A new comprehensive surface temperature data set for India is used to document changes in Indian temperature over seven decades, in order to examine the patterns and possible effects of global ...warming. The data set is subdivided into pre-monsoon, monsoon, and post-monsoon categories in order to study the temperature patterns in each of these periods. When the decade means in maximum, minimum and daily mean temperature for the 2000s are compared to those of the 1950s, a consistent pattern of warming is found over northwestern and southern India, and a pattern of cooling is seen in a broad zone anchored over northeastern India and extending southwestward across central India. These patterns are explained by the presence of a large region of anthropogenic brown haze over India and adjacent ocean regions. These aerosols absorb solar radiation, leading to warming of the haze layer over northeastern and central India and to cooling of the surface air beneath. The heated air rises and then sinks to the north and south of the haze region over northwestern and southern India, warming the air by compression as it sinks in those regions. The possible impact of these temperature patterns on Indian agriculture is considered.
Indian summer monsoon exhibits strong northward propagation in intraseasonal timescale intraseasonal oscillations (ISO) from the equatorial Indian Ocean (EIO) to the foothills of the Himalayas. ...Initiation of this northward march is often marked by a strong convective activity over the EIO, which could be associated with Madden–Julian Oscillation. Satellite derived rainfall and reanalysis products are used to unravel different characteristics of ISO in terms of strength, extent and speed at different times. Eastern EIO convective events are grouped into two categories based on the intensities of the simultaneous rainfall anomalies over central India (CI). Northward propagation from EIO is observed to be much stronger and slower when CI experiences strong dry anomaly at the time of initiation of northward march of convection near the equator. Essentially, a first northward propagating dry lobe seems to impact the behavior of the next arriving wet lobe. Distinctive features in Rossby waves emanated from the eastern EIO and different surface and atmospheric conditions over the region in the two different cases are observed. Strong dry conditions over CI favors strong easterly vertical wind-shear, which eventually helps destabilizing the atmosphere. Upper tropospheric meridional temperature gradient over the region modulates the variation in vertical wind-shear via thermal wind balance. These conditions are favorable for generation of strong boundary layer convergence to the north of the convective band, producing strong northward propagation. These results indicate the dual role of convection near the equator and dryness over CI in modulating the northward march of rainfall over India.
Monsoon depressions, that form during the Indian summer monsoon season (June to September) are known to be baroclinic disturbances (horizontal scale 2,000–3,000 km) and are driven by deep convection ...that carries a very large vertical slope towards cold air aloft in the upper troposphere. Deep convection is nearly always organized around the scale of these depressions. In the maintenance of the monsoon depression the generation of eddy kinetic energy on the scale of the monsoon depression is largely governed by the “in scale” covariance of heating and temperature and of vertical velocity and temperature over the region of the monsoon depression. There are normally about 6–8 monsoon depressions during a summer monsoon season. Recent years 2009, 2010 and 2011 saw very few (around 1, 0 and 1 per season respectively). The best numerical models such as those from ECMWF and US (GFS) carried many false alarms in their 3–5 day forecasts, more like 6–8 disturbances. Even in recent years with fewer observed monsoon depressions a much larger number of depressions is noted in ECMWF forecasts. These are fairly comprehensive models that carry vast data sets (surface and satellite based), detailed data assimilation, and are run at very high resolutions. The monsoon depression is well resolved by these respective horizontal resolutions in these models (at 15 and 35 km). These models carry complete and detailed physical parameterizations. The false alarms in their forecasts leads us to suggest that some additional important ingredient may be missing in these current best state of the art models. This paper addresses the effects of pollution for the enhancement of cloud condensation nuclei and the resulting disruption of the organization of convection in monsoon depressions. Our specific studies make use of a high resolution mesoscale model (WRF/CHEM) to explore the impacts of the first and second aerosol indirect effects proposed by Twomey and Albrecht. We have conducted preliminary studies including examination of the evolution of radar reflectivity (computed inversely from the model hydrometeors) for normal and enhanced CCN effects (arising from enhanced monsoon pollution). The time lapse histories show a major disruption in the organization of convection of the monsoon depressions on the time scale of a week to 10 days in these enhanced CCN scenarios.
Abstract
Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is ...necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.
This study provides a monsoonal link to the rapid Arctic ice melt. Each year the planetary-scale African-Asian monsoonal outflow near the tropopause carries a large anticyclonic gyre that has a ...longitudinal spread that occupies nearly half of the entire tropics. In recent years, the South Asian summer monsoon has experienced increased rainfall over northwestern India and Pakistan and it has also contributed to more intense local anticyclonic outflows from this region. The western lobes of these intense upper-high-pressure areas carry outflows with large heat fluxes from the monsoon belt toward central Asia and eventually to the region of the rapid ice melt of the Canadian Arctic. In this study this spectacular pathway has been defined from airflow trajectories, heat content, and heat flux anomalies. Most of these show slow increasing trends in the last 20 years. The monsoonal connection to the rapid Arctic ice melt is a new contribution of this study. This is shown from the passage of a vertical column of large positive values of the heat content anomaly that can be traced from the Asian monsoon belt to the Canadian Arctic. The heat flux along these episodic and intermittently active pathways is shown to be considerably larger than the atmospheric poleward flux across latitude circles and from the oceans. This study contrasts these thermodynamic wave trains (defining this pathway) for the more conventional dynamic wave trains.
