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- Author or Editor: T. P. Barnett x
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Abstract
Results of a recent study show eastward propagation of information in the low-frequency variations of the tropical sea-level pressure (SLP) field. The current work extends that analysis to investigate the vertical structure of this signal. It is found that the propagating signal exists to a height of at least 850 mb. At 500 mb the signal is not so clear, while at 200 mb there is little evidence of propagation of information. Instead, the low-frequency variations in 200 rnb height appear coherent for the tropical belt around the entire globe. The analysis suggests that the anomalies discussed here appear first at the surface and later at higher levels in the atmosphere.
Abstract
Results of a recent study show eastward propagation of information in the low-frequency variations of the tropical sea-level pressure (SLP) field. The current work extends that analysis to investigate the vertical structure of this signal. It is found that the propagating signal exists to a height of at least 850 mb. At 500 mb the signal is not so clear, while at 200 mb there is little evidence of propagation of information. Instead, the low-frequency variations in 200 rnb height appear coherent for the tropical belt around the entire globe. The analysis suggests that the anomalies discussed here appear first at the surface and later at higher levels in the atmosphere.
Abstract
Analysis of a near-global sea level pressure (SLP) data set for the period 1951-80 was carried out in terms of complex empirical orthogonal function
A large-scale, propagating SLP signal was discovered that seems to include variability previously associated with the Southern Oscillation, monsoons and tropical midlatitude teleconnection patterns In this sense, the new feature offers an empirical framework for many of the well-known features of the global climate system. Extensive comparisons show the feature to be observable in the raw data field, and thus not a figment of the analysis technique. The large SLP signal may have its origins in a SLP/snow feedback loop that acts over the region from Siberia to India. It is suggested that the maintenance of the SLP signal involves SLP/precipation feedbacks and, more importantly, the excitation of a major natural mode of climate variation. The SLP signal is only excited at time scales larger than about 18 mouths, i.e., time scales characteristic of oceanic processes. The GCM results of Lau also suggest the importance of ocean-atmosphere interactions in maintaining the SLP signal. However, forcing solely by SST does not appear capable of explaining the entire SLP signal.
The North Atlantic 0scillation (NAO) was found to be another natural mode of global climate variation. The spatial response of this mode. which closely resembled a simple standing wave, may be excited over a wide range of frequencies. The appearance of this mode in both a Geophysics Fluid Dynamic Laboratory (GFDL) model simulation and NCAR Community Climate Model (CCM) simulation with limited interannual forcing suggests the NAO to be a singular expression of internal atmospheric dynamics The NAO unrelated in both space and time to the large SLP signal discussed above.
Both the NAO and the large-scale SLP modes exhibit dichotomous behavior in time. On one hand they can appear as quasi-regular, continuous elements of the global climate system. Alternatively, they exhibit behavior characteristic of a multiequilibrium system.
Abstract
Analysis of a near-global sea level pressure (SLP) data set for the period 1951-80 was carried out in terms of complex empirical orthogonal function
A large-scale, propagating SLP signal was discovered that seems to include variability previously associated with the Southern Oscillation, monsoons and tropical midlatitude teleconnection patterns In this sense, the new feature offers an empirical framework for many of the well-known features of the global climate system. Extensive comparisons show the feature to be observable in the raw data field, and thus not a figment of the analysis technique. The large SLP signal may have its origins in a SLP/snow feedback loop that acts over the region from Siberia to India. It is suggested that the maintenance of the SLP signal involves SLP/precipation feedbacks and, more importantly, the excitation of a major natural mode of climate variation. The SLP signal is only excited at time scales larger than about 18 mouths, i.e., time scales characteristic of oceanic processes. The GCM results of Lau also suggest the importance of ocean-atmosphere interactions in maintaining the SLP signal. However, forcing solely by SST does not appear capable of explaining the entire SLP signal.
The North Atlantic 0scillation (NAO) was found to be another natural mode of global climate variation. The spatial response of this mode. which closely resembled a simple standing wave, may be excited over a wide range of frequencies. The appearance of this mode in both a Geophysics Fluid Dynamic Laboratory (GFDL) model simulation and NCAR Community Climate Model (CCM) simulation with limited interannual forcing suggests the NAO to be a singular expression of internal atmospheric dynamics The NAO unrelated in both space and time to the large SLP signal discussed above.
Both the NAO and the large-scale SLP modes exhibit dichotomous behavior in time. On one hand they can appear as quasi-regular, continuous elements of the global climate system. Alternatively, they exhibit behavior characteristic of a multiequilibrium system.
Abstract
Five million ship observations have been used to obtain a description of the Pacific trade wind fields in terms of empirical orthogonal functions. The description covers the period 1950–72 in two-month intervals.
