Search

The extremes of Indian summer monsoon rainfall (ISMR) are largely driven by the modulation of vertically integrated moisture flux over the Arabian sea (70oE) and the Bay of Bengal (90oE). The droughts and floods are resulted due to strong divergence and convergence of the moisture-laden winds over India associated with various external forcings. Therefore, identifying the association of the zonal moisture flux with ISMR in the observation and Climate Forecast System version 2 (CFSv2) model is essential to improve the prediction of the ISMR extremes. We find that, unlike observation, ISMR extremes in CFSv2 are all ENSO-related and mainly driven by the moisture flux over the Bay of Bengal and remain unresponsive to eastward boundary flux at 70oE. Further decomposition of the fluxes into dynamical (winds) and thermodynamical (moisture) components shows that both moisture and winds terms over the Arabian sea are necessary for determining extremes. However, in CFSv2, only the winds component of the eastern boundary (90oE) flux plays a significant role in driving the ISMR extremes. It is due to the presence of strong heating over the Western Pacific which results in strong eastward moisture flux over the Bay of Bengal through a Gill-type response.

High-resolution regional climate model (RCM) simulations are found to be very useful in deriving realistic climate change projection information. This study uses high-resolution dynamical downscaling framework (CCSM4-WRF) for India. To delineate the advantage of high resolution, we compared the results of 9-km resolution CCSM4-WRF simulations against the 50-km resolution RCM simulations under Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) programme. Quantitative estimations show that majority of CORDEX-SA models exhibit large dry bias (

Understanding local SST control on precipitation during monsoon is important for deducing climate change due to global warming, particularly for warm oceans. Studies of the relationship of the precipitation over tropical oceans with local sea surface temperature (SST), on the monthly scale, have shown that the propensity for precipitation is high for SST above a threshold of 27.5 $$^{\circ }$$ C/28 $$^{\circ }$$ C. However, for warm oceans with SST above the threshold such as the Bay of Bengal and South China Sea, for each SST, there is a large variation of precipitation and the SST–precipitation relationship is weak. On daily scale mean precipitation increases slightly with SST when precipitation lags SST by a few days, but the relationship between them is rather weak. But, when SST is above the threshold, for daily, pentad, 10-day and monthly time scales, and with or without time lag, the curve depicting the variation of mean precipitation with SST explains only a small fraction of precipitation variance and hence cannot be considered to be representative of the SST–precipitation relationship, or used to deduce the impact of SST on precipitation. On the other hand, the local control on precipitation is predominantly atmospheric dynamics with the relationship of variation of precipitation to low-level convergence on all timescales, being strong. This suggests that for warm oceans, the limiting resource for precipitation/convection is not SST but dynamics.

Indian summer monsoon seasonal reforecasts by CFSv2, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), are analysed to investigate causes for the highest Indian summer monsoon rainfall (ISMR) forecast skill of CFSv2 with February (3-month lead time, L3) ICs. Although theory suggests forecast skill should degrade with increase in lead-time, CFSv2 shows highest skill with L3, due to its forecasting of ISMR excess of 1983 which other ICs failed to forecast. In contrast to observation, in CFSv2, ISMR extremes are largely decided by sea surface temperature (SST) variation over central Pacific (NINO3.4) associated with El Niño-Southern Oscillation (ENSO), where ISMR excess (deficit) is associated with La Niña (El Niño) or cooling (warming) over NINO3.4. In 1983, CFSv2 with L3 ICs forecasted strong La Niña during summer, which resulted in 1983 ISMR excess. In contrast, in observation, near normal SSTs prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean, which CFSv2 fails to capture with all ICs. CFSv2 reforecasts with late-April/early-May ICs are found to have highest deterministic ISMR forecast skill, if 1983 is excluded and Indian monsoon seasonal biases are also reduced. During the transitional ENSO in Boreal summer of 1983, faster and intense cooling of NINO3.4 SSTs in L3, could be due to larger dynamical drift with longer lead time of forecasting, compared to L0. Boreal summer ENSO forecast skill is also found to be lowest for L3 which gradually decreases from June to September. Rainfall occurrence with strong cold bias over NINO3.4, is because of the existence of stronger ocean-atmosphere coupling in CFSv2, but with a shift of the SST-rainfall relationship pattern to slightly colder SSTs than the observed. Our analysis suggests the need for a systematic approach to minimize bias in SST boundary forcing in CFSv2, to achieve improved ISMR forecasts.

Traditionally, monsoon teleconnections are measured in terms of the strength of a simultaneous linear relationship. Such associative metrics do not quantify precipitation variations through physical parameters directly related to the moisture budget of the atmosphere. In this study, for the first time, we develop a linear model for the Indian summer monsoon rainfall (ISMR) based on surface pressure over regions surrounding it and sea surface temperature (SST) forcing from tropics and midlatitude. This surface pressure acts as a dynamical link between SST forcing and convective processes over the Indian region, which was missing in previous studies. We also use this novel approach to understand the ISMR prediction skill in the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). We find that the interannual variability of ISMR does not rely solely on tropical processes, but the midlatitude phenomenon also plays a crucial role in modulating it. The model, however, derived most of its variability from the ENSO mode. The understated midlatitude forcing in the model can be attributed to its low prediction skill.

Association of higher (lower) rainfall with lower (higher) Aerosol Optical Depth (AOD) is consistent with the understanding that increased washout (build-up) and shorter (longer) life-time of aerosols occur in wetter (drier) conditions. Given the life-time of aerosols, it is imperative to examine how aerosols impact active/break (wetter/drier than normal) spells, prominent intraseasonal variability (ISV) of Indian summer monsoon (ISM), through their composite analysis using recent satellite observations of aerosols and cloud properties, circulation and rainfall. Dust aerosols can act as CCN and participate efficiently in cloud processes during active phase. During breaks, build-up of desert dust transported by prevalent circulation, is associated with lower cloud effective radius implying aerosols’ indirect effect where they can inhibit cloud growth in the presence of reduced moisture and decrease precipitation efficiency/rainfall. Correspondingly, correlation albeit small, between intraseasonal anomalies of AOD and rainfall is negative, when AOD leads rainfall by 3–5 days implying that indirect aerosols impact is effective during breaks, though it is not the dominant responsible factor. During breaks, lower shortwave flux at top of atmosphere hints at dust-induced semi-direct effect. As breaks are permanent features of ISM, incorporation of dust-induced feedbacks in models, is essential for improved ISV simulation and ISM prediction.

We analyse the performance of global climate models of 6th generation of Coupled Model Intercomparison Project (CMIP6) in simulating climatological summer monsoon rainfall over India, interannual variability (IAV) of all-India summer monsoon rainfall (ISMR) and its teleconnections with rainfall variability over equatorial Pacific and Indian Oceans. The multimodel ensemble mean (MME) of 61 CMIP6 models shows the best skill in simulating mean monsoon rainfall over India compared to the MMEs of 6th generation atmosphere-only models (AMIP6) and the previous generations of Atmospheric and Coupled Model Intercomparison Projects (AMIPs and CMIPs). Systematic improvement and reduction in bias are evident from lower to