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Previous research regarding the intraseasonal variability of the wintertime Pacific jet has employed empirical orthogonal function (EOF)/principal component (PC) analysis to characterize two leading modes of variability: a zonal extension or retraction and a ∼20° meridional shift of the jet exit region. These leading modes are intimately tied to the large-scale structure, sensible weather phenomena, and forecast skill in and around the vast North Pacific basin. However, variability within the wintertime Pacific jet and the relative importance of tropical and extratropical processes in driving such variability, is poorly understood. Here, a self-organizing maps (SOM) analysis is applied to 73 Northern Hemisphere cold seasons of 250-hPa zonal winds from the NCEP–NCAR reanalysis data to identify 12 characteristic physical jet states, some of which resemble the leading EOF Pacific jet patterns and combinations of them. Examination of teleconnection patterns such as El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) provide insight into the varying nature of the 12 SOM nodes at inter- and intraseasonal time scales. These relationships suggest that the hitherto more common EOF/PC analysis of jet variability obscures important subtleties of jet structure, revealed by the SOM analysis, which bear on the underlying physical processes associated with Pacific jet variability as well as the nature of its downstream impacts. [ABSTRACT FROM AUTHOR]

The premonsoon circulation over South Asia in May shows remarkable interannual variations, and it can modulate the onset of the Asian summer monsoon. The present study derives two dominant stratosphere–troposphere modes over the North Atlantic in April via applying principal component (PC) analysis to regional geopotential height during 1979–2015. Both of the modes reflect the North Atlantic Oscillation (NAO)-like circulation at the midtroposphere, but the circulation patterns and planetary wave activity between the two modes are quite different. The first mode represents a stratosphere–troposphere-coupled mode. Further analysis indicates that during the years with a positive phase of the first mode, the low-level anomalous southerlies over the northern Barents Sea (BS) lead to a reduction in local sea ice, which could persist into May through the ice-albedo feedback. The BS sea ice anomalies in May could generate a southeastward propagating Rossby wave train, producing an anomalous anticyclonic circulation to the west of Qinghai–Tibet Plateau. This anomalous anticyclone induces increases in local air temperature and the meridional temperature gradient, resulting in a strengthened premonsoon circulation. Given that prediction of the premonsoon circulation over South Asia remains a challenging issue, the stratosphere–troposphere-coupling modes in April provide another potential predictability source. On the other hand, ENSO has a significant effect on the South Asian premonsoon circulations in May, and can also lead to the NAO-like circulation by influencing atmospheric Rossby waves. Such an NAO-like circulation is closely related to the second mode. [ABSTRACT FROM AUTHOR]

Enhanced surface melt over the ice shelves of the Antarctic Peninsula (AP) is one of the precursors to their collapse, which can be proceeded by accelerated ground glacier flow and increased contribution to sea level rise. With the collapse of Larsen A and B and the major 2017 calving event from Larsen C, whether Larsen C is bound for a similar fate has received increasing attention. Here, the interannual variation of regional circulation over the AP region is studied using the empirical orthogonal function (EOF)/principal component (PC) analysis on the sea level pressure of ERA5. The EOF modes capture the variations of depth, location, and extent of Amundsen Sea low and Weddell Sea low in each season. Statistically significant positive correlations exist between Larsen C surface temperature and the PC time series of EOF mode 1 in winter and spring through northerly/northwesterly wind anomalies west of the AP. The PC time series of EOF mode 2 is negatively correlated with Larsen C surface temperature in autumn and summer and surface melt in summer, all due to southerly wind anomalies east of the AP. Surface energy budget analysis associated with EOF mode 2 shows that downwelling longwave radiation over Larsen C has negative statistically significant correlations with EOF mode 2 and is the major atmospheric forcing regulating the variation of Larsen C surface melt. Positively enhanced EOF mode 2 since 2004 is responsible for the recent cooling and decline of surface melt over Larsen C. [ABSTRACT FROM AUTHOR]

State-of-the-art general circulation models show important systematic errors in their simulation of sea surface temperatures (SST), especially in the tropical Atlantic. In this work the spread in the simulation of climatological SST in the tropical Atlantic by 24 CMIP5 models is examined, and its relationship with the mean systematic biases in the region is explored. The modes of intermodel variability are estimated by applying principal component (PC) analysis to the SSTs in the region 70°W–20°E, 20°S–20°N. The intermodel variability is approximately explained by the first three modes. The first mode is related to warmer SSTs in the basin, shows worldwide connections with same-signed loads over most of the tropics, and is connected with lower low cloud cover over the eastern parts of the subtropical oceans. The second mode is restricted to the Atlantic, where it shows negative and positive loads to the north and south of the equator, respectively, and is connected to a too weak Atlantic meridional overturning circulation (AMOC). The third mode is related to the double intertropical convergence zone bias in the Pacific and to an interhemispheric asymmetry in the net radiation at the top of the atmosphere. The structure of the second mode is closer to the mean bias than that of the others in the tropical Atlantic, suggesting that model difficulties with the AMOC contribute to the regional biases. [ABSTRACT FROM AUTHOR]

The monthly mean precipitation patterns of the Atmospheric Model Intercomparison Project decadal simulations over the United States and adjoining oceans are intercompared. A simple harmonic analysis of the 12- month seasonal mean precipitation values and a principal component (PC) analysis of the 120 monthly values were carried out. Emphasis is placed on the basic seasonal variation for three subregions: the eastern and central United States and the U.S. west coast. It is vital for GCM simulations to accurately portray the seasonal cycle. The results indicate the following. 1) There are problems for almost all the models in capturing the seasonal variation of the precipitation over the eastern United States. The models typically overemphasize the summer- spring rainfall amounts. The PC analysis indicates that many of the models tend to extend the precipitation regime typical of the central United States too far to the east, resulting in a precipitation maxima occurring in the summer for the eastern region. 2) The seasonal variation of the West Coast is handled with the greatest fidelity. This result cuts across all the models and may be attributable to the fact the SST forcing is specified and common to all the simulations. The common SST forcing is apparently a dominant factor in determining this region's precipitation climatology. 3) On the space scales of the regions selected, there is little consistent evidence that points to any specific model feature as a predictor of model performance. None of the obvious candidates such as horizontal resolution, convective closure schemes, or land surface schemes are reliable discriminators of a model's ability to simulate precipitation. 4) For one smaller subregion centered over Arizona, chosen because of the dominance of the semiannual cycle, there is evidence that increased horizontal resolution has an effect. For this intermountain region the higher-resolution models as a whole do better than the... [ABSTRACT FROM AUTHOR]

