatically important, such as turbulence and cloud formation. These processes are not explicitly resolvable, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary AI-based data-driven methods to model subgrid-scale processes. This strategy focuses on employing AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. Increasing resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distributions, but it must still allow the generation of large ensembles for model calibration and the broad exploration of possible climate outcomes – currently O (10 km) horizontal resolutions are feasible. By synergizing decades of scientific development with advanced AI techniques, this approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. [ABSTRACT FROM AUTHOR]

The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea-ice variability and trends, and they thus help to disentangle the role of forced and internal sea-ice changes over the satellite record. In particular, we identify a mode of decadal ocean–atmosphere–sea-ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012 but accounts for at most 10 % of the decline since 1979. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea but has contributed less to trends in the Barents and Kara seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea-ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments. [ABSTRACT FROM AUTHOR]

Antarctic sea ice gradually increased from the late 1970s until 2016, when it experienced an abrupt decline. A number of mechanisms have been proposed for both the gradual increase and abrupt decline of Antarctic sea ice, but how each mechanism manifests spatially and temporally remains poorly understood. Here, we use a statistical method called low-frequency component analysis to analyze the spatial-temporal structure of observed Antarctic sea-ice concentration variability. The identified patterns reveal distinct modes of low-frequency sea ice variability. The leading mode, which accounts for the large-scale, gradual expansion of sea ice, is associated with the Interdecadal Pacific Oscillation and resembles the observed sea-surface temperature trend pattern that climate models have trouble reproducing. The second mode is associated with the central Pacific El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode, and accounts for most of the sea ice variability in the Ross Sea. The third mode is associated with the eastern Pacific ENSO and Amundsen Sea Low, and accounts for most of the pan-Antarctic sea-ice variability and almost all of the sea ice variability in the Weddell Sea. This mode is associated with periods of abrupt Antarctic sea-ice decline and is related to a weakening of the circumpolar westerlies, which favors surface warming through a shoaling of the ocean mixed layer and decreased northward Ekman heat convergence. Broadly, these results suggest that climate model biases in long-term Antarctic sea-ice and global sea-surface temperature trends are related to each other and that eastern Pacific ENSO variability causes abrupt sea ice changes. [ABSTRACT FROM AUTHOR]

Observed surface temperature trends over recent decades are characterized by (a) intensified warming in the Indo‐Pacific Warm Pool and slight cooling in the eastern equatorial Pacific, consistent with Walker circulation strengthening, and (b) Southern Ocean cooling. In contrast, state‐of‐the‐art coupled climate models generally project enhanced warming in the eastern equatorial Pacific, Walker circulation weakening, and Southern Ocean warming. Here we investigate the ability of 16 climate model large ensembles to reproduce observed sea‐surface temperature and sea‐level pressure trends over 1979–2020 through a combination of externally forced climate change and internal variability. We find large‐scale differences between observed and modeled trends that are very unlikely (<5% probability) to occur due to internal variability as represented in models. Disparate trends in the ratio of Indo‐Pacific Warm Pool to tropical‐mean warming, which shows little multi‐decadal variability in models, hint that model biases in the response to historical forcing constitute part of the discrepancy. Plain Language Summary: Regional climate change depends not only on the magnitude of global warming, but also on the spatial pattern of warming. We show that the spatial pattern of observed surface temperature changes since 1979 is highly unusual, and many aspects of it cannot be reproduced in current climate models, even when accounting for the influence of natural variability. We find a particularly large discrepancy in the rate of warming within the western Pacific Ocean and eastern Indian Ocean, which suggests that models have systematic biases in the response of sea‐surface temperature patterns to anthropogenic forcing, because the contribution of natural variability to multi‐decadal trends is thought to be relatively small in this region. Our work raises the possibility that the recent trends toward more La‐Niña‐like conditions may be partly a response to anthropogenic forcing, even though most existing climate model and paleoclimate evidence suggests that trends will eventually reverse toward more El‐Niño‐like conditions, with an associated shift in regional climate trends. Key Points: The patterns of observed sea‐surface temperature and sea‐level pressure trends differ significantly from CMIP5/6 historical simulationsThe rate of Indo‐Pacific Warm Pool warming relative to tropical‐mean warming is particularly anomalous in observations compared to modelsPatterns of observed changes that climate models do not reproduce are isolated with a signal‐to‐noise maximizing pattern analysis [ABSTRACT FROM AUTHOR]

