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- Author or Editor: Prashant D. Sardeshmukh x
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Abstract
The impact of a warming climate on El Niño–Southern Oscillation (ENSO) is investigated in large-ensemble simulations of the Community Earth System Model (CESM1). These simulations are forced by historical emissions for the past and the RCP8.5-scenario emissions for future projections. The simulated variance of the Niño-3.4 ENSO index increases from 1.4°C2 in 1921–80 to 1.9°C2 in 1981–2040 and 2.2°C2 in 2041–2100. The autocorrelation time scale of the index also increases, consistent with a narrowing of its spectral peak in the 3–7-yr ENSO band, raising the possibility of greater seasonal to interannual predictability in the future. Low-order linear inverse models (LIMs) fitted separately to the three 60-yr periods capture the CESM1 increase in ENSO variance and regularity. Remarkably, most of the increase can be attributed to the increase in the 23-month damping time scale of a single damped oscillatory ENSO eigenmode of these LIMs by 5 months in 1981–2040 and 6 months in 2041–2100. These apparently robust projected increases may, however, be compromised by CESM1 biases in ENSO amplitude and damping time scale. An LIM fitted to the 1921–80 observations has an ENSO eigenmode with a much shorter 8-month damping time scale, similar to that of several other eigenmodes. When the mode’s damping time scale is increased by 5 and 6 months in this observational LIM, a much smaller increase of ENSO variance is obtained than in the CESM1 projections. This may be because ENSO is not as dominated by a single ENSO eigenmode in reality as it is in the CESM1.
Abstract
The impact of a warming climate on El Niño–Southern Oscillation (ENSO) is investigated in large-ensemble simulations of the Community Earth System Model (CESM1). These simulations are forced by historical emissions for the past and the RCP8.5-scenario emissions for future projections. The simulated variance of the Niño-3.4 ENSO index increases from 1.4°C2 in 1921–80 to 1.9°C2 in 1981–2040 and 2.2°C2 in 2041–2100. The autocorrelation time scale of the index also increases, consistent with a narrowing of its spectral peak in the 3–7-yr ENSO band, raising the possibility of greater seasonal to interannual predictability in the future. Low-order linear inverse models (LIMs) fitted separately to the three 60-yr periods capture the CESM1 increase in ENSO variance and regularity. Remarkably, most of the increase can be attributed to the increase in the 23-month damping time scale of a single damped oscillatory ENSO eigenmode of these LIMs by 5 months in 1981–2040 and 6 months in 2041–2100. These apparently robust projected increases may, however, be compromised by CESM1 biases in ENSO amplitude and damping time scale. An LIM fitted to the 1921–80 observations has an ENSO eigenmode with a much shorter 8-month damping time scale, similar to that of several other eigenmodes. When the mode’s damping time scale is increased by 5 and 6 months in this observational LIM, a much smaller increase of ENSO variance is obtained than in the CESM1 projections. This may be because ENSO is not as dominated by a single ENSO eigenmode in reality as it is in the CESM1.
Abstract
This study investigates the mechanisms by which short time-scale perturbations to atmospheric processes can affect El Niño–Southern Oscillation (ENSO) in climate models. To this end a control simulation of NCAR’s Community Climate System Model is compared to a simulation in which the model’s atmospheric diabatic tendencies are perturbed every time step using a Stochastically Perturbed Parameterized Tendencies (SPPT) scheme. The SPPT simulation compares better with ECMWF’s twentieth-century reanalysis in having lower interannual sea surface temperature (SST) variability and more irregular transitions between El Niño and La Niña states, as expressed by a broader, less peaked spectrum. Reduced-order linear inverse models (LIMs) derived from the 1-month lag covariances of selected tropical variables yield good representations of tropical interannual variability in the two simulations. In particular, the basic features of ENSO are captured by the LIM’s least damped oscillatory eigenmode. SPPT reduces the damping time scale of this eigenmode from 17 to 11 months, which is in better agreement with the 8 months obtained from reanalyses. This noise-induced stabilization is consistent with perturbations to the frequency of the ENSO eigenmode and explains the broadening of the SST spectrum (i.e., the greater ENSO irregularity). Although the improvement in ENSO shown here was achieved through stochastic physics parameterizations, it is possible that similar improvements could be realized through changes in deterministic parameterizations or higher numerical resolution. It is suggested that LIMs could provide useful insight into model sensitivities, uncertainties, and biases also in those cases.