This paper describes a framework for identifying dominant atmospheric drivers of ocean variability. The method combines statistics of atmosphere–ocean fluxes with physics from an ocean general circulation model to derive atmospheric patterns optimized to excite variability in a specified ocean quantity of interest. We first derive the method as a weighted principal components analysis and illustrate its capabilities in a toy problem. Next, we apply our analysis to the problem of interannual upper ocean heat content (HC) variability in the North Atlantic Subpolar Gyre (SPG) using the adjoint of the MITgcm and atmosphere–ocean fluxes from the ECCOv4-r4 state estimate. An unweighted principal components analysis reveals that North Atlantic heat and momentum fluxes in ECCOv4-r4 have a range of spatiotemporal patterns. By contrast, dynamics-weighted principal components analysis collapses the space of these patterns onto a small subset—principally associated with the North Atlantic Oscillation—that dominates interannual SPG HC variance. By perturbing the ECCOv4-r4 state estimate, we illustrate the pathways along which variability propagates from the atmosphere to the ocean in a nonlinear ocean model. This technique is applicable across a range of problems across Earth system components, including in the absence of a model adjoint. Significance Statement: While the oceans have absorbed 90% of the excess heat associated with human-forced climate change, the change in the ocean's heat content is not steady, with peaks and troughs superimposed upon a general increase. These fluctuations come from chaotic changes in the atmosphere and ocean and can be hard to disentangle. We use this case of ocean heat content variability to introduce a new method for determining the patterns of weather and climate in the atmosphere that are most effective at generating fluctuations in the ocean. To do this, we combine the statistics of recent atmospheric activity with output from a state-of-the-art numerical ocean model that reveals physical processes driving changes in ocean quantities including ocean heat content. This approach suggests that the atmospheric patterns that stimulate the most energetic changes in ocean heat content in the northern North Atlantic are not necessarily the most energetic patterns present in the atmosphere. We test our findings by preventing these patterns from affecting the ocean in our numerical model and measure a strong reduction in ocean heat content fluctuations. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (DSD) retrievals from two years of data gathered by the Tropical Rainfall Measuring Mission (TRMM) satellite and processed with a combined radar-radiometer algorithm over the oceans equatorward of 35° are examined for relationships with variables describing properties of the vertical precipitation profile, mesoscale organization, and background environment. In general, higher freezing levels and relative humidities (tropical environments) are associated with smaller reflectivity-normalized median drop size ( εϵDSD) than in the extratropics. Within the tropics, the smallest εϵDSD values are found in large, shallow convective systems where warm rain formation processes are thought to be predominant, whereas larger sizes are found in the stratiform regions of organized deep convection. In the extratropics, the largest εϵDSD values are found in the scattered convection that occurs when cold, dry continental air moves over the much warmer ocean after the passage of a cold front. These relationships are formally attributed to variables describing the large-scale environment, mesoscale organization, and profile characteristics via principal component (PC) analysis. The leading three PCs account for 23% of the variance in εϵDSD at the individual profile level and 45% of the variance in 1°-gridded mean values. The geographical distribution of εϵDSD is consistent with many of the observed regional reflectivity-rainfall ( Z- R) relationships found in the literature as well as discrepancies between the TRMM radar-only and radiometer-only precipitation products. In particular, midlatitude and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Ocean intertropical convergence zone. [ABSTRACT FROM AUTHOR]

An analysis method previously used to detect observed intra- to multidecadal (IMD) climate regimes was adapted to compare observed and modeled IMD climate variations. Pending the availability of the more appropriate phase 5 Coupled Model Intercomparison Project (CMIP-5) simulations, the method is demonstrated using CMIP-3 model simulations. Although the CMIP-3 experimental design will almost certainly prevent these model runs from reproducing features of historical IMD climate variability, these simulations allow for the demonstration of the method and illustrate how the models and observations disagree. This method samples a time series's data rankings over moving time windows, converts those ranking sets to a Mann-Whitney U statistic, and then normalizes the U statistic into a Z statistic. By detecting optimally significant IMD ranking regimes of arbitrary onset and varying duration, this process generates time series of Z values that are an adaptively low-passed and normalized transformation of the original time series. Principal component (PC) analysis of the Z series derived from observed annual temperatures at 92 U.S. grid locations during 1919-2008 shows two dominant modes: a PC1 mode with cool temperatures before the late 1960s and warm temperatures after the mid-1980s, and a PC2 mode indicating a multidecadal temperature cycle over the Southeast. Using a graphic analysis of a Z error metric that compares modeled and observed Z series, the three CMIP-3 model simulations tested here are shown to reproduce the PC1 mode but not the PC2 mode. By providing a way to compare grid-level IMD climate response patterns in observed and modeled data, this method can play a useful diagnostic role in future model development and decadal climate forecasting. [ABSTRACT FROM AUTHOR]

The objectives of this study are to describe the local interannual variability in southern Québec, Canada, streamflow, based on wavelet analysis, and to identify plausible climatic teleconnections that could explain these local variations. Scale-averaged wavelet power spectra are used to simultaneously assess the interannual and spatial variability in 18 contiguous annual streamflow time series. The span of available observations, 1938–2000, allows depicting the variance for periods up to about 12 yr. The most striking feature, in the 2–3-yr band and in the 3–6-yr band—the 6–12-yr band is dominated by white noise and is not considered further—is a net distinction between the timing of the interannual variability in local western and eastern streamflows, which may be linked to the local climatology. This opens up the opportunity to construct two regional time series using principal component (PC) analysis. Then, for each band, linear relationships are sought between the regional streamflow and five selected climatic indices: the Pacific–North America (PNA), the North Atlantic Oscillation (NAO), the Northern Hemisphere annular mode (NAM), the Baffin Island–West Atlantic (BWA) and the sea surface temperature anomalies over the Niño-3 region (ENSO3). The correlation analysis revealed the presence of a change point in the streamflow time series, as reported by others, occurring around 1970. For example, the west and east 2–3-yr bands are positively correlated to PNA since 1970, which was not the case prior to that change point. The proposed regional east–west divide is particularly evident prior to 1970, with a negative NAM correlation for the west and a positive NAM (and negative ENSO3) for the east. The picture for the less energetic 3–6-yr band is mixed, with alternating dominance of teleconnection patterns, but the 1970 change point holds. [ABSTRACT FROM AUTHOR]

Weather regimes defined through cluster analysis concisely categorize the anomalous regional circulation pattern on any given day. Owing to their persistence and low dimensionality, regimes are increasingly used in subseasonal-to-seasonal prediction and in analysis of climate variability and change. However, a limitation of existing regime classifications for North America is their seasonal dependence, with most existing studies defining regimes for winter only. Here, we normalize the seasonal cycle in daily geopotential height variance and use empirical orthogonal function analysis combined with k-means clustering to define a new set of year-round North American weather regimes: the Pacific Trough, Pacific Ridge, Alaskan Ridge, and Greenland High regimes. We additionally define a "No Regime" state to represent conditions close to climatology. To demonstrate the robustness of the classification, a thorough assessment of the sensitivity of the clustering solution to various methodological choices is provided. The median persistence of all four regimes, obtained without imposing a persistence criterion, is found to be one week, approximately 3 times longer than the median persistence of the No Regime state. Regime-associated temperature and precipitation anomalies are reported, together with the relationship between the regimes and modes of climate variability. We also quantify historical trends in the frequency of the regimes since 1979, finding a decrease in the annual frequency of the Pacific Trough regime and an increase in the summertime frequency of the Greenland High regime. This study serves as a foundation for the future use of these regimes in a variety of weather and climate applications. [ABSTRACT FROM AUTHOR]

