Abstract

El Niño–Southern Oscillation (ENSO) phenomenon, a periodic warming of sea surface temperatures in the eastern and central equatorial Pacific, generates a significant proportion of short-term climate variations globally, second only to the seasonal cycle. Global economic losses of tens of billions of dollars are attributed to extremes of ENSO (i.e., El Niño and La Niña), suggesting that these events disproportionately trigger socioeconomic disasters on the global scale. Since global El Niño/La Niña–associated climate impacts were first documented in the 1980s, the prevailing assumption has been that more severe and widespread climate anomalies, and, therefore, greater climate-related socioeconomic losses, should be expected during ENSO extremes. Contrary to expectations, climate anomalies associated with such losses are not greater overall during ENSO extremes than during neutral periods. However, during El Niño and La Niña events climate forecasts are shown to be more accurate. Stronger ENSO events lead to greater predictability of the climate and, potentially, the socioeconomic outcomes. Thus, the prudent use of climate forecasts could mitigate adverse impacts and lead instead to increased beneficial impacts, which could transform years of ENSO extremes into the least costly to life and property.

Extreme phases of the El Niño–Southern Oscillation (ENSO) phenomenon have been blamed for precipitation anomalies in many areas of the world. In some areas the probability of above-normal precipitation may be increased during warm or cold events, while in others below-normal precipitation may be more likely. The percentages of times that seasonal precipitation over land areas was above, near, and below normal during the eight strongest El Niño and La Nina episodes are tabulated, and the significance levels of the posterior probabilities are calculated using the hypergeometric distribution. These frequencies may provide a useful starting point for probabilistic climate forecasts during strong ENSO events. Areas with significantly high or low frequencies or above- or below-normal precipitation are highlighted, and attempts are made to estimate the proportion of land areas with significant ENSO-related precipitation signals.

There is a danger of overstating the global impact of ENSO events because only about 20%–30% of land areas experience significantly increased probabilities of above- or below-normal seasonal precipitation during at least some part of the year. Since different areas are affected at different times of the year, the fraction of global land affected in any particular season is only about 15%—25%. The danger of focusing on the impact of only warm-phase events is emphasized also: the global impact of La Nina seems to be at least as widespread as that of El Niño. Furthermore, there are a number of notable asymmetries in precipitation responses to El Niño and La Nina events. For many areas it should not be assumed that the typical climate anomaly of one ENSO extreme is likely to be the opposite of the other extreme. A high frequency of above-normal precipitation during strong El Niño conditions, for example, does not guarantee a high frequency of below-normal precipitation during La Nina events, or vice versa. On a global basis El Niño events are predominantly associated with below-normal seasonal precipitation over land, whereas La Nina events result in a wider extent of above-normal precipitation.

Abstract

A technique for producing regional rainfall forecasts for southern Africa is developed that statistically maps or “recalibrates” large-scale circulation features produced by the ECHAM3.6 general circulation model (GCM) to observed regional rainfall for the December–February (DJF) season. The recalibration technique, model output statistics (MOS), relates archived records of GCM fields to observed DJF rainfall through a set of canonical correlation analysis (CCA) equations. After screening several potential predictor fields, the 850-hPa geopotential height field is selected as the single predictor field in the CCA equations that is subsequently used to produce MOS-recalibrated rainfall patterns. The recalibrated forecasts outscore area-averaged GCM-simulated rainfall anomalies, as well as forecasts produced using a simple linear forecast model. The MOS recalibration is applied to two sets of GCM experiments: for the “simulation” experiment, simultaneous observed sea surface temperature (SST) serves as the lower boundary forcing; for the “hindcast” experiment, the prescribed SSTs are obtained by persisting the previous month's SST anomaly through the forecast period. Pattern analyses performed on the predictor–predictand pairs confirm a robust relationship between the GCM 850-hPa height fields and the rainfall fields. The structure and variability of the large-scale circulation is well characterized by the GCM in both simulation and hindcast mode. Measures of retroactive skill for a 9-yr independent period (1991/92–1999/2000) using the hindcast MOS are obtained for both deterministic and probabilistic forecasts, suggesting that a probabilistic representation of MOS forecasts is potentially more valuable. Finally, MOS is employed to investigate its potential to downscale the GCM large-scale circulation to more specific forecasts of land surface characteristics such as streamflow.

