Abstract

The effects of nonlinear (positive only or conditional) heating on moist Kelvin waves are examined with a simple equatorial zonal-plane model describing the gravest baroclinic mode.

The unstable perturbation subject to nonlinear beating emerges as a wave packet. A typical amplifying, eastward-moving wave packet is characterized by an asymmetric structure: 1) the ascending branch (wet region) is much narrower than the two descending ones (dry regions); and 2) the circulation cell to the east of the wet region center is smaller and stronger than its counterpart to the west of the center. The wet-dry asymmetry is primarily caused by the nonlinear beating effect, while the east-west asymmetry is a result of the movement of the wave packet relative to mean flow. The existence of Newtonian cooling and Rayleigh friction enhances the structural asymmetries.

The unstable wave packet is characterized by two zonal length scales: the ascending branch length (ABL) and total circulation extent (TCE). For a given basic state, the growth rate of a wave packet increases with decreasing ABL or TCE. However, up to a moderate growth rate (order of day⁻¹) the energy spectra of all wave packets are dominated by zonal wavenumber one regardless of ABL size. In particular, the slowly growing (low frequency) wave packets normally exhibit TCEs of planetary scale and ABLs of synoptic scale.

Observed equatorial intraseasonal disturbances often display a narrow convection region in between two much broader dry regions and a total circulation of planetary scale. These structure and scale characteristics are caused by the effects of nonlinear heating and the cyclic geometry of the equator. It is argued that the unstable disturbance found in numerical experiments (e.g., Lau and Peng; Hayashi and Sumi) is a manifestation of the nonlinear wave packet.

Abstract

A series of seasonally varying linear Markov models are constructed in a reduced multivariate empirical orthogonal function (MEOF) space of observed sea surface temperature, surface wind stress, and sea level analysis. The Markov models are trained in the 1980–95 period and are verified in the 1964–79 period. It is found that the Markov models that include seasonality fit to the data better in the training period and have a substantially higher skill in the independent period than the models without seasonality. The authors conclude that seasonality is an important component of ENSO and should be included in Markov models. This conclusion is consistent with that of statistical models that take seasonality into account using different methods.

The impact of each variable on the prediction skill of Markov models is investigated by varying the weightings among the three variables in the MEOF space. For the training period the Markov models that include sea level information fit the data better than the models without sea level information. For the independent 1964–79 period, the Markov models that include sea level information have a much higher skill than the Markov models without sea level information. The authors conclude that sea level contains the most essential information for ENSO since it contains the filtered response of the ocean to noisy wind forcing.

The prediction skill of the Markov model with three MEOFs is competitive for both the training and independent periods. This Markov model successfully predicted the 1997/98 El Niño and the 1998/99 La Niña.

Abstract

Seasonal prediction skill of North Pacific sea surface temperature anomalies (SSTAs) and the Pacific decadal oscillation (PDO) in the NCEP Climate Forecast System (CFS) retrospective forecasts is assessed. The SST forecasts exhibit significant skills over much of the North Pacific for two seasons in advance and outperform persistence over much of the North Pacific except near the Kuroshio–Oyashia Extension. Similar to the “spring barrier” feature in the El Niño–Southern Oscillation forecasts, the central North Pacific SST experiences a faster drop in prediction skill for forecasts initialized from November to February than those from May to August. Forecasts for the PDO displayed a constant phase shift from the observation with respect to lead time. The PDO skill has a clear seasonality with highest skill for forecasts initialized in boreal spring.

The impact of ENSO on the PDO and North Pacific SST prediction was investigated. The analysis revealed that seasonal prediction skill in the central North Pacific mainly results from the skillful prediction of ENSO. As a result, the PDO is more skillful than persistence at all lead times during ENSO years. On the other hand, persistence is superior to the CFS forecast during ENSO-neutral conditions owing to errors in initial conditions and deficiencies in model physics. Examination of seasonal variance and predictability (signal-to-noise ratio) further articulates the influence of ENSO on the PDO skill. The results suggest that improvement of ENSO prediction as well as reduction in model biases in the western North Pacific will lead to improvements in the PDO and North Pacific SST predictions.