Abstract
This study investigates the mechanisms by which short time-scale perturbations to atmospheric processes can affect El Niño–Southern Oscillation (ENSO) in climate models. To this end a control simulation of NCAR’s Community Climate System Model is compared to a simulation in which the model’s atmospheric diabatic tendencies are perturbed every time step using a Stochastically Perturbed Parameterized Tendencies (SPPT) scheme. The SPPT simulation compares better with ECMWF’s twentieth-century reanalysis in having lower interannual sea surface temperature (SST) variability and more irregular transitions between El Niño and La Niña states, as expressed by a broader, less peaked spectrum. Reduced-order linear inverse models (LIMs) derived from the 1-month lag covariances of selected tropical variables yield good representations of tropical interannual variability in the two simulations. In particular, the basic features of ENSO are captured by the LIM’s least damped oscillatory eigenmode. SPPT reduces the damping time scale of this eigenmode from 17 to 11 months, which is in better agreement with the 8 months obtained from reanalyses. This noise-induced stabilization is consistent with perturbations to the frequency of the ENSO eigenmode and explains the broadening of the SST spectrum (i.e., the greater ENSO irregularity). Although the improvement in ENSO shown here was achieved through stochastic physics parameterizations, it is possible that similar improvements could be realized through changes in deterministic parameterizations or higher numerical resolution. It is suggested that LIMs could provide useful insight into model sensitivities, uncertainties, and biases also in those cases.
Abstract
Given the reality of anthropogenic global warming, it is tempting to seek an anthropogenic component in any recent change in the statistics of extreme weather. This paper cautions that such efforts may, however, lead to wrong conclusions if the distinctively skewed and heavy-tailed aspects of the probability distributions of daily weather anomalies are ignored or misrepresented. Departures of several standard deviations from the mean, although rare, are far more common in such a distinctively non-Gaussian world than they are in a Gaussian world. This further complicates the problem of detecting changes in tail probabilities from historical records of limited length and accuracy.
A possible solution is to exploit the fact that the salient non-Gaussian features of the observed distributions are captured by so-called stochastically generated skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise and as such represent the simplest physically based prototypes of the observed distributions. The tails of SGS distributions can also be directly linked to generalized extreme value (GEV) and generalized Pareto (GP) distributions. The Markov process model can be used to provide rigorous confidence intervals and to investigate temporal persistence statistics. The procedure is illustrated for assessing changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the period 1872–2011. No significant changes in these indices are found from the first to the second half of the period.
Abstract
Given the reality of anthropogenic global warming, it is tempting to seek an anthropogenic component in any recent change in the statistics of extreme weather. This paper cautions that such efforts may, however, lead to wrong conclusions if the distinctively skewed and heavy-tailed aspects of the probability distributions of daily weather anomalies are ignored or misrepresented. Departures of several standard deviations from the mean, although rare, are far more common in such a distinctively non-Gaussian world than they are in a Gaussian world. This further complicates the problem of detecting changes in tail probabilities from historical records of limited length and accuracy.
A possible solution is to exploit the fact that the salient non-Gaussian features of the observed distributions are captured by so-called stochastically generated skewed (SGS) distributions that include Gaussian distributions as special cases. SGS distributions are associated with damped linear Markov processes perturbed by asymmetric stochastic noise and as such represent the simplest physically based prototypes of the observed distributions. The tails of SGS distributions can also be directly linked to generalized extreme value (GEV) and generalized Pareto (GP) distributions. The Markov process model can be used to provide rigorous confidence intervals and to investigate temporal persistence statistics. The procedure is illustrated for assessing changes in the observed distributions of daily wintertime indices of large-scale atmospheric variability in the North Atlantic and North Pacific sectors over the period 1872–2011. No significant changes in these indices are found from the first to the second half of the period.