Abstract

Data from a realistic model of the ocean, forced with observed atmospheric conditions for the period 1953–92, are analyzed to determine the energetics of interannual variability in the tropical Pacific. The work done by the winds on the ocean, rather than generating kinetic energy, does work against pressure gradients and generates buoyancy power, which in turn is responsible for the rate of change of available potential energy (APE). This means interannual fluctuations in work done by the wind have a phase that leads variations in APE. Variations in the sea surface temperature (SST) of the eastern equatorial Pacific and in APE are highly correlated and in phase so that changes in the work done by the wind are precursors of El Niño. The wind does positive work on the ocean during the half cycle that starts with the peak of El Niño and continues into La Niña; it does negative work during the remaining half cycle.

The results corroborate the delayed oscillator mechanism that qualitatively describes the deterministic behavior of ENSO. In that paradigm, a thermocline perturbation appearing in the western equatorial Pacific affects the transition from one phase of ENSO to the next when that perturbation arrives in the eastern equatorial Pacific where it influences SST. The analysis of energetics indicates that the transition starts earlier, during La Niña, when the perturbation is still in the far western equatorial Pacific. Although the perturbation at that stage affects the thermal structure mainly in the thermocline, at depth, the associated currents are manifest at the surface and immediately affect work done by the wind. For the simulation presented here, the change in energy resulting from adjustment processes far outweighs that due to stochastic processes, such as intraseasonal wind bursts, at least during periods of successive El Niño and La Niña events.

Abstract

El Niño brings widespread drought (i.e., precipitation deficit) to the tropics. Stronger or more frequent El Niño events in the future and/or their intersection with local changes in the mean climate toward a future with reduced precipitation would exacerbate drought risk in highly vulnerable tropical areas. Projected changes in El Niño characteristics and associated teleconnections are investigated between the twentieth and twenty-first centuries. For climate change models that reproduce realistic oceanic variability of the El Niño–Southern Oscillation (ENSO) phenomenon, results suggest no robust changes in the strength or frequency of El Niño events. These models exhibit realistic patterns, magnitude, and spatial extent of El Niño–induced drought patterns in the twentieth century, and the teleconnections are not projected to change in the twenty-first century, although a possible slight reduction in the spatial extent of droughts is indicated over the tropics as a whole. All model groups investigated show similar changes in mean precipitation for the end of the twenty-first century, with increased precipitation projected between 10°S and 10°N, independent of the ability of the models to replicate ENSO variability. These results suggest separability between climate change and ENSO-like climate variability in the tropics. As El Niño–induced precipitation drought patterns are not projected to change, the observed twentieth-century variability is used in combination with model-projected changes in mean precipitation for assessing year-to-year drought risk in the twenty-first century. Results suggest more locally consistent changes in regional drought risk among models with good fidelity in reproducing ENSO variability.

Abstract

This study examines skill of retrospective forecasts using the ECHAM4.5 atmospheric general circulation model (AGCM) forced with predicted sea surface temperatures (SSTs) from methods of varying complexity. The SST fields are predicted in three ways: persisted observed SST anomalies, empirically predicted SSTs, and predicted SSTs from a dynamically coupled ocean–atmosphere model. Investigation of relative skill of the three sets of retrospective forecasts focuses on the ensemble mean, which constitutes the portion of the model response attributable to the prescribed boundary conditions. The anomaly correlation skill analyses for precipitation and 2-m air temperature indicate that dynamically predicted SSTs generally improve upon persisted and empirically predicted SSTs when they are used as boundary forcing in the AGCM predictions. This is particularly the case for precipitation forecasts. The skill differences in these experiments are ascribed to the skill of SST predictions in the tropical ocean basins. The multiscenario forecast by averaging the three retrospective experiments performs, overall, as well as or better than the best of the three individual experiments in specific seasons and regions. The advantage of multiscenario forecast manifests both in the deterministic and probabilistic skill. In particular, the multiscenario precipitation forecast for the December–February season demonstrates better skill than the best of the three scenarios over several regions, such as the western United States and southeastern South America. These results suggest the potential value in producing superensembles spanning different SST prediction scenarios.

Abstract

In the past 50 years, sea surface temperatures (SSTs) in the tropical latitudes have trended toward a warmer ocean state. As a response, tropical land surface temperatures, as well as tropical tropospheric temperatures (as manifested in the variations in the 200-mb tropical heights), have also trended upward. Analysis of trends in the tropical precipitation fields, however, remains problematic because of the scarcity of the observed data over the tropical oceans.

Using both observed data and data from atmospheric general circulation model simulations, trends in tropical precipitation over the ocean and land are analyzed. The analysis reveals that in the tropical latitudes over land, the precipitation trend differs from the trend in the surface temperature. Oceanic precipitation has an increasing trend that is consistent with increasing SSTs, whereas over the tropical land regions precipitation decreases. In contrast, land temperatures increase in phase with the trend in SSTs. It is suggested that the combination of increasing surface temperature and decreasing precipitation could produce considerably greater societal consequences compared with the traditionally argued scenario in which both temperature and precipitation increase in response to increasing SSTs.