The major conclusions are as follows: 1) The core regions of the trade winds are quasi-permanent in position. 2) The winds in these regions experience large (20–40%) interannual fluctuations in strength, thus suggesting similarly large changes in the momentum and kinetic energy of the trade wind system. 3) The area of maximum trades expands and contracts at interannual time scales. 4) The ITCZ and the thermal equator in the ocean are congruent on annual and interannual time scales. 5) The northeast and southeast trades are seasonally out of phase. However, the winds immediately poleward of a given trade system fluctuate in phase with the trade system of the opposite hemisphere. The northeast and southeast trades are weakly coupled at interannual time scales. 6) The maximum interannual variations in the trades occur away from the region of maximum intensity, particularly in the western and southwest Pacific. 7) The trade wind field is closely and interactively coupled to the underlying equatorial ocean. 8) Interannual variations in the sea-level pressure field over the North Pacific lead by about a season associated changes in the zonal component of the trades.
Abstract
Five million ship observations have been used to obtain a description of the Pacific trade wind fields in terms of empirical orthogonal functions. The description covers the period 1950–72 in two-month intervals.
The major conclusions are as follows: 1) The core regions of the trade winds are quasi-permanent in position. 2) The winds in these regions experience large (20–40%) interannual fluctuations in strength, thus suggesting similarly large changes in the momentum and kinetic energy of the trade wind system. 3) The area of maximum trades expands and contracts at interannual time scales. 4) The ITCZ and the thermal equator in the ocean are congruent on annual and interannual time scales. 5) The northeast and southeast trades are seasonally out of phase. However, the winds immediately poleward of a given trade system fluctuate in phase with the trade system of the opposite hemisphere. The northeast and southeast trades are weakly coupled at interannual time scales. 6) The maximum interannual variations in the trades occur away from the region of maximum intensity, particularly in the western and southwest Pacific. 7) The trade wind field is closely and interactively coupled to the underlying equatorial ocean. 8) Interannual variations in the sea-level pressure field over the North Pacific lead by about a season associated changes in the zonal component of the trades.
Abstract
Many parameters that measure climatic variability have nonstationary statistics, that is, they depend strongly on the phase of the annual cycle. In this case normal statistical analysis techniques based on time-invariant models are inappropriate. Generalized methods accounting for seasonal nonstationarity (phase averaged or cyclostationary models) have been developed to treat such data.
The methods are applied to the problem of predicting El Niño off South America. It is shown that El Niños may be predicted up to a year in advance with considerably more confidence and accuracy using phase-averaged models than with time-invariant models.
In a second application surface air temperature anomalies are predicted over North America from Pacific Ocean sea surface temperatures. Again, the phase-averaged models consistently outperform models based on standard statistical procedures.
Abstract
Many parameters that measure climatic variability have nonstationary statistics, that is, they depend strongly on the phase of the annual cycle. In this case normal statistical analysis techniques based on time-invariant models are inappropriate. Generalized methods accounting for seasonal nonstationarity (phase averaged or cyclostationary models) have been developed to treat such data.
The methods are applied to the problem of predicting El Niño off South America. It is shown that El Niños may be predicted up to a year in advance with considerably more confidence and accuracy using phase-averaged models than with time-invariant models.
In a second application surface air temperature anomalies are predicted over North America from Pacific Ocean sea surface temperatures. Again, the phase-averaged models consistently outperform models based on standard statistical procedures.
Abstract
A dynamical model incorporating observed field data is used to estimate the potential importance of linear and nonlinear vorticity advection to climate forecast models. Forecasts of 30-day averages benefit from inclusion of the linear advection term, but the nonlinear advection appears only marginally helpful. For intermediate averaging times (e.g., 10 days), both advection terms appear to be important. Analysis of the nonlinear terms suggests that they could be most adequately parameterized as a noise process that is “white” in wavenumber space and “red” in the time domain.
Abstract
A dynamical model incorporating observed field data is used to estimate the potential importance of linear and nonlinear vorticity advection to climate forecast models. Forecasts of 30-day averages benefit from inclusion of the linear advection term, but the nonlinear advection appears only marginally helpful. For intermediate averaging times (e.g., 10 days), both advection terms appear to be important. Analysis of the nonlinear terms suggests that they could be most adequately parameterized as a noise process that is “white” in wavenumber space and “red” in the time domain.
Abstract
A theoretical framework is developed to consider the abilities of analog techniques for the prediction of short-term climate fluctuations. The basic element of the framework is the definition of a “climate state vector.” This vector points to the position of a “climate particle” whose motion in a multi-dimensional hyper-space represents the time evolution of the climate system. The particle has a number of properties that describe regional covariability of various climatic fields.
A series of metrics are assigned to the space in which the climate particle moves. These metrics are used to select past states of the climate system which are analogs to a “current” state. The subsequent prediction is made based on the past evolution of the climate state Vector. Forecasts made with the analog selection techniques are evaluated in terms of the local and global skills that attend them. Thus both the spatial and temporal dependence of the skill score field is examined.
Predictions were made for the seasonal average surface air temperature anomaly fields over the North American continent at lead times of one to four seasons in advance. Significant predictive skill was found in the experiments, particularly for the summer season. The result suggests that high predictability is associated with the degree of exactness with which the climate particle retraces its trajectory in hyperspace. This in turn suggests that more accurate predictions can he made with a longer data base than the one used in this study since better analog fits would presumably he found. The results also suggest that both the current state and recent history of the climate system are important in determining the future evolution of climatic anomalies.
