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Abstract
In this study, the authors compare skills of forecasts of tropical Pacific sea surface temperatures from the National Centers for Environmental Prediction (NCEP) coupled general circulation model that were initiated using different sets of ocean initial conditions. These were produced with and without assimilation of observed subsurface upper-ocean temperature data from expendable bathythermographs (XBTs) and from the Tropical Ocean Global Atmosphere–Tropical Atmosphere Ocean (TOGA–TAO) buoys.
These experiments show that assimilation of observed subsurface temperature data in the determining of the initial conditions, especially for summer and fall starts, results in significantly improved forecasts for the NCEP coupled model. The assimilation compensates for errors in the forcing fields and inadequate physical parameterizations in the ocean model. Furthermore, additional skill improvements, over that provided by XBT assimilation, result from assimilation of subsurface temperature data collected by the TOGA–TAO buoys. This is a consequence of the current predominance of TAO data in the tropical Pacific in recent years.
Results suggest that in the presence of erroneous wind forcing and inadequate physical parameterizations in the ocean model ocean data assimilation can improve ocean initialization and thus can improve the skill of the forecasts. However, the need for assimilation can create imbalances between the mean states of the oceanic initial conditions and the coupled model. These imbalances and errors in the coupled model can be significant limiting factors to forecast skill, especially for forecasts initiated in the northern winter. These limiting factors cannot be avoided by using data assimilation and must be corrected by improving the models and the forcing fields.
Abstract
In this study, the authors compare skills of forecasts of tropical Pacific sea surface temperatures from the National Centers for Environmental Prediction (NCEP) coupled general circulation model that were initiated using different sets of ocean initial conditions. These were produced with and without assimilation of observed subsurface upper-ocean temperature data from expendable bathythermographs (XBTs) and from the Tropical Ocean Global Atmosphere–Tropical Atmosphere Ocean (TOGA–TAO) buoys.
These experiments show that assimilation of observed subsurface temperature data in the determining of the initial conditions, especially for summer and fall starts, results in significantly improved forecasts for the NCEP coupled model. The assimilation compensates for errors in the forcing fields and inadequate physical parameterizations in the ocean model. Furthermore, additional skill improvements, over that provided by XBT assimilation, result from assimilation of subsurface temperature data collected by the TOGA–TAO buoys. This is a consequence of the current predominance of TAO data in the tropical Pacific in recent years.
Results suggest that in the presence of erroneous wind forcing and inadequate physical parameterizations in the ocean model ocean data assimilation can improve ocean initialization and thus can improve the skill of the forecasts. However, the need for assimilation can create imbalances between the mean states of the oceanic initial conditions and the coupled model. These imbalances and errors in the coupled model can be significant limiting factors to forecast skill, especially for forecasts initiated in the northern winter. These limiting factors cannot be avoided by using data assimilation and must be corrected by improving the models and the forcing fields.
Abstract
A dynamical model-based ocean analysis system has been implemented at the National Meteorological Center (NMC). This is used to provide retrospective and routine weekly analyses for the Pacific and Atlantic Oceans. Retrospective analyses have been performed for the period mid-1982 to mid-1993. The analyses are used for diagnostics of past climatic variability, real-time climate monitoring, and as initial conditions for coupled multiseason forecasts. The assimilation system is based on optimal interpolation objective analysis solved using an equivalent variational formulation. Analysis errors are estimated by comparisons to independent datasets such as temperature data from moorings and sea level information from tide gauges. In the near equatorial zone rms errors in thermocline depth are of order of 6–15 m. Comparisons of sea level estimates from the reanalyses with the records from tide gauges indicate that the rms sea level errors for monthly analysis are of the order of 0.04–0.09 m. For the weekly analyses, which potentially have more accurate forcing fields, the rms sea level errors am about 0.02–0.06 m.
The analysis system can be used to infer the net heat flux at the air–sea interface on mean annual and interannual timescales. Examination of the dominant components to the oceanic heat budget shows that advection, storage changes, and the net surface heat flux can all be of the same order of magnitude; however, frequently the net surface heat flux is much smaller than the other components. The annual variations in the components are as large or larger than the interannual variability. In the equatorial region interannual changes are of the order of 50–100 W m−2 and act as a negative feedback to the anomalous SSTs. In the subtropics the interannual variability is only in the order of 5–10 W m−2.
Principal component analysis of the monthly analyzed ocean fields revealed an interannual sea level and SST empirical orthogonal function that has an intradecadal timescale. This mode is characterized by meridional adjustments of the thermal field. It is probably forced by the changes in the curl of the stress caused by changes in the intensity and location of the trade winds associated with the ENSO.
Abstract
A dynamical model-based ocean analysis system has been implemented at the National Meteorological Center (NMC). This is used to provide retrospective and routine weekly analyses for the Pacific and Atlantic Oceans. Retrospective analyses have been performed for the period mid-1982 to mid-1993. The analyses are used for diagnostics of past climatic variability, real-time climate monitoring, and as initial conditions for coupled multiseason forecasts. The assimilation system is based on optimal interpolation objective analysis solved using an equivalent variational formulation. Analysis errors are estimated by comparisons to independent datasets such as temperature data from moorings and sea level information from tide gauges. In the near equatorial zone rms errors in thermocline depth are of order of 6–15 m. Comparisons of sea level estimates from the reanalyses with the records from tide gauges indicate that the rms sea level errors for monthly analysis are of the order of 0.04–0.09 m. For the weekly analyses, which potentially have more accurate forcing fields, the rms sea level errors am about 0.02–0.06 m.