Three very different views of the mean structure and variability of deep convection over the tropical east Indian and west Pacific Oceans, provided by three different reanalysis datasets for 1980–93, are highlighted. The datasets were generated at the National Centers for Environmental Prediction, the National Aeronautics and Space Administration's Goddard Laboratory for Atmospheres, and the European Centre for Medium-Range Weather Forecasts (ECMWF). Precipitation, outgoing longwave radiation (OLR), and 200-mb wind divergence fields from the three datasets are compared with one another and with satellite observations. Climatological means as well as interannual and intraseasonal (30–70 day) variability are discussed. For brevity the focus is restricted to northern winter (DJF).
The internal consistency of the datasets is high, in the sense that the geographical extremes of rainfall, OLR, and divergence in each dataset correspond closely to one another. On the other hand, the external consistency, that is, the agreement between the datasets, is so low as to defy a simple summary. Indeed, the differences are such as to raise fundamental questions concerning 1) whether there is a single or a split ITCZ over the west Pacific Ocean with a strong northern branch, 2) whether there is more convection to the west or the east of Sumatra over the equatorial Indian Ocean, and 3) whether there is a relative minimum of convection near New Guinea. Geographical maps of interannual and intraseasonal variances also show similar order 1 uncertainties, as do regressions against the principal component time series of the Madden–Julian oscillation. The annual cycle of convection is also different in each reanalysis. Overall, the ECMWF reanalysis compares best with observations in this region, but it too has important errors.
Finally, it is noted that although 200-mb divergence fields in the three datasets are highly inconsistent with one another, the 200-mb vorticity fields are highly consistent. This reaffirms the relevance of diagnosing divergence from knowledge of the vorticity using the method described in Sardeshmukh (1993). This would yield divergence fields from the three datasets that are not only more consistent with each other, but also more consistent with the 200-mb vorticity balance. Further, as proxies of deep convection, they would help resolve many of the issues raised above.
Three very different views of the mean structure and variability of deep convection over the tropical east Indian and west Pacific Oceans, provided by three different reanalysis datasets for 1980–93, are highlighted. The datasets were generated at the National Centers for Environmental Prediction, the National Aeronautics and Space Administration's Goddard Laboratory for Atmospheres, and the European Centre for Medium-Range Weather Forecasts (ECMWF). Precipitation, outgoing longwave radiation (OLR), and 200-mb wind divergence fields from the three datasets are compared with one another and with satellite observations. Climatological means as well as interannual and intraseasonal (30–70 day) variability are discussed. For brevity the focus is restricted to northern winter (DJF).
The internal consistency of the datasets is high, in the sense that the geographical extremes of rainfall, OLR, and divergence in each dataset correspond closely to one another. On the other hand, the external consistency, that is, the agreement between the datasets, is so low as to defy a simple summary. Indeed, the differences are such as to raise fundamental questions concerning 1) whether there is a single or a split ITCZ over the west Pacific Ocean with a strong northern branch, 2) whether there is more convection to the west or the east of Sumatra over the equatorial Indian Ocean, and 3) whether there is a relative minimum of convection near New Guinea. Geographical maps of interannual and intraseasonal variances also show similar order 1 uncertainties, as do regressions against the principal component time series of the Madden–Julian oscillation. The annual cycle of convection is also different in each reanalysis. Overall, the ECMWF reanalysis compares best with observations in this region, but it too has important errors.
Finally, it is noted that although 200-mb divergence fields in the three datasets are highly inconsistent with one another, the 200-mb vorticity fields are highly consistent. This reaffirms the relevance of diagnosing divergence from knowledge of the vorticity using the method described in Sardeshmukh (1993). This would yield divergence fields from the three datasets that are not only more consistent with each other, but also more consistent with the 200-mb vorticity balance. Further, as proxies of deep convection, they would help resolve many of the issues raised above.
Climate variability and global change studies are increasingly focused on understanding and predicting regional changes of daily weather statistics. Assessing the evidence for such variations over the last 100 yr requires a daily tropospheric circulation dataset. The only dataset available for the early twentieth century consists of error-ridden hand-drawn analyses of the mean sea level pressure field over the Northern Hemisphere. Modern data assimilation systems have the potential to improve upon these maps, but prior to 1948, few digitized upper-air sounding observations are available for such a “reanalysis.” We investigate the possibility that the additional number of newly recovered surface pressure observations is sufficient to generate useful weather maps of the lower-tropospheric extratropical circulation back to 1890 over the Northern Hemisphere, and back to 1930 over the Southern Hemisphere. Surprisingly, we find that by using an advanced data assimilation system based on an ensemble Kalman filter, it would be feasible to produce high-quality maps of even the upper troposphere using only surface pressure observations. For the beginning of the twentieth century, the errors of such upper-air circulation maps over the Northern Hemisphere in winter would be comparable to the 2-3-day errors of modern weather forecasts.
