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David G. DeWitt

Abstract

A large number of ensemble hindcasts (or retrospective forecasts) of tropical Pacific sea surface temperature (SST) have been made with a coupled atmosphere–ocean general circulation model (CGCM) that does not employ flux correction in order to evaluate the potential skill of the model as a seasonal forecasting tool. Oceanic initial conditions are provided by an ocean data assimilation system. Ensembles of seven forecasts of 6-month length are made starting each month in the 1982 to 2002 period. Skill of the coupled model is evaluated from both a deterministic and a probabilistic perspective. The skill metrics are calculated using both the bulk method, which includes all initial condition months together, and as a function of initial condition month. The latter method allows a more objective evaluation of how the model has performed in the context in which forecasts are actually made and applied. The deterministic metrics used are the anomaly correlation and the root-mean-square error. The coupled model deterministic skill metrics are compared with those from persistence and damped persistence reference forecasts. Despite the fact that the coupled model has a large cold bias in the central and eastern equatorial Pacific this coupled model is shown to have forecast skill that is competitive with other state-of-the-art forecasting techniques.

Potential skill from probabilistic forecasts made using the coupled model ensemble members are evaluated using the relative operating characteristics method. This analysis indicates that for most initial condition months this coupled model has more skill at forecasting cold events than warm or neutral events in the central Pacific.

In common with other forecasting systems, the coupled model forecast skill is found to be lowest for forecasts passing through the Northern Hemisphere (NH) spring. Diagnostics of this so-called spring predictability barrier in the context of this coupled model indicate that two factors likely contribute to this predictability barrier. First, the coupled model shows a too-weak coupling of the surface and subsurface temperature anomalies during NH spring. Second, the coupled-model-simulated signal-to-noise ratio for SST anomalies is much lower during NH spring than at other times of the year, indicating that the model’s potential predictability is low at this time.

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David G. DeWitt and Edwin K. Schneider

Abstract

The annual cycle of sea surface temperature (SST) in the tropical Atlantic of a directly coupled atmosphere–ocean general circulation model (CGCM) is decomposed into the parts forced by different surface fluxes (denoted as modes) for the two extreme months of March and August using forced ocean experiments. Almost all previous diagnostic work of the forcing of the SST annual cycle in the Atlantic has concentrated on the near-equatorial region. Here, the annual cycle is examined within the latitude range of 25°S–25°N to facilitate comparison with the interannual variability. The structure of the response to the different surface flux forcings bears some resemblance to the interannual SST modes in the tropical Atlantic, which are diagnosed using rotated empirical orthogonal function (REOF) analysis. Diagnosis of the forcing of the annual cycle modes and the interannual modes shows that they do not always have a common cause. Hence, the simple interpretation that the leading interannual modes are perturbations to the annual cycle is not always valid.

In particular, the equatorial SST annual cycle mode is primarily driven by variations in vertical velocity while the equatorial interannual mode is associated with eastward-propagating thermocline anomalies and is forced by both thermocline anomalies and vertical velocity anomalies. As for the interannual modes, there exist off-equatorial annual cycle modes in both the Northern and Southern Hemispheres. The annual cycle off-equatorial mode in both hemispheres is shown to be primarily driven by heat flux variations. The Southern Hemisphere interannual mode is primarily driven by heat flux variations while the Northern Hemisphere interannual mode shows a strong influence of thermocline depth anomalies. In addition, the Southern Hemisphere interannual mode is centered about 10° south of the annual cycle mode. An interannual mode that has maximum variability along the South American coast south of the equator is shown to be associated with thermocline depth anomalies. This interannual mode has no analog in the annual cycle modes.

The coupled model simulation of the annual cycle is found to be fairly realistic so that the results presented here could have applicability to the observed Atlantic.

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David G. DeWitt and Edwin K. Schneider

Abstract

The origin of the annual cycle of equatorial sea surface temperature (SST) is diagnosed using a global coupled atmosphere–ocean general circulation model (CGCM) that realistically simulates this annual cycle. The simulated heat flux, wind stress, upper ocean thermal structure, and mixed-layer depth, which are critical to the correct simulation of the near-equatorial SST, are compared with observations for realism. Based on this analysis, it is concluded that the model results should be applicable to the actual coupled system in the Indian and Atlantic Oceans. In the Pacific, errors in the simulated zonal wind stress and heat flux imply that, even though the SST annual cycle is realistic, the processes that govern its evolution may be different than those found in nature.

