Modeling, Simulating, and Forecasting Subseasonal Atmospheric Variability II: Modeling
Presiding: D Waliser, California Institute of Technology; K Weickmann, National Oceanic and Atmospheric Administration
A32A-01 INVITED 10:30h
Extended intraseasonal predictions using Bayesian empirical methods and slow manifold climate modeling
A major goal of GEWEX is the prediction of precipitation from daily to seasonal time scales. Of all of these scales, prediction of intraseasonal variations stands as probably the most useful for agriculture, water resource management and disaster mitigation and relief planning. Unfortunately, it is the time scale that has proven most difficult to either simulate or predict even though the intraseasonal signal is strong, and the phenomena well described, especially in the tropics. We have approached the problem of intraseasonal prediction from a hypothesis that the strong intraseasonal signal observed in nature is eroded in weather and climate models by errors in high frequency convective parameterization. The following procedures were adopted: We develop a Bayesian physically based empirical scheme that uses wavelet banding to separate physically significant bands. This procedure was adopted for the predictor (e.g., regional precipitation in a sector of India, Brahmaputra river discharge.) and a set of predictors. Linear regression and recombination of the bands provides pentad forecasts at 20 days (4 lags) with correlation coefficients of > 0.8. The ECMWF coupled ocean-atmosphere model was run in ensemble mode for 30 day periods initialized daily for 15 days before to 15 days after major intraseasonal oscillations thus allowing the examination of the success and failure of a climate model relative to the phase of the oscillation. Two cases were chosen: the December period of 1992/93 during TOGA COARE and the onset of the monsoon in 2004. The results compare well with observations for about 10 days after which the fields rapidly diverge. We develop a model that applies the philosophy of the banded wavelet empirical scheme to the coupled ocean-atmosphere general circulation model and use for 30-day forecasts for the two cases mentioned above. Adherence with observations is greatly improved and a full evolution of the monsoon ISO predicted throughout June 2004. The new Slow Manifold Model appears to show great promise and has been designed with use as an operational system in mind. As distinct from the empirical Bayesian model which is regionally bound and for which new predictors would have to be found for forecasting different intraseasonal system, the SMM model provides consistent global forecasts on the intraseasonal time scale.
A32A-02 INVITED 10:45h
Impact of Atmosphere-Ocean Interaction on the Predictability of Monsoon Intraseasonal Oscillations
A hybrid coupled general circulation model and its atmospheric component have been used to explore the possible impact of air-sea coupling on the predictability of tropical intraseasonal oscillations (ISO). This study focuses on the intraseasonal variability associated with the Asian-western Pacific summer monsoon (so-called Monsoon Intraseasonal Oscillation or MISO). During 15-year control run with the coupled model, twenty intraseasonal oscillation events have been selected from 15 summers (MJJASO). Four phases (i.e., peak dry, dry-to-wet, peak wet, and wet-to-dry) have been identified for each ISO event. A series of 'twin' perturbation experiments have been conducted for each phase of all ISO events using both the coupled model and the stand-alone atmospheric model. Two measures have been introduced to quantify the predictability of the MISO for both the coupled forecasts and the stand-alone atmospheric forecasts: the ratio of the signal associated with the ISO rainfall to the mean square error of the forecasts and the anomalous pattern correlation coefficient (ACC) as a function of leading time. Both measures indicate that air-sea coupling significantly enhances the predictability of the MISO. When measured with the ratio of signal to forecast error, the predictability of the MISO rainfall in the coupled model reaches about 25 days averaged over the Asian-western Pacific region (60oE-160oE,10oS-30oN). The averaged predictability in the atmosphere-only model is about 19 days. This result suggests that air-sea coupling is able to extend the predictability of the MISO by about a week. Almost the same conclusion is reached when the predictability is measured by the anomalous pattern correlation. The dependence of predictability on different ISO phases in both the coupled and stand-alone atmospheric forecasts is also discussed.
A32A-03 11:00h
Prediction of Sub-seasonal Atmospheric Variability Using Coupled-Multi-Model Superensemble
Seasonal forecast data sets from four different versions of the Florida State University (FSU) coupled model are examined to study the predictability issues of sub-seasonal atmospheric variability on the time scales of Madden-Julian Oscillations (MJO) and Intra-Seasonal Oscillations (ISO). These four versions of the coupled model are identical except for the choice of parameterization schemes for cumulus convection and radiative transfer. As many as 720 seasonal forecast experiments were carried out with these models for the period from 1987 to 2002. The multimodel superensemble methodology developed at the FSU has shown some promising results in the areas of numerical weather prediction, hurricane track and intensity forecasts, and seasonal climate forecasts. This methodology is extended to the FSU coupled model daily forecast data sets in order to examine the predictability issues on the sub-seasonal time scales. The results of this study are encouraging, where the superensemble forecasts show higher skill in predicting these low-frequency modes compared to individual member models and the conventional ensemble mean. Some episodes of MJO and ISO are examined in detail and the deterministic and probabilistic skills are evaluated using standard measures for verification.