Seasonal predictions of tropical cyclone (TC) landfalls are challenging because seasonal landfall count not only depends on the number and spatial distribution of TC genesis, but also whether those TCs are steered toward land or not. Past studies have separately examined genesis and landfall as a function of large-scale ocean and atmospheric environmental conditions. Here, we introduce a practical statistical framework for estimating the seasonal count of TC landfalls as the product of a Poisson model for seasonal TC genesis and a logistic model for landfall probability. We compute spatial variations in TC landfall and genesis by decomposing TC activity in the western North Pacific (WNP) basin into 10° × 10° bins, then identify coherent regions where El Niño–Southern Oscillation (ENSO) and the western extent of the Pacific subtropical high (WPSH) have significant influences on seasonal landfall count. Our framework shows that ENSO and the WPSH are weakly related to basinwide landfalls but strongly related to regional genesis and landfall probability. ENSO modulates the zonal distribution of TC genesis, consistent with past work, whereas the WPSH modulates the meridional distribution of landfall probability due to variations in steering flow associated with the Pacific subtropical high. These spatial patterns result in four coherent subregions of the WNP basin that define seasonal landfall variations: landfall count increases in the southwestern WNP during a positive WPSH and La Niña, the south-central WNP during a positive WPSH and El Niño, the eastern WNP during a negative WPSH and El Niño, and the northern WNP during a negative WPSH and La Niña. [ABSTRACT FROM AUTHOR]

Main modes of atmospheric variability exert a significant influence on weather and climate at local and regional scales on all time scales. However, their past changes and variability over the instrumental record are not well constrained due to limited availability of observations, particularly over the oceans. Here we couple a reconstruction method with an evolutionary algorithm to yield a new 1° × 1° optimized reconstruction of monthly North Atlantic sea level pressure since 1750 from a network of meteorological land and ocean observations. Our biologically inspired optimization technique finds an optimal set of weights for the observing network that maximizes the reconstruction skill of sea level pressure fields over the North Atlantic Ocean, bringing significant improvements over poorly sampled oceanic regions, as compared to non-optimized reconstructions. It also reproduces realistic variations of regional climate patterns such as the winter North Atlantic Oscillation and the associated variability of the subtropical North Atlantic high and the subpolar low pressure system, including the unprecedented strengthening of the Azores high in the second half of the twentieth century. We find that differences in the winter North Atlantic Oscillation indices are partially explained by disparities in estimates of its Azores high center. Moreover, our reconstruction also shows that displacements of the summer Azores high center toward the northeast coincided with extremely warm events in western Europe including the anomalous summer of 1783. Overall, our results highlight the importance of improving the characterization of the Azores high for understanding the climate of the Euro-Atlantic sector and the added value of artificial intelligence in this avenue. [ABSTRACT FROM AUTHOR]

In this study, interannual and interdecadal variations in the extreme high-temperature event (EHE) frequency over northern Asia (NA) and the associated possible mechanisms are explored. On an interannual time scale, the first two empirical orthogonal function modes of the NA EHE frequency exhibit a meridional dipole pattern (EOF1) and diagonal tripolar pattern (EOF2), respectively. The higher NA EHE frequency is related to anomalous local highs, reduced mid- to low clouds, and more solar radiation. The warmer ground further heats the overlying atmosphere through longwave radiation and sensible heat. The warm temperature advection in the lower troposphere and the drier soil conditions also favor higher EHE frequency. Further analysis reveals that the EOF1 mode is related to the Polar–Eurasian teleconnection pattern (POL), while the EOF2 mode is associated with North Atlantic Oscillation (NAO) and Pacific–Japan/East Asia–Pacific pattern (PJ/EAP). The fitted EHE frequency based on the atmospheric factors (POL, NAO, and PJ/EAP) can explain the interannual variation in the regionally averaged EHE frequency by 33.8%. Furthermore, three anomalous sea surface temperature (SST) patterns over the North Atlantic–Mediterranean Sea region and around the Maritime Continent are associated with the two EHE modes by intensifying the pronounced atmospheric teleconnections. Analysis on the simulation of five models in the Atmospheric Model Intercomparison Project experiment further confirms the impact of the pronounced SST patterns on the POL, NAO and PJ/EAP. In addition, NA EHE frequency experienced a significant interdecadal increase around the mid-1990s, which could be associated with the phase shift of the Atlantic multidecadal oscillation and long-term global warming trend. [ABSTRACT FROM AUTHOR]

The subpolar North Atlantic (SPNA) experienced extreme cold during 2015, an event often called the "cold blob." The evolution of this event in the Community Earth System Model version 1 Decadal Prediction Large Ensemble (CESM1-DPLE) hindcast initialized in November 2014 is compared to observations. This CESM1-DPLE hindcast failed to predict cold conditions during 2015 despite already cold SPNA initial conditions and despite having high sea surface temperature skill in the SPNA in all other years. The goal of this paper is to understand what led to this prediction failure in order to provide insight for future decadal prediction efforts. Our analysis shows that strongly positive North Atlantic Oscillation (NAO) conditions during winter and spring 2015 likely sustained the cold blob but were not simulated in any CESM1-DPLE members. We examine the rarity of the 2015 event using the CESM1-DPLE's uninitialized counterpart, the CESM1 Large Ensemble (CESM1-LE). Results from the CESM1-LE indicate that the exceptional state of the observed NAO in the winter of 2015 is at least part of the explanation for why this event was not encompassed in the CESM1-DPLE spread. To test another possibility—namely, that deficiencies in the initial conditions degraded the prediction—we performed additional hindcasts using the CESM1-DPLE protocol but different initial conditions. Altering the initial conditions did not improve the simulation of the 2015 cold blob, and in some cases, degraded it. Given the difficulty of predicting this event, this case could be a useful test bed for future prediction system development. [ABSTRACT FROM AUTHOR]

The CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1) provides a large (96 member) ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatiotemporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere, and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. For the atmosphere, we evaluate CAFE60v1 in comparison to empirical indices of the major climate teleconnections and blocking with various reanalysis products. Estimates of the large-scale ocean structure, transports, and biogeochemistry are compared to those derived from gridded observational products and climate model projections (CMIP). Sea ice (extent, concentration, and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model and observational products. Our results show that CAFE60v1 is a useful, comprehensive, and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multiyear to decadal time scales. [ABSTRACT FROM AUTHOR]

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 time scales) to total variance. Lead–lag regressions of atmospheric and ocean variables onto the LFC time series 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. [ABSTRACT FROM AUTHOR]

Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconomic and environmental implications for many regions around the world. Physics-based deterministic models show limited ability to predict precipitation as the lead time increases, due to imperfect representation of physical processes and incomplete knowledge of initial conditions. Similarly, statistical methods drawing upon established climate teleconnections have low prediction skill due to the complex nature of the climate system. Recently, promising data-driven approaches have been proposed, but they often suffer from overparameterization and overfitting due to the short observational record, and they often do not account for spatiotemporal dependencies among covariates (i.e., predictors such as sea surface temperatures). This study addresses these challenges via a predictive model based on a graph-guided regularizer that simultaneously promotes similarity of predictive weights for highly correlated covariates and enforces sparsity in the covariate domain. This approach both decreases the effective dimensionality of the problem and identifies the most predictive features without specifying them a priori. We use large ensemble simulations from a climate model to construct this regularizer, reducing the structural uncertainty in the estimation. We apply the learned model to predict winter precipitation in the southwestern United States using sea surface temperatures over the entire Pacific basin, and demonstrate its superiority compared to other regularization approaches and statistical models informed by known teleconnections. Our results highlight the potential to combine optimally the space--time structure of predictor variables learned from climate models with new graph-based regularizers to improve seasonal prediction. [ABSTRACT FROM AUTHOR]