The analysis system can be used to infer the net heat flux at the air–sea interface on mean annual and interannual timescales. Examination of the dominant components to the oceanic heat budget shows that advection, storage changes, and the net surface heat flux can all be of the same order of magnitude; however, frequently the net surface heat flux is much smaller than the other components. The annual variations in the components are as large or larger than the interannual variability. In the equatorial region interannual changes are of the order of 50–100 W m−2 and act as a negative feedback to the anomalous SSTs. In the subtropics the interannual variability is only in the order of 5–10 W m−2.
Principal component analysis of the monthly analyzed ocean fields revealed an interannual sea level and SST empirical orthogonal function that has an intradecadal timescale. This mode is characterized by meridional adjustments of the thermal field. It is probably forced by the changes in the curl of the stress caused by changes in the intensity and location of the trade winds associated with the ENSO.
Abstract
An improved forecast system has been developed for El Niño–Southern Oscillation (ENSO) prediction at the National Centers for Environmental Prediction. Improvements have been made both to the ocean data assimilation system and to the coupled ocean–atmosphere forecast model. In Part I of a two-part paper the authors describe the new assimilation system. The important changes are 1) the incorporation of vertical variation in the first-guess error variance that concentrates temperature corrections in the thermocline and 2) the overall reduction in the magnitude of the estimated first-guess error. The new system was used to produce a set of retrospective ocean analyses for 1980–95. The new analyses are less noisy than their earlier counterparts and compare more favorably with independent measurements of temperature, currents, and sea surface height variability. Part II of this work presents the results of using these analyses to initialize the coupled forecast model for ENSO prediction.
Abstract
An improved forecast system has been developed for El Niño–Southern Oscillation (ENSO) prediction at the National Centers for Environmental Prediction. Improvements have been made both to the ocean data assimilation system and to the coupled ocean–atmosphere forecast model. In Part I of a two-part paper the authors describe the new assimilation system. The important changes are 1) the incorporation of vertical variation in the first-guess error variance that concentrates temperature corrections in the thermocline and 2) the overall reduction in the magnitude of the estimated first-guess error. The new system was used to produce a set of retrospective ocean analyses for 1980–95. The new analyses are less noisy than their earlier counterparts and compare more favorably with independent measurements of temperature, currents, and sea surface height variability. Part II of this work presents the results of using these analyses to initialize the coupled forecast model for ENSO prediction.
Abstract
An improved forecast system has been developed and implemented for ENSO prediction at the National Centers for Environmental Prediction (NCEP). This system consists of a new ocean data assimilation system and an improved coupled ocean–atmosphere forecast model (CMP12) for ENSO prediction. The new ocean data assimilation system is described in Part I of this two-part paper.
The new coupled forecast model (CMP12) is a variation of the standard NCEP coupled model (CMP10). Major changes in the new coupled model are improved vertical mixing for the ocean model; relaxation of the model’s surface salinity to the climatological annual cycle; and incorporation of an anomalous freshwater flux forcing. Also, the domain in which the oceanic SST couples to the atmosphere is limited to the tropical Pacific.
Evaluation of ENSO prediction results show that the new coupled model, using the more accurate ocean initial conditions, achieves higher prediction skill. However, two sets of hindcasting experiments (one using the more accurate ocean initial conditions but the old coupled model, the other using the new coupled model but the less accurate ocean initial conditions), result in no improvement in prediction skill. These results indicate that future improvement in ENSO prediction skill requires systematically improving both the coupled model and the ocean analysis system. The authors’ results also suggest that for the purpose of initializing the coupled model for ENSO prediction, care should be taken to give sufficient weight to the model dynamics during the ocean data assimilation. This can reduce the danger of aliasing large-scale model biases into the low-frequency variability in the ocean initial conditions, and also reduce the introduction of small-scale noise into the initial conditions caused by overfitting the model to sparse observations.
Abstract
An improved forecast system has been developed and implemented for ENSO prediction at the National Centers for Environmental Prediction (NCEP). This system consists of a new ocean data assimilation system and an improved coupled ocean–atmosphere forecast model (CMP12) for ENSO prediction. The new ocean data assimilation system is described in Part I of this two-part paper.
The new coupled forecast model (CMP12) is a variation of the standard NCEP coupled model (CMP10). Major changes in the new coupled model are improved vertical mixing for the ocean model; relaxation of the model’s surface salinity to the climatological annual cycle; and incorporation of an anomalous freshwater flux forcing. Also, the domain in which the oceanic SST couples to the atmosphere is limited to the tropical Pacific.
Evaluation of ENSO prediction results show that the new coupled model, using the more accurate ocean initial conditions, achieves higher prediction skill. However, two sets of hindcasting experiments (one using the more accurate ocean initial conditions but the old coupled model, the other using the new coupled model but the less accurate ocean initial conditions), result in no improvement in prediction skill. These results indicate that future improvement in ENSO prediction skill requires systematically improving both the coupled model and the ocean analysis system. The authors’ results also suggest that for the purpose of initializing the coupled model for ENSO prediction, care should be taken to give sufficient weight to the model dynamics during the ocean data assimilation. This can reduce the danger of aliasing large-scale model biases into the low-frequency variability in the ocean initial conditions, and also reduce the introduction of small-scale noise into the initial conditions caused by overfitting the model to sparse observations.
