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Abstract
Observations of oceanic temperature in the upper 400 m reveal decadal signals that propagate in the thermocline along lines of constant potential vorticity from the ventilation region in the central North Pacific to approximately 18°N in the western Pacific. The propagation path and speed are well described by the geostrophic mean circulation and by a model of the ventilated thermocline. The approximate southward speed of the thermal signal of 7 mm s−1 yields a transit time of approximately eight years. The thermal anomalies appear to be forced by perturbations of the mixed layer heat budget in the subduction region of the central North Pacific east of the date line. A warm pulse was generated in the central North Pacific by a series of mild winters from 1973 to 1976 and reached 18°N around 1982. After 1978 a succession of colder winters initiated a cold anomaly in the central North Pacific that propagated along a similar path and with a similar speed as the warm anomaly, then arrived in the western tropical Pacific at 18°N around 1991. Tropical Ekman pumping, rather than further propagation of the midlatitude signal, caused the subsequent spread into the equatorial western Pacific and an increase in amplitude. Historical data show that anomalous sea surface temperature in the equatorial central Pacific is correlated with tropical Ekman pumping while the correlation with thermal anomalies in the North Pacific eight years earlier is not significant. These results indicate no significant coupling in the Pacific of Northern Hemisphere midlatitudes and the equatorial region via advection of thermal anomalies along the oceanic thermocline.
Abstract
Observations of oceanic temperature in the upper 400 m reveal decadal signals that propagate in the thermocline along lines of constant potential vorticity from the ventilation region in the central North Pacific to approximately 18°N in the western Pacific. The propagation path and speed are well described by the geostrophic mean circulation and by a model of the ventilated thermocline. The approximate southward speed of the thermal signal of 7 mm s−1 yields a transit time of approximately eight years. The thermal anomalies appear to be forced by perturbations of the mixed layer heat budget in the subduction region of the central North Pacific east of the date line. A warm pulse was generated in the central North Pacific by a series of mild winters from 1973 to 1976 and reached 18°N around 1982. After 1978 a succession of colder winters initiated a cold anomaly in the central North Pacific that propagated along a similar path and with a similar speed as the warm anomaly, then arrived in the western tropical Pacific at 18°N around 1991. Tropical Ekman pumping, rather than further propagation of the midlatitude signal, caused the subsequent spread into the equatorial western Pacific and an increase in amplitude. Historical data show that anomalous sea surface temperature in the equatorial central Pacific is correlated with tropical Ekman pumping while the correlation with thermal anomalies in the North Pacific eight years earlier is not significant. These results indicate no significant coupling in the Pacific of Northern Hemisphere midlatitudes and the equatorial region via advection of thermal anomalies along the oceanic thermocline.
Abstract
The output from an ocean general circulation model (OGCM) driven by observed surface forcing is used in conjunction with simpler dynamical models to examine the physical mechanisms responsible for interannual to interdecadal pycnocline variability in the northeast Pacific Ocean during 1958–97, a period that includes the 1976–77 climate shift. After 1977 the pycnocline deepened in a broad band along the coast and shoaled in the central part of the Gulf of Alaska. The changes in pycnocline depth diagnosed from the model are in agreement with the pycnocline depth changes observed at two ocean stations in different areas of the Gulf of Alaska. A simple Ekman pumping model with linear damping explains a large fraction of pycnocline variability in the OGCM. The fit of the simple model to the OGCM is maximized in the central part of the Gulf of Alaska, where the pycnocline variability produced by the simple model can account for ∼70%–90% of the pycnocline depth variance in the OGCM. Evidence of westward-propagating Rossby waves is found in the OGCM, but they are not the dominant signal. On the contrary, large-scale pycnocline depth anomalies have primarily a standing character, thus explaining the success of the local Ekman pumping model. The agreement between the Ekman pumping model and OGCM deteriorates in a large band along the coast, where propagating disturbances within the pycnocline, due to either mean flow advection or boundary waves, appear to play an important role in pycnocline variability. Coastal propagation of pycnocline depth anomalies is especially relevant in the western part of the Gulf of Alaska, where local Ekman pumping-induced changes are anticorrelated with the OGCM pycnocline depth variations. The pycnocline depth changes associated with the 1976–77 climate regime shift do not seem to be consistent with Sverdrup dynamics, raising questions about the nature of the adjustment of the Alaska Gyre to low-frequency wind stress variability.