Water mass transformation (WMT) in the North Atlantic plays a key role in driving the Atlantic Meridional Overturning Circulation (AMOC) and its variability. Here, we analyze subpolar North Atlantic WMT in high‐ and low‐resolution versions of the Community Earth System Model version 1 (CESM1) and investigate whether differences in resolution and climatological WMT impact low‐frequency AMOC variability and the atmospheric response to this variability. We find that high‐resolution simulations reproduce the WMT found in a reanalysis‐forced high‐resolution ocean simulation more accurately than low‐resolution simulations. We also find that the low‐resolution simulations, including one forced with the same atmospheric reanalysis data, have larger biases in surface heat fluxes, sea‐surface temperatures, and salinities compared to the high‐resolution simulations. Despite these major climatological differences, the mechanisms of low‐frequency AMOC variability are similar in the high‐ and low‐resolution versions of CESM1. The Labrador Sea WMT plays a major role in driving AMOC variability, and a similar North Atlantic Oscillation‐like sea‐level pressure pattern leads AMOC changes. However, the high‐resolution simulation shows a pronounced atmospheric response to the AMOC variability not found in the low‐resolution version. The consistent role of Labrador Sea WMT in low‐frequency AMOC variability across high‐ and low‐resolution coupled simulations, including a simulation which accurately reproduces the WMT found in an atmospheric‐reanalysis‐forced high‐resolution ocean simulation, suggests that the mechanisms may be similar in nature. Plain Language Summary: Water mass transformation, which is a measure of density change in the surface ocean, plays an important role in variations in the Atlantic Meridional Overturning Circulation (AMOC). Here, we use high‐ and low‐resolution‐coupled model simulations to determine whether resolution and time‐mean water mass transformation patterns impact AMOC variations. We find that a high‐resolution coupled simulation reproduces the water mass transformation from our closest analog to observations and yields more realistic surface ocean features than its low‐resolution counterpart. Despite these differences, the mechanisms governing decadal and multidecadal AMOC variations are similar in the two simulations. In both simulations, the Labrador Sea plays a prominent role in driving decadal and multidecadal AMOC variations. Given that the high‐resolution simulation accurately reproduces the water mass transformation patterns found in our closest analog to observations, it is likely that the Labrador Sea plays a major role in driving AMOC variations in nature as well. Key Points: A high‐resolution coupled simulation skillfully reproduces climatological water mass transformation in the subpolar North AtlanticDespite climatological differences between low‐ and high‐resolution models, the Labrador Sea plays a major role in AMOC variability in bothHigh‐resolution simulations show a larger atmospheric response to low‐frequency AMOC variability [ABSTRACT FROM AUTHOR]