Climate variability and global change studies are increasingly focused on understanding and predicting regional changes of daily weather statistics. Assessing the evidence for such variations over the last 100 yr requires a daily tropospheric circulation dataset. The only dataset available for the early twentieth century consists of error-ridden hand-drawn analyses of the mean sea level pressure field over the Northern Hemisphere. Modern data assimilation systems have the potential to improve upon these maps, but prior to 1948, few digitized upper-air sounding observations are available for such a “reanalysis.” We investigate the possibility that the additional number of newly recovered surface pressure observations is sufficient to generate useful weather maps of the lower-tropospheric extratropical circulation back to 1890 over the Northern Hemisphere, and back to 1930 over the Southern Hemisphere. Surprisingly, we find that by using an advanced data assimilation system based on an ensemble Kalman filter, it would be feasible to produce high-quality maps of even the upper troposphere using only surface pressure observations. For the beginning of the twentieth century, the errors of such upper-air circulation maps over the Northern Hemisphere in winter would be comparable to the 2-3-day errors of modern weather forecasts.
Abstract
The predictability of weekly averaged circulation anomalies in the Northern Hemisphere, and diabatic heating anomalies in the Tropics, is investigated in a linear inverse model (LIM) derived from their observed simultaneous and time-lag correlation statistics. In both winter and summer, the model's forecast skill at week 2 (days 8–14) and week 3 (days 15–21) is comparable to that of a comprehensive global medium-range forecast (MRF) model developed at the National Centers for Environmental Prediction (NCEP). Its skill at week 3 is actually higher on average, partly due to its better ability to forecast tropical heating variations and their influence on the extratropical circulation. The geographical and temporal variations of forecast skill are also similar in the two models. This makes the much simpler LIM an attractive tool for assessing and diagnosing atmospheric predictability at these forecast ranges.
The LIM assumes that the dynamics of weekly averages are linear, asymptotically stable, and stochastically forced. In a forecasting context, the predictable signal is associated with the deterministic linear dynamics, and the forecast error with the unpredictable stochastic noise. In a low-order linear model of a high-order chaotic system, this stochastic noise represents the effects of both chaotic nonlinear interactions and unresolved initial components on the evolution of the resolved components. Its statistics are assumed here to be state independent.
An average signal-to-noise ratio is estimated at each grid point on the hemisphere and is then used to estimate the potential predictability of weekly variations at the point. In general, this predictability is about 50% higher in winter than summer over the Pacific and North America sectors; the situation is reversed over Eurasia and North Africa. Skill in predicting tropical heating variations is important for realizing this potential skill. The actual LIM forecast skill has a similar geographical structure but weaker magnitude than the potential skill.
In this framework, the predictable variations of forecast skill from case to case are associated with predictable variations of signal rather than of noise. This contrasts with the traditional emphasis in studies of shorter-term predictability on flow-dependent instabilities, that is, on the predictable variations of noise. In the LIM, the predictable variations of signal are associated with variations of the initial state projection on the growing singular vectors of the LIM's propagator, which have relatively large amplitude in the Tropics. At times of strong projection on such structures, the signal-to-noise ratio is relatively high, and the Northern Hemispheric circulation is not only potentially but also actually more predictable than at other times.
Abstract
The predictability of weekly averaged circulation anomalies in the Northern Hemisphere, and diabatic heating anomalies in the Tropics, is investigated in a linear inverse model (LIM) derived from their observed simultaneous and time-lag correlation statistics. In both winter and summer, the model's forecast skill at week 2 (days 8–14) and week 3 (days 15–21) is comparable to that of a comprehensive global medium-range forecast (MRF) model developed at the National Centers for Environmental Prediction (NCEP). Its skill at week 3 is actually higher on average, partly due to its better ability to forecast tropical heating variations and their influence on the extratropical circulation. The geographical and temporal variations of forecast skill are also similar in the two models. This makes the much simpler LIM an attractive tool for assessing and diagnosing atmospheric predictability at these forecast ranges.
