The dynamics of phase locking

Many low-frequency phenomena such as the Madden-Julian oscillation (MJO) or the El Niño-Southern Oscillation (ENSO) exhibit rapid growth where they appear to be undergoing a phase locking with other time scales such as the annual cycle. The purpose of this paper is to illustrate an example of phase locking of two different time scales. In this instance it is shown that during such epochs of phase locking a large increase in nonlinear energy exchange occurs from one time scale to the other. This paper utilizes the ECMWF Re-Analysis (ERA-40) datasets for the year 2001 to examine this problem. This study is a sequel to a recent modeling study where the maintenance of the MJO time scale was examined from scale interactions, especially with synoptic-scale waves with a 2-7 day periods. It was shown that a pair of waves on the synoptic time scale can satisfy certain selection rules and undergo triad interactions (kinetic energy to kinetic energy exchanges) and transfer energy. This present study illustrates the fact that during epochs of phase locking such nonlinear interactions can become very large, thus portraying the importance of phase locking. These explosive exchanges are shown from two perspectives: an approach based on kinetic energy exchanges in the frequency domain and another that invokes the boundary layer dynamics in the frequency domain

Prediction of the diurnal change using a multimodel superensemble. Part I: Precipitation

Modeling the geographical distribution of the phase and amplitude of the diurnal change is a challenging problem. This paper addresses the issues of modeling the diurnal mode of precipitation over the Tropics. Largely an early morning precipitation maximum over the oceans and an afternoon rainfall maximum over land areas describe the first-order diurnal variability. However, large variability in phase and amplitude prevails even within the land and oceanic areas. This paper addresses the importance of a multimodel superensemble for much improved prediction of the diurnal mode as compared to what is possible from individual models. To begin this exercise, the skills of the member models, the ensemble mean of the member models, a unified cloud model, and the superensemble for the prediction of total rain as well as its day versus night distribution were examined. Here it is shown that the distributions of total rain over the earth (tropical belt) and over certain geographical regions are predicted reasonably well (RMSE less than 18) from the construction of a multimodel superensemble. This dataset is well suited for addressing the diurnal change. The large errors in phase of the diurnal modes in individual models usually stem from numerous physical processes such as the cloud radiation, shallow and deep cumulus convection, and the physics of the planetary boundary layer. The multimodel superensemble is designed to reduce such systematic errors and provide meaningful forecasts. That application for the diurnal mode appears very promising. This paper examines some of the regions such as the Tibetan Plateau, the eastern foothills of the Himalayas, and the Amazon region of South America that are traditionally difficult for modeling the diurnal change. In nearly all of these regions, errors in phase and amplitude of the diurnal mode of precipitation increase with the increased length of forecasts. Model forecast errors on the order of 6-12 h for phase and 50 for the amplitude are often seen from the member models. The multimodel superensemble reduces these errors and provides a close match (RMSE < 6 h) to the observed phase. The percent of daily rain and their phases obtained from the multimodel superensemble at 3-hourly intervals for different regions of the Tropics showed a closer match (pattern correlation about 0.4) with the satellite estimates. This is another area where the individual member models conveyed a much lower skill

Prediction of the diurnal cycle using a multimodel superensemble. Part II: Clouds

This study addresses the issue of cloud parameterization in general circulation models utilizing a twofold approach. Four versions of the Florida State University (FSU) global spectral model (GSM) were used, including four different cloud parameterization schemes in order to construct ensemble forecasts of cloud covers. Next, a superensemble approach was used to combine these model forecasts based on their past performance. It was shown that it is possible to substantially reduce the 1-5-day forecast errors of phase and amplitude of the diurnal cycle of clouds from the use of a multimodel superensemble. Further, the statistical information generated in the construction of a superensemble was used to develop a unified cloud parameterization scheme for a single model. This new cloud scheme, when implemented in the FSU GSM, carried a higher forecast accuracy compared to those of the individual cloud schemes and their ensemble mean for the diurnal cycle of cloud cover up to day 5 of the forecasts. This results in a 5-10 W m-2 improvement in the root-mean-square error to the upward longwave and shortwave flux at the top of the atmosphere, especially over deep convective regions. It is shown that while the multimodel superensemble is still the best product in forecasting the diurnal cycle of clouds, a unified cloud parameterization scheme, implemented in a single model, also provides higher forecast accuracy compared to the individual cloud models. Moreover, since this unified scheme is an integral part of the model, the forecast accuracy of the single model improves in terms of radiative fluxes and thus has greater impacts on weather and climate time scales. This new cloud scheme will be tested in real-time simulations