The processes controlling the annual cycle of SST in the CGCM are diagnosed with experiments in which the ocean component model is forced with the CGCM surface fluxes of heat, momentum, and freshwater. In the eastern Pacific, the annual cycle of SST is due in large part to upwelling variations. Near the coast of South America, the upwelling is caused in nearly equal parts by the zonal and meridional wind stress, whereas farther to the west the upwelling induced by the zonal wind stress is dominant. The surface heat flux forces a significant portion of the annual cycle in the eastern Atlantic. However, as was found for the eastern Pacific, upwelling is the dominant process determining the annual cycle of SST. The annual cycle of SST in the Indian Ocean is dominated by the response to the varying heat flux. The annual cycle of surface heat flux in the Atlantic and Indian Oceans is due about equally to the latent and shortwave fluxes. In both the Atlantic and Indian Oceans, variations in the magnitude of the surface heat flux are more important than mixed-layer depth variations in determining the SST response to the flux of heat across the ocean surface.

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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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Shuhua Li, Lisa Goddard, and David G. DeWitt

Abstract

This study examines skill of retrospective forecasts using the ECHAM4.5 atmospheric general circulation model (AGCM) forced with predicted sea surface temperatures (SSTs) from methods of varying complexity. The SST fields are predicted in three ways: persisted observed SST anomalies, empirically predicted SSTs, and predicted SSTs from a dynamically coupled ocean–atmosphere model. Investigation of relative skill of the three sets of retrospective forecasts focuses on the ensemble mean, which constitutes the portion of the model response attributable to the prescribed boundary conditions. The anomaly correlation skill analyses for precipitation and 2-m air temperature indicate that dynamically predicted SSTs generally improve upon persisted and empirically predicted SSTs when they are used as boundary forcing in the AGCM predictions. This is particularly the case for precipitation forecasts. The skill differences in these experiments are ascribed to the skill of SST predictions in the tropical ocean basins. The multiscenario forecast by averaging the three retrospective experiments performs, overall, as well as or better than the best of the three individual experiments in specific seasons and regions. The advantage of multiscenario forecast manifests both in the deterministic and probabilistic skill. In particular, the multiscenario precipitation forecast for the December–February season demonstrates better skill than the best of the three scenarios over several regions, such as the western United States and southeastern South America. These results suggest the potential value in producing superensembles spanning different SST prediction scenarios.

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David G. DeWitt and Edwin K. Schneider

Abstract

Changes in the tropical oceans caused by a shift of 6 months in the date of perihelion are examined using a coupled atmosphere–ocean general circulation model (GCM). The changes in the annual cycle of sea surface temperature (SST) near the equator are described, and the mechanism for the changes is diagnosed. The GCM results are diagnosed using the ocean component model forced by the time mean fluxes from the coupled integration. This diagnosis shows that the changes in the annual cycle of near-equatorial SST are caused by different mechanisms in different regions. An extensive analysis of the changes in the eastern Pacific is given because of the importance of this region in modulating the global climate through teleconnections associated with ENSO.

In the eastern Pacific, the change in the annual cycle of SST is found to be primarily due to zonal wind stress differences. The zonal wind stress differences are caused for the most part by changes in the precipitation distribution. The changes in the precipitation distribution are consistent with being caused by changes in both the low-level convergence forced by the surface temperature gradients and changes in the local evaporation. The physical process responsible for the change in low-level zonal wind and convergence are diagnosed using a steady-state linearized version of the atmospheric model that is forced by time mean fields from the coupled model.

Interannual variability of eastern Pacific (Niño-3) SST under the modified solar forcing is found to have an amplitude and period similar to those observed in modern times. The only major difference in the interannual variability of tropical Pacific SST is found to be the timing of SST anomalies. This occurs because the interannual SST variability in the tropical eastern Pacific is phase locked to the annual cycle of SST, whose phase is itself dependent on the solar forcing.

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David G. Dewitt, Edwin K. Schneider, and Anandu D. Vernekar

Abstract

The roles played by the large-scale motion induced by vertical diffusion of heat from the lower boundary and condensational heating due to deep convection in maintaining the precipitation zones in the Tropics of an atmospheric general circulation model (GCM) are explored. A steady linearized version of the GCM is used to diagnose the wind forced by these processes. The wind field obtained from the linear model is combined with the time-mean moisture field from the GCM in order to determine the zonally asymmetric moisture flux convergence, which is the primary factor maintaining the zonally asymmetric precipitation distribution. The role of the other diabatic heating processes is explored as is the role of the orographic forcing in maintaining the precipitation distribution.

The vertically integrated moisture flux convergence forced by vertical diffusion of heat and condensational heating are found to be in phase over the ocean and 180 degrees out of phase over the land. Over the ocean, both of these forcings contribute to moisture flux convergence in the regions of largest precipitation. The moisture flux convergence forced by the vertical diffusion of heat tends to narrow the precipitation zones in the meridional direction over the ocean. Over the land, the condensational heating leads to moisture flux convergence in the regions of large precipitation, while the vertical diffusion of heat leads to moisture flux divergence. This indicates that the motions forced by the surface temperature provide a negative feedback on the precipitation. This feedback is apparently due to the relatively cool surface temperatures present in the regions of large precipitation over land. This locally cool surface temperature leads to a low-level divergent circulation from the cool region to warmer regions. Other forcing functions are found to play a minor role in the moisture flux convergence by the time-mean flow with the exception of the orographic forcing in some regions.