Abstract

The NCEP Climate Forecast System Reanalysis (CFSR) represents a new effort with the first guess from a high-resolution coupled system and offers prospects for improved simulation of mesoscale air–sea coupled variability. This study aims to describe the characteristics of ocean–atmosphere covariability associated with tropical instability waves (TIWs) in the Pacific for the CFSR, and to assess how well they agree with in situ and satellite observations.

Multiyear daily high-resolution CFSR data are used to describe variability associated with TIWs. Results show that TIW-induced SST variations exhibit pronounced seasonal and interannual variability that are tightly connected with cold tongue variations. The analysis illustrates coherent patterns associated with TIWs, both in the ocean and the atmosphere. Moisture and air temperature maximums are located west of SST maximums, leading to downstream displacement of surface pressure minimums relative to SST maximums. Surface winds accelerate (decelerate) over warm (cold) water, and a thermally direct circulation is created. Significant signals are observed in low-level cloud cover, which are closely in phase with surface wind convergences. The magnitudes of TIW-induced surface wind, surface pressure, and cloud cover perturbations agree well with in situ and satellite observations. Further analysis shows that surface net heat flux perturbations are dominated by latent heat fluxes and have a large negative feedback on TIW SST variability (~40 W m⁻² °C⁻¹). Water vapor perturbation is the primary factor contributing to changes in latent heat fluxes, while SST-induced wind perturbation plays a secondary role. The analysis presented here highlights that the CFSR provides an unprecedented opportunity to study the physical mechanisms for the TIWs, as well as their influences on climate variability.

Abstract

To estimate the state of the ocean in the context of monitoring and prediction, ocean analysis products combine observed information from various sources that include both in situ ocean measurements and estimates of atmospheric forcings derived either from numerical models or from objective analysis methods. In the context of El Niño–Southern Oscillation (ENSO) variability in the equatorial tropical Pacific, this study discusses two questions: 1) the role of surface forcings in resolving the observed variability of subsurface ocean temperatures, and 2) which component of surface forcings plays a more important role.

The analysis approach is based on ocean model simulations where specification of surface forcings is controlled and the resulting ocean state is either compared among various simulations or is compared with an independent ocean analysis (where information from in situ ocean temperature measurements is included). The results highlight the importance of the contribution of observed sea surface temperature (via its influence on surface winds due to coupled air–sea interactions) and the observed surface wind forcing in determining the evolution of subsurface ocean temperatures. Implications for assessing the feasibility of extending ocean analysis and forecasts back in time when in situ observations were limited are also discussed.

Abstract

Subsurface ocean observations in the equatorial tropical Pacific Ocean dramatically increased after the 1990s because of the completion of the TAO moored array and a steady increase in Argo floats. In this analysis the question explored is whether a steady increase in ocean observations can be discerned in improvements in skill of predicting sea surface temperature (SST) variability associated with El Niño–Southern Oscillation (ENSO)? The analysis is based on the time evolution of skill of sea surface temperatures in the equatorial tropical Pacific since 1982 based on a seasonal prediction system. It is found that for forecasts up to a 6-month lead time, a clear fingerprint of increases in subsurface ocean observations is not readily apparent in the time evolution of prediction skill that is dominated much more by the signal-to-noise consideration of SSTs to be predicted. Finding no clear relationship between an increase in ocean observations and prediction skill of SSTs, various possibilities for why it may be so are discussed. This discussion is to motivate further exploration on the question of the tropical Pacific observing system, its influence on the skill of ENSO prediction, and the capabilities of the current generation of coupled models and ocean data assimilation systems to take advantage of ocean observations.

