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Abstract The response of zonal-mean precipitation minus evaporation ( P − E ) to global warming is investigated using a moist energy balance model (MEBM) with a simple Hadley cell parameterization. The MEBM accurately emulates zonal-mean P − E change simulated by a suite of global climate models (GCMs) under greenhouse gas forcing. The MEBM also accounts for most of the intermodel differences in GCM P − E change and better emulates GCM P − E change when compared to the “wet-gets-wetter, dry-gets-drier” thermodynamic mechanism. The intermodel spread in P − E change is attributed to intermodel differences in radiative feedbacks, which account for 60%–70% of the intermodel variance, with smaller contributions from radiative forcing and ocean heat uptake. Isolating the intermodel spread of feedbacks to specific regions shows that tropical feedbacks are the primary source of intermodel spread in zonal-mean P − E change. The ability of the MEBM to emulate GCM P − E change is further investigated using idealized feedback patterns. A less negative and narrowly peaked feedback pattern near the equator results in more atmospheric heating, which strengthens the Hadley cell circulation in the deep tropics through an enhanced poleward heat flux. This pattern also increases gross moist stability, which weakens the subtropical Hadley cell circulation. These two processes in unison increase P − E in the deep tropics, decrease P − E in the subtropics, and narrow the intertropical convergence zone. Additionally, a feedback pattern that produces polar-amplified warming partially reduces the poleward moisture flux by weakening the meridional temperature gradient. It is shown that changes to the Hadley cell circulation and the poleward moisture flux are crucial for understanding the pattern of GCM P − E change under warming. Significance Statement Changes to the hydrological cycle over the twenty-first century are predicted to impact ecosystems and socioeconomic activities throughout the world. While it is broadly expected that dry regions will get drier and wet regions will get wetter, the magnitude and spatial structure of these changes remains uncertain. In this study, we use an idealized climate model, which assumes how energy is transported in the atmosphere, to understand the processes setting the pattern of precipitation and evaporation under global warming. We first use the idealized climate model to explain why comprehensive climate models predict different changes to precipitation and evaporation across a range of latitudes. We show this arises primarily from climate feedbacks, which act to amplify or dampen the amount of warming. Ocean heat uptake and radiative forcing play secondary roles but can account for a significant amount of the uncertainty in regions where ocean circulation influences the rate of warming. We further show that uncertainty in tropical feedbacks (mainly from clouds) affects changes to the hydrological cycle across a range of latitudes. We then show how the pattern of climate feedbacks affects how the patterns of precipitation and evaporation respond to climate change through a set of idealized experiments. These results show how the pattern of climate feedbacks impacts tropical hydrological changes by affecting the strength of the Hadley circulation and polar hydrological changes by affecting the transport of moisture to the high latitudes. 

The polar regions are predicted to experience the largest relative change in precipitation in response to increased greenhouse-gas concentrations, where a substantial absolute increase in precipitation coincides with small precipitation rates in the present-day climate. The reasons for this amplification, however, are still debated. Here, we use an atmospheric energy budget to decompose regional precipitation change from climate models under greenhouse-gas forcing into contributions from atmospheric radiative feedbacks, dry-static energy flux divergence changes, and surface sensible heat flux changes. The polar-amplified relative precipitation change is shown to be a consequence of the Planck feedback, which, when combined with larger polar warming, favors substantial atmospheric radiative cooling that balances increases in latent heat release from precipitation. Changes in the dry-static energy flux divergence contribute modestly to the polar-amplified pattern. Additional contributions to the polar-amplified response come, in the Arctic, from the cloud feedback and, in the Antarctic, from both the cloud and water vapor feedbacks. The primary contributor to the intermodel spread in the relative precipitation change in the polar region is also the Planck feedback, with the lapse rate feedback and dry-static energy flux divergence changes playing secondary roles. For all regions, there are strong covariances between radiative feedbacks and changes in the dry-static energy flux divergence that impact the intermodel spread. These results imply that constraining regional precipitation change, particularly in the polar regions, will require constraining not only individual feedbacks but also the covariances between radiative feedbacks and atmospheric energy transport.

 

Abstract. 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. The Arctic sea ice cover is strongly influenced by internal variability on decadal time scales, 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 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. 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.

 

Over recent decades, the Southern Ocean (SO) has experienced multi‐decadal surface cooling despite global warming. Earlier studies have proposed that recent SO cooling has been caused by the strengthening of surface westerlies associated with a positive trend of the Southern Annular Mode (SAM) forced by ozone depletion. Here we revisit this hypothesis by examining the relationships between the SAM, zonal winds and SO sea‐surface temperature (SST). Applying a low‐frequency component analysis to observations, we show that while positive SAM anomalies can induce SST cooling as previously found, this seasonal‐to‐interannual modulation makes only a small contribution to the observed long‐term SO cooling. Global climate models well capture the observed interannual SAM‐SST relationship, and yet generally fail to simulate the observed multi‐decadal SO cooling. The forced SAM trend in recent decades is thus unlikely the main cause of the observed SO cooling, pointing to a limited role of the Antarctic ozone hole.