Abstract
The output from an ocean general circulation model (OGCM) driven by observed surface forcing is used in conjunction with simpler dynamical models to examine the physical mechanisms responsible for interannual to interdecadal pycnocline variability in the northeast Pacific Ocean during 1958–97, a period that includes the 1976–77 climate shift. After 1977 the pycnocline deepened in a broad band along the coast and shoaled in the central part of the Gulf of Alaska. The changes in pycnocline depth diagnosed from the model are in agreement with the pycnocline depth changes observed at two ocean stations in different areas of the Gulf of Alaska. A simple Ekman pumping model with linear damping explains a large fraction of pycnocline variability in the OGCM. The fit of the simple model to the OGCM is maximized in the central part of the Gulf of Alaska, where the pycnocline variability produced by the simple model can account for ∼70%–90% of the pycnocline depth variance in the OGCM. Evidence of westward-propagating Rossby waves is found in the OGCM, but they are not the dominant signal. On the contrary, large-scale pycnocline depth anomalies have primarily a standing character, thus explaining the success of the local Ekman pumping model. The agreement between the Ekman pumping model and OGCM deteriorates in a large band along the coast, where propagating disturbances within the pycnocline, due to either mean flow advection or boundary waves, appear to play an important role in pycnocline variability. Coastal propagation of pycnocline depth anomalies is especially relevant in the western part of the Gulf of Alaska, where local Ekman pumping-induced changes are anticorrelated with the OGCM pycnocline depth variations. The pycnocline depth changes associated with the 1976–77 climate regime shift do not seem to be consistent with Sverdrup dynamics, raising questions about the nature of the adjustment of the Alaska Gyre to low-frequency wind stress variability.
Abstract
Relationships among the atmospheric phenomena associated with the Southern Oscillation and El Niño are investigated, using the Comprehensive Ocean-Atmosphere Data Set (COADS) of marine surface observations from ships of opportunity and the World Monthly Surface [Land] Station Climatology (WMSSC) for the period 1950-79. Annual mean (April–March) sea level pressure at Darwin, Australia is used as an index of the Southern Oscillation. Results are based on simple linear correlation techniques stratified by season as in the Rasmusson and Carpenter (1982) composite.
Correlations on the order of +0.9 are observed between Darwin pressure, sea surface temperature (SST) and rainfall in the equatorial central Pacific, and zonal wind in the equatorial western Pacific. Relations among these variables are strongest from July through November, when the month to month autocorrelation is also at its strongest. Sea surface temperature along the Peruvian coast and pressure in the eastern Pacific are also most strongly coupled to the Southern Oscillation during thew months, which correspond to the cool season in that region.
The amplitude of the tropical pressure and central Pacific SST anomalies associated with the Southern Oscillation appear to be just as large and the relationships between them just as coherent during positive excursions of the Southern Oscillation (cold episodes) as during negative excursions (warm episodes).
Lead/lag relationships among climatic variables associated with the Southern Oscillation and El Niño events along the South American coast are also examined in the context of the same seasonal stratification. Our results are generally consistent with the traditional view that the Southern Oscillation is, to first order, a standing oscillation with geographically fixed nodes and antinodes.
Abstract
Relationships among the atmospheric phenomena associated with the Southern Oscillation and El Niño are investigated, using the Comprehensive Ocean-Atmosphere Data Set (COADS) of marine surface observations from ships of opportunity and the World Monthly Surface [Land] Station Climatology (WMSSC) for the period 1950-79. Annual mean (April–March) sea level pressure at Darwin, Australia is used as an index of the Southern Oscillation. Results are based on simple linear correlation techniques stratified by season as in the Rasmusson and Carpenter (1982) composite.
Correlations on the order of +0.9 are observed between Darwin pressure, sea surface temperature (SST) and rainfall in the equatorial central Pacific, and zonal wind in the equatorial western Pacific. Relations among these variables are strongest from July through November, when the month to month autocorrelation is also at its strongest. Sea surface temperature along the Peruvian coast and pressure in the eastern Pacific are also most strongly coupled to the Southern Oscillation during thew months, which correspond to the cool season in that region.