State‐of‐the‐art climate models simulate a large spread in the projected decline of Arctic sea‐ice area (SIA) over the 21st century. Here we diagnose causes of this intermodel spread using a simple model that approximates future SIA based on present SIA and the sensitivity of SIA to Arctic temperatures. This model accounts for 70%–95% of the intermodel variance, with the majority of the spread arising from present‐day biases. The remaining spread arises from intermodel differences in Arctic warming, with some contribution from differences in the local sea‐ice sensitivity. Using observations to constrain the projections moves the probability of an ice‐free Arctic forward by 10–35 years when compared to unconstrained projections. Under a high‐emissions scenario, an ice‐free Arctic will likely (>66% probability) occur between 2036 and 2056 in September and between 2050 and 2068 from July to October. Under a medium‐emissions scenario, the "likely" date occurs between 2040 and 2062 in September and much later in the 21st century from July to October. Plain Language Summary: Arctic sea ice coverage has declined substantially over the past few decades and is projected to continue to decline over the next century. These projections, however, are marred by large uncertainties which arise primarily due to differences between climate models. In this study, we use a simple model that emulates the future evolution of Arctic sea ice as simulated by climate models to explain where this uncertainty comes from. We show that biases in simulating present‐day Arctic sea ice contribute most of the uncertainty, with differences in the simulated amount of Arctic warming from climate models contributing much of the rest. We use observations to constrain our simple model and show that under a high emissions scenario it is likely the Arctic will be free of sea ice in September sometime between 2036 and 2056 and from July to October sometime between 2050 and 2068. We also show that the emissions pathway impacts the length of ice free summers in the Arctic, indicating a low‐emissions pathway will reduce the likelihood of seeing ice‐free Arctic summers. Key Points: A model relating future sea‐ice area (SIA) to present SIA and local sea‐ice sensitivity is used to explain the intermodel spread in Arctic SIA projectionsBiases in simulating present‐day SIA contribute most to the spread, with model differences in Arctic warming contributing the restUnder a high‐emissions scenario, the Arctic will likely be ice‐free in September between 2036 and 2056 and from July to October between 2050 and 2068 [ABSTRACT FROM AUTHOR]

The high Atlantic surface salinity has sometimes been interpreted as a signature of the Atlantic meridional overturning circulation and an associated salt advection feedback. Here, the role of oceanic and atmospheric processes for creating the surface salinity difference between the Atlantic and Indo-Pacific is examined using observations and a conceptual model. In each basin, zonally averaged data are represented in diagrams relating net evaporation E ˜ and surface salinity S. The data-pair curves in the E ˜ –S plane share common features in both basins. However, the slopes of the curves are generally smaller in the Atlantic than in the Indo-Pacific, indicating a weaker sensitivity of the Atlantic surface salinity to net evaporation variations. To interpret these observations, a conceptual advective–diffusive model of the upper-ocean salinity is constructed. Notably, the E ˜ –S relations can be qualitatively reproduced with only meridional diffusive salt transport. In this limit, the interbasin difference in salinity is caused by the spatial structure of net evaporation, which in the Indo-Pacific oceans contains lower meridional wavenumbers that are weakly damped by the diffusive transport. The observed Atlantic E ˜ –S relationship at the surface reveals no clear influence of northward advection associated with the meridional overturning circulation; however, a signature of northward advection emerges in the relationship when the salinity is vertically averaged over the upper kilometer. The results indicate that the zonal-mean near-surface salinity is shaped primarily by the spatial pattern of net evaporation and the diffusive meridional salt transport due to wind-driven gyres and mesoscale ocean eddies, rather than by salt advection within the meridional overturning circulation. [ABSTRACT FROM AUTHOR]