A stochastically forced linear inverse model (LIM) of the combined modes of variability from the tropical and South Pacific Oceans is used to investigate the linear growth of optimal initial perturbations and to identify the spatiotemporal features of the stochastic forcing associated with the atmospheric Pacific–South American patterns 1 and 2 (PSA1 and PSA2). Optimal initial perturbations are shown to project onto El Niño–Southern Oscillation (ENSO) and South Pacific decadal oscillation (SPDO), where the inclusion of subsurface South Pacific Ocean temperature variability significantly increases the multiyear linear predictability of the deterministic system. We show that the optimal extratropical sea surface temperature (SST) precursor is associated with the South Pacific meridional mode, which takes from 7 to 9 months to linearly evolve into the final ENSO and SPDO peaks in both the observations and as simulated in an atmosphere-forced ocean model. The optimal subsurface precursor resembles its peak phase, but with a weak amplitude, representing oceanic Rossby waves in the extratropical South Pacific. The stochastic forcing is estimated as the residual by removing the deterministic dynamics from the actual tendency under a centered difference approximation. The resulting stochastic forcing time series satisfies the Gaussian white noise assumption of the LIM. We show that the PSA-like variability is strongly associated with stochastic SST forcing in the tropical and South Pacific Oceans and contributes not only to excite the optimal initial perturbations associated with ENSO and the SPDO but in general to activate the entire stochastic SST forcing, especially in austral summer. [ABSTRACT FROM AUTHOR]

The adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models' representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. For ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement. [ABSTRACT FROM AUTHOR]

The Atlantic and Pacific basin are found linked in the context of multidecadal SST variability from analyses of 118 years of observational data. Recurrent spatiotemporal variability, including multidecadal modes, was identified using the extended-EOF technique in a longitudinally global domain, allowing unfettered expression of interbasin interactions. The physicality of the obtained decadal modes was assessed using fishery records and analog counts. A three-mode structure with bi-directional interbasin links frames the new perspective on the cycling of multidecadal SST variability. The three modes are the Atlantic multidecadal oscillation (AMO), low-frequency North Atlantic Oscillation (LF-NAO), and North Pacific decadal variability [PDV-NP; resembling negative (–ve) PDO]. The two previously documented links AMO→PDV-NP (with ~12.5-yr lead) and LF-NAO→AMO (with 16-yr lead) are corroborated, while a third one, PDV-NP→(−LF-NAO) with ~6.5-yr lead, is uncovered. The interaction triad closes the loop on the cycling of multidecadal SST variability, generating AMO's phase reversal in ~35 years, consistent with its widely noted ~70-yr time scale. The two previously noted links—one intrabasin and one interbasin—were unsuccessful in this regard. Other findings include the deeper subsurface extensions of Atlantic multidecadal SST variability, and the hitherto unrecognized similarity of Pan-Pacific decadal variability and North Pacific Gyre Oscillation. Instrumental records, albeit short in the context of multidecadal variability, must continue to be mined for insights into the functioning of the climate system as its model representations while improving, remain inadequate. [ABSTRACT FROM AUTHOR]

Recent studies have stressed the key role of the east Atlantic (EA) pattern and its interactions with the North Atlantic Oscillation (NAO) in Euro-Atlantic climate variability. However, instrumental records of these leading patterns of variability are short, hampering a proper characterization of the atmospheric circulation beyond the mid-nineteenth century. In this work, we present the longest (1685–2014) observational-based records of winter NAO and EA indices as well as estimates of the North Atlantic eddy-driven jet stream speed and latitude for the same period. The time series display large variability from interannual to multidecadal time scales, with, for example, positive (negative) EA (NAO) phases dominating before 1750 (during much of the nineteenth century). By identifying winters with different combinations of NAO/EA phases in the twentieth century, our results highlight the additional role of EA in shaping the North Atlantic action centers and the European climate responses to NAO. The EA interference with the NAO signal is stronger in precipitation than in temperature and affects areas with strong responses to NAO such as Greenland and the western Mediterranean, which prevents simplistic relationships of natural proxies with NAO. The last three centuries uncover multidecadal periods dominated by specific NAO/EA states and substantial interannual-to-centennial variability in the North Atlantic jet stream, thus providing new evidence of the dynamics behind some outstanding periods. Transitions in the NAO/EA phase space have been recurrent and pin down long-lasting anomalies, such as the displacement of the North Atlantic action centers in the late twentieth century, besides some disagreements between NAO indices. [ABSTRACT FROM AUTHOR]

A frequency–wavenumber power () spectrum was constructed using satellite-derived outgoing longwave radiation (OLR) and brightness temperature for the tropical latitudes. Since the two datasets overlap for over 34 years with nonintersecting sources in error and compare relatively well with each other, it is possible to diagnose trends in the tropical wave activity from the two datasets with confidence. The results suggest a weakening trend in characterized by high interannual variability for wave activity occurring in the low-frequency part of the spectrum and a steady increase in with relatively low interannual variability for wave activity occurring in the high-frequency part of the spectrum. The results show the parts of the spectrum representing the Madden–Julian oscillation and equatorial Rossby wave losing and other parts of the spectrum representing Kelvin waves, mixed Rossby–gravity waves, and tropical disturbance–like wave activity gaining . Similar results were obtained when trends in variance corresponding to the first principal component were produced using spectrally filtered OLR data representative of atmospheric equatorial waves. Spatial trends in the active phase of wave events and the mean duration of events are also shown for the different wave types. Linear trends in for the entire spectrum and regional means in the spectrum corresponding to the abovementioned five wave types with confidence intervals are also presented in the paper. Finally, we demonstrate that El Niño–Southern Oscillation variability does not appear to control the overall spatial patterns and trends observed in the spectrum. [ABSTRACT FROM AUTHOR]

Widespread multiday convective bursts in the southwestern United States during the North American monsoon are often triggered by Gulf of California moisture surges (GoC surges). However, how GoC surges, and the amount and intensity of associated precipitation, will change in response to CO2-induced warming remains little known, not least because the most widely available climate models do not currently resolve the relevant mesoscale dynamics because of their coarse resolution (100 km or more). In this study, a 50-km-resolution global coupled model is used to address this question. It is found that the mean number of GoC surge events remains unchanged under CO2 doubling, but intermediate-to-high intensity surgerelated precipitation tends to become less frequent, thus reducing the mean summertime rainfall. Lowlevel moisture fluxes associated with GoC surges as well as their convergence over land to the east of the GoC intensify, but the increases in low-level moisture are not matched by the larger increments in the near-surface saturation specific humidity because of amplified land warming. This results in a more unsaturated low-level atmospheric environment that disfavors moist convection. These thermodynamic changes are accompanied by dynamic changes that are also detrimental to convective activity, with the midlevel monsoonal ridge projected to expand and move to the west of its present-day climatological maximum. Despite the overall reduction in precipitation, the frequency of very intense, localized daily surge-related precipitation in Arizona and surrounding areas is projected to increase with increased precipitable water. [ABSTRACT FROM AUTHOR]

