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- Author or Editor: Matthew J. Widlansky x
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Abstract
Forty years ago, Klaus Wyrtki of the University of Hawaii launched an “El Niño Watch” expedition to the eastern equatorial Pacific to document oceanographic changes that were expected to develop during the onset of an El Niño event in early 1975. He and his colleagues used a very simple atmospheric pressure index to predict the event and convinced the National Science Foundation and Office of Naval Research to support an expedition to the eastern Pacific on relatively short notice. An anomalous warming was detected during the first half of the expedition, but it quickly dissipated. Given the state of the art in El Niño research at the time, Wyrtki and colleagues could offer no explanation for why the initial warming failed to amplify, nor could they connect what they observed to what was happening in other parts of the basin prior to and during the expedition. With the benefit of hindsight, the authors provide a basin-scale context for what the expedition observed, elucidate the dynamical processes that gave rise to the abbreviated warming that was detected, and present retrospective forecasts of the event using modern coupled ocean–atmosphere dynamical model prediction systems. Reviewing this history highlights how early pioneers in El Niño research, despite the obstacles they faced, were able to make significant progress through bold initiatives that advanced the frontiers of our knowledge. It is also evident that, even though the scientific community today has a much deeper understanding of climate variability, more advanced observational capabilities, and sophisticated seasonal forecasting tools, skillful predictions of El Niño and its cold counterpart La Niña remain a major challenge.
Abstract
Forty years ago, Klaus Wyrtki of the University of Hawaii launched an “El Niño Watch” expedition to the eastern equatorial Pacific to document oceanographic changes that were expected to develop during the onset of an El Niño event in early 1975. He and his colleagues used a very simple atmospheric pressure index to predict the event and convinced the National Science Foundation and Office of Naval Research to support an expedition to the eastern Pacific on relatively short notice. An anomalous warming was detected during the first half of the expedition, but it quickly dissipated. Given the state of the art in El Niño research at the time, Wyrtki and colleagues could offer no explanation for why the initial warming failed to amplify, nor could they connect what they observed to what was happening in other parts of the basin prior to and during the expedition. With the benefit of hindsight, the authors provide a basin-scale context for what the expedition observed, elucidate the dynamical processes that gave rise to the abbreviated warming that was detected, and present retrospective forecasts of the event using modern coupled ocean–atmosphere dynamical model prediction systems. Reviewing this history highlights how early pioneers in El Niño research, despite the obstacles they faced, were able to make significant progress through bold initiatives that advanced the frontiers of our knowledge. It is also evident that, even though the scientific community today has a much deeper understanding of climate variability, more advanced observational capabilities, and sophisticated seasonal forecasting tools, skillful predictions of El Niño and its cold counterpart La Niña remain a major challenge.
Abstract
The South Pacific convergence zone (SPCZ) is simulated as too zonal a feature in the current generation of climate models, including those in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This zonal bias induces errors in tropical convective heating, with subsequent effects on global circulation. The SPCZ structure, particularly in the subtropics, is governed by the tropical–extratropical interaction between transient synoptic systems and the mean background state. In this study, analysis of synoptic variability in the simulated subtropical SPCZ reveals that the basic mechanism of tropical–extratropical interaction is generally well simulated, with storms approaching the SPCZ along comparable trajectories to observations. However, there is a broad spread in mean precipitation and its variability across the CMIP5 ensemble. Intermodel spread appears to relate to a biased background state in which the synoptic waves propagate. In particular, the region of mean negative zonal stretching deformation or “storm graveyard” in the upper troposphere is displaced in CMIP5 models to the northeast of its position in reanalysis data, albeit with pronounced (≈25°) intermodel longitudinal spread. Precipitation along the eastern edge of the SPCZ shifts in accordance with a storm graveyard shift, and in general models with stronger storm graveyards show higher precipitation variability. Building on prior SPCZ research, it is suggested that SPCZs simulated by CMIP5 models are not simply too zonal; rather, in models the subtropical SPCZ manifests a diagonal tilt similar to observations while SST biases force an overly zonal tropical SPCZ, resulting in a more discontinuous SPCZ than observed.