Evaluation of several different planetary boundary layer schemes within a single model, a unified model and a multimodel superensemble

This paper addresses the forecasts of latent heat fluxes from five different formulations of the planetary boundary layer (PBL). Different formulations are deployed within the Florida State University global spectral model. Hundreds of short range forecast experiments are carried out using daily data sets for summer 2002 with each model. The primary goal of this study is to compare the performance of the diverse family of PBL algorithms for the latent heat fluxes within the PBL. Benchmark fluxes are calculated from the vertical integrals of Yanai's formulation of the apparent moisture sink and a precipitation using Physical Initialization. This provides indirectly observed estimates of the vertical fluxes of latent heat in the PBL. This comparison reveals that no single scheme shows a global spread of improvement over other models for forecasts of latent heat fluxes in the PBL. Among these diverse models the turbulent kinetic energy based closure provides somewhat better results. The construction of a multimodel superensemble provides a synthesis of these different PBL formulations and shows improved forecasts of the surface fluxes. A single unified model utilizing weighted PBL algorithms where all the five schemes are retained within a single model shows some promise for improving a single model

Determination of forecasts errors arising from different components of model physics and dynamics

This paper addresses a procedure to extract error estimates for the physical and dynamical components of a forecast model. This is a two-step process in which contributions to the forecast tendencies from individual terms of the model equations are first determined using an elaborate bookkeeping of the forecast. The second step regresses these estimates of tendencies from individual terms of the model equations against the observed total tendencies. This process is executed separately for the entire horizontal and vertical transform grid points of a global model. The summary of results based on the corrections to the physics and dynamics provided by the regression coefficients highlights the component errors of the model arising from its formulation. This study provides information on geographical and vertical distribution of forecast errors contributed by features such as nonlinear advective dynamics, the rest of the dynamics, deep cumulus convection, large-scale condensation physics, radiative processes, and the rest of physics. Several future possibilities from this work are also discussed in this paper

On the weakening of Hurricane Lili, October 2002

This paper addresses the weakening of Hurricane Lili of October 2002 just before it made landfall in Louisiana. This hurricane weakened from a category 4 storm on October 3, 2002 at 0000 UTC to a category 1 storm on October 3, 2002 at 1300 UTC. This sudden drop in intensity has been a subject of considerable interest. In this paper we explore a forecast model diagnostic approach that explores the contribution to the hurricane intensity changes arising from a number of dynamical and physical possibilities. Running several versions of a global model at very high resolution, the relative contribution to the intensity drop of Lili arising from cooler sea surface temperatures, dry air advection into the storm, advective non-linear dynamics, non-advective dynamics, and shallow and deep cumulus convection was examined. This line of inquiry led to the conclusion that dry air advection from the north into the storm and the slightly cold sea surface temperatures were not the primary contribution to the observed pressure rise by 22 hPa. The primary contribution to the pressure rise was found to be the 'rest of dynamics' (the non-advective dynamics). The shallow convection contributed slightly to an overall cooling, i.e. a weakening of the warm core of Lili. The effects of deep cumulus convection appeared to be opposite, i.e. towards maintaining a strong storm. A primary term in the 'rest of dynamics', the advection of Earth's angular momentum into the storm, is identified as a major contributor for the intensity change in the analysis. This feature resembles an intrusion of dry air into the core of the storm. This intrusion contributes to a reduction of spin and an overall rapid weakening of the hurricane. The angular momentum partitioning appears quite revealing on the sudden demise of Lili

Seasonal climate forecasts of the South Asian monsoon using multiple coupled models