Recent observed climate trends result from a combination of external radiative forcing and internally generated variability. To better contextualize these trends and forecast future ones, it is necessary to properly model the spatiotemporal properties of the internal variability. Here, a statistical model is developed for terrestrial temperature and precipitation, and global sea level pressure, based upon monthly gridded observational datasets that span 1921–2014. The model is used to generate a synthetic ensemble, each member of which has a unique sequence of internal variability but with statistical properties similar to the observational record. This synthetic ensemble is combined with estimates of the externally forced response from climate models to produce an observational large ensemble (OBS-LE). The 1000 members of the OBS-LE display considerable diversity in their 50-yr regional climate trends, indicative of the importance of internal variability on multidecadal time scales. For example, unforced atmospheric circulation trends associated with the northern annular mode can induce winter temperature trends over Eurasia that are comparable in magnitude to the forced trend over the past 50 years. Similarly, the contribution of internal variability to winter precipitation trends is large across most of the globe, leading to substantial regional uncertainties in the amplitude and, in some cases, the sign of the 50-yr trend. The OBS-LE provides a real-world counterpart to initial-condition model ensembles. The approach could be expanded to using paleo-proxy data to simulate longer-term variability. [ABSTRACT FROM AUTHOR]

The major El Niño of 2015/16 brought significantly less precipitation to California than previous events of comparable strength, much to the disappointment of residents suffering through the state's fourth consecutive year of severe drought. Here, California's weak precipitation in 2015/16 relative to previous major El Niño events is investigated within a 40-member ensemble of atmosphere-only simulations run with historical sea surface temperatures (SSTs) and constant radiative forcing. The simulations reveal significant differences in both California precipitation and the large-scale atmospheric circulation between 2015/16 and previous strong El Niño events, which are similar to (albeit weaker than) the differences found in observations. Principal component analysis indicates that these ensemble-mean differences were likely related to a pattern of tropical SST variability with a strong signal in the Indian Ocean and western Pacific and a weaker signal in the eastern equatorial Pacific and subtropical North Atlantic. This SST pattern was missed by the majority of forecast models, which could partly explain their erroneous predictions of above-average precipitation in California in 2015/16. [ABSTRACT FROM AUTHOR]

Observational studies link a persistent dipole of autumn and winter snow cover anomalies over the Tibetan Plateau (TP) and Mongolia with winter Pacific-North American (PNA)-like atmospheric variations. This study investigates atmospheric responses to such snow forcings using multiple ensemble transient integrations of the CAM4 and CLM4.0 models. Model boundary conditions are based on climatological sea ice extent and sea surface temperature, and satellite observations of snow cover extent (SCE) and snow water equivalent (SWE) over the TP and Mongolia from October to March in 1997/98 (heavy TP and light Mongolia snow) and 1984/85 (light TP and heavy Mongolia snow), with model-derived SCE and SWE elsewhere. In various forcing experiments, the ensemble-mean difference between simulations with these two extreme snow states identifies local, distant, concurrent, and delayed climatic responses. The main atmospheric responses to a dipole of high TP and low Mongolia SCE persisting from October to March (versus the opposite extreme) are strong TP surface cooling, warming in the surrounding China and Mongolia region, and a winter positive PNA-like response. The localized response is maintained by persistent diabatic cooling or heating, and the remote PNA response results mainly from the increased horizontal eastward propagation of stationary Rossby wave energy due to persistent TP snow forcing and also a winter transient eddy feedback mechanism. With a less persistent dipole anomaly in autumn or winter only, local responses are similar depending on the specific anomalies, but the winter PNA-like response is nearly absent or noticeably reduced. [ABSTRACT FROM AUTHOR]

Atmospheric reanalyses have been widely used to study large-scale atmospheric circulation and its links to local weather and to validate climate models. Only little effort has so far been made to compare reanalyses over the Euro-Atlantic domain, with the exception of a few studies analyzing North Atlantic cyclones. In particular, studies utilizing automated classifications of circulation patterns-one of the most popular methods in synoptic climatology-have paid little or no attention to the issue of reanalysis evaluation. Here, five reanalyses [ERA-40; NCEP-1; JRA-55; Twentieth Century Reanalysis, version 2 (20CRv2); and ECMWF twentieth-century reanalysis (ERA-20C)] are compared as to the frequency of occurrence of circulation types (CTs) over eight European domains in winters 1961-2000. Eight different classifications are used in parallel with the intention to eliminate possible artifacts of individual classification methods. This also helps document how substantial effect a choice of method can have if one quantifies differences between reanalyses. In general, ERA-40, NCEP-1, and JRA-55 exhibit a fairly small portion of days (under 8%) classified to different CTs if pairs of reanalyses are compared, with two exceptions: over Iceland, NCEP-1 shows disproportionately high frequencies of CTs with cyclones shifted south- and eastward; over the eastern Mediterranean region, ERA-40 and NCEP-1 disagree on classification of about 22% of days. The 20CRv2 is significantly different from other reanalyses over all domains and has a clearly suppressed frequency of zonal CTs. Finally, validation of 32 CMIP5 models over the eastern Mediterranean region reveals that using different reanalyses can considerably alter errors in the CT frequency of models and their rank. [ABSTRACT FROM AUTHOR]

Conventional analysis methods in weather and climate science (e.g., EOF analysis) exhibit a number of drawbacks including scaling and mixing. These methods focus mostly on the bulk of the probability distribution of the system in state space and overlook its tail. This paper explores a different method, the archetypal analysis (AA), which focuses precisely on the extremes. AA seeks to approximate the convex hull of the data in state space by finding 'corners' that represent 'pure' types or archetypes through computing mixture weight matrices. The method is quite new in climate science, although it has been around for about two decades in pattern recognition. It encompasses, in particular, the virtues of EOFs and clustering. The method is presented along with a new manifold-based optimization algorithm that optimizes for the weights simultaneously, unlike the conventional multistep algorithm based on the alternating constrained least squares. The paper discusses the numerical solution and then applies it to the monthly sea surface temperature (SST) from HadISST and to the Asian summer monsoon (ASM) using sea level pressure (SLP) from ERA-40 over the Asian monsoon region. The application to SST reveals, in particular, three archetypes, namely, El Niño, La Niña, and a third pattern representing the western boundary currents. The latter archetype shows a particular trend in the last few decades. The application to the ASM SLP anomalies yields archetypes that are consistent with the ASM regimes found in the literature. Merits and weaknesses of the method along with possible future development are also discussed. [ABSTRACT FROM AUTHOR]

Spectral analyses of the North Atlantic temperature field in the Simple Ocean Data Analysis (SODA) reanalysis identify prominent and statistically significant interannual oscillations along the Gulf Stream front and in large regions of the North Atlantic. A 7-8-yr oscillatory mode is characterized by a basin wide southwest-to-northeast–oriented propagation pattern in the sea surface temperature (SST) field. This pattern is found to be linked to a seesaw in the meridional dipole structure of the zonal wind stress forcing (TAUX). In the subpolar gyre, the SST and TAUX fields of this mode are shown to be in phase opposition, which suggests a cooling effect of the wind stress on the upper ocean layer. Over all, this mode's temperature field is characterized by a strong equivalent-barotropic component, as shown by covariations in SSTs and sea surface heights, and by phase-coherent behavior of temperature layers at depth with the SST field. Recent improvements of multivariate singular spectrum analysis (M-SSA) help separate spatiotemporal patterns. This methodology is developed further and applied to studying the ocean's response to variability in the atmospheric forcing. Statistical evidence is shown to exist for other mechanisms generating oceanic variability of similar 7-8-yr periodicity in the Gulf Stream region; the latter variability is likewise characterized by a strongly equivalent-barotropic component. Two other modes of biennial variability in the Gulf Stream region are also identified, and it is shown that interannual variability in this region cannot be explained by the ocean's response to similar variability in the atmospheric forcing alone. [ABSTRACT FROM AUTHOR]