Abstract
The South Pacific convergence zone (SPCZ) is simulated as too zonal a feature in the current generation of climate models, including those in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This zonal bias induces errors in tropical convective heating, with subsequent effects on global circulation. The SPCZ structure, particularly in the subtropics, is governed by the tropical–extratropical interaction between transient synoptic systems and the mean background state. In this study, analysis of synoptic variability in the simulated subtropical SPCZ reveals that the basic mechanism of tropical–extratropical interaction is generally well simulated, with storms approaching the SPCZ along comparable trajectories to observations. However, there is a broad spread in mean precipitation and its variability across the CMIP5 ensemble. Intermodel spread appears to relate to a biased background state in which the synoptic waves propagate. In particular, the region of mean negative zonal stretching deformation or “storm graveyard” in the upper troposphere is displaced in CMIP5 models to the northeast of its position in reanalysis data, albeit with pronounced (≈25°) intermodel longitudinal spread. Precipitation along the eastern edge of the SPCZ shifts in accordance with a storm graveyard shift, and in general models with stronger storm graveyards show higher precipitation variability. Building on prior SPCZ research, it is suggested that SPCZs simulated by CMIP5 models are not simply too zonal; rather, in models the subtropical SPCZ manifests a diagonal tilt similar to observations while SST biases force an overly zonal tropical SPCZ, resulting in a more discontinuous SPCZ than observed.
Abstract
Performance of a newly developed decadal climate prediction system is examined using the low-resolution Community Earth System Model (CESM). To identify key sources of predictability and determine the role of upper and deeper ocean data assimilation, we first conduct a series of perfect model experiments. These experiments reveal the importance of upper ocean temperature and salinity assimilation in reducing sea surface temperature biases. However, to reduce biases in the sea surface height, data assimilation below 300 m in the ocean is necessary, in particular for high-latitude regions. The perfect model experiments clearly emphasize the key role of combined three-dimensional ocean temperature and salinity assimilation in reproducing mean state and model trajectories. Applying this knowledge to the realistic decadal climate prediction system, we conducted an ensemble of ocean assimilation simulations with the fully coupled CESM covering the period 1960–2014. In this system, we assimilate three-dimensional ocean temperature and salinity data into the ocean component of CESM. Instead of assimilating direct observations, we assimilate temperature and salinity anomalies obtained from the ECMWF Ocean Reanalysis version 4 (ORA-S4). Anomalies are calculated relative to the sum of the ORA-S4 climatology and an estimate of the externally forced signal. As a result of applying the balanced ocean conditions to the model, our hindcasts show only very little drift and initialization shocks. This new prediction system exhibits multiyear predictive skills for decadal climate variations of the Atlantic meridional overturning circulation (AMOC) and North Pacific decadal variability.
Abstract
Performance of a newly developed decadal climate prediction system is examined using the low-resolution Community Earth System Model (CESM). To identify key sources of predictability and determine the role of upper and deeper ocean data assimilation, we first conduct a series of perfect model experiments. These experiments reveal the importance of upper ocean temperature and salinity assimilation in reducing sea surface temperature biases. However, to reduce biases in the sea surface height, data assimilation below 300 m in the ocean is necessary, in particular for high-latitude regions. The perfect model experiments clearly emphasize the key role of combined three-dimensional ocean temperature and salinity assimilation in reproducing mean state and model trajectories. Applying this knowledge to the realistic decadal climate prediction system, we conducted an ensemble of ocean assimilation simulations with the fully coupled CESM covering the period 1960–2014. In this system, we assimilate three-dimensional ocean temperature and salinity data into the ocean component of CESM. Instead of assimilating direct observations, we assimilate temperature and salinity anomalies obtained from the ECMWF Ocean Reanalysis version 4 (ORA-S4). Anomalies are calculated relative to the sum of the ORA-S4 climatology and an estimate of the externally forced signal. As a result of applying the balanced ocean conditions to the model, our hindcasts show only very little drift and initialization shocks. This new prediction system exhibits multiyear predictive skills for decadal climate variations of the Atlantic meridional overturning circulation (AMOC) and North Pacific decadal variability.