The amplitude of the tropical pressure and central Pacific SST anomalies associated with the Southern Oscillation appear to be just as large and the relationships between them just as coherent during positive excursions of the Southern Oscillation (cold episodes) as during negative excursions (warm episodes).
Lead/lag relationships among climatic variables associated with the Southern Oscillation and El Niño events along the South American coast are also examined in the context of the same seasonal stratification. Our results are generally consistent with the traditional view that the Southern Oscillation is, to first order, a standing oscillation with geographically fixed nodes and antinodes.
Abstract
The mean-state bias and the associated forecast errors of the El Niño–Southern Oscillation (ENSO) are investigated in a suite of 2-yr-lead retrospective forecasts conducted with the Community Earth System Model, version 1, for 1954–2015. The equatorial Pacific cold tongue in the forecasts is too strong and extends excessively westward due to a combination of the model’s inherent climatological bias, initialization imbalance, and errors in initial ocean data. The forecasts show a stronger cold tongue bias in the first year than that inherent to the model due to the imbalance between initial subsurface oceanic states and model dynamics. The cold tongue bias affects not only the pattern and amplitude but also the duration of ENSO in the forecasts by altering ocean–atmosphere feedbacks. The predicted sea surface temperature anomalies related to ENSO extend to the far western equatorial Pacific during boreal summer when the cold tongue bias is strong, and the predicted ENSO anomalies are too weak in the central-eastern equatorial Pacific. The forecast errors of pattern and amplitude subsequently lead to errors in ENSO phase transition by affecting the amplitude of the negative thermocline feedback in the equatorial Pacific and tropical interbasin adjustments during the mature phase of ENSO. These ENSO forecast errors further degrade the predictions of wintertime atmospheric teleconnections, land surface air temperature, and rainfall anomalies over the Northern Hemisphere. These mean-state and ENSO forecast biases are more pronounced in forecasts initialized in boreal spring–summer than other seasons due to the seasonal intensification of the Bjerknes feedback.
Abstract
The mean-state bias and the associated forecast errors of the El Niño–Southern Oscillation (ENSO) are investigated in a suite of 2-yr-lead retrospective forecasts conducted with the Community Earth System Model, version 1, for 1954–2015. The equatorial Pacific cold tongue in the forecasts is too strong and extends excessively westward due to a combination of the model’s inherent climatological bias, initialization imbalance, and errors in initial ocean data. The forecasts show a stronger cold tongue bias in the first year than that inherent to the model due to the imbalance between initial subsurface oceanic states and model dynamics. The cold tongue bias affects not only the pattern and amplitude but also the duration of ENSO in the forecasts by altering ocean–atmosphere feedbacks. The predicted sea surface temperature anomalies related to ENSO extend to the far western equatorial Pacific during boreal summer when the cold tongue bias is strong, and the predicted ENSO anomalies are too weak in the central-eastern equatorial Pacific. The forecast errors of pattern and amplitude subsequently lead to errors in ENSO phase transition by affecting the amplitude of the negative thermocline feedback in the equatorial Pacific and tropical interbasin adjustments during the mature phase of ENSO. These ENSO forecast errors further degrade the predictions of wintertime atmospheric teleconnections, land surface air temperature, and rainfall anomalies over the Northern Hemisphere. These mean-state and ENSO forecast biases are more pronounced in forecasts initialized in boreal spring–summer than other seasons due to the seasonal intensification of the Bjerknes feedback.
Abstract
Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.
Abstract
Ensembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.
Abstract
Stratospheric conditions are increasingly being recognized as an important driver of North Atlantic and Eurasian climate variability. Mindful that the observational record is relatively short, and that internal climate variability can be large, the authors here analyze a new 10-member ensemble of integrations of a stratosphere-resolving, atmospheric general circulation model, forced with the observed evolution of sea surface temperature (SST) during 1952–2003. Previous studies are confirmed, showing that El Niño conditions enhance the frequency of occurrence of stratospheric sudden warmings (SSWs), whereas La Niña conditions do not appear to affect it. However, large differences are noted among ensemble members, suggesting caution when interpreting the relatively short observational record. More importantly, it is emphasized that the majority of SSWs are not caused by anomalous tropical Pacific SSTs. Comparing composites of winters with and without SSWs in each ENSO phase separately, it is demonstrated that stratospheric variability gives rise to large and statistically significant anomalies in tropospheric circulation and surface conditions over the North Atlantic and Eurasia. This indicates that, for those regions, climate variability of stratospheric origin is comparable in magnitude to variability originating from tropical Pacific SSTs, so that the occurrence of a single SSW in a given winter is able to completely alter seasonal climate predictions based solely on ENSO conditions. These findings, corroborating other recent studies, highlight the importance of accurately forecasting SSWs for improved seasonal prediction of North Atlantic and Eurasian climate.