The signatures of large-scale annular variability on the vertical structure of clouds and cloud radiative effects are examined in vertically resolved CloudSat and other satellite and reanalysis data products. The northern and southern 'barotropic' annular modes (the NAM and SAM) have a complex vertical structure. Both are associated with a meridional dipole in clouds between subpolar and middle latitudes, but the sign of the anomalies changes between upper, middle, and lower tropospheric levels. In contrast, the northern and southern baroclinic annular modes have a much simpler vertical structure. Both are linked to same-signed anomalies in clouds extending throughout the troposphere at middle to high latitudes. The changes in cloud incidence associated with both the barotropic and baroclinic annular modes are consistent with dynamical forcing by the attendant changes in static stability and/or vertical motion. The results also provide the first observational estimates of the vertically resolved atmospheric cloud radiative effects associated with hemispheric-scale extratropical variability. In general, the anomalies in atmospheric cloud radiative effects associated with the annular modes peak in the middle to upper troposphere, and are consistent with the anomalous trapping of longwave radiation by variations in upper tropospheric clouds. The southern baroclinic annular mode gives rise to periodic behavior in longwave cloud radiative effects at the top of the atmosphere averaged over Southern Hemisphere midlatitudes. [ABSTRACT FROM AUTHOR]

The relationship between climate modes and Antarctic sea ice is explored by separating the variability into intraseasonal, interannual, and decadal time scales. Cross-spectral analysis shows that geopotential height and Antarctic sea ice extent are most coherent at periods between about 20 and 40 days (the intraseasonal time scale). In this period range, where the atmospheric circulation and the sea ice extent are most tightly coupled, sea ice variability responds strongly to Rossby waves with the structure of the Pacific-South American (PSA) pattern. The PSA pattern in this time scale is not directly related to El Niño-Southern Oscillation (ENSO) or the southern annular mode (SAM), which have received much attention for explaining Antarctic sea ice variability. On the interannual time scale, ENSO and SAM are important, but a large fraction of sea ice variance can also be explained by Rossby wave-like structures in the Drake Passage region. After regressing out the sea ice extent variability associated with ENSO, the observed positive sea ice trends in Ross Sea and Indian Ocean during the satellite era become statistically insignificant. Regressing out SAM makes the sea ice trend in the Indian Ocean insignificant. Thus, the positive trends in sea ice in the Ross Sea and the Indian Ocean sectors may be explained by the variability and decadal trends of known interannual climate modes. [ABSTRACT FROM AUTHOR]

The decline in Barents Sea ice has been implicated in forcing the 'warm-Arctic cold-Siberian' (WACS) anomaly pattern via enhanced turbulent heat flux (THF). This study investigates interannual variability in winter [December-February (DJF)] Barents Sea THF and its relationship to Barents Sea ice and the large-scale atmospheric flow. ERA-Interim and observational data from 1979/80 to 2011/12 are used. The leading pattern (EOF1: 33%) of winter Barents Sea THF variability is relatively weakly correlated ( r = 0.30) with Barents Sea ice and appears to be driven primarily by atmospheric variability. The sea ice-related THF variability manifests itself as EOF2 (20%, r = 0.60). THF EOF2 is robust over the entire winter season, but its link to the WACS pattern is not. However, the WACS pattern emerges consistently as the second EOF (20%) of Eurasian surface air temperature (SAT) variability in all winter months. When Eurasia is cold, there are indeed weak reductions in Barents Sea ice, but the associated THF anomalies are on average negative, which is inconsistent with the proposed direct atmospheric response to sea ice variability. Lead-lag correlation analyses on shorter time scales support this conclusion and indicate that atmospheric variability plays an important role in driving observed variability in Barents Sea THF and ice cover, as well as the WACS pattern. [ABSTRACT FROM AUTHOR]

Variability in tropical cyclone activity in the eastern Pacific basin has been linked to a wide range of climate factors, yet the dominant factors driving this variability have yet to be identified. Using Poisson regressions and a track clustering method, the authors analyze and compare the climate influence on cyclone activity in this region. The authors show that local sea surface temperature and upper-ocean heat content as well as large-scale conditions in the northern Atlantic are the dominant influence in modulating eastern North Pacific tropical cyclone activity. The results also support previous findings suggesting that the influence of the Atlantic Ocean occurs through changes in dynamical conditions over the eastern Pacific. Using model selection algorithms, the authors then proceed to construct a statistical model of eastern Pacific tropical cyclone activity. The various model selection techniques used agree in selecting one predictor from the Atlantic (northern North Atlantic sea surface temperature) and one predictor from the Pacific (relative sea surface temperature) to represent the best possible model. Finally, we show that this simple model could have predicted the anomalously high level of activity observed in 2014. [ABSTRACT FROM AUTHOR]

Trend tests, linear regression, and canonical correlation analysis were used to quantify changes in National Weather Service Cooperative Observer (COOP) snow depth data and derived quantities, precipitation, snowfall, and temperature over the study period 1950-2010. Despite widespread warming, historical trends in snowfall and snow depth are generally mixed owing to competing influences of trends in precipitation. Trends toward later snow-cover onset in the western half of the conterminous United States and earlier onset in the eastern half and a widespread trend toward earlier final meltoff of snow cover combined to produce trends toward shorter snow seasons in the eastern half of the United States and in the west and longer snow seasons in the Great Plains and southern Rockies. The annual total number of days with snow cover exhibited a widespread decline. Monthly trend patterns show the dominant influence of temperature trends on occurrence of snow cover in the warmer snow-season months and a combination of temperature and precipitation trends in the colder months. A canonical correlation analysis indicated that most trends presented here took hold in the 1970s, consistent with the temporal pattern of global warming during the study period. [ABSTRACT FROM AUTHOR]

Understanding non-Gaussian statistics of atmospheric synoptic and low-frequency variability has important consequences in the atmospheric sciences, not least because weather and climate risk assessment depends on knowing and understanding the exact shape of the system's probability density function. While there is no doubt that many atmospheric variables exhibit non-Gaussian statistics on many time (and spatial) scales, a full and complete understanding of this phenomenon remains a challenge. Various mechanisms behind the observed atmospheric non-Gaussian statistics have been proposed but remain, however, multifaceted and scattered in the literature: nonlinear dynamics, multiplicative noise, cross-frequency coupling, nonlinear boundary layer drag, and others. Given the importance of this subject for weather and climate research, and in an attempt to contribute to this topic, a thorough review and discussion of the different mechanisms that lead to non-Gaussian weather and climate variability are presented in this paper and an outlook is given. [ABSTRACT FROM AUTHOR]

