T. N. Krishnamurti

Indian Ocean Experiment: An integrated analysis of the climate forcing and effects of the great Indo-Asian haze

Every year, from December to April, anthropogenic haze spreads over most of the North Indian Ocean, and South and Southeast Asia. The Indian Ocean Experiment (INDOEX) documented this Indo-Asian haze at scales ranging from individual particles to its contribution to the regional climate forcing. This study integrates the multiplatform observations (satellites, aircraft, ships, surface stations, and balloons) with one- and four-dimensional models to derive the regional aerosol forcing resulting from the direct, the semidirect and the two indirect effects. The haze particles consisted of several inorganic and carbonaceous species, including absorbing black carbon clusters, fly ash, and mineral dust. The most striking result was the large loading of aerosols over most of the South Asian region and the North Indian Ocean. The January to March 1999 visible optical depths were about 0.5 over most of the continent and reached values as large as 0.2 over the equatorial Indian ocean due to long-range transport. The aerosol layer extended as high as 3 km. Black carbon contributed about 14% to the fine particle mass and 11% to the visible optical depth. The single-scattering albedo estimated by several independent methods was consistently around 0.9 both inland and over the open ocean. Anthropogenic sources contributed as much as 80% (±10%) to the aerosol loading and the optical depth. The in situ data, which clearly support the existence of the first indirect effect (increased aerosol concentration producing more cloud drops with smaller effective radii), are used to develop a composite indirect effect scheme. The Indo-Asian aerosols impact the radiative forcing through a complex set of heating (positive forcing) and cooling (negative forcing) processes. Clouds and black carbon emerge as the major players. The dominant factor, however, is the large negative forcing (-20±4 W m^(−2)) at the surface and the comparably large atmospheric heating. Regionally, the absorbing haze decreased the surface solar radiation by an amount comparable to 50% of the total ocean heat flux and nearly doubled the lower tropospheric solar heating. We demonstrate with a general circulation model how this additional heating significantly perturbs the tropical rainfall patterns and the hydrological cycle with implications to global climate.

Multimodel Ensemble Forecasts for Weather and Seasonal Climate

Abstract In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm. The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a “nature run” were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed...

Cumulus Parameterization and Rainfall Rates I

Abstract Modeling of convective rainfall rates is a central problem in tropical meteorology. Toward numerical weather prediction efforts the semi-prognostic approach (i.e., a one time-step prediction of rainfall rates) provides a relevant test of cumulus parameterization methods. In this paper we compare five currently available cumulus parameterization schemes using the semi-prognostic approach. The calculated rainfall rates are compared with observed estimates provided in the recent publication of Hudlow and Patterson (1979). Among these the scheme proposed by Kuo (1974) provides the least root-mean-square error between the calculated and the observed estimates, slightly better than that of Arakawa and Schubert (1974), which was used by Lord (1978a). The simplicity of the approach holds promise for numerical weather prediction. Unlike some of the other schemes this method is not sensitive to and does not require computation of internal parameters such as profiles of cloud mass flux updrafts and downdraf...

Improvement of the multimodel superensemble technique for seasonal forecasts

Abstract The superensemble technique has previously been demonstrated to provide an improved seasonal forecast compared to the bias-removed ensemble of equally weighted models. This paper offers a further improvement to the superensemble method by modifying the regression coefficients used in the weighting of the models for the construction of the superensemble. The improvement is achieved by use of singular value decomposition of the covariance matrix, and selecting only the largest singular value, corresponding to maximal explained variance, for the calculation of the regression coefficients. The results shown here are based on calculations done with 10 yr worth of monthly forecasts from the Atmospheric Model Intercomparison Project (AMIP) dataset, using cross validation.

Improving Tropical Precipitation Forecasts from a Multianalysis Superensemble

Abstract This paper utilizes forecasts from a multianalysis system to construct a superensemble of precipitation forecasts. This method partitions the computations into two time lines. The first of those is a control (or a training) period and the second is a forecast period. The multianalysis is derived from a physical initialization–based data assimilation of “observed rainfall rates.” The different members of the reanalysis are produced by using different rain-rate algorithms for physical initialization. The basic rain-rate datasets are derived from satellites’ microwave radiometers, including those from the Tropical Rainfall Measuring Mission (TRMM) satellites and the Special Sensor Microwave Imager (SSM/I) data from three current U.S. Air Force Defense Meteorological Satellite Program (DMSP) satellites. During the training period, 155 experiments were conducted to find the relationship between forecasts from the multianalysis dataset and the best “observed” estimates of daily rainfall totals. This re...

