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In a suite of idealized experiments with the Community Atmospheric Model version 3 coupled to a slab ocean, we show that the atmospheric circulation response to CO2 increase is sensitive to extratropical cloud feedback that is potentially nonlinear. Doubling CO2 produces a poleward shift of the Southern Hemisphere (SH) midlatitude jet that is driven primarily by cloud shortwave feedback and modulated by ice albedo feedback, in agreement with earlier studies. More surprisingly, for CO2 increases smaller than ~25 %, the SH jet shifts equatorward. Nonlinearities are also apparent in the Northern Hemisphere, but with less zonal symmetry. Baroclinic instability theory and climate feedback analysis suggest that as the CO2 forcing amplitude is reduced, there is a transition from a regime in which cloud and circulation changes are largely decoupled to a regime in which they are highly coupled. In the dynamically coupled regime, there is an apparent cancellation between cloud feedback due to warming and cloud feedback due to the shifting jet, and this allows the ice albedo feedback to dominate in the high latitudes. The extent to which dynamical coupling effects exceed thermodynamic forcing effects is strongly influenced by cloud microphysics: an alternate model configuration with slightly increased cloud liquid (LIQ) produces poleward jet shifts regardless of the amplitude of CO2 forcing. Altering the cloud microphysics also produces substantial spread in the circulation response to CO2 doubling: the LIQ configuration produces a poleward SH jet shift approximately twice that produced under the default configuration. Analysis of large ensembles of the Canadian Earth System Model version 2 demonstrates that nonlinear, cloud-coupled jet shifts are also possible in comprehensive models. We still expect a poleward trend in SH jet latitude for timescales on which CO2 increases by more than ~25 %. But on shorter timescales, our results give good reason to expect significant equatorward deviations. We also discuss the implications for understanding the circulation response to small external forcings from other sources, such as the solar cycle.

We use a multilevel vector autoregressive model (VAR-L), to forecast sea surface temperature anomalies (SSTAs) in the Atlantic hurricane Main Development Region (MDR). VAR-L is a linear regression model using global SSTA data from L prior months as predictors. In hindcasts for the recent 30 years, the multilevel VAR-L outperforms a state-of-the-art dynamic forecast model, as well as the commonly used linear inverse model (LIM). The multilevel VAR-L model shows skill in 6–12 month forecasts, with its greatest skill in the months of the active hurricane season. The optimized model for the best long-range skill score in the MDR, chosen by a cross-validation procedure, has 12 time levels and 12 empirical orthogonal function modes. We investigate the optimal initial conditions for MDR SSTA prediction using a generalized singular vector decomposition of the propagation matrix. We find that the added temporal degrees of freedom for the predictands in VAR12 as compared with a LIM model, which allow the model to capture both the local wind–evaporation–SST feedback in the Tropical Atlantic and the impact on the Atlantic of an improved medium-range ENSO forecast, elevate the long-range forecast skill in the MDR.

A multiscale-modeling framework for daily rainfall is considered and diagnostic results are presented for an application to the winter season in Northwest India. The daily rainfall process is considered to follow a hidden Markov model (HMM), with the hidden states assumed to be an unknown random function of slowly varying climatic modulation of the winter jet stream and moisture transport dynamics. The data used are from 14 stations over Satluj River basin in winter (December–January–February–March). The period considered is 1977/78–2005/06. The HMM identifies four discrete weather states, which are used to describe daily rainfall variability over study region. Each state was found to be associated with a distinct atmospheric circulation pattern, with the driest and drier states, State 1 and 2 respectively, characterized by a lack of synoptic wave activity. In contrast, the wetter and wettest states, States 3 and 4 respectively, are characterized by a zonally oriented wave train extending across Eurasia between 20N and 40N, identified with ‘western disturbances’ (WD). The occurrence of State 4 is strongly conditioned by the El Nino and Indian Ocean Dipole (IOD) phenomena in winter, which is demonstrated using large-scale correlation maps based on mean sea level pressure and sea surface temperature. This suggests that there is a tendency of higher frequency of the wet days and intense WD activities in winter during El Nino and positive IOD years. These findings, derived from daily rainfall station records, help clarify the sequence of Northern Hemisphere mid-latitude storms bringing winter rainfall over Northwest India, and their association with potentially predictable low frequency modes on seasonal time scales and longer.

The presence of rich ENSO variability in the long unforced simulation of GFDL's CM2.1 motivates the use of tools from dynamical systems theory to study vari- ability in ENSO predictability, and its connections to ENSO magnitude, frequency, and physical evolution. Local Lyapunov exponents (LLEs) estimated from the monthly NINO3 SSTa model output are used to charac- terize periods of increased or decreased predictability. The LLEs describe the growth of infinitesimal perturbations due to internal variability, and are a measure of the immediate predictive uncertainty at any given point in the system phase-space. The LLE-derived predictability esti- mates are compared with those obtained from the error growth in a set of re-forecast experiments with CM2.1. It is shown that the LLEs underestimate the error growth for short forecast lead times (less than 8 months), while they overestimate it for longer lead times. The departure of LLE-derived error growth rates from the re-forecast rates is a linear function of forecast lead time, and is also sensitive to the length of the time series used for the LLE calcula- tion. The LLE-derived error growth rate is closer to that estimated from the re-forecasts for a lead time of 4 months. In the 2,000-year long simulation, the LLE-derived pre- dictability at the 4-month lead time varies (multi)decadally only by 9-18 %. Active ENSO periods are more predict- able than inactive ones, while epochs with regular peri- odicity and moderate magnitude are classified as the most predictable by the LLEs. Events with a deeper thermocline in the west Pacific up to five years prior to their peak, along with an earlier deepening of the thermocline in the east Pacific in the months preceding the peak, are classified as more predictable. Also, the GCM is found to be less pre- dictable than nature under this measure of predictability.