Two questions are addressed in this paper: whether ENSO can be adequately characterized by simple, seasonally invariant indices and whether the time series of a single component-SST or OLR-provides a sufficiently complete representation of ENSO for the purpose of quantifying U.S. climate impacts. Here, ENSO is defined as the leading mode of seasonally varying canonical correlation analysis (CCA) between anomalies of tropical Pacific SST and outgoing longwave radiation (OLR). The CCA reveals that the strongest regions of coupling are mostly invariant as a function of season and correspond to an OLR region located in the central Pacific Ocean (CP-OLR) and an SST region in the eastern Pacific that coincides with the Niño-3 region. In a linear context, the authors explore whether the use of a combined index of these SST and OLR regions explains additional variance of North American temperature and precipitation anomalies beyond that described by using a single index alone. Certain seasons and regions benefit from the use of a combined index. In particular, a combined index describes more variability in winter/spring precipitation and summer temperature. [ABSTRACT FROM AUTHOR]

The optimal ranking regime (ORR) method was used to identify intradecadal to multidecadal (IMD) time windows containing significant ranking sequences in U.S. climate division temperature data. The simplicity of the ORR procedure's output-a time series' most significant nonoverlapping periods of high or low rankings-makes it possible to graphically identify common temporal breakpoints and spatial patterns of IMD variability in the analyses of 102 climate division temperature series. This approach is also applied to annual Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) climate indices, a Northern Hemisphere annual temperature (NHT) series, and divisional annual and seasonal temperature data during 1896-2012. In addition, Pearson correlations are calculated between PDO, AMO, and NHT series and the divisional temperature series. Although PDO phase seems to be an important influence on spring temperatures in the northwestern United States, eastern temperature regimes in annual, winter, summer, and fall temperatures are more coincident with cool and warm phase AMO regimes. Annual AMO values also correlate significantly with summer temperatures along the Eastern Seaboard and fall temperatures in the U.S. Southwest. Given evidence of the abrupt onset of cold winter temperatures in the eastern United States during 1957/58, possible climate mechanisms associated with the cause and duration of the eastern U.S. warming hole period-identified here as a cool temperature regime occurring between the late 1950s and late 1980s-are discussed. [ABSTRACT FROM AUTHOR]

Predictability of sea ice concentrations (SICs) in the Barents Sea in early winter (November-December) is studied using canonical correlation analysis with atmospheric and ocean anomalies from the NCEP Climate Forecast System Reanalysis (CFSR) data. It is found that the highest prediction skill for a single-predictor model is obtained from the 13-month lead subsurface temperature at 200-m depth (T200) and the in-phase meridional surface wind ( Vsfc). T200 skillfully predicts SIC variability in 35% of the Barents Sea, mainly in the eastern side. The T200 for negative sea ice anomalies exhibits warm anomalies in the subsurface ocean temperature downstream of the Norwegian Atlantic Slope Current (NwASC) on a decadal time scale. The diagnostic analysis of NCEP CFSR data suggests that the subsurface temperature anomaly stored below the thermocline during summer reemerges in late autumn by atmospheric cooling and affects the sea ice. The subsurface temperature anomaly of the NwASC is advected from the North Atlantic subpolar gyre over ~3 years. Also, Vsfc skillfully predicts SIC variability in 32% of the Barents Sea, mainly in the western side. The Vsfc for the negative sea ice anomalies exhibits southerly wind anomalies; Vsfc is related to the large-scale atmospheric circulation patterns from the subtropical North Atlantic to the Eurasian continent. This study suggests that both atmospheric and oceanic remote effects have a potential impact on the forecasting accuracy of SIC. [ABSTRACT FROM AUTHOR]

Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate System Model, version 3 (CCSM3) ensemble for the period 2000-61. Each member is subject to an identical greenhouse gas emissions scenario and differs only in the atmospheric model component's initial condition. September Arctic sea ice extent trends during 2020-59 range from −2.0 × 106 to −5.7 × 106 km2 across the 39 ensemble members, indicating a substantial role for internal variability in future Arctic sea ice loss projections. A similar nearly threefold range (from −7.0 × 103 to −19 × 103 km3) is found for summer sea ice volume trends. Higher rates of summer Arctic sea ice loss in CCSM3 are associated with enhanced transpolar drift and Fram Strait ice export driven by surface wind and sea level pressure patterns. Over the Arctic, the covarying atmospheric circulation patterns resemble the so-called Arctic dipole, with maximum amplitude between April and July. Outside the Arctic, an atmospheric Rossby wave train over the Pacific sector is associated with internal ice loss variability. Interannual covariability patterns between sea ice and atmospheric circulation are similar to those based on trends, suggesting that similar processes govern internal variability over a broad range of time scales. Interannual patterns of CCSM3 ice-atmosphere covariability compare well with those in nature and in the newer CCSM4 version of the model, lending confidence to the results. Atmospheric teleconnection patterns in CCSM3 suggest that the tropical Pacific modulates Arctic sea ice variability via the aforementioned Rossby wave train. Large ensembles with other coupled models are needed to corroborate these CCSM3-based findings. [ABSTRACT FROM AUTHOR]

During boreal winter, anomalous temperature regimes (ATRs), including cold air outbreaks (CAOs) and warm waves (WWs), provide important societal influences upon the United States. The current study analyzes reanalysis and model data for the period from 1949 to 2011 to assess (i) long-term variability in ATRs, (ii) interannual modulation of ATRs by low-frequency modes, and (iii) the representation of ATR behavior in models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). No significant trends in either WWs or CAOs are identified for the continental United States. On interannual time scales, CAOs are modulated by the (i) North Atlantic Oscillation (NAO) over the U.S. Southeast and (ii) the Pacific-North American (PNA) pattern over the Northwest. WW frequency is modulated by (i) the NAO over the eastern United States and (ii) the combined influence of the PNA, Pacific decadal oscillation (PDO), and ENSO over the southern United States. In contrast to previous studies of seasonal-mean temperature, the influence of ENSO upon ATRs is found to be mainly limited to a modest modulation of WWs over the southern United States. Multiple linear regression analysis reveals that the regional collective influence of low-frequency modes accounts for as much as 50% of interannual ATR variability. Although similar behavior is observed in CMIP5 models, WW (CAO) frequency is typically overestimated (underestimated). All models considered are unable to replicate observed associations between ATRs and the PDO. Further, the collective influence of low-frequency modes upon ATRs is generally underestimated in CMIP5 models. The results indicate that predictions of future ATR behavior are limited by climate model ability to represent the evolving behavior of low-frequency modes of variability. [ABSTRACT FROM AUTHOR]

Variability of the equatorial East Africa 'short rains' (EASR) has intensified significantly since the turn of the twentieth century. This increase toward more extreme rainfall events has not been gradual but is strongly characterized by epochs. The rain gauge-based Global Precipitation Climatology Centre (GPCC) monthly precipitation dataset for the period 1901-2009 is used to demonstrate that the epochal changes were dictated by shifts in the Indian Ocean dipole (IOD) mode. These shifts occurred during 1961 and 1997. In the pre-1961 period, there was virtually no significant linear link between the IOD and the EASR. But a relatively strong coupling between the two occurred abruptly in 1961 and was generally maintained at that level until 1997, when another sudden shift to even a greater level occurred. The first principal component (PC1) extracted from the EASR spatial domain initially merely explained about 50% of the rainfall variability before 1961, and then catapulted to about 73% for the period from 1961 to 1997, before eventually shifting to exceed 82% after 1997. The PC1 for each successive epoch also displayed loadings with notably improved spatial coherence. This systematic pattern of increase was accompanied by both a sharp increase in the frequency of rainfall extremes and spatial coherence of the rainfall events over the region. Therefore, it is most likely that the 1961 and 1997 IOD shifts are responsible for the epochal modulation of the EASR in both the spatial and temporal domain. [ABSTRACT FROM AUTHOR]

