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Two independent atmospheric general circulation models reveal that the positive (negative) phase of Atlantic multidecadal variability (AMV) can reduce (amplify) the variance of the shorter time-scale (e.g., ENSO related) precipitation fluctuations in the United States, especially in the Southwest, as well as decrease (increase) the long-term seasonal mean precipitation for the cold season. The variance is modulated because of changes in 1) dry day frequency and 2) maximum daily rainfall intensity. With positive AMV forcing, the upper-level warming originating from the increased precipitation over the tropical Atlantic Ocean changes the mean vertical thermal structure over the United States continent to a profile less favorable for rain-inducing upward motions. In addition, a northerly low-level dry advection associated with the local overturning leaves less available column moisture for condensation and precipitation. The opposite conditions occur during cold AMV periods.

During the strong 2015/16 El Niño, only normal to below-average precipitation fell across California in the late winter. This disagrees with both predictions by the ensemble mean of forecast models and expectations for strong El Niños. The authors examine one of the possible reasons why this event did not bring expected precipitation to California in the late winter. The maximum equatorial Pacific sea surface temperature anomalies (SSTAs) were located, compared to the 1982/83 and 1997/98 strong El Niños, farther to the west in the 2015/16 winter, which possibly caused less convection in the eastern tropical Pacific and shifted the teleconnection patterns westward in the North Pacific, thus weakening the influences on California. The SSTA and precipitation forecast for February–April 2016, based on the North American Multimodel Ensemble, showed large discrepancies from observations, with the ensemble mean of most of the models overestimating SSTAs in the eastern tropical Pacific and California precipitation. Atmospheric general circulation model (AGCM) experiments were conducted to test the hypothesis that the warmer eastern tropical Pacific SSTA forecast may have caused the wetter forecast in California in 2015/16 compared to observations. The AGCM experiments suggest it is difficult to assert that the eastern tropical Pacific SSTAs caused the too-wet California precipitation forecast, especially in Southern California, given that the models disagree. Results indicate forecast error can be influenced by atmosphere-model sensitivity to forecast SSTs, but they also indicate atmospheric internal variability may have been responsible for the combination of a strong El Niño and near-normal California precipitation.

A neural-network-based cluster technique, the so-called self-organizing map (SOM), was performed to extract distinct sea surface temperature (SST) anomaly patterns during boreal winter. The SOM technique has advantages in nonlinear feature extraction compared to the commonly used empirical orthogonal function analysis and is widely used in meteorology. The eight distinguishable SOM patterns so identified represent three La Niña–like patterns, two near-normal patterns, and three El Niño–like patterns. These patterns show the varied amplitude and location of the SST anomalies associated with El Niño and La Niña, such as the central Pacific (CP) and eastern Pacific (EP) El Niño. The impact of each distinctive SOM pattern on winter-mean surface temperature and precipitation changes over North America was examined. Based on composite maps with observational data, each SOM pattern corresponds to a distinguishable spatial structure of temperature and precipitation anomaly over North America, which seems to result from differing wave train patterns, extending from the tropics to mid–high latitudes induced by longitudinally shifted tropical heating. The corresponding teleconnection as represented by the National Center for Atmospheric Research Community Atmospheric Model, version 4 (CAM4), was compared with the observational results. It was found that the 16-member ensemble average of the CAM4 experiments with prescribed SST can reproduce the observed atmospheric circulation responses to the different SST SOM patterns, which suggests that the circulation differences are largely SST driven rather than due to internal atmospheric variability.

A suite of empirical model experiments under the empirical model reduction framework are conducted to advance the understanding of ENSO diversity, nonlinearity, seasonality, and the memory effect in the simulation and prediction of tropical Pacific sea surface temperature (SST) anomalies. The model training and evaluation are carried out using 4000-yr preindustrial control simulation data from the coupled model GFDL CM2.1. The results show that multivariate models with tropical Pacific subsurface information and multilevel models with SST history information both improve the prediction skill dramatically. These two types of models represent the ENSO memory effect based on either the recharge oscillator or the time-delayed oscillator viewpoint. Multilevel SST models are a bit more efficient, requiring fewer model coefficients. Nonlinearity is found necessary to reproduce the ENSO diversity feature for extreme events. The nonlinear models reconstruct the skewed probability density function of SST anomalies and improve the prediction of the skewed amplitude, though the role of nonlinearity may be slightly overestimated given the strong nonlinear ENSO in GFDL CM2.1. The models with periodic terms reproduce the SST seasonal phase locking but do not improve the prediction appreciably. The models with multiple ingredients capture several ENSO characteristics simultaneously and exhibit overall better prediction skill for more diverse target patterns. In particular, they alleviate the spring/autumn prediction barrier and reduce the tendency for predicted values to lag the target month value.