 

In recent years, the Southern Ocean has experienced unprecedented surface warming and sea ice loss—a stark reversal of the sea ice expansion and surface cooling that prevailed over the preceding decades. Here, we examine the mechanisms that led to the abrupt circumpolar surface warming events that occurred in late 2016 and 2019 and assess the role of internal climate variability. A mixed layer heat budget analysis reveals that these recent circumpolar surface warming events were triggered by a weakening of the circumpolar westerlies, which decreased northward Ekman transport and accelerated the seasonal shoaling of the mixed layer. We emphasize the underappreciated effect of the latter mechanism, which played a dominant role and amplified the warming effect of air–sea heat fluxes during months of peak solar insolation. An examination of the CESM1 large ensemble demonstrates that these recent circumpolar warming events are consistent with the internal variability associated with the Southern Annular Mode (SAM), whereby negative SAM in austral spring favors shallower mixed layers and anomalously high summertime SST. A key insight from this analysis is that the seasonal phasing of springtime mixed layer depth shoaling is an important contributor to summertime SST variability in the Southern Ocean. Thus, future Southern Ocean summertime SST extremes will depend on the coevolution of mixed layer depth and surface wind variability.

This study examines how reductions in the strength of the circumpolar westerlies can produce abrupt and extreme surface warming across the Southern Ocean. A key insight is that the mixed layer temperature is most sensitive to surface wind perturbations in late austral spring, when the regional mixed layer depth and solar insolation approach their respective seasonal minimum and maximum. This heightened surface temperature response to surface wind variability was realized during the austral spring of 2016 and 2019, when a dramatic weakening of the circumpolar westerlies triggered unprecedented warming across the Southern Ocean. In both cases, the anomalously weak circumpolar winds reduced the northward Ekman transport of cool subpolar waters and caused the mixed layer to shoal more rapidly in the spring, with the latter mechanism being more dominant. Using results from an ensemble of coupled climate simulations, we demonstrate that the 2016 and 2019 Southern Ocean warming events are consistent with the internal variability associated with the Southern Annular Mode (SAM). These results suggest that future Southern Ocean surface warming extremes will depend on both the evolution of regional mixed layer depths and interannual wind variability.

 

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal‐mean precipitation‐minus‐evaporation (P − E) under greenhouse‐gas forcing. It is shown that cloud feedbacks act to narrow bands of tropicalP − Eand increaseP − Ein the deep tropics. The surface‐albedo feedback shifts the location of maximum tropicalP − Eand increasesP − Ein the polar regions. The intermodel spread in theP − Echanges associated with feedbacks arises mainly from cloud feedbacks, with the lapse‐rate and surface‐albedo feedbacks playing important roles in the polar regions. TheP − Echange associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the “wet‐gets‐wetter, dry‐gets‐drier” paradigm.

 

Improved knowledge of the contributing sources of uncertainty in projections of Arctic sea ice over the 21st century is essential for evaluating impacts of a changing Arctic environment. Here, we consider the role of internal variability, model structure and emissions scenario in projections of Arctic sea-ice area (SIA) by using six single model initial-condition large ensembles and a suite of models participating in Phase 5 of the Coupled Model Intercomparison Project. For projections of September Arctic SIA change, internal variability accounts for as much as 40%–60% of the total uncertainty in the next decade, while emissions scenario dominates uncertainty toward the end of the century. Model structure accounts for 60%–70% of the total uncertainty by mid-century and declines to 30% at the end of the 21st century in the summer months. For projections of wintertime Arctic SIA change, internal variability contributes as much as 50%–60% of the total uncertainty in the next decade and impacts total uncertainty at longer lead times when compared to the summertime. In winter, there exists a considerable scenario dependence of model uncertainty with relatively larger model uncertainty under strong forcing compared to weak forcing. At regional scales, the contribution of internal variability can vary widely and strongly depends on the calendar month and region. For wintertime SIA change in the Greenland-Iceland-Norwegian and Barents Seas, internal variability contributes 60%–70% to the total uncertainty over the coming decades and remains important much longer than in other regions. We further find that the relative contribution of internal variability to total uncertainty is state-dependent and increases as sea ice volume declines. These results demonstrate that internal variability is a significant source of uncertainty in projections of Arctic sea ice.

 

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.

 

The decline of Arctic sea ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically driven SIM export and negative feedbacks from sea ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice thickness observations collected after melt onset are particularly critical for summer Arctic sea ice predictions.