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- Author or Editor: Tim P. Barnett x
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Abstract
The behavior of large-scale patterns of sea level pressure is documented using a simple composite analysis over the period 1951–85. One interpretation of the maps shows that anomalies of a given sign appear sequentially along a closed, counterclockwise trajectory that transits Asia, eastward through the tropics of the Indo-Pacific then into the eastern Pacific, and finally back to Asia via the North Pacific. A typical time scale for this process is approximately 2 yr. Unfortunately, the composites are noisy and often poorly defined, thus allowing alternate interpretations.
Abstract
The behavior of large-scale patterns of sea level pressure is documented using a simple composite analysis over the period 1951–85. One interpretation of the maps shows that anomalies of a given sign appear sequentially along a closed, counterclockwise trajectory that transits Asia, eastward through the tropics of the Indo-Pacific then into the eastern Pacific, and finally back to Asia via the North Pacific. A typical time scale for this process is approximately 2 yr. Unfortunately, the composites are noisy and often poorly defined, thus allowing alternate interpretations.
Abstract
Ensemble forecasts of global climate conditions during the seven largest Pacific warm/cold events of the last 23 years have been made with a new two-tiered climate forecast technique. The internal model variability (IMV) within the atmospheric model used in the forecasts was large, approximately ⅓ to ½ the variability obtained from the same model forced by observed SST over the last 20 yr. This variability can lead to a wide range of realistic-looking forecasts, even when the equatorial SST forcing was nil. For example, single model forecasts forced by climatological SST produced excellent simulations of the extreme cold event of 1988/89 and the large warm event of 1982/83. Clearly, single simulations are totally inadequate for forecasting and sensitivity studies. The 10 member ensembles used in this study appear to be the minimum for reliable forecasts/sensitivity studies in midlatitudes, although fewer realizations may be needed in the Tropics.
The long-range forecasts are shown to be insensitive to initial conditions (which the model “forgets” after several months), but critically dependent on the nature of the SST forecast in the central equatorial Pacific.
The spectrum of IMV is described quantitatively. A regional phase space representation of this spectrum is obtained and used to demonstrate a new significance test for model-derived climate forecasts. The results presented suggest the traditional atmospheric GCM may not be the best tool with which to make long-range climate forecasts.
Abstract
Ensemble forecasts of global climate conditions during the seven largest Pacific warm/cold events of the last 23 years have been made with a new two-tiered climate forecast technique. The internal model variability (IMV) within the atmospheric model used in the forecasts was large, approximately ⅓ to ½ the variability obtained from the same model forced by observed SST over the last 20 yr. This variability can lead to a wide range of realistic-looking forecasts, even when the equatorial SST forcing was nil. For example, single model forecasts forced by climatological SST produced excellent simulations of the extreme cold event of 1988/89 and the large warm event of 1982/83. Clearly, single simulations are totally inadequate for forecasting and sensitivity studies. The 10 member ensembles used in this study appear to be the minimum for reliable forecasts/sensitivity studies in midlatitudes, although fewer realizations may be needed in the Tropics.
The long-range forecasts are shown to be insensitive to initial conditions (which the model “forgets” after several months), but critically dependent on the nature of the SST forecast in the central equatorial Pacific.
The spectrum of IMV is described quantitatively. A regional phase space representation of this spectrum is obtained and used to demonstrate a new significance test for model-derived climate forecasts. The results presented suggest the traditional atmospheric GCM may not be the best tool with which to make long-range climate forecasts.
Abstract
Two different sea-level pressure datasets for the strip between 20° and 40°S are intercompared. At large space-time scales the sets are virtually identical. However, for studies of short-term, regional changes caution must be exercised because the datasets may differ substantially.
Abstract
Two different sea-level pressure datasets for the strip between 20° and 40°S are intercompared. At large space-time scales the sets are virtually identical. However, for studies of short-term, regional changes caution must be exercised because the datasets may differ substantially.
Abstract
This study summarizes results of an analysis of the TOGA drifting buoy observations in the Southern Hemisphere. The data were first quality controlled for gross errors and then screened against climatology and products from national weather centers. The characteristic space scales of the SLP, SST, and air temperature fields for the summer months of December, January, and February, and the winter months of June, July, and August were determined next. Typical decorrelation distances for all fields were between 1200–2800 km with the correlations being generally isotropic. This information suggests that roughly 30–40 fully functional buoys evenly distributed over the southern oceans from 15° to 60°S should be able to resolve the major scales of Southern Hemisphere climate change.
Abstract
This study summarizes results of an analysis of the TOGA drifting buoy observations in the Southern Hemisphere. The data were first quality controlled for gross errors and then screened against climatology and products from national weather centers. The characteristic space scales of the SLP, SST, and air temperature fields for the summer months of December, January, and February, and the winter months of June, July, and August were determined next. Typical decorrelation distances for all fields were between 1200–2800 km with the correlations being generally isotropic. This information suggests that roughly 30–40 fully functional buoys evenly distributed over the southern oceans from 15° to 60°S should be able to resolve the major scales of Southern Hemisphere climate change.