A32A-04 11:15h
Predictability and Prediction of the Intraseasonal Oscillation With the ECHAM5 GCM
The present study analyzes the predictability and prediction of the intraseasonal oscillation (ISO) during northern summer. The ECHAM5 atmospheric general circulation model (GCM) is utilized to assess the ISO predictability and to predict observed ISOs. In the former case, the three strongest ISO events of a 10-year control simulation forced with climatological SSTs are predicted by a 14-member ensemble forecast. In the latter case, three strong ISO events observed during the years 1990, 1992 and 1996 are predicted. In each case, the leading extended empirical orthogonal functions of precipitation are used to define four different phases of the ISO. Fourteen-member ensembles of 90-day hindcasts are run for each phase of the three strongest ISO events. For the predictability study, initial conditions for each ensemble are created from the control simulation using a breeding method. For the prediction of observed events, the GCM is nudged every six hours toward ERA40 dynamics and SSTs. During the forecast period nudging is turned off and climatological SST is used. Different initial condictions are obtained by variations in the nudging coefficients. The signal-to-noise ratio is analyzed over a region that covers the core of the Asian summer monsoon activity. Theoretical predictability of more than 20 days is found for 200 hPa zonal wind. Precipitation is predictable for more than two weeks. A spatial analysis of the predictability of different phases of the ISO reveals that the area of high predictability follows the westward propagating subtropical Rossby-waves during the active and break phases of the monsoon, and additionally it follows the eastward propagating ISO during the active phase. This predictability is considerably higher than for numerical forecasts of typical weather variations, particularly for the Tropics, indicating that useful forecasts of monsoon active and break events may be possible with lead times of more than two weeks for precipitation and more than 20 days for the dynamics. A closer look at the breeding method used here to initialize the hindcasts, shows the importance of appropriate ensemble experiment designs. Analysis of the prediction skill study is ongoing and results will be presented at the meeting.
A32A-05 11:30h
Impact of ocean surface condition on the simulation of the MJO by NCEP GFS and CFS models
Maintenance of correct activities of the Madden-Julian Oscillation (MJO) has been one of the major difficulties in the forecast of atmospheric subseasonal variability with numerical models. Possible reasons for the unsatisfactory representation of the MJO in the numerical models include inaccurate parameterization of physical processes in the atmosphere, insufficient model resolution, and incorrect treatment of the ocean surface. In this study, we examine the impact of ocean surface condition on the simulation of the MJO by NCEP models. Results from both uncoupled atmospheric T62/64-layer version of the Global Forecast System (GFS) model and the Coupled Forecast System (CFS) model consisting of GFS and Modular Ocean Model version 3 (MOM3) will be presented. Simulations with GFS model are made with both climatological and interannually varying sea surface temperatures (SSTs) to test the sensitivity of the GFS to the change in sea surface condition. Simulations with the CFS model are conducted with and without ocean-surface flux correction to investigate the impact of air-sea coupling and errors in SSTs in the coupled model. Diagnoses are based on 20-year simulations.
A32A-06 11:45h
A Study of Extreme Events in Subseasonal Forecasts Made by a High Resolution Version of the NCEP Climate Forecast System
Extreme events in nature are of great importance due to their societal impact. For any numerical model, there is an obvious question about skill in predicting such extremes. In addition, due to the brevity of observational records, and the assumption that models are a good proxy for reality, we may learn more about extremes from a study of a long series of integrations by these models. Here we investigate the impact of increasing horizontal resolution on the prediction of extremes in a fully coupled ocean-atmosphere-land model, which has a high vertical resolution atmospheric component. A series of retrospective forecasts, each integrated over a period of 60+ days during the 1997 to 2004 timeframe with the new NCEP Climate Forecast System are studied for the occurrence of, and the skill in predicting, extreme events. In addition to the question of deterministic predictability out to 2 or 3 weeks, these forecasts, beyond two weeks, provide an opportunity to study the occurrence of extreme events in climate models.