Abstract

The influence of El Niño–Southern Oscillation (ENSO) on Pacific decadal variability (PDV) is investigated by comparing two 500-yr simulations with the National Centers for Environmental Prediction (NCEP) Climate Forecast System coupled model. One simulation is a no-ENSO run, in which model daily sea surface temperature (SST) in the tropical Pacific Ocean is relaxed to the observed climatology. The other simulation is a fully coupled run and retains ENSO variability. The PDV considered in this study is the first two empirical orthogonal functions of monthly SST anomalies in the North Pacific: the Pacific decadal oscillation (PDO) and the North Pacific Gyre Oscillation (NPGO). The PDO in the no-ENSO run can be clearly identified. Without ENSO, the PDO displays relatively higher variance at the decadal time scale and no spectral peak at the interannual time scale. In the ENSO run, the PDO variability increases slightly. ENSO not only enhances the variability of the PDO at the interannual time scale, but also shifts the PDO to longer time scales—both consistent with observations. ENSO modulates the Aleutian low and associated surface wind over the North Pacific. The latter, in turn, helps establish a more persistent PDO in the ENSO run. The results also indicate a PDO modulation of global ENSO impacts and the linearity in the superposition of the ENSO-forced and PDO-related atmospheric anomalies. Compared to observations, the NPGO in both simulations lacks power at the time scale longer than 30 yr. On the decadal time scale, the variability of the NPGO is weaker in the ENSO run than in the no-ENSO run.

Abstract

The seasonality of the Pacific decadal oscillation (PDO) is examined using North Pacific sea surface temperature (SST) in observations and in a 480-yr simulation with the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) coupled model. The PDO, both in observations and in the CFS, shows similar seasonality, with increasing SST variance during spring and a maximum in late spring and early summer. The vertical structure of the ocean temperature anomaly associated with the PDO in the CFS displays a significant transition from a deep to a shallow structure during late spring, consistent with the seasonal variation of the mean ocean mixed layer depth (MLD). An analysis of atmospheric surface wind and SST anomalies from the CFS simulation indicates that there is a 1-month delay in the PDO-related SST response to the atmospheric wind forcing. The results based on the CFS simulation are generally consistent with observations, including both atmospheric data from the NCEP/Department of Energy (DOE) Global Reanalysis 2 (GR-2) and ocean data from the NCEP Global Ocean Data Assimilation System (GODAS). The 1-month delay together with the seasonal variation of the mean MLD tends to amplify the PDO-related SST response to the atmospheric surface wind in late spring to early summer, and the combination leads to the maximum variability of the PDO, which is a 3-month delay from the peak phase of the surface wind in February and March.

Abstract

Lagged pentad composites of surface air temperature and precipitation are analyzed for the winter season (December–February) to assess the influence of the Madden–Julian oscillation (MJO) on the climate of the contiguous United States. Composites are based on the Wheeler and Hendon MJO index as well as an index developed and maintained at NOAA’s Climate Prediction Center (CPC), which is based on extended empirical orthogonal function analysis of upper-level velocity potential. Significant positive temperature anomalies develop in the eastern United States 5–20 days following Wheeler and Hendon MJO index phase 3, which corresponds to enhanced convection centered over the eastern Indian Ocean. At the same lag, positive precipitation anomalies are observed from the southern Plains to the Great Lakes region. Negative temperature anomalies appear in the central and eastern United States 10–20 days following Wheeler and Hendon MJO phase 7. These impacts are supported by an analysis of the evolution of 200-hPa geopotential height and zonal wind anomalies. Composites based on the CPC velocity potential MJO index generally yield similar results; however, they capture more cases since the index contains both interannual and subseasonal variability. There are some cases where the CPC index differs from that of WH in both MJO phase identification and its North American impacts, especially near the West Coast. This analysis suggests that MJO-related velocity potential anomalies can be used without the Wheeler and Hendon MJO index to predict MJO impacts.

Abstract

The focus of the analysis is to investigate the question to what extent the specification of sea surface temperature (SST) in coupled model integration can impart realistic evolution of subsurface ocean temperature in the equatorial tropical Pacific. In the context of El Niño–Southern Oscillation (ENSO) prediction, the analysis is of importance from two aspects: such a system can be considered as a simple coupled ocean data assimilation system that can provide ocean initial conditions; and what additional components of the ocean observing system may be crucial for skillful ENSO prediction.

The results indicate that coupled model integration where SST is continuously nudged toward the observed state can generate a realistic evolution of subsurface ocean temperature. The evolution of slow variability related to ENSO, in particular, has a good resemblance against the observational counterpart. The realism of subsurface ocean temperature variability is highest near the date line and least in the far eastern Pacific where the thermocline is shallowest. The results are also discussed in the context of ocean observing system requirements for ENSO prediction.