Abstract

Regional temperature change projections for the twenty-first century are generated using a multimodel ensemble of atmosphere–ocean general circulation models. The models are assigned coefficients jointly, using a Bayesian linear model fitted to regional observations and simulations of the climate of the twentieth century. Probability models with varying degrees of complexity are explored, and a selection is made based on Bayesian deviance statistics, coefficient properties, and a classical cross-validation measure utilizing temporally averaged data. The model selected is shown to be superior in predictive skill to a naïve model consisting of the unweighted mean of the underlying atmosphere–ocean GCM (AOGCM) simulations, although the skill differential varies regionally. Temperature projections for the A2 and B1 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios are presented.

Abstract

The global responses of two atmospheric general circulation models (AGCM), the National Centers for Environmental Prediction–Medium Range Forecast (NCEP–MRF9) and the University of Hamburg climate model–3 (ECHAM), to simultaneous global SST forcing are examined on a 3-month timescale. Rotated principal components analysis of the model and observations is also used to identify and compare their leading modes of coherent variability. The scope of the present analyses is largely descriptive and does not attempt to explain the differences in model behavior in terms of their formulations. The authors’ main focus is to quantify the simulation skill of the two comprehensive AGCMs on seasonal timescales and compare it to skill obtained using empirical prediction models.

Both models are found to exhibit realistic responses to El Niño–Southern Oscillation (ENSO)-related forcing, with the ECHAM response slightly more accurate in the spatial phasing and structure of the atmospheric anomalies. The ECHAM model exhibits realistic atmospheric responses to tropical Pacific SST forcing as well as patterns associated with extratropical internal atmospheric dynamics [e.g., North Atlantic oscillation (NAO) and a high latitude north–south dipole in the Pacific]. It shows a slightly higher signal-to-noise ratio than that found in the real world, while the NCEP model’s signal-to-noise ratio is approximately equal to that in nature. The NCEP model responds with more zonally symmetric atmospheric patterns than observed, although this does not prevent it from forming realistic responses to ENSO over the Pacific–North American region. The NCEP model’s NAO variability is only about half as strong as that observed.

In terms of simulation skill with respect to observations, the ECHAM model generally tends to outperform the NCEP model for global 500-hPa geopotential height and surface climate. A decomposition of the observed and model data into rotated principal components indicates that both models reproduce the ENSO-related anomalies in circulation and surface climate of the real atmosphere quite well. The ECHAM model, which handles ENSO variability and impacts slightly better than the NCEP model, shows a larger increment of capability in reproducing other global climate processes. Two linear statistical benchmarks, which are used as skill control measures, sometimes outperform the NCEP model but are more comparable, on average, to the skill of the ECHAM model. Thus as noted in other recent studies, the dynamical models and the statistical models have roughly the same simulation skill and would be expected to have similar forecast skill if the models used forecasted SSTs as their boundary conditions.

To first order, the linear component of the relationships appears to be modeled well by the two dynamical models. It is undetermined whether instances of better performance of the dynamical models than the statistical benchmarks are partly attributable to the models’ effective exploitation of nonlinearities in the relationships between tropical SST and global climate. One reason for this inconclusiveness is that evidence for nonlinearities in the present analyses is not compelling. Hence, the question of whether dynamical models have untapped potential to consistently outperform statistical models on the seasonal timescale remains open and may require close examination of each physical formulation in the dynamical models.

Abstract

The eastern United States experienced an unusually cold winter season during the 2002/03 El Niño event. The U.S. seasonal forecasts did not suggest an enhanced likelihood for below-normal temperatures over the eastern United States in that season. A postmortem analysis examining the observed temperatures and the associated forecast is motivated by two fundamental questions: what are these temperature anomalies attributable to, and to what extent were these temperature anomalies predictable? The results suggest that the extreme seasonal temperatures experienced in the eastern United States during December–February (DJF) 2002/03 can be attributed to a combination of several constructively interfering factors that include El Niño conditions in the tropical Pacific, a persistent positive Pacific–North American (PNA) mode, a persistent negative North Atlantic Oscillation (NAO) mode, and persistent snow cover over the northeastern United States. According to the simulations and predictions from several dynamical atmospheric models, which were not rigorously included in the U.S. forecast, much of the observed temperature pattern was potentially predictable.