Storm tracks play a major role in regulating the precipitation and hydrological cycle in midlatitudes. The changes in the location and amplitude of the storm tracks in response to global warming will have significant impacts on the poleward transport of heat, momentum and moisture and on the hydrological cycle. Recent studies have indicated a poleward shift of the storm tracks and the midlatitude precipitation zone in the warming world that will lead to subtropical drying and higher latitude moistening. This study agrees with this key feature for not only the annual mean but also different seasons and for the zonal mean as well as horizontal structures based on the analysis of Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 model simulations. Further analyses show that the meridional sensible and latent heat fluxes associated with the storm tracks shift poleward and intensify in both boreal summer and winter in the late twenty-first century (years 2081-2100) relative to the latter half of the twentieth century (years 1961-2000). The maximum dry Eady growth rate is examined to determine the effect of global warming on the time mean state and associated available potential energy for transient growth. The trend in maximum Eady growth rate is generally consistent with the poleward shift and intensification of the storm tracks in the middle latitudes of both hemispheres in both seasons. However, in the lower troposphere in northern winter, increased meridional eddy transfer within the storm tracks is more associated with increased eddy velocity, stronger correlation between eddy velocity and eddy moist static energy, and longer eddy length scale. The changing characteristics of baroclinic instability are, therefore, needed to explain the storm track response as climate warms. Diagnosis of the latitude-by-latitude energy budget for the current and future climate demonstrates how the coupling between radiative and surface heat fluxes and eddy heat and moisture transport influences the midlatitude storm track response to global warming. Through radiative forcing by increased atmospheric carbon dioxide and water vapor, more energy is gained within the tropics and subtropics, while in the middle and high latitudes energy is reduced through increased outgoing terrestrial radiation in the Northern Hemisphere and increased ocean heat uptake in the Southern Hemisphere. This enhanced energy imbalance in the future climate requires larger atmospheric energy transports in the midlatitudes which are partially accomplished by intensified storm tracks. Finally a sequence of cause and effect for the storm track response in the warming world is proposed that combines energy budget constraints with baroclinic instability theory.

A hierarchy of ENSO (El Nino/Southern Oscillation) prediction schemes has been developed which includes statistical schemes and physical models. The statistical models are, in general, based on advanced statistical techniques and can be classified into models which use either low-frequency variations in the atmosphere (sea level pressure or surface wind) or upper ocean heat content as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the lsquoshallow waterrsquo-type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast on predicting typical indices of ENSO on lead times of 6 to 12 months. The most successful prediction schemes, the fully physical coupled ocean-atmosphere models, show significant prediction abilities at lead times exceeding one year period. We therefore conclude that ENSO is predictable at least one year in advance. However, all of this applies to gross indices of ENSO such as the Southern Oscillation Index. Despite the demonstrated predictability, little is known about the predictability of specific features known to be associated with ENSO (e.g. Indian Monsoon rainfall, Southern African drought, or even off-equatorial sea surface temperature). Nor has the relative importance for prediction of different regional anomalies or different physical processes yet been established. A seasonal dependence in predictability is well established, but the processes responsible for it are not fully understood.

An intercomparison is undertaken of the tropical behavior of 17 coupled ocean-atmosphere models in which at least one component may be termed a general circulation model (GCM). The aim is to provide a taxonomy—a description and rough classification—of behavior across the ensemble of models, focusing on interannual variability. The temporal behavior of the sea surface temperature (SST) field along the equator is presented for each model, SST being chosen as the primary variable for intercomparison due to its crucial role in mediating the coupling and because it is a sensitive indicator of climate drift. A wide variety of possible types of behavior are noted among the models. Models with substantial interannual tropical variability may be roughly classified into cases with propagating SST anomalies and cases in which the SST anomalies develop in place. A number of the models also exhibit significant drift with respect to SST climatology. However, there is not a clear relationship between climate drift and the presence or absence of interannual oscillations. In several cases, the mode of climate drift within the tropical Pacific appears to involve coupled feedback mechanisms similar to those responsible for El Niño variability. Implications for coupled-model development and for climate prediction on seasonal to interannual time scales are discussed. Overall, the results indicate considerable sensitivity of the tropical coupled ocean-atmosphere system and suggest that the simulation of the warm-pool/cold-tongue configuration in the equatorial Pacific represents a challenging test for climate model parameterizations.