Abstract
Stratospheric conditions are increasingly being recognized as an important driver of North Atlantic and Eurasian climate variability. Mindful that the observational record is relatively short, and that internal climate variability can be large, the authors here analyze a new 10-member ensemble of integrations of a stratosphere-resolving, atmospheric general circulation model, forced with the observed evolution of sea surface temperature (SST) during 1952–2003. Previous studies are confirmed, showing that El Niño conditions enhance the frequency of occurrence of stratospheric sudden warmings (SSWs), whereas La Niña conditions do not appear to affect it. However, large differences are noted among ensemble members, suggesting caution when interpreting the relatively short observational record. More importantly, it is emphasized that the majority of SSWs are not caused by anomalous tropical Pacific SSTs. Comparing composites of winters with and without SSWs in each ENSO phase separately, it is demonstrated that stratospheric variability gives rise to large and statistically significant anomalies in tropospheric circulation and surface conditions over the North Atlantic and Eurasia. This indicates that, for those regions, climate variability of stratospheric origin is comparable in magnitude to variability originating from tropical Pacific SSTs, so that the occurrence of a single SSW in a given winter is able to completely alter seasonal climate predictions based solely on ENSO conditions. These findings, corroborating other recent studies, highlight the importance of accurately forecasting SSWs for improved seasonal prediction of North Atlantic and Eurasian climate.
Abstract
Internal variability in the climate system gives rise to large uncertainty in projections of future climate. The uncertainty in future climate due to internal climate variability can be estimated from large ensembles of climate change simulations in which the experiment setup is the same from one ensemble member to the next but for small perturbations in the initial atmospheric state. However, large ensembles are invariably computationally expensive and susceptible to model bias.
Here the authors outline an alternative approach for assessing the role of internal variability in future climate based on a simple analytic model and the statistics of the unforced climate variability. The analytic model is derived from the standard error of the regression and assumes that the statistics of the internal variability are roughly Gaussian and stationary in time. When applied to the statistics of an unforced control simulation, the analytic model provides a remarkably robust estimate of the uncertainty in future climate indicated by a large ensemble of climate change simulations. To the extent that observations can be used to estimate the amplitude of internal climate variability, it is argued that the uncertainty in future climate trends due to internal variability can be robustly estimated from the statistics of the observed climate.
Abstract
Internal variability in the climate system gives rise to large uncertainty in projections of future climate. The uncertainty in future climate due to internal climate variability can be estimated from large ensembles of climate change simulations in which the experiment setup is the same from one ensemble member to the next but for small perturbations in the initial atmospheric state. However, large ensembles are invariably computationally expensive and susceptible to model bias.
Here the authors outline an alternative approach for assessing the role of internal variability in future climate based on a simple analytic model and the statistics of the unforced climate variability. The analytic model is derived from the standard error of the regression and assumes that the statistics of the internal variability are roughly Gaussian and stationary in time. When applied to the statistics of an unforced control simulation, the analytic model provides a remarkably robust estimate of the uncertainty in future climate indicated by a large ensemble of climate change simulations. To the extent that observations can be used to estimate the amplitude of internal climate variability, it is argued that the uncertainty in future climate trends due to internal variability can be robustly estimated from the statistics of the observed climate.
abstract
The role of sampling variability in ENSO composites of winter surface air temperature and precipitation over North America during the period 1920–2013 is assessed for observations and ensembles of coupled model simulations in which sea surface temperature anomalies in the tropical eastern Pacific are nudged to those of the real world. The individual members of each model ensemble show a surprising amount of diversity in their ENSO composites, despite being constructed from the same observed set of 18 El Niño and 14 La Niña events. For a given model, this ensemble spread can only be due to sampling variability, that is, aliasing of internal variability that is unrelated to ENSO, which in turn is shown to arise from internal atmospheric dynamics rather than coupled ocean–atmosphere processes. Analogous ensemble spread is evident in 2000 synthetic ENSO composites based on observations using random sampling techniques. These synthetic composites provide information on the range of spatial patterns and amplitudes associated with imperfect estimation of the forced ENSO signal in the observational record. In some locations, the amplitude of the estimated ENSO signal can vary by more than a factor of two. This observational uncertainty necessitates an approach to model assessment that considers not only the model’s forced response to ENSO, given by its ensemble-mean ENSO composite, but also its representation of internal variability unrelated to ENSO. Such an approach is used to reveal fidelities and shortcomings in the Community Earth System Model, version 1.
