This study investigates the physical mechanisms involved in the generation and decay of El Niño–Southern Oscillation episodes in a coupled GCM simulation. Warm and cold events found in a 100-yr-long record are separated into groups by means of a clustering technique that objectively discriminates common features in the evolution of the tropical Pacific heat content anomalies leading to the event’s peak. Through an analysis of the composites obtained from this classification, insight is gained as to the processes responsible for the presence of different behaviors. Three classes of warm events were identified. The first is characterized by the westward propagation of warm heat content anomalies north of the equator before the onset of the episode. This propagation characteristic of the delayed oscillator paradigm appears weakened in the decay of the episode. In the second class, local development of heat content anomalies in the northwest tropical Pacific, associated with overlying wind stress curl anomalies, dominates both the generation and the decay of the warm event. In addition, subsurface cold anomalies form in the equatorial western Pacific in association with the poleward flow considered by the recharge–discharge oscillator model. The third class is characterized by a relatively quick development of the warm episode. Attention is focused on the first two classes. The suitability of different conceptual models to explain them is addressed. Previous analyses of the simulation are reviewed throughout this work. Differences between the classes are related to a regime shift that occurs toward the middle of the record.
Long-range empirical forecasts of North Atlantic anomalous conditions are issued, using sea ice concentration anomalies in the same region as predictors. Conditions in the North Atlantic are characterized by anomalies of sea surface temperature, of 850 hPa air temperature and of sea level pressure. Using the Singular Value Decomposition of the cross-covariance matrix between the sea ice field (the predictor) and each of the predictand variables, empirical models are built, and forecasts at lead times from 3 to 18 months are presented. The forecasts of the air temperature anomalies score the highest levels of the skill, while forecasts of the sea level pressure anomalies are the less sucessful ones.To investigate the sources of the forecast skill, we analyze their spatial patterns. In addition, we investigate the influence of major climatic signals on the forecast skill. In the case of the air temperature anomalies, the spatial pattern of the skill may be connected to El Niñ o Southern Oscillation (ENSO) influences. The ENSO signature is present in the predictor field, as shown in the composite analysis. The composite pattern indicates a higher (lower) sea ice concentration in the Labrador Sea and the opposite situation in the Greenland-Barents Seas during the warm (cold) phase of ENSO. The forecasts issued under the El Niñ o conditions show improved skill in the Labrador region, the Iberian Peninsula and south of Greenland for the lead times considered in this paper. For the Great Lakes region the skill increases when the predictor is under the influence of a cold phase. Some features in the spatial structure of the skill of the forecasts issued in the period of the Great Salinity Anomaly present similarities with those found for forecasts made during the cold phase of ENSO. The strength of the dependence on the Great Salinity Anomaly makes it very difficult to determine the influence of the North Atlantic Oscillation.
In this study, we introduce a sensitivity analysis of modelled CO2 aviation emissions to changes in the model parameters, which is intended as a contribution to the understanding of the atmospheric composition stabilization issue. The two variable dynamic model incorporates the effects of the technological innovations on the emissions rate, the environmental feedback, and a non-linear control term on the passengers rate. The model parameters, estimated from different air traffic sources, are subject to considerable uncertainty. The stability analysis of Monte Carlo simulations revealed that, for certain values of the non-linear term parameter and depending on the type of flight, the passengers number at some equilibrium points exceeded its initial value, while the emissions level was below the initial corresponding one. The results of two global sensitivity analyses indicated that the influence of the non-linear term prevailed on the passengers number rate, followed distantly by the environmental feedback. For the emissions rate, the non-linear term contribution dominated, with the technological term influence placing second.
Long‐range empirical forecasts of North Atlantic anomalous conditions are issued, using sea ice concentration anomalies in the same region as predictors. Conditions in the North Atlantic are characterized by anomalies of sea surface temperature, of 850 hPa air temperature and of sea level pressure. Using the Singular Value Decomposition of the cross‐covariance matrix between the sea ice field (the predictor) and each of the predictand variables, empirical models are built, and forecasts at lead times from 3 to 18 months are presented. The forecasts of the air temperature anomalies score the highest levels of the skill, while forecasts of the sea level pressure anomalies are the less sucessful ones. To investigate the sources of the forecast skill, we analyze their spatial patterns. In addition, we investigate the influence of major climatic signals on the forecast skill. In the case of the air temperature anomalies, the spatial pattern of the skill may be connected to El Niño Southern Oscillation (ENSO) influences. The ENSO signature is present in the predictor field, as shown in the composite analysis. The composite pattern indicates a higher (lower) sea ice concentration in the Labrador Sea and the opposite situation in the Greenland–Barents Seas during the warm (cold) phase of ENSO. The forecasts issued under the El Niño conditions show improved skill in the Labrador region, the Iberian Peninsula and south of Greenland for the lead times considered in this paper. For the Great Lakes region the skill increases when the predictor is under the influence of a cold phase. Some features in the spatial structure of the skill of the forecasts issued in the period of the Great Salinity Anomaly present similarities with those found for forecasts made during the cold phase of ENSO. The strength of the dependence on the Great Salinity Anomaly makes it very difficult to determine the influence of the North Atlantic Oscillation.
The interannual variability of the tropical Atlantic is characterized by warmings and coolings similar to the Pacific ones (El Nifio), and by an interhemispheric signal of decadal variability. The magnitudes of the Gulf of Guinea warmings are less and, therefore, they do not significantly influence the earth's climate, as the El NiiidSouthern Oscillation (ENSO) does. In the past, they have been studied because of their connections with the recurrent droughts in the Sahel region. Recently, a number of modelling studies have tried to establish their dependence on ENSO. The real existence of an interhemispheric decadal signal, and its predictability, is also a widely discussed topic. Forecast studies have recently appeared for both the north tropical Atlantic and the Gulf of Guinea regions, and are now operationally available from the National Oceanic and Atmospheric Administration.In the present work we try first to understand the tropical Atlantic variability in terms of forcings external to the basin. These are identified from 48 years of monthly anomalies of sea surface temperature (SST) data obtained by combining the Comprehensive Ocean and Atmosphere Dataset (COADS) and the Integrated Global Ocean Services System (IGOSS) dataset, and then using the Bayesian theory of estimation. Besides the ENSOrelated scales, our analysis retains a decadal time scale in the data variability.Next, a model is built to forecast the most important features of the equatorial Atlantic interannual variability. These features are monitored through two indices, the Gulf of Guinea and the north tropical Atlantic indices ('the predictands'). Predictor fields are identified from our preliminary analysis, as those time series significantly correlated with the predictands. These correspond to grid points in the tropical Pacific (mainly the Nifio3 region, (5OS-5' N. 150°W-900W)) and tropical Indian oceans. Forecasts were issued for 28 years, at three-monthly intervals. For the north tropical Atlantic index, we have a good forecast skill at leads greater than four months with predictors obtained from east equatorial Pacific time series. For the Gulf of Guinea index, a good forecast skill can be obtained only when we include time series of the equatorial Indian ocean, as well as the east equatorial Pacific, among the predictors. Any of the forecasts presented here show useful forecast skill that, at least, beats persistence at leads greater than four months
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