Abstract
When a numerical model's representation of a physical field is to be compared with a corresponding real observed field, it is usually the case that the numbers of realizations of model and observed field are relatively small, so that the natural procedure of producing histograms of pertinent statistics of the fields (e.g., means, variances) from the data sets themselves cannot be usually carried out. Also, it is not always safe to adopt assumptions of normality and independence of the data values. This prevents the confident use of classical statistical methods to make significance statements about the success or failure of the model's replication of the data. Here we suggest two techniques of determinable statistical power, in which small samples of spatially extensive physical fields can be made to blossom into workably large samples on which significance decisions can be based. We also introduce some new measures of location, spread and shape of multivariate data sets which may be used in conjunction with the two techniques. The result is a pair of new data intercomparison procedures which we illustrate using GCM simulations of the January sea-level pressure field and regional ocean model simulations of the new-shore velocity field of South America. We include with these procedures a method of determining the spatial and temporal locations of non-random errors between the model and data fields so that models can be improved accordingly.
Abstract
When a numerical model's representation of a physical field is to be compared with a corresponding real observed field, it is usually the case that the numbers of realizations of model and observed field are relatively small, so that the natural procedure of producing histograms of pertinent statistics of the fields (e.g., means, variances) from the data sets themselves cannot be usually carried out. Also, it is not always safe to adopt assumptions of normality and independence of the data values. This prevents the confident use of classical statistical methods to make significance statements about the success or failure of the model's replication of the data. Here we suggest two techniques of determinable statistical power, in which small samples of spatially extensive physical fields can be made to blossom into workably large samples on which significance decisions can be based. We also introduce some new measures of location, spread and shape of multivariate data sets which may be used in conjunction with the two techniques. The result is a pair of new data intercomparison procedures which we illustrate using GCM simulations of the January sea-level pressure field and regional ocean model simulations of the new-shore velocity field of South America. We include with these procedures a method of determining the spatial and temporal locations of non-random errors between the model and data fields so that models can be improved accordingly.
Abstract
The relative roles of heat and freshwater fluxes in forcing the tropical Pacific on interannual timescales are investigated using sophisticated atmospheric and oceanic general circulation models.
Interannual density flux anomalies due to anomalous precipitation and shortwave and longwave radiation are highly correlated since they all depend on clouds. Their respective contributions to the anomalous surface density flux are of comparable magnitude, with precipitation and longwave anomalies opposing shortwave radiation. This implies that anomalous radiation and precipitation associated with the eastward shift of the centers of deep convection during El Niño change the density flux little since they largely balance. This near cancellation also causes the evaporative component to dominate interannual anomalies of the density flux in the eastern Pacific and in the Indian Ocean and implies that anomalous net surface density fluxes there can be approximated by anomalous evaporation alone. However, in the central and western Pacific, evaporative anomalies are negatively correlated to shortwave anomalies as well, and interannual anomalies of the net density flux are therefore small and deviate considerably from the evaporative component alone.
Forcing an oceanic circulation model with the interannual anomalies of the fluxes of heat and freshwater alone yields salinity and temperature anomalies of the same order as observed. Model salinity anomalies explain approximately half of the observations, while temperature anomalies have reversed signs compared to observations. This reflects the negative feedback between surface heat fluxes and the warming caused by interannual anomalies of the wind not included in this simulation.
Over most of the tropical ocean, interannual anomalies of surface density are dominated by temperature anomalies. In the central Pacific, salinity anomalies diminish up to half of the effect of temperature. Anomalies of the velocity fields due to interannual anomalies of the surface heat and freshwater fluxes are largest in the eastern equatorial ocean, where the thermocline is shallow and anomalies of the surface flux have the largest impact on vertical mixing.
Abstract
The relative roles of heat and freshwater fluxes in forcing the tropical Pacific on interannual timescales are investigated using sophisticated atmospheric and oceanic general circulation models.
Interannual density flux anomalies due to anomalous precipitation and shortwave and longwave radiation are highly correlated since they all depend on clouds. Their respective contributions to the anomalous surface density flux are of comparable magnitude, with precipitation and longwave anomalies opposing shortwave radiation. This implies that anomalous radiation and precipitation associated with the eastward shift of the centers of deep convection during El Niño change the density flux little since they largely balance. This near cancellation also causes the evaporative component to dominate interannual anomalies of the density flux in the eastern Pacific and in the Indian Ocean and implies that anomalous net surface density fluxes there can be approximated by anomalous evaporation alone. However, in the central and western Pacific, evaporative anomalies are negatively correlated to shortwave anomalies as well, and interannual anomalies of the net density flux are therefore small and deviate considerably from the evaporative component alone.
Forcing an oceanic circulation model with the interannual anomalies of the fluxes of heat and freshwater alone yields salinity and temperature anomalies of the same order as observed. Model salinity anomalies explain approximately half of the observations, while temperature anomalies have reversed signs compared to observations. This reflects the negative feedback between surface heat fluxes and the warming caused by interannual anomalies of the wind not included in this simulation.