abstract
The role of sampling variability in ENSO composites of winter surface air temperature and precipitation over North America during the period 1920–2013 is assessed for observations and ensembles of coupled model simulations in which sea surface temperature anomalies in the tropical eastern Pacific are nudged to those of the real world. The individual members of each model ensemble show a surprising amount of diversity in their ENSO composites, despite being constructed from the same observed set of 18 El Niño and 14 La Niña events. For a given model, this ensemble spread can only be due to sampling variability, that is, aliasing of internal variability that is unrelated to ENSO, which in turn is shown to arise from internal atmospheric dynamics rather than coupled ocean–atmosphere processes. Analogous ensemble spread is evident in 2000 synthetic ENSO composites based on observations using random sampling techniques. These synthetic composites provide information on the range of spatial patterns and amplitudes associated with imperfect estimation of the forced ENSO signal in the observational record. In some locations, the amplitude of the estimated ENSO signal can vary by more than a factor of two. This observational uncertainty necessitates an approach to model assessment that considers not only the model’s forced response to ENSO, given by its ensemble-mean ENSO composite, but also its representation of internal variability unrelated to ENSO. Such an approach is used to reveal fidelities and shortcomings in the Community Earth System Model, version 1.
Abstract
Multidecadal variability in the North Atlantic jet stream in general circulation models (GCMs) is compared with that in reanalysis products of the twentieth century. It is found that almost all models exhibit multidecadal jet stream variability that is entirely consistent with the sampling of white noise year-to-year atmospheric fluctuations. In the observed record, the variability displays a pronounced seasonality within the winter months, with greatly enhanced variability toward the late winter. This late winter variability exceeds that found in any GCM and greatly exceeds expectations from the sampling of atmospheric noise, motivating the need for an underlying explanation. The potential roles of both external forcings and internal coupled ocean–atmosphere processes are considered. While the late winter variability is not found to be closely connected with external forcing, it is found to be strongly related to the internally generated component of Atlantic multidecadal variability (AMV) in sea surface temperatures (SSTs). In fact, consideration of the seasonality of the jet stream variability within the winter months reveals that the AMV is far more strongly connected to jet stream variability during March than the early winter months or the winter season as a whole. Reasoning is put forward for why this connection likely represents a driving of the jet stream variability by the SSTs, although the dynamics involved remain to be understood. This analysis reveals a fundamental mismatch between late winter jet stream variability in observations and GCMs and a potential source of long-term predictability of the late winter Atlantic atmospheric circulation.
Abstract
Multidecadal variability in the North Atlantic jet stream in general circulation models (GCMs) is compared with that in reanalysis products of the twentieth century. It is found that almost all models exhibit multidecadal jet stream variability that is entirely consistent with the sampling of white noise year-to-year atmospheric fluctuations. In the observed record, the variability displays a pronounced seasonality within the winter months, with greatly enhanced variability toward the late winter. This late winter variability exceeds that found in any GCM and greatly exceeds expectations from the sampling of atmospheric noise, motivating the need for an underlying explanation. The potential roles of both external forcings and internal coupled ocean–atmosphere processes are considered. While the late winter variability is not found to be closely connected with external forcing, it is found to be strongly related to the internally generated component of Atlantic multidecadal variability (AMV) in sea surface temperatures (SSTs). In fact, consideration of the seasonality of the jet stream variability within the winter months reveals that the AMV is far more strongly connected to jet stream variability during March than the early winter months or the winter season as a whole. Reasoning is put forward for why this connection likely represents a driving of the jet stream variability by the SSTs, although the dynamics involved remain to be understood. This analysis reveals a fundamental mismatch between late winter jet stream variability in observations and GCMs and a potential source of long-term predictability of the late winter Atlantic atmospheric circulation.