The lowest model sigma-level wind field over the tropical Pacific Ocean is examined. In general both the zonal and meridional wind fields are dominated by the response to convective condensational heating. Exceptions include the meridional wind in the western Pacific and the zonal wind along the equator. In these regions, the response to low-level temperature gradients is found to be nonnegligible in comparison with the response to convective condensational heating. The role of the orographic forcing is also significant along the coasts of the tropical continents and in the western Pacific.

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Rong-Hua Zhang, Antonio J. Busalacchi, and David G. DeWitt

Abstract

The El Niño–Southern Oscillation (ENSO) has been observed to exhibit decadal changes in its properties; the cause and implication of such changes are strongly debated. Here the authors examine the influences of two particular attributors of the ocean–atmospheric system. The roles of stochastic forcing (SF) in the atmosphere and decadal changes in the temperature of subsurface water entrained into the mixed layer (Te) in modulating ENSO are compared to one another using coupled ocean–atmosphere models of the tropical Pacific climate system. Two types of coupled models are used. One is an intermediate coupled model (ICM) and another is a hybrid coupled model (HCM), both of which consist of the same intermediate ocean model (IOM) with an empirical parameterization for Te, constructed via singular value decomposition (SVD) analysis of the IOM simulated historical data. The differences in the ICM and HCM are in the atmospheric component: the one in the ICM is an empirical feedback model for wind stress (τ), and that in the HCM is an atmospheric general circulation model (AGCM; ECHAM4.5). The deterministic component of atmospheric τ variability, representing its signal response (τ Sig) to an external SST forcing, is constructed statistically by an SVD analysis from a 24-member ensemble mean of the ECHAM4.5 AGCM simulations forced by observed SST; the SF component (τ SF) is explicitly estimated from the ECHAM4.5 AGCM ensemble and HCM simulations. Different SF representations are specified in the atmosphere: the SF effect can be either absent or present explicitly in the ICM, or implicitly in the HCM where the ECHAM4.5 AGCM is used as a source for SF. Decadal changes in the ocean thermal structure observed in the late 1970s are incorporated into the coupled systems through the Te parameterizations for the two subperiods before (1963–79) and after (1980–96) the climate shift (T 63–79 e and T 80–96 e), respectively.

The ICM and HCM simulations well reproduce interannual variability associated with El Niño in the tropical Pacific. Model sensitivity experiments are performed using these two types of coupled models with different realizations of SF in the atmosphere and specifications of decadal Te changes in the ocean. It is demonstrated that the properties of ENSO are modulated differently by these two factors. The decadal Te changes in the ocean can be responsible for a systematic shift in the phase propagation of ENSO, while the SF in the atmosphere can contribute to the amplitude and period modulation in a random way. The relevance to the observed decadal ENSO variability in the late 1970s is discussed.

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Michael K. Tippett, Anthony G. Barnston, David G. DeWitt, and Rong-Hua Zhang

Abstract

This paper is about the statistical correction of systematic errors in dynamical sea surface temperature (SST) prediction systems using linear regression approaches. The typically short histories of model forecasts create difficulties in developing regression-based corrections. The roles of sample size, predictive skill, and systematic error are examined in evaluating the benefit of a linear correction. It is found that with the typical 20 yr of available model SST forecast data, corrections are worth performing when there are substantial deviations in forecast amplitude from that determined by correlation with observations. The closer the amplitude of the uncorrected forecasts is to the optimum squared error-minimizing amplitude, the less likely is a correction to improve skill. In addition to there being less “room for improvement,” this rule is related to the expected degradation in out-of-sample skill caused by sampling error in the estimate of the regression coefficient underlying the correction.

Application of multivariate [canonical correlation analysis (CCA)] correction to three dynamical SST prediction models having 20 yr of data demonstrates improvement in the cross-validated skills of tropical Pacific SST forecasts through reduction of systematic errors in pattern structure. Additional beneficial correction of errors orthogonal to the CCA modes is achieved on a per-gridpoint basis for features having smaller spatial scale. Until such time that dynamical models become freer of systematic errors, statistical corrections such as those shown here can make dynamical SST predictions more skillful, retaining their nonlinear physics while also calibrating their outputs to more closely match observations.

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Anthony G. Barnston, Simon J. Mason, Lisa Goddard, David G. DeWitt, and Stephen E. Zebiak

The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM forecasts may render them more sufficient in their own right.

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