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Ben P. Kirtman

Abstract

Results are described from a large sample of coupled ocean–atmosphere retrospective forecasts during 1980–99. The prediction system includes a global anomaly coupled general circulation model and a state-of-the-art ocean data assimilation system. The retrospective forecasts are initialized each January, April, July, and October of each year, and ensembles of six forecasts are run for each initial month, yielding a total of 480 1-yr predictions.

In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective. The probabilistic approach is used to quantify the uncertainty in any given forecast. The deterministic measures of skill for eastern tropical Pacific SST anomalies (SSTAs) suggest that the ensemble mean forecasts are useful up to lead times of 7–9 months. At somewhat shorter leads, the forecasts capture some aspects of the variability in the tropical Indian and Atlantic Oceans. The ensemble mean precipitation anomaly has disappointingly low correlation with observed rainfall. The probabilistic measures of skill (relative operating characteristics) indicate that the distribution of the ensemble provides useful forecast information that could not easily be gleaned from the ensemble mean. In particular, the prediction system has more skill at forecasting cold ENSO events compared to warm events. Despite the fact that the ensemble mean rainfall is not well correlated with the observed, the ensemble distribution does indicate significant regions where there is useful information in the forecast ensemble. In fact, it is possible to detect that droughts over land are more predictable than floods. It is argued that probabilistic verification is an important complement to any deterministic verification, and provides a useful and quantitative way to measure uncertainty.

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Ben P. Kirtman

Abstract

Tropical ocean wave dynamics associated with the El Niño–Southern Oscillation cycle in a coupled model are examined. The ocean–atmosphere model consists of statistical atmosphere coupled to a simple reduced gravity model of the tropical Pacific Ocean. The statistical atmosphere is simple enough to allow for the structure and position of the wind stress anomalies to be externally specified. In a control simulation, where the structure of the wind stress anomaly is determined from observations, the model produces a regular 5-yr oscillation. This simulation is consistent with the so-called delayed oscillator theory in that subsurface wave dynamics determine the slow timescale of the oscillation and surface-layer processes are found to be of secondary importance. Kelvin and Rossby wave propagation is detected along the equator, with periods considerably shorter than the simulated oscillation period. The way in which these relatively fast waves are related to the simulated 5-yr oscillation is discussed.

In order to understand the mechanism responsible for the 5-yr period in the control simulation, two sets of sensitivity experiments were conducted. The first set of experiments focused on how the meridional structure of the wind stress anomaly influences the model ENSO period. Relatively broad (narrow) meridional structures lead to relatively long (short) periods. While the gravest Rossby wave appears to be important in these simulations, it is found that the maximum variability in the thermocline is associated with off-equatorial Rossby waves (i.e., Rossby waves that have a maximum amplitude beyond ±7° of the equator). The second set of sensitivity experiments was designed to examine how these off-equatorial Rossby waves influence the ENSO cycle. Without the effects of the off-equatorial Rossby waves at the western boundary, the model produces a 2-yr oscillation regardless of the meridional structure of the wind stress anomaly. The mechanism by which these off-equatorial Rossby waves influence the ENSO period is described. Based on these experiments, it is shown that the reflection of the gravest Rossby wave off the western boundary is required to produce oscillatory behavior in the model, but the period of the oscillation is determined by the off-equatorial Rossby waves and the latitude at which they are forced.

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Ben P. Kirtman
and
Dughong Min

Abstract

Results are described from a large sample of coupled ocean–atmosphere retrospective forecasts during 1982–98. The prediction system is based on the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3 (CCSM3.0), and a state-of-the-art ocean data assimilation system made available by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL). The retrospective forecasts are initialized in January, April, July, and November of each year, and ensembles of 6 forecasts are run for each initial month, yielding a total of 408 1-yr predictions. In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective, it is then compared to the operational NOAA Climate Forecast System (CFS), and the forecasts are combined with CFS to produce a multimodel prediction system. While the skill scores for each model are highly dependent on lead time and initialization month, the overall level of skill of the individual models is quite comparable. The multimodel combination (i.e., the unweighted average of the forecast), while not always the most skillful, is generally as skillful as the best model, using either deterministic or probabilistic skill metrics.

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Ben P. Kirtman
and
Stephen E. Zebiak

Abstract

A hybrid coupled model (HCM) consisting of a tropical Pacific Ocean and global atmosphere is presented. The ocean component is a linear reduced gravity model of the upper ocean in the tropical Pacific. The atmospheric component is a triangular 30 horizontal resolution global spectral general circulation model with 18 unevenly spaced levels in the vertical. In coupling these component models, an anomaly coupling strategy is employed. A 40-yr simulation was made with HCM and the variability in the tropical Pacific was compared to the observed variability. The HCM produces irregular ENSO events with a broad spectrum of periods between 12 and 48 months. On longer timescales, approximately 48 months, the simulated variability was weaker than the observed and on shorter timescales (approximately 24 months) the simulated variability was too strong. The simulated variability is asymmetric in the sense that the amplitude of the warm events is realistic, but there are no significant cold events.