The LIM assumes that the dynamics of weekly averages are linear, asymptotically stable, and stochastically forced. In a forecasting context, the predictable signal is associated with the deterministic linear dynamics, and the forecast error with the unpredictable stochastic noise. In a low-order linear model of a high-order chaotic system, this stochastic noise represents the effects of both chaotic nonlinear interactions and unresolved initial components on the evolution of the resolved components. Its statistics are assumed here to be state independent.
An average signal-to-noise ratio is estimated at each grid point on the hemisphere and is then used to estimate the potential predictability of weekly variations at the point. In general, this predictability is about 50% higher in winter than summer over the Pacific and North America sectors; the situation is reversed over Eurasia and North Africa. Skill in predicting tropical heating variations is important for realizing this potential skill. The actual LIM forecast skill has a similar geographical structure but weaker magnitude than the potential skill.
In this framework, the predictable variations of forecast skill from case to case are associated with predictable variations of signal rather than of noise. This contrasts with the traditional emphasis in studies of shorter-term predictability on flow-dependent instabilities, that is, on the predictable variations of noise. In the LIM, the predictable variations of signal are associated with variations of the initial state projection on the growing singular vectors of the LIM's propagator, which have relatively large amplitude in the Tropics. At times of strong projection on such structures, the signal-to-noise ratio is relatively high, and the Northern Hemispheric circulation is not only potentially but also actually more predictable than at other times.
Abstract
The relative contributions to mean global atmospheric moisture transport by both the time-mean circulation and by synoptic and low-frequency (periods greater than 10 days) anomalies are evaluated from the vertically integrated atmospheric moisture budget based on 40 yr of “chi corrected” NCEP–NCAR reanalysis data. In the extratropics, while the time-mean circulation primarily moves moisture zonally within ocean basins, low-frequency and synoptic anomalies drive much of the mean moisture transport both from ocean to land and toward the poles. In particular, during the cool-season low-frequency variability is the largest contributor to mean moisture transport into southwestern North America, Europe, and Australia. While some low-frequency transport originates in low latitudes, much is of extratropical origin due to large-scale atmospheric anomalies that extract moisture from the northeast Pacific and Atlantic Oceans. Low-frequency variability is also integral to the Arctic (latitudes > 70°N) mean moisture budget, especially during summer, when it drives mean poleward transport from relatively wet high-latitude continental regions. Synoptic variability drives about half of the mean poleward moisture transport in the midlatitudes of both hemispheres, consistent with simple “lateral mixing” arguments. Extratropical atmospheric transport is also particularly focused within “atmospheric rivers” (ARs), relatively narrow poleward-moving moisture plumes associated with frontal dynamics. AR moisture transport, defined by compositing fluxes over those locations and times where column-integrated water vapor and poleward low-level wind anomalies are both positive, represents most of the total extratropical meridional moisture transport. These results suggest that understanding potential anthropogenic changes in the earth ’s hydrological cycle may require understanding corresponding changes in atmospheric variability, especially on low-frequency time scales.
Abstract
The relative contributions to mean global atmospheric moisture transport by both the time-mean circulation and by synoptic and low-frequency (periods greater than 10 days) anomalies are evaluated from the vertically integrated atmospheric moisture budget based on 40 yr of “chi corrected” NCEP–NCAR reanalysis data. In the extratropics, while the time-mean circulation primarily moves moisture zonally within ocean basins, low-frequency and synoptic anomalies drive much of the mean moisture transport both from ocean to land and toward the poles. In particular, during the cool-season low-frequency variability is the largest contributor to mean moisture transport into southwestern North America, Europe, and Australia. While some low-frequency transport originates in low latitudes, much is of extratropical origin due to large-scale atmospheric anomalies that extract moisture from the northeast Pacific and Atlantic Oceans. Low-frequency variability is also integral to the Arctic (latitudes > 70°N) mean moisture budget, especially during summer, when it drives mean poleward transport from relatively wet high-latitude continental regions. Synoptic variability drives about half of the mean poleward moisture transport in the midlatitudes of both hemispheres, consistent with simple “lateral mixing” arguments. Extratropical atmospheric transport is also particularly focused within “atmospheric rivers” (ARs), relatively narrow poleward-moving moisture plumes associated with frontal dynamics. AR moisture transport, defined by compositing fluxes over those locations and times where column-integrated water vapor and poleward low-level wind anomalies are both positive, represents most of the total extratropical meridional moisture transport. These results suggest that understanding potential anthropogenic changes in the earth ’s hydrological cycle may require understanding corresponding changes in atmospheric variability, especially on low-frequency time scales.
