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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.

 

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.

 

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.

 

Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100‐year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions.

 

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.

 

Warming drives ocean memory loss leading to noisier, less predictable sea surface temperature variability. 

Abstract Decadal sea surface temperature (SST) fluctuations in the North Atlantic Ocean influence climate over adjacent land areas and are a major source of skill in climate predictions. However, the mechanisms underlying decadal SST variability remain to be fully understood. This study isolates the mechanisms driving North Atlantic SST variability on decadal time scales using low-frequency component analysis, which identifies the spatial and temporal structure of low-frequency variability. Based on observations, large ensemble historical simulations, and preindustrial control simulations, we identify a decadal mode of atmosphere–ocean variability in the North Atlantic with a dominant time scale of 13–18 years. Large-scale atmospheric circulation anomalies drive SST anomalies both through contemporaneous air–sea heat fluxes and through delayed ocean circulation changes, the latter involving both the meridional overturning circulation and the horizontal gyre circulation. The decadal SST anomalies alter the atmospheric meridional temperature gradient, leading to a reversal of the initial atmospheric circulation anomaly. The time scale of variability is consistent with westward propagation of baroclinic Rossby waves across the subtropical North Atlantic. The temporal development and spatial pattern of observed decadal SST variability are consistent with the recent observed cooling in the subpolar North Atlantic. This suggests that the recent cold anomaly in the subpolar North Atlantic is, in part, a result of decadal SST variability. 

Abstract Ocean heat transport (OHT) plays a key role in climate and its variability. Here, we identify modes of low-frequency North Atlantic OHT variability by applying a low-frequency component analysis (LFCA) to output from three global climate models. The first low-frequency component (LFC), computed using this method, is an index of OHT variability that maximizes the ratio of low-frequency variance (occurring at decadal and longer timescales) to total variance. Lead-lag regressions of atmospheric and ocean variables onto the LFC timeseries illuminate the dominant mechanisms controlling low-frequency OHT variability. Anomalous northwesterly winds from eastern North America over the North Atlantic act to increase upper ocean density in the Labrador Sea region, enhancing deep convection, which later increases OHT via changes in the strength of the Atlantic Meridional Overturning Circulation (AMOC). The strengthened AMOC carries warm, salty water into the subpolar gyre, reducing deep convection and weakening AMOC and OHT. This mechanism, where changes in AMOC and OHT are driven primarily by changes in Labrador Sea deep convection, holds not only in models where the climatological (i.e., time-mean) deep convection is concentrated in the Labrador Sea, but also in models where the climatological deep convection is concentrated in the Greenland-Iceland-Norwegian (GIN) Seas or the Irminger and Iceland Basins. These results suggest that despite recent observational evidence suggesting that the Labrador Sea plays a minor role in driving the climatological AMOC, the Labrador Sea may still play an important role in driving low-frequency variability in AMOC and OHT. 

Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal climate variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large scale spatial averaging. This paper demonstrates a pattern recognition method (forced pattern filtering) that extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Ni˜no, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El-Ni˜no-like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to effectively identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.