The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño–Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VAR model implicitly captures subsurface forcing observable in the LIM residual as red noise. Optimal skill is achieved using a state vector of order 14–17 months in an exhaustive 120-yr cross-validated hindcast assessment. It is found that VAR outperforms LIM, increasing forecast skill by 3 months, in a 30-yr retrospective forecast experiment.

This study analyzes the association between wintertime high-wind events (HWEs) in the northeastern United States and extratropical cyclones. Sustained wind maxima in the daily summary data from the National Climatic Data Center’s integrated surface database are analyzed for 1979–2012. For each station, a generalized Pareto distribution is fit to the upper tail of the daily maximum wind speed data, and probabilistic return levels at 1, 3, and 5 yr are derived. Wind events meeting the return-level criteria are termed HWEs. The HWEs occurring on the same day are grouped into simultaneous wind exceedance dates, termed multistation events. In a separate analysis, extratropical cyclones are tracked using ERA-Interim. The multistation events are associated with the extratropical cyclone tracks on the basis of cyclone proximity on the day of the event. The multistation wind events are found to be most often associated with cyclones traveling from southwest to northeast, originating west of the Appalachian Mountains. To quantify the relative frequency of the strong-wind-associated cyclones, the full set of northeastern cyclone tracks is separated on the basis of path, using a crosshairs algorithm designed for this region. The tracks separate into an evenly distributed set of four pathways approaching the northeastern United States: from due west, from the southwest, and from the southeast and storms starting off the coast north of the Carolinas. Using the frequency of the tracks in each of the pathways, it is shown that the storms associated with multistation wind events are most likely to approach the northeastern United States from the southwest.

The performance of the Twentieth-Century Reanalysis (20CR) in reproducing observed monthly mean precipitation over the global domain is compared to that of comprehensive reanalyses that also assimilate upper-air and satellite observations [the Climate Forecast System Reanalysis (CFSR), ECMWF Interim Re-Analysis (ERA-Interim), and NCEP–U.S. Department of Energy reanalysis (NCEP2)] and to that of an atmospheric general circulation model (GCM) ensemble simulation [Global Ocean Global Atmosphere (GOGA)] that is forced with observed sea surface temperature (SST). Wintertime rainfall variability in the midlatitude continents and storm tracks is captured with great accuracy, similar to the comprehensive reanalyses, but summertime rainfall is not, probably in consequence of the greater importance of convection in the summer season. Over the tropics, the accuracy of all reanalyses is much less than over the midlatitudes. Over tropical land, the performance of 20CR is better than NCEP2 and similar to ERA-Interim and CFSR, but over the tropical oceans the most recent reanalyses perform significantly better. Across the twentieth century, the clearest gain from the assimilation of a denser observational dataset is the expansion of the area of good skill. A comparison of the accuracy and ensemble spread in the 20CR and GOGA ensembles highlights regions where SST forcing is a stronger source of skill than data assimilation for 20CR. In contrast, for some tropical regions such as the Sahel, the assimilation of sea level pressure is effective in constraining precipitation values—but model biases in the teleconnections with global SST limit the performance of 20CR.

The causes of the Texas–northern Mexico drought during 2010–11 are shown, using observations, reanalyses, and model simulations, to arise from a combination of ocean forcing and internal atmospheric variability. The drought began in fall 2010 and winter 2010/11 as a La Niña event developed in the tropical Pacific Ocean. Climate models forced by observed sea surface temperatures (SSTs) produced dry conditions in fall 2010 through spring 2011 associated with transient eddy moisture flux divergence related to a northward shift of the Pacific–North American storm track, typical of La Niña events. In contrast the observed drought was not associated with such a clear shift of the transient eddy fields and instead was significantly influenced by internal atmospheric variability including the negative North Atlantic Oscillation of winter 2010/11, which created mean flow moisture divergence and drying over the southern Plains and southeast United States. The models suggest that drought continuation into summer 2011 was not strongly SST forced. Mean flow circulation and moisture divergence anomalies were responsible for the summer 2011 drought, arising from either internal atmospheric variability or a response to dry summer soils not captured by the models. The summer of 2011 was one of the two driest and hottest summers over recent decades but it does not represent a clear outlier to the strong inverse relation between summer precipitation and temperature in the region. Seasonal forecasts at 3.5-month lead time did predict onset of the drought in fall and winter 2010/11 but not intensification into summer 2011, demonstrating the current, and likely inherent, inability to predict important aspects of North American droughts.

Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface temperature (SST) anomalies are used to force the atmospheric general circulation model. Two coupled models and one two-tiered model are considered here, and they are, respectively, the ECHAM4.5–version 3 of the Modular Ocean Model (MOM3-DC2), the ECHAM4.5-GML–NCEP Coupled Forecast System (CFSSST), and the ECHAM4.5 atmospheric model that is forced with SST anomalies predicted by a statistical model. The 850-hPa geopotential height fields of the three models are statistically downscaled to South African Weather Service district rainfall data by retroactively predicting 3-month seasonal rainfall totals over the 14-yr period from 1995/96 to 2008/09. Retroactive forecasts are produced for lead times of up to 4 months, and probabilistic forecast performance is evaluated for three categories with the outer two categories, respectively, defined by the 25th and 75th percentile values of the climatological record. The resulting forecast skill levels are also compared with skill levels obtained by downscaling forecasts produced by forcing the atmospheric model with simultaneously observed SST in order to produce a reference forecast set. Downscaled forecasts from the coupled systems generally outperform the downscaled forecasts from the two-tiered system, but neither of the two systems outscores the reference forecasts, suggesting that further improvement in operational seasonal rainfall forecast skill for South Africa is still achievable.

Prescribing sea surface temperature (SST) for the atmospheric general circulation models (GCM) may not lead to underestimation of the coupled variability. In this study, a set of SST-driven atmospheric GCM experiments, starting from slightly different multiple initial conditions, is performed. The SST used here is prepared by a coupled GCM, which has the same atmospheric GCM component as the AGCM used in the experiment with the prescribed SST. The results indicate that prescribing SST leads to underestimation of the coupled air temperature variance only in subtropics. Meanwhile, in midlatitudes, prescribing SST may result in the overestimation of the coupled air temperature variance, where the major role of ocean–atmosphere contrast is to provide damping for SST. The simple stochastically driven coupled model is revisited with an extension to the direct wind-driven forcing for SST. In addition to the previous setup relying exclusively on the stochastic perturbation for air temperature, the ocean temperature is also forced by the pure random wind. By this extended model, it is speculated that the coupled air temperature variance can be overestimated by prescribing SST, depending on the sensitivity of SST to the wind-driven heat flux. The midlatitude is the most probable place for the overestimation since the wind-driven ocean dynamics can enhance the wind-driven surface heat flux due to the dominant zonal wind anomaly.

In this study, a new modeling approach is used to look for potential causes of the North Pacific decadal climate regime shift. This new modeling approach is specifically designed to assess not only how changes of the wind-driven ocean circulation induce SST variability, but also the subsequent feedback to climate. Observations appear to indicate that the 1970s North Pacific climate regime shift may be attributed to the coupled ocean–atmosphere interaction over the North Pacific in response to persistent wind stress anomalies in the previous decade. This tends to be supported by modeling results, which suggest that the delayed adjustment of the subtropical ocean circulation may generate sea surface temperature (SST) anomalies in the western subtropical Pacific that may potentially induce a shift of atmospheric circulation, leading to a change of SST in the central and midlatitude North Pacific. This study appears to unify the recent contradictory views of the roles of ocean circulation in the North Pacific decadal climate variability.

Pacific decadal variability is studied in a series of coupled global ocean‐atmosphere simulations aided by two ‘‘modeling surgery’’ strategies: partial coupling (PC) and partial blocking (PB). The PC experiments retain full ocean‐atmosphere coupling in selected regions, but constrain ocean‐atmosphere coupling elsewhere by prescribing the model climatological SST to force the atmospheric component of the coupled system. In PB experiments, sponge walls are inserted into the ocean component of the coupled model at specified latitudinal bands to block the extratropical‐tropical oceanic teleconnection. Both modeling and observational studies suggest that Pacific decadal variability is composed of two distinct modes: a decadal to bidecadal tropical Pacific mode (TPM) and a multidecadal North Pacific mode (NPM). The PC and PB experiments showed that the tropical Pacific mode originates predominantly from local coupled ocean‐atmosphere interaction within the tropical Pacific. Extratropical‐tropical teleconnections, although not a necessary precondition for the genesis of the tropical decadal variability, can enhance SST variations in the Tropics. The decadal memory in the Tropics seems to be associated with tropical higher baroclinic modes. The North Pacific mode originates from local atmospheric stochastic processes and coupled ocean‐atmosphere interaction. Atmospheric stochastic forcing can generate a weaker NPM-like pattern in both the atmosphere and ocean, but with no preferred timescales. In contrast, coupled ocean‐atmosphere feedback can enhance the variability substantially and generate a basin-scale multidecadal mode in the North Pacific. The multidecadal memory in the midlatitudes seems to be associated with the delayed response of the subtropical/subpolar gyre to wind stress variation in the central North Pacific and the slow growing/decaying of SST anomalies that propagate eastward in the Kuroshio Extension region. Oceanic dynamics, particularly the advection of the mean temperature by anomalous meridional surface Ekman flow and western boundary currents, plays an important role in generating the North Pacific mode.