An ensemble of 60 hindcast predictions was made with the HCM and the skill was compared to other prediction systems. In forecasting sea surface temperature anomalies in the eastern Pacific, the HCM is comparable to the other prediction systems for lead times up to 10 months. The anomaly correlation coefficient for the eastern Pacific SSTA remains above 0.6 for lead times of up to 11 months. Consistent with the 40-yr simulation, hindcasts of cold events have little skill, particularly when compared to hindcasts of warm events. Specific hindcasts also demonstrate that the HCM also has difficulty predicting the transition from warm conditions to normal or cold conditions.

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Ben P. Kirtman
and
David G. DeWitt

Abstract

The Geophysical Fluid Dynamics Laboratory ocean model has been used to diagnose the sensitivity of the Center for Ocean–Land–Atmosphere Studies atmospheric general circulation model wind stress to convective parameterization. In a previous study this atmospheric model was integrated for seven years with observed sea surface temperatures to test three different convective parameterizations: Kuo, Betts–Miller, and relaxed Arakawa–Schubert. In this study, the three wind stress fields are then used to force the ocean model. For comparison, an ocean model simulation with the subjectively analyzed Florida State University wind stress product is also made. The resulting ocean temperature field is compared with the National Centers for Environmental Prediction ocean analyses in terms of the annual cycle and interannual variability in order to identify errors in the ocean model and errors that are unique to the particular wind stress field. Overall, the ocean simulation with the wind stress resulting from the relaxed Arakawa–Schubert parameterization gives a thermal structure that is in best agreement with the analyzed wind stress simulation and the ocean analyses.

Based on the simulation of the annual cycle of sea surface temperature and heat content in the deep Tropics, it is concluded that the wind stress is too strong with the Betts–Miller parameterization and too weak with the Kuo parameterization, particularly in the boreal spring. The simulation with the wind stress from the relaxed Arakawa–Schubert parameterization minimizes the errors in the heat content between 10°S and 10°N and has smaller errors than the analyzed wind stress simulation in some regions. In terms of interannual variability, the anomalies from all three wind stress products produce sea surface temperature anomalies that are concentrated in the western Pacific with little or no sea surface temperature anomaly in the east indicating that the ocean model has some difficulty capturing the remote response to anomalous wind stress forcing. However, the subsurface temperature anomalies along the equator are best simulated with the wind stress from the relaxed Arakawa–Schubert parameterization.

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Ray Bell
and
Ben Kirtman

Abstract

This study assesses the skill of multimodel forecasts of 10-m wind speed, significant wave height, and mean wave period in the North Atlantic for the winter months. The 10-m winds from four North American multimodel ensemble models and three European Multimodel Seasonal-to-Interannual Prediction project (EUROSIP) models are used to force WAVEWATCH III experiments. Ten ensembles are used for each model. All three variables can be predicted using December initial conditions. The spatial maps of rank probability skill score are explained by the impact of the North Atlantic Oscillation (NAO) on the large-scale wind–wave relationship. Two winter case studies are investigated to understand the relationship between large-scale environmental conditions such as sea surface temperature, geopotential height at 500 hPa, and zonal wind at 200 hPa to the NAO and the wind–wave climate. The very strong negative NAO in 2008/09 was not well forecast by any of the ensembles while most models correctly predicted the sign of the event. This led to a poor forecast of the surface wind and waves. A Monte Carlo model combination analysis is applied to understand how many models are needed for a skillful multimodel forecast. While the grand multimodel ensemble provides robust skill, in some cases skill improves once some models are not included.

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Rameshan Kallummal
and
Ben P. Kirtman

Abstract

Calculations of the optimal perturbations of an anomaly coupled ocean–atmosphere general circulation model (ACGCM) have been performed using a set of linear (Markov) models best fit to 300 years of continuous simulations. This study aims (i) to verify some of the findings regarding optimals from earlier studies based on relatively less complex coupled models and (ii) to assess how well a linear stochastic model reproduces the interannual variability of the tropical Pacific in the ACGCM.

The Markov models are built in a multivariate (i.e., sea surface temperature, heat content, zonal and meridional components of wind stress, and total heat flux) empirical orthogonal function (MEOF) space with reduced dimension while retaining the important covariability. These empirical models are trained in the first 300 yr and verified in the last 50 yr of the data. The Markov model with eight retained MEOFs (MK8) shows, in terms of the anomaly correlations, the best predictive skill of interannual variability in the ACGCM for up to about a year.