Abstract
A theoretical framework is developed to consider the abilities of analog techniques for the prediction of short-term climate fluctuations. The basic element of the framework is the definition of a “climate state vector.” This vector points to the position of a “climate particle” whose motion in a multi-dimensional hyper-space represents the time evolution of the climate system. The particle has a number of properties that describe regional covariability of various climatic fields.
A series of metrics are assigned to the space in which the climate particle moves. These metrics are used to select past states of the climate system which are analogs to a “current” state. The subsequent prediction is made based on the past evolution of the climate state Vector. Forecasts made with the analog selection techniques are evaluated in terms of the local and global skills that attend them. Thus both the spatial and temporal dependence of the skill score field is examined.
Predictions were made for the seasonal average surface air temperature anomaly fields over the North American continent at lead times of one to four seasons in advance. Significant predictive skill was found in the experiments, particularly for the summer season. The result suggests that high predictability is associated with the degree of exactness with which the climate particle retraces its trajectory in hyperspace. This in turn suggests that more accurate predictions can he made with a longer data base than the one used in this study since better analog fits would presumably he found. The results also suggest that both the current state and recent history of the climate system are important in determining the future evolution of climatic anomalies.
Abstract
The sensitivity of the global climate system to interannual variability of he Eurasian snow cover has been investigated with numerical models. It was found that heavier than normal Eurasian snow cover in spring leads to a “poor” monsoon over Southeast Asia thereby verifying an idea over 100 years old. The poor monsoon was characterized by reduced rainfall over India and Burma, reduced wind stress over the Indian Ocean, lower than normal temperatures on the Asian land mass and in the overlying atmospheric column, reduced tropical jet, increased soil moisture, and other features associated with poor monsoons. Lighter than normal snow cover led to a “good” monsoon with atmospheric anomalies like those described above but of opposite sign. Remote responses from the snow field perturbation include readjustment of the Northern Hemispheric mass field in midlatitude, an equatorially symmetric response of the tropical geopotential height and temperature field and weak, but significant, perturbations in the surface wind stress and heat flux in the tropical Pacific.
The physics responsible for the regional response involves all elements of both the surface heat budget and heat budget of the full atmospheric column. In essence, the snow, soil and atmospheric moisture all act to keep the land and overlying atmospheric column colder than normal during a heavy snow simulation thus reducing the land–ocean temperature contrast needed to initiate the monsoon. The remote responses are driven by heating anomalies associated with both large scale air-sea interactions and precipitation events.
The model winds from the heavy snow experiment were used to drive an ocean model. The SST field in that model developed a weak El Niño in the equatorial Pacific. A coupled ocean-atmosphere model simulation perturbed only by anomalous Eurasian snow cover was also run and it developed a much stranger El Niño in the Pacific. The coupled system clearly amplified the wind stress anomaly associated with the poor monsoon. These results show the important role of an evolving (not specified) sea surface temperature in numerical experiments and the real climate system. Our general results also demonstrate the importance of land processes in global climate dynamics and their possible role as one of the factors that could trigger ENSO events.
Abstract
The sensitivity of the global climate system to interannual variability of he Eurasian snow cover has been investigated with numerical models. It was found that heavier than normal Eurasian snow cover in spring leads to a “poor” monsoon over Southeast Asia thereby verifying an idea over 100 years old. The poor monsoon was characterized by reduced rainfall over India and Burma, reduced wind stress over the Indian Ocean, lower than normal temperatures on the Asian land mass and in the overlying atmospheric column, reduced tropical jet, increased soil moisture, and other features associated with poor monsoons. Lighter than normal snow cover led to a “good” monsoon with atmospheric anomalies like those described above but of opposite sign. Remote responses from the snow field perturbation include readjustment of the Northern Hemispheric mass field in midlatitude, an equatorially symmetric response of the tropical geopotential height and temperature field and weak, but significant, perturbations in the surface wind stress and heat flux in the tropical Pacific.
The physics responsible for the regional response involves all elements of both the surface heat budget and heat budget of the full atmospheric column. In essence, the snow, soil and atmospheric moisture all act to keep the land and overlying atmospheric column colder than normal during a heavy snow simulation thus reducing the land–ocean temperature contrast needed to initiate the monsoon. The remote responses are driven by heating anomalies associated with both large scale air-sea interactions and precipitation events.
The model winds from the heavy snow experiment were used to drive an ocean model. The SST field in that model developed a weak El Niño in the equatorial Pacific. A coupled ocean-atmosphere model simulation perturbed only by anomalous Eurasian snow cover was also run and it developed a much stranger El Niño in the Pacific. The coupled system clearly amplified the wind stress anomaly associated with the poor monsoon. These results show the important role of an evolving (not specified) sea surface temperature in numerical experiments and the real climate system. Our general results also demonstrate the importance of land processes in global climate dynamics and their possible role as one of the factors that could trigger ENSO events.