The year 2009 was a major drought year for the Indian summer monsoon with a seasonal deficit of rainfall by 21.6%. Standard oceanic predictors such as ENSO and the Indian Ocean dipole are not ...consistent for these dry spells. There are a host of other parameters such as the Himalayan ice cover, the Eurasian snow cover, the passage of intraseasonal waves, and even accumulated effects of Asian pollution that have been considered for analysis of the dry spells of the monsoon. This paper presents another factor, the western Asian desert air incursions toward central India, and emphasizes the formation of a blocking high over western Asia as an important feature for these dry spells. The blocking high advects descending very dry air toward central India, portrayed using swaths of three-dimensional trajectories. This is a robust indicator for dry spells of the monsoon during the last several decades. This dry air above the 3-km level over central India strongly inhibits the vertical growth of deep convection. Some of the interesting antecedents of the formation of the blocking high include an eastward and somewhat northward extension of the ITCZ over North Africa, a stronger than normal local Hadley cell over North Africa, a strong subtropical jet stream over the southern Mediterranean, and strong conversions of anticyclonic shear vorticity to anticyclonic curvature vorticity. The dynamical antecedents of the aforementioned scenario in this study are related to many aspects of North African weather features. They are portrayed using both reanalysis datasets and ensemble modeling using a suite of coupled atmosphere-ocean models.
A multimodel superensemble developed by the Florida State University combines multiple model forecasts based on their past performance (training phase) to make a consensus forecast. Because observed ...precipitation reflects local characteristics such as orography, quantitative high-resolution precipitation products are useful for downscaling coarse model outputs. The Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and Tropical Rainfall Measuring Mission (TRMM) 3B43 products are used for downscaling and as training data in the superensemble training phase. Seven years (1998–2004) of monthly precipitation (June–August) over the Asian monsoon region (0°–50°N, 60°–150°E) and results of four coupled climate models were used. TRMM 3B43 was adjusted by APHRODITE (m-TRMM). For seasonal climate forecasts, a synthetic superensemble technique was used. A cross-validation technique was adopted, in which the year to be forecast was excluded from the calculations for obtaining the regression coefficients. The principal results are as follows: 1) Seasonal forecasts of Asian monsoon precipitation were considerably improved by use of APHRODITE rain gauge–based data or the m-TRMM product. These forecasts are much superior to those from the best model of the suite and ensemble mean. 2) Use of a statistical downscaling and synthetic superensemble method for multimodel forecasts of seasonal climate significantly improved precipitation prediction at higher resolution. This is confirmed by cross-evaluation of superensemble with using other observation data than the data used in the training phase. 3) Availability of a dense rain gauge network–based analysis was essential for the success of this work.
This study addresses numerical prediction of atmospheric wave trains that provide a monsoonal link to the Arctic ice melt. The monsoonal link is one of several ways that heat is conveyed to the ...Arctic region. This study follows a detailed observational study on thermodynamic wave trains that are initiated by extreme rain events of the northern summer south Asian monsoon. These wave trains carry large values of heat content anomalies, heat transports and convergence of flux of heat. These features seem to be important candidates for the rapid melt scenario. This present study addresses numerical simulation of the extreme rains, over India and Pakistan, and the generation of thermodynamic wave trains, simulations of large heat content anomalies, heat transports along pathways and heat flux convergences, potential vorticity and the diabatic generation of potential vorticity. We compare model based simulation of many features such as precipitation, divergence and the divergent wind with those evaluated from the reanalysis fields. We have also examined the snow and ice cover data sets during and after these events. This modeling study supports our recent observational findings on the monsoonal link to the rapid Arctic ice melt of the Canadian Arctic. This numerical modeling suggests ways to interpret some recent episodes of rapid ice melts that may require a well-coordinated field experiment among atmosphere, ocean, ice and snow cover scientists. Such a well-coordinated study would sharpen our understanding of this one component of the ice melt, i.e. the monsoonal link, which appears to be fairly robust.
This study addresses the predictability of rainfall variations over South America and the Amazon basin. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse ...resolution data utilized by coupled models during the training phase. By using MERRA reanalysis and statistical downscaling along with the superensemble methodology, it is possible to obtain more precise forecast of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of member models also allows for increased accuracy of superensemble forecasts. The use of coupled atmospheric–ocean numerical models to predict the rainfall anomalies has had mixed results. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of downscaling and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well.
This review provides a summary of work in the area of ensemble forecasts for weather, climate, oceans, and hurricanes. This includes a combination of multiple forecast model results that does not ...dwell on the ensemble mean but uses a unique collective bias reduction procedure. A theoretical framework for this procedure is provided, utilizing a suite of models that is constructed from the well‐known Lorenz low‐order nonlinear system. A tutorial that includes a walk‐through table and illustrates the inner workings of the multimodel superensemble's principle is provided. Systematic errors in a single deterministic model arise from a host of features that range from the model's initial state (data assimilation), resolution, representation of physics, dynamics, and ocean processes, local aspects of orography, water bodies, and details of the land surface. Models, in their diversity of representation of such features, end up leaving unique signatures of systematic errors. The multimodel superensemble utilizes as many as 10 million weights to take into account the bias errors arising from these diverse features of multimodels. The design of a single deterministic forecast models that utilizes multiple features from the use of the large volume of weights is provided here. This has led to a better understanding of the error growths and the collective bias reductions for several of the physical parameterizations within diverse models, such as cumulus convection, planetary boundary layer physics, and radiative transfer. A number of examples for weather, seasonal climate, hurricanes and sub surface oceanic forecast skills of member models, the ensemble mean, and the superensemble are provided.
Key Points
Improved weather forecasts are possible from a combination of many models to produce a consensus forecast
Model consensus is achieved through using a weighted mean of models; the weights (as many as 10 million ) vary in space and time
Model ensembles utilize a suite of models and the methodology entails the reduction of systematic bias errors of each member model
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