The main conclusions of this study are as follows:

(i) The singular values of MK8 show a strong seasonal dependence, indicating seasonal variation in the Markov models, which in turn implies the importance of seasonality in the internal dynamics. From the perspective of a stochastic model, this may also indicate that neither the nonlinearity in the system nor the seasonality in the stochastic forcing is necessary for the phase locking of the model ENSO to the annual cycle.

(ii) In the tropical Pacific, a narrow region straddling the equator is conducive for the transient growth of perturbations, independent of the season; however, a migration of the maximum growth region from the central to the eastern Pacific from spring to summer to winter is also observed. The western warm pool region is capable of generating transient growth throughout the year. However, the amplitude of the optimal shows some modulation with the annual cycle. Another region where considerable transient growth can occur is the northern subtropics. Therefore, the optimals in this study encompass the features of a coupled model with a statistical atmosphere as well as the features of the coupled model with a dynamical atmosphere in which deep convective parameterization is included.

(iii) In general, the geographical locations of the optimals do not depend on whether EOF analysis is performed on a covariance matrix or on a correlation matrix. Nevertheless, their spatial extent is larger when the correlation matrix is used.

(iv) The spatial distribution of the ratio of local amplitudes of the residual (misfit of the linear models) to the respective local anomaly points to the fact that, in general, a narrow region between 5°S and 5°N in the Pacific Ocean is in a linear regime. However, in the off-equatorial region this linear approximation generally fails. In the equatorial western Pacific west of 160°E, a region where abundant convective activity occurs both in the model and in nature, nonlinear dynamics is important. Therefore, this analysis hints at the possibility of accommodating the two competing points of views of ENSO into a single framework, by virtue of the fact that both linear and nonlinear dynamics seems to operate in a nonoverlapping manner in both space and time.

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Ben P. Kirtman
and
Paul S. Schopf

Abstract

A simple coupled model is used to examine decadal variations in El Niño–Southern Oscillation (ENSO) prediction skill and predictability. Without any external forcing, the coupled model produces regular ENSO-like variability with a 5-yr period. Superimposed on the 5-yr oscillation is a relatively weak decadal amplitude modulation with a 20-yr period. External uncoupled atmospheric “weather noise” that is determined from observations is introduced into the coupled model. Including the weather noise leads to irregularity in the ENSO events, shifts the dominant period to 4 yr, and amplifies the decadal signal. The decadal signal results without any external prescribed changes to the mean climate of the model.

Using the coupled simulation with weather noise as initial conditions and for verification, a large ensemble of prediction experiments were made. The forecast skill and predictability were examined and shown to have a strong decadal dependence. During decades when the amplitude of the interannual variability is large, the forecast skill is relatively high and the limit of predictability is relatively long. Conversely, during decades when the amplitude of the interannual variability is low, the forecast skill is relatively low and the limit of predictability is relatively short. During decades when the predictability is high, the delayed oscillator mechanism drives the sea surface temperature anomaly (SSTA), and during decades when the predictability is low, the atmospheric noise strongly influences the SSTA. Additional experiments indicate that the relative effectiveness of the delayed oscillator mechanism versus the external noise forcing in determining interannual SSTA variability is strongly influenced by much slower timescale (decadal) variations in the state of the coupled model.

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Sang-Wook Yeh
and
Ben P. Kirtman

Abstract

Four climate system models are chosen here for an analysis of ENSO amplitude changes in 4 × CO2 climate change projections. Despite the large changes in the tropical Pacific mean state, the changes in ENSO amplitude are highly model dependant. To investigate why similar mean state changes lead to very different ENSO amplitude changes, the characteristics of sea surface temperature anomaly (SSTA) variability simulated in two coupled general circulation models (CGCMs) are analyzed: the Meteorological Research Institute (MRI) and Geophysical Fluid Dynamics Laboratory (GFDL) models. The skewed distribution of tropical Pacific SSTA simulated in the MRI model suggests the importance of nonlinearities in the ENSO physics, whereas the GFDL model lies in the linear regime. Consistent with these differences in ENSO regime, the GFDL model is insensitive to the mean state changes, whereas the MRI model is sensitive to the mean state changes associated with the 4 × CO2 scenario. Similarly, the low-frequency modulation of ENSO amplitude in the GFDL model is related to atmospheric stochastic forcing, but in the MRI model the amplitude modulation is insensitive to the noise forcing. These results suggest that the understanding of changes in ENSO statistics among various climate change projections is highly dependent on whether the model ENSO is in the linear or nonlinear regime.

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