North Pacific atmospheric and oceanic circulations are key missing pieces in our understanding of the reorganization of the global climate system since the Last Glacial Maximum. Here, using a basin‐wide compilation of planktic foraminiferal δ18O, we show that the North Pacific subpolar gyre extended ~3° further south during the Last Glacial Maximum, consistent with sea surface temperature and productivity proxy data. Climate models indicate that the expansion of the subpolar gyre was associated with a substantial gyre strengthening, and that these gyre circulation changes were driven by a southward shift of the midlatitude westerlies and increased wind stress from the polar easterlies. Using single‐forcing model runs, we show that these atmospheric circulation changes are a nonlinear response to ice sheet topography/albedo and CO2. Our reconstruction indicates that the gyre boundary (and thus westerly winds) began to migrate northward at ~16.5 ka, driving changes in ocean heat transport, biogeochemistry, and North American hydroclimate. Plain language summary: Despite the North Pacific's importance in the global climate system, changes in the circulation of this region since the last ice age are poorly understood. Today, the North Pacific Ocean has distinct properties north and south of ~40°N: To the south, the warm surface waters form a circulation cell that moves clockwise (the subtropical gyre); to the north, the cold surface waters form a circulation cell that moves anticlockwise (the subpolar gyre). This difference in surface ocean circulation north and south of ~40°N is determined by the wind patterns. Here, using a compilation of oxygen isotopes measured in the carbonate shells of fossil plankton from sediment cores across the basin, which tracks changes in the spatial pattern of temperature, we reconstruct how the position of the boundary between the gyres changed since the last ice age. Our results show that the boundary between the gyres was shifted southward by ~3° during the last ice age; this indicates that the westerly winds were also shifted southward at this time. Using numerical simulations of the climate, we find that this ice age shift in the westerly winds is primarily due to the presence of a large ice sheet over North America. Key Points: Planktic foraminiferal δ18O data indicate that the North Pacific subpolar gyre expanded southward by ~3° during the Last Glacial MaximumClimate models show that changes in gyre extent/strength are driven by a nonlinear response of the westerlies to ice sheet albedo/topography and CO2Proxy data and model simulations indicate that the gyre boundary and westerlies began to migrate northward at ~16.5 ka, during Heinrich Stadial 1 [ABSTRACT FROM AUTHOR]

The Pacific Decadal Oscillation (PDO) is the dominant pattern of observed sea surface temperature variability in the North Pacific. Its characteristic pattern of eastern intensified warming and cooling within the Kuroshio‐Oyashio Extension is pervasive across timescales. We investigate the mechanisms for its decadal persistence in coupled climate models, focusing on the role of ocean circulation changes. We use low‐frequency component analysis to isolate the mechanisms relevant at decadal and longer timescales from those acting at shorter timescales. The PDO warm phase is associated with strengthening and expansion of the North Pacific subpolar gyre in response to a deepening of the Aleutian Low. The subpolar gyre takes several years to respond to wind stress forcing through baroclinic ocean Rossby wave adjustment, such that white noise atmospheric forcing is integrated into red noise, increasing variability at long timescales. Sea level anomalies within the Kuroshio‐Oyashio Extension provide an observable ocean circulation signature of North Pacific decadal variability. Plain Language Summary: North Pacific sea surface temperatures vary from decade to decade with a characteristic pattern, where temperature anomalies near North America are opposite to those off the coast of Japan. These ocean changes influence fish populations as well as climate over the surrounding land regions. Here we investigate the physical mechanisms for this sea surface temperature variability using global climate models that include interactions between the atmosphere and ocean. We find that ocean currents change together with the changes in sea surface temperature and that the time it takes for these ocean currents to adjust to changes in the prevailing wind patterns gives this variability its persistence from decade to decade. Key Points: Decadal sea surface temperature variability in the North Pacific is linked to variability of the ocean gyre circulationThe gyre circulation integrates white noise Aleutian Low forcing, giving variability that is strongest at decadal and longer timescalesSea level anomalies illustrate the ocean';s role in North Pacific decadal variability [ABSTRACT FROM AUTHOR]

Ocean memory, the persistence of ocean conditions, is a major source of predictability in the climate system beyond weather time scales. We show that ocean memory, as measured by the year-to-year persistence of sea surface temperature anomalies, is projected to steadily decline in the coming decades over much of the globe. This global decline in ocean memory is predominantly driven by shoaling of the upper-ocean mixed layer depth in response to global surface warming, while thermodynamic and dynamic feedbacks can contribute substantially regionally. As the mixed layer depth shoals, stochastic forcing becomes more effective in driving sea surface temperature anomalies, increasing high-frequency noise at the expense of persistent signals. Reduced ocean memory results in shorter lead times of skillful persistence-based predictions of sea surface thermal conditions, which may present previously unknown challenges for predicting climate extremes and managing marine biological resources under climate change.