Abstract
During strong El Niño events, sea level drops around some tropical western Pacific islands by up to 20–30 cm. Such events (referred to as taimasa in Samoa) expose shallow reefs, thereby causing severe damage to associated coral ecosystems and contributing to the formation of microatolls. During the termination of strong El Niño events, a southward movement of weak trade winds and the development of an anomalous anticyclone in the Philippine Sea are shown to force an interhemispheric sea level seesaw in the tropical Pacific that enhances and prolongs extreme low sea levels in the southwestern Pacific. Spectral features, in addition to wind-forced linear shallow water ocean model experiments, identify a nonlinear interaction between El Niño and the annual cycle as the main cause of these sea level anomalies.
Abstract
During strong El Niño events, sea level drops around some tropical western Pacific islands by up to 20–30 cm. Such events (referred to as taimasa in Samoa) expose shallow reefs, thereby causing severe damage to associated coral ecosystems and contributing to the formation of microatolls. During the termination of strong El Niño events, a southward movement of weak trade winds and the development of an anomalous anticyclone in the Philippine Sea are shown to force an interhemispheric sea level seesaw in the tropical Pacific that enhances and prolongs extreme low sea levels in the southwestern Pacific. Spectral features, in addition to wind-forced linear shallow water ocean model experiments, identify a nonlinear interaction between El Niño and the annual cycle as the main cause of these sea level anomalies.
Abstract
Potential changing climate threats in the tropical and subtropical North Pacific Ocean were assessed, using coupled ocean–atmosphere and atmosphere-only general circulation models, to explore their response to projected increasing greenhouse gas emissions. Tropical cyclone occurrence, described by frequency and intensity, near islands housing major U.S. defense installations was the primary focus. Four island regions—Guam and Kwajalein Atoll in the tropical northwestern Pacific, Okinawa in the subtropical northwestern Pacific, and Oahu in the tropical north-central Pacific—were considered, as they provide unique climate and geographical characteristics that either enhance or reduce the tropical cyclone risk. Guam experiences the most frequent and severe tropical cyclones, which often originate as weak systems close to the equator near Kwajalein and sometimes track far enough north to affect Okinawa, whereas intense storms are the least frequent around Oahu. From assessments of models that simulate well the tropical Pacific climate, it was determined that, with a projected warming climate, the number of tropical cyclones is likely to decrease for Guam and Kwajalein but remain about the same near Okinawa and Oahu; however, the maximum intensity of the strongest storms may increase in most regions. The likelihood of fewer but stronger storms will necessitate new localized assessments of the risk and vulnerabilities to tropical cyclones in the North Pacific.
Abstract
Potential changing climate threats in the tropical and subtropical North Pacific Ocean were assessed, using coupled ocean–atmosphere and atmosphere-only general circulation models, to explore their response to projected increasing greenhouse gas emissions. Tropical cyclone occurrence, described by frequency and intensity, near islands housing major U.S. defense installations was the primary focus. Four island regions—Guam and Kwajalein Atoll in the tropical northwestern Pacific, Okinawa in the subtropical northwestern Pacific, and Oahu in the tropical north-central Pacific—were considered, as they provide unique climate and geographical characteristics that either enhance or reduce the tropical cyclone risk. Guam experiences the most frequent and severe tropical cyclones, which often originate as weak systems close to the equator near Kwajalein and sometimes track far enough north to affect Okinawa, whereas intense storms are the least frequent around Oahu. From assessments of models that simulate well the tropical Pacific climate, it was determined that, with a projected warming climate, the number of tropical cyclones is likely to decrease for Guam and Kwajalein but remain about the same near Okinawa and Oahu; however, the maximum intensity of the strongest storms may increase in most regions. The likelihood of fewer but stronger storms will necessitate new localized assessments of the risk and vulnerabilities to tropical cyclones in the North Pacific.
Abstract
Hawaii experienced record-high sea levels during 2017, which followed the 2015 strong El Niño and coincided with weak trade winds in the tropical northeastern Pacific. The record sea levels were associated with a combination of processes, an important contributing factor of which was the persistent high sea level (~10 cm above normal) over a large region stretching between Hawaii and Mexico. High sea levels at Mexico are known to occur during strong El Niño as the coastal thermocline deepens. Planetary wave theory predicts that these coastal anomalies propagate westward into the basin interior; however, high sea levels at Hawaii do not occur consistently following strong El Niño events. In particular, Hawaii sea levels remained near normal following the previous strong El Niño of 1997. The processes controlling whether Hawaii sea levels rise after El Niño have so far remained unknown. Atmosphere-forced ocean model experiments show that anomalous surface cooling, controlled by variable trade winds, impacts sea level via mixed layer density, explaining much of the difference in Hawaiian sea level response after the two recent strong El Niño events. In climate model projections with greenhouse warming, more frequent weak trade winds following El Niño events are expected, suggesting that the occurrence of high sea levels at Hawaii will increase as oceanic anomalies more often traverse the basin.