Abstract
Low-order linear inverse models (LIMs) have been shown to be competitive with comprehensive coupled atmosphere–ocean models at reproducing many aspects of tropical oceanic variability and predictability. This paper presents an extended cyclostationary linear inverse model (CS-LIM) that includes the annual cycles of the background state and stochastic forcing of tropical sea surface temperature (SST) and sea surface height (SSH) anomalies. Compared to a traditional stationary LIM that ignores such annual cycles, the CS-LIM is better at representing the seasonal modulation of ENSO-related SST anomalies and their phase locking to the annual cycle. Its deterministic as well as probabilistic hindcast skill is comparable to the skill of the North American Multimodel Ensemble (NMME) of comprehensive global coupled models. The explicit inclusion of annual-cycle effects in the CS-LIM improves the forecast skill of both SST and SSH anomalies through SST–SSH coupling. The impact on the SSH skill is particularly marked at longer forecast lead times over the western Pacific and in the vicinity of the Pacific North Equatorial Countercurrent (NECC), consistent with westward propagating oceanic Rossby waves that reflect off the western boundaries as eastward propagating Kelvin waves and influence El Niño development in the region. The higher CS-LIM skill is thus associated with the improved representation of both ENSO phase-locking and Pacific NECC variations. These improvements result from explicitly accounting for not only the annual cycle of the background state, but also that of the stochastic forcing.
Abstract
Low-order linear inverse models (LIMs) have been shown to be competitive with comprehensive coupled atmosphere–ocean models at reproducing many aspects of tropical oceanic variability and predictability. This paper presents an extended cyclostationary linear inverse model (CS-LIM) that includes the annual cycles of the background state and stochastic forcing of tropical sea surface temperature (SST) and sea surface height (SSH) anomalies. Compared to a traditional stationary LIM that ignores such annual cycles, the CS-LIM is better at representing the seasonal modulation of ENSO-related SST anomalies and their phase locking to the annual cycle. Its deterministic as well as probabilistic hindcast skill is comparable to the skill of the North American Multimodel Ensemble (NMME) of comprehensive global coupled models. The explicit inclusion of annual-cycle effects in the CS-LIM improves the forecast skill of both SST and SSH anomalies through SST–SSH coupling. The impact on the SSH skill is particularly marked at longer forecast lead times over the western Pacific and in the vicinity of the Pacific North Equatorial Countercurrent (NECC), consistent with westward propagating oceanic Rossby waves that reflect off the western boundaries as eastward propagating Kelvin waves and influence El Niño development in the region. The higher CS-LIM skill is thus associated with the improved representation of both ENSO phase-locking and Pacific NECC variations. These improvements result from explicitly accounting for not only the annual cycle of the background state, but also that of the stochastic forcing.
Abstract
It is well known that randomly perturbing an atmospheric model’s diabatic tendencies can increase its probabilistic forecast skill, mainly by increasing the spread of ensemble forecasts and making it more consistent with the errors of ensemble-mean forecasts. Less obvious and less well established is that such perturbations can also reduce the errors of the ensemble-mean forecasts and improve the model’s mean climate, variability, and sensitivity to forcing. A clear reduction in ensemble-mean forecast errors is demonstrated here in large ensembles of 15-day forecasts made with NOAA’s Global Forecast System model. The nearly ubiquitous reduction around the globe, obtained throughout the forecast range, is interpreted as arising in effect from a modification of the model’s deterministic evolution operator by a stochastic noise-induced drift. The effect is general in systems with state-dependent noise, and occurs even if the noise is not white. In the atmospheric context considered here, the effect is suggested to arise largely from noise-induced reductions of mechanical and thermal damping by chaotic boundary layer and cloud-radiative processes, which also tend to increase model sensitivity to forcing. The results presented here are consistent with many previous studies performed with models ranging from simple stochastically forced models to comprehensive global weather and climate models. They suggest that the diabatic interactions in most current global atmospheric models may not be sufficiently chaotic and this deficiency could be partly remedied by specifying additional stochastic terms. Using some empirical guidance in such specifications may be unavoidable, given the generally intractable complexity of the diabatic interactions.