Over most of the tropical ocean, interannual anomalies of surface density are dominated by temperature anomalies. In the central Pacific, salinity anomalies diminish up to half of the effect of temperature. Anomalies of the velocity fields due to interannual anomalies of the surface heat and freshwater fluxes are largest in the eastern equatorial ocean, where the thermocline is shallow and anomalies of the surface flux have the largest impact on vertical mixing.
Abstract
The signature of ENSO in the wintertime frequencies of heavy precipitation and temperature extremes is derived from both observations and atmospheric general circulation model output for the contiguous United States. ENSO signals in the frequency of occurrence of heavy rainfall are found in the Southeast, Gulf Coast, central Rockies, and the general area of the Mississippi–Ohio River valleys. Strong, nonlinear signals in extreme warm temperature frequencies are found in the southern and eastern United States. Extreme cold temperature frequencies are found to be less sensitive to ENSO forcing than extreme warm temperature frequencies. Observed ENSO signals in extreme temperature frequencies are not simply manifestations of shifts in mean seasonal temperature. These signals in the wintertime frequency of extreme rainfall and temperature events appear strong enough to be useful in long-range regional statistical prediction.
Comparisons of observational and model results show that the model climate is sensitive to ENSO on continental scales and provide some encouragement to modeling studies of intraseasonal sensitivity to low-frequency climatic forcing. However, large regional disagreements exist in all variables. Continental-scale El Niño signatures in intraseasonal temperature variability are not correctly modeled. Modeled signals in extreme temperature event frequencies are much more directly related to shifts in seasonal mean temperature than they are in nature.
Abstract
The signature of ENSO in the wintertime frequencies of heavy precipitation and temperature extremes is derived from both observations and atmospheric general circulation model output for the contiguous United States. ENSO signals in the frequency of occurrence of heavy rainfall are found in the Southeast, Gulf Coast, central Rockies, and the general area of the Mississippi–Ohio River valleys. Strong, nonlinear signals in extreme warm temperature frequencies are found in the southern and eastern United States. Extreme cold temperature frequencies are found to be less sensitive to ENSO forcing than extreme warm temperature frequencies. Observed ENSO signals in extreme temperature frequencies are not simply manifestations of shifts in mean seasonal temperature. These signals in the wintertime frequency of extreme rainfall and temperature events appear strong enough to be useful in long-range regional statistical prediction.
Comparisons of observational and model results show that the model climate is sensitive to ENSO on continental scales and provide some encouragement to modeling studies of intraseasonal sensitivity to low-frequency climatic forcing. However, large regional disagreements exist in all variables. Continental-scale El Niño signatures in intraseasonal temperature variability are not correctly modeled. Modeled signals in extreme temperature event frequencies are much more directly related to shifts in seasonal mean temperature than they are in nature.
Seasonal climate anomalies over North America exhibit rather large variability between years characterized by the same ENSO phase. This lack of consistency reduces potential statistically based ENSO-related climate predictability. The authors show that the North Pacific oscillation (NPO) exerts a modulating effect on ENSO teleconnections. Sea level pressure (SLP) data over the North Pacific, North America, and the North Atlantic and daily rainfall records in the contiguous United States are used to demonstrate that typical ENSO signals tend to be stronger and more stable during preferred phases of the NPO. Typical El Niño patterns (e.g., low pressure over the northeastern Pacific, dry northwest, and wet southwest, etc.) are strong and consistent only during the high phase of the NPO, which is associated with an anomalously cold northwestern Pacific. The generally reversed SLP and precipitation patterns during La Niña winters are consistent only during the low NPO phase. Climatic anomalies tend to be weak and spatially incoherent during low NPO–E1 Niño and high NPO–La Niña winters. These results suggest that confidence in ENSO-based long-range climate forecasts for North America should reflect interdecadal climatic anomalies in the North Pacific.
Seasonal climate anomalies over North America exhibit rather large variability between years characterized by the same ENSO phase. This lack of consistency reduces potential statistically based ENSO-related climate predictability. The authors show that the North Pacific oscillation (NPO) exerts a modulating effect on ENSO teleconnections. Sea level pressure (SLP) data over the North Pacific, North America, and the North Atlantic and daily rainfall records in the contiguous United States are used to demonstrate that typical ENSO signals tend to be stronger and more stable during preferred phases of the NPO. Typical El Niño patterns (e.g., low pressure over the northeastern Pacific, dry northwest, and wet southwest, etc.) are strong and consistent only during the high phase of the NPO, which is associated with an anomalously cold northwestern Pacific. The generally reversed SLP and precipitation patterns during La Niña winters are consistent only during the low NPO phase. Climatic anomalies tend to be weak and spatially incoherent during low NPO–E1 Niño and high NPO–La Niña winters. These results suggest that confidence in ENSO-based long-range climate forecasts for North America should reflect interdecadal climatic anomalies in the North Pacific.