Abstract
Hawaii experienced record-high sea levels during 2017, which followed the 2015 strong El Niño and coincided with weak trade winds in the tropical northeastern Pacific. The record sea levels were associated with a combination of processes, an important contributing factor of which was the persistent high sea level (~10 cm above normal) over a large region stretching between Hawaii and Mexico. High sea levels at Mexico are known to occur during strong El Niño as the coastal thermocline deepens. Planetary wave theory predicts that these coastal anomalies propagate westward into the basin interior; however, high sea levels at Hawaii do not occur consistently following strong El Niño events. In particular, Hawaii sea levels remained near normal following the previous strong El Niño of 1997. The processes controlling whether Hawaii sea levels rise after El Niño have so far remained unknown. Atmosphere-forced ocean model experiments show that anomalous surface cooling, controlled by variable trade winds, impacts sea level via mixed layer density, explaining much of the difference in Hawaiian sea level response after the two recent strong El Niño events. In climate model projections with greenhouse warming, more frequent weak trade winds following El Niño events are expected, suggesting that the occurrence of high sea levels at Hawaii will increase as oceanic anomalies more often traverse the basin.
Abstract
Sea level anomaly extremes impact tropical Pacific Ocean islands, often with too little warning to mitigate risks. With El Niño, such as the strong 2015/16 event, comes weaker trade winds and mean sea level drops exceeding 30 cm in the western Pacific that expose shallow-water ecosystems at low tides. Nearly opposite climate conditions accompany La Niña events, which cause sea level high stands (10–20 cm) and result in more frequent tide- and storm-related inundations that threaten coastlines. In the past, these effects have been exacerbated by decadal sea level variability, as well as continuing global sea level rise. Climate models, which are increasingly better able to simulate past and future evolutions of phenomena responsible for these extremes (i.e., El Niño–Southern Oscillation, Pacific decadal oscillation, and greenhouse warming), are also able to describe, or even directly simulate, associated sea level fluctuations. By compiling monthly sea level anomaly predictions from multiple statistical and dynamical (coupled ocean–atmosphere) models, which are typically skillful out to at least six months in the tropical Pacific, improved future outlooks are achieved. From this multimodel ensemble comes forecasts that are less prone to individual model errors and also uncertainty measurements achieved by comparing retrospective forecasts with the observed sea level. This framework delivers online a new real-time forecasting product of monthly mean sea level anomalies and will provide to the Pacific island community information that can be used to reduce impacts associated with sea level extremes.
Abstract
Sea level anomaly extremes impact tropical Pacific Ocean islands, often with too little warning to mitigate risks. With El Niño, such as the strong 2015/16 event, comes weaker trade winds and mean sea level drops exceeding 30 cm in the western Pacific that expose shallow-water ecosystems at low tides. Nearly opposite climate conditions accompany La Niña events, which cause sea level high stands (10–20 cm) and result in more frequent tide- and storm-related inundations that threaten coastlines. In the past, these effects have been exacerbated by decadal sea level variability, as well as continuing global sea level rise. Climate models, which are increasingly better able to simulate past and future evolutions of phenomena responsible for these extremes (i.e., El Niño–Southern Oscillation, Pacific decadal oscillation, and greenhouse warming), are also able to describe, or even directly simulate, associated sea level fluctuations. By compiling monthly sea level anomaly predictions from multiple statistical and dynamical (coupled ocean–atmosphere) models, which are typically skillful out to at least six months in the tropical Pacific, improved future outlooks are achieved. From this multimodel ensemble comes forecasts that are less prone to individual model errors and also uncertainty measurements achieved by comparing retrospective forecasts with the observed sea level. This framework delivers online a new real-time forecasting product of monthly mean sea level anomalies and will provide to the Pacific island community information that can be used to reduce impacts associated with sea level extremes.