This study addresses seasonal climate forecasts using coupled atmosphere-ocean multimodels. Using as many as 67 different seasonal-forecast runs per season from a variety of coupled (atmosphere-ocean) models consensus seasonal forecasts have been prepared from about 4500 experiments. These include the European Center's DEMETER (Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction) database and a suite of Florida State University (FSU) models (based on different combinations of physical parametrizations). This is one of the largest databases on coupled models. The monsoon region was selected to examine the predictability issue. The methodology involves construction of seasonal anomalies of all model forecasts for a number of variables including precipitation, 850 hPa winds, 2-m/surface temperatures, and sea surface temperatures. This study explores the skills of the ensemble mean and the FSU multimodel superensemble. The metrics for forecast evaluation include computation of hindcast and verification anomalies from model/ observed climatology, time-series of specific climate indices, and standard deterministic ensemble mean scores such as anomaly correlation coefficient and root mean square error. The results were deliberately prepared to match the metrics used by European DEMETER models. Invariably in all modes of evaluation, the results from the FSU multimodel superensemble demonstrate greater skill for most of the variables tested here than those obtained in earlier studies. The specific inquiry of this study was on this question: is it going to be wetter or drier, warmer or colder than the long-term recent climatology of the monsoon; and where and when during the next season?These results are most encouraging, and they suggest that this vast database and the superensemble methodology are able to provide some useful answers to the seasonal monsoon forecast issue compared to the use of single climate models or from the conventional ensemble averaging

A multi-model superensemble algorithm for seasonal climate prediction using DEMETER forecasts

In this paper, a multi-model ensemble approach with statistical correction for seasonal precipitation forecasts using a coupled DEMETER model data set is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability; consequently, the predictive skill of extended forecasts remains quite low. One of the approaches to the improvement of seasonal prediction is the empirical weighted multi-model ensemble, or superensemble, combination. In the superensemble approach, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The skill of a superensemble relies strongly on the past performance of the individual member models used in its construction. The algorithm proposed here involves empirical orthogonal function (EOF) filtering of the actual data set prior to the construction of a multi-model ensemble or superensemble as an alternative solution for seasonal prediction. This algorithm generates a new data set from the input multi-model data set by finding a consistent spatial pattern between the observed analysis and the individual model forecast. This procedure is a multiple linear regression problem in the EOF space. The newly generated EOF-filtered data set is then used as an input data set for the construction of a multi-model ensemble and superensemble. The skill of forecast anomalies is assessed using statistics of categorical forecast, spatial anomaly correlation and root mean square (RMS) errors. The various verifications show that the unbiased multi-model ensemble of DEMETER forecasts improves the prediction of spatial patterns (i.e. the anomaly correlation), but it shows poor skill in categorical forecast. Due to the removal of seasonal mean biases of the different models, the forecast errors of the bias-corrected multi-model ensemble and superensemble are already quite small. Based on the anomaly correlation and RMS measures, the forecasts produced by the proposed method slightly outperform the other conventional forecasts

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space-time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multimodel ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, biascorrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models

Hurricane Forecasts with a Mesoscale Suite of Models

A suite of mesoscale models are being used in the present study to examine experimental forecast performance for tracks and intensity of hurricanes covering the years 2004, 2005 and 2006. Fifty-eight storm cases are being considered in the present study. Most of the mesoscale models are being run at a horizontal resolution at around 9 km. This includes the WRF (two versions), MM5, HWRF, GFDL and DSHP. The performances of forecasts are evaluated using absolute errors for storm track and intensity. Our consensus forecasts utilize ensemble mean and a bias corrected ensemble mean for these member models on the mesoscale and the large-scale model suites. Comparing the forecast statistics for the mesoscale suite, the large-scale suite and the combined suite we find that the mesoscale suite provided the best track forecasts for 60 and 72 h. However, the forecast from the combined suite of model were also very close to the track errors of the mesoscale at 60 and 72 h. Overall track forecast errors were least for the combined suite. The intensity forecasts of the bias corrected ensemble mean of the mesoscale suite were comparable to DSHP and GFDL at the later part of the forecast periods