The anthropogenic climate change impacts on the eddy-jet system include an intensified midlatitude jet stream and an elevated tropopause, as well as a poleward-shifted jet. While both responses are evident in phase 3 of the Coupled Model Intercomparison Project (CMIP3) ensemble mean twenty-first-century projections, uncertainty in the poleward shift response is large enough that even the sign of the shift is not consistent among all models, especially in the Northern Hemisphere. The present analysis finds that twenty-first-century projections of the ensemble mean zonal wind change at 300 hPa predict a weakening and poleward expansion of the Pacific jet and an overall expansion of the Atlantic jet. In contrast with the direct zonal mean climate change signal of increasing midlatitude upper-level winds, zonal winds are projected to decrease in the core of the Pacific and Atlantic jets, with increasing zonal winds located primarily in the jet exit regions and the meridional flanks of the jets. Uncertainties in SST changes from the twentieth century to the twenty-first century between models are shown to impact modeled Northern Hemisphere jet stream changes. In particular, El Niño-Southern Oscillation-like mean winter SST changes explain 30% of intermodel variance of midlatitude zonal wind compared to the 8% explained by the domain-averaged warming SST signal. This suggests that a reduction of uncertainty in the tropical Pacific SST response to global warming will significantly reduce uncertainty in the Northern Hemisphere zonal wind response to climate change. [ABSTRACT FROM AUTHOR]

The present study focuses on diagnosing the intermodel variability of nonzonally averaged NH winter jet stream portrayal in 17 global climate models (GCMs) from phase three of the Coupled Model Intercomparison Project (CMIP3). Relative to the reanalysis, the ensemble-mean 300-hPa Atlantic jet is too zonally extended and located too far equatorward in GCMs. The Pacific jet varies significantly between modeling groups, with large biases in the vicinity of the jet exit region that cancel in the ensemble mean. After seeking relationships between twentieth-century model wind biases and 1) the internal modes of jet variability or 2) tropical sea surface temperatures (SSTs), it is found that biases in upper-level winds are strongly related to an ENSO-like pattern in winter-mean tropical Pacific Ocean SST biases. The spatial structure of the leading modes of variability of the upper-level jet in the twentieth century is found to be accurately modeled in all 17 GCMs. Also, it is shown that Pacific model biases in the longitude of EOFs 1 and 2 are strongly linked to the modeled longitude of the Pacific jet exit, indicating that the improved characterization of the mean state of the Pacific jet may positively impact the modeled variability. This work suggests that improvements in model portrayal of the tropical Pacific mean state may significantly advance the portrayal of the mean state of the Pacific and Atlantic jets, which will consequently improve the modeled jet stream variability in the Pacific. To complement these findings, a companion paper examines the twenty-first-century GCM projections of the nonzonally averaged NH jet streams. [ABSTRACT FROM AUTHOR]

Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western Australia (CWWA), however, has exhibited comparatively much weaker coastal trends, but a more prominent inland increase during the wet summer season. Analysis of seasonally averaged rainfall data from a group of stations, representative of both the coastal and inland regions of CWWA, revealed that rainfall trends during the 1958-2010 period in the wet months of November-April were primarily associated with El Niño-Southern Oscillation (ENSO), and with the southern annular mode (SAM) farther inland. During the wet months of May-October, the Indian Ocean dipole (IOD) showed the most robust relationships. Those results hold when the effects of ENSO or IOD are excluded, and were confirmed using a principal component analysis of sea surface temperature (SST) anomalies, rainfall wavelet analyses, and point-by-point correlations of rainfall with global SST anomaly fields. Although speculative, given their long-term averages, reanalysis data suggest that from 1958 to 2010 the increase in CWWA inland rainfall largely is attributable to an increasing cyclonic anomaly trend over CWWA, bringing onshore moist tropical flow to the Pilbara coast. During May-October, the flow anomaly exhibits a transition from an onshore to offshore flow regime in the 2001-10 decade, which is consistent with the observed weaker drying trend during this period. [ABSTRACT FROM AUTHOR]

The authors present an analysis of the global midtropospheric CO2 retrieved for all-sky (clear and cloudy) conditions from measurements by the Atmospheric Infrared Radiation Sounder on board the Aqua satellite in 2003-09. The global data coverage allows the identification of the set of CO2 spatial patterns and their time variability by applying principal component analysis and empirical mode decomposition. The first, dominant pattern represents 93% of the variability and exhibits the linear trend of 2 ± 0.2 ppm yr−1, as well as annual and interannual dependencies. The single-site record of CO2 at Mauna Loa compares well with variability of this pattern. The first principal component is phase shifted relative to the Southern Oscillation, indicating a causative relationship between the atmospheric CO2 and ENSO. The higher-order patterns show regional details of CO2 distribution and display the semiannual oscillation. The CO2 distributions are compared with the distribution of two major characteristics of air transport: the vertical velocity and potential temperature surfaces at the same height. In agreement with modeling, CO2 concentration closely traces the potential temperature surfaces (isentropes) in middle and high latitudes. However, its vertical transport in the tropics, where these surfaces are mostly horizontal, is suppressed. The results are in agreement with the previous results on annual and interannual CO2 time variability obtained by using the network flask data. This knowledge of the global CO2 spatial patterns can be useful in climate analyses and potentially in the challenging task of connecting CO2 sources and sinks with its distribution in the atmosphere. [ABSTRACT FROM AUTHOR]

The observed relationships between anomalous Antarctic sea ice concentration (SIC) and the leading patterns of Southern Hemisphere (SH) large-scale climate variability are examined as a function of season over 1980-2008. Particular emphasis is placed on 1) the interactions between SIC, the southern annular mode (SAM), and El Niño-Southern Oscillation (ENSO); and 2) the contribution of these two leading modes to the 29-yr trends in sea ice. Regression, composite, and principal component analyses highlight a seasonality in SH sea ice-atmosphere interactions, whereby Antarctic sea ice variability exhibits the strongest linkages to the SAM and ENSO during the austral cold season months. As noted in previous work, a dipole in SIC anomalies emerges in relation to the SAM, characterized by centers of action located near the Bellingshausen/Weddell and Amundsen/eastern Ross Seas. The structure and magnitude of this SIC dipole is found to vary considerably as a function of season, consistent with the seasonality of the overlying atmospheric circulation anomalies. Relative to the SAM, the pattern of sea ice anomalies linked to ENSO exhibits a similar seasonality but tends to be weaker in amplitude and more diffuse in structure. The relationships between ENSO and sea ice also exhibit a substantial nonlinear component, highlighting the need to consider both season and phase of the ENSO cycle when diagnosing ENSO-SIC linkages. Trends in SIC over 1980-2008 are not significantly related to trends in either the SAM or ENSO during any season, including austral summer when the trend in the SAM is most pronounced. [ABSTRACT FROM AUTHOR]