Abstract
It is well known that randomly perturbing an atmospheric model’s diabatic tendencies can increase its probabilistic forecast skill, mainly by increasing the spread of ensemble forecasts and making it more consistent with the errors of ensemble-mean forecasts. Less obvious and less well established is that such perturbations can also reduce the errors of the ensemble-mean forecasts and improve the model’s mean climate, variability, and sensitivity to forcing. A clear reduction in ensemble-mean forecast errors is demonstrated here in large ensembles of 15-day forecasts made with NOAA’s Global Forecast System model. The nearly ubiquitous reduction around the globe, obtained throughout the forecast range, is interpreted as arising in effect from a modification of the model’s deterministic evolution operator by a stochastic noise-induced drift. The effect is general in systems with state-dependent noise, and occurs even if the noise is not white. In the atmospheric context considered here, the effect is suggested to arise largely from noise-induced reductions of mechanical and thermal damping by chaotic boundary layer and cloud-radiative processes, which also tend to increase model sensitivity to forcing. The results presented here are consistent with many previous studies performed with models ranging from simple stochastically forced models to comprehensive global weather and climate models. They suggest that the diabatic interactions in most current global atmospheric models may not be sufficiently chaotic and this deficiency could be partly remedied by specifying additional stochastic terms. Using some empirical guidance in such specifications may be unavoidable, given the generally intractable complexity of the diabatic interactions.
Can an individual weather event be attributed to El Niño? This question is addressed quantitatively using ensembles of medium-range weather forecasts made with and without tropical sea surface temperature anomalies. The National Centers for Environmental Prediction (NCEP) operational medium-range forecast model is used. It is found that anomalous tropical forcing affects forecast skill in midlatitudes as early as the fifth day of the forecast. The effect of the anomalous sea surface temperatures in the medium range is defined as the synoptic El Niño signal. The synoptic El Niño signal over North America is found to vary from case to case and sometimes can depart dramatically from the pattern classically associated with El Niño. This method of parallel ensembles of medium-range forecasts provides information about the changing impacts of El Niño on timescales of a week or two that is not available from conventional seasonal forecasts.
Knowledge of the synoptic El Niño signal can be used to attribute aspects of individual weather events to El Niño. Three large-scale weather events are discussed in detail: the January 1998 ice storm in the northeastern United States and southeastern Canada, the February 1998 rains in central and southern California, and the October 1997 blizzard in Colorado. Substantial impacts of El Nino are demonstrated in the first two cases. The third case is inconclusive.
Can an individual weather event be attributed to El Niño? This question is addressed quantitatively using ensembles of medium-range weather forecasts made with and without tropical sea surface temperature anomalies. The National Centers for Environmental Prediction (NCEP) operational medium-range forecast model is used. It is found that anomalous tropical forcing affects forecast skill in midlatitudes as early as the fifth day of the forecast. The effect of the anomalous sea surface temperatures in the medium range is defined as the synoptic El Niño signal. The synoptic El Niño signal over North America is found to vary from case to case and sometimes can depart dramatically from the pattern classically associated with El Niño. This method of parallel ensembles of medium-range forecasts provides information about the changing impacts of El Niño on timescales of a week or two that is not available from conventional seasonal forecasts.
Knowledge of the synoptic El Niño signal can be used to attribute aspects of individual weather events to El Niño. Three large-scale weather events are discussed in detail: the January 1998 ice storm in the northeastern United States and southeastern Canada, the February 1998 rains in central and southern California, and the October 1997 blizzard in Colorado. Substantial impacts of El Nino are demonstrated in the first two cases. The third case is inconclusive.