The Summer Monsoon of 1987

Abstract In this paper we have examined the evolution of a number of parameters we believe were important for our understanding of the drought over India during the summer of 1987. The list of parameters includes monthly means or anomalies of the following fields: sea surface temperatures, divergent circulations, outgoing longwave radiation, streamfunction of the lower and upper troposphere, and monthly precipitation (expressed as a percentage departure from a long-term mean). The El Nino related warm sea surface temperature anomaly and a weaker warm sea surface temperature anomaly over the equatorial Indian Ocean provide sustained convection, as reflected by the negative values of the outgoing longwave radiation. With the seasonal heating, a pronounced planetary-scale divergent circulation evolved with a center along the western Pacific Ocean. The monsoonal divergent circulation merged with that related to the El Nino, maintaining most of the heavy rainfall activity between the equatorial Pacific Ocean a...

Real-Time Multianalysis-Multimodel Superensemble Forecasts of Precipitation Using TRMM and SSM/I Products

This paper addresses real-time precipitation forecasts from a multianalysis‐multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis‐multimodel system studied here. In this paper, ‘‘multimodel’’ refers to different models whose forecasts are being assimilated for the construction of the superensemble. ‘‘Multianalysis’’ refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a ‘‘best’’ rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis‐multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis‐multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model’s skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually biasremoved models. The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1‐3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

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 multimodel 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.

Multimodel Superensemble Forecasting of Tropical Cyclones in the Pacific

Using currently available operational forecast datasets on the tracks and intensities of tropical cyclones over the Pacific Ocean for the years 1998, 1999, and 2000 a multimodel superensemble has been constructed following the earlier work of the authors on the Atlantic hurricanes. The models included here include forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction/Environmental Modeling Center [NCEP/EMC, the Aviation (AVN) and Medium-Range Forecast (MRF) Models], the U.S. Navy [Naval Operational Global Atmospheric Prediction System, (NOGAPS)], the U.K. Met Office (UKMO), and the Japan Meteorological Agency (JMA). The superensemble methodology includes a collective bias estimation from a training phase in which a multiple-regression-based least squares minimization principle for the model forecasts with respect to the observed measures is employed. This is quite different from a simple bias correction, whereby a mean value is simply shifted. These bias estimates are described by separate weights at every 12 h during the forecasts for each of the member models. Superensemble forecasts for track and intensity are then constructed up to 144 h into the future using these weights. Some 100 past forecasts of tropical cyclone days are used to define the training phase for each basin. The findings presented herein show a marked improvement for the tracks and intensities of forecasts from the proposed multimodel superensemble as compared to the forecasts from member models and the ensemble mean. This note includes detailed statistics on the Pacific Ocean tropical cyclone forecasts for the years 1998, 1999, and 2000.

A Pronounced Continental-Scale Diurnal Mode of the Asian Summer Monsoon

Abstract A pronounced continental-scale diurnal mode of the Asian summer monsoon is mapped using data from recent satellites Meteosat-5 and TRMM. These datasets were available at high temporal resolutions. A result that stands out is the diurnal divergent circulation that in the afternoon hours has an ascending lobe over north-central India and has a descending lobe that reaches out radially toward central China, the southern part of China, the equatorial Indian Ocean, and the western Arabian Sea. The reverse circulation is clearly seen during the early morning hours. This diurnal pulsation of continental-scale divergent circulation appears to be an integral part of the monsoon. Another finding relates to the diurnal slowing down and speeding up of the Tibetan high circulations, especially in the southern flanks where the tropical easterly jet resides and exhibits a pulsation of intensity. The amplitude of pulsation was found to reach up to 7 m s−1. Thus this continental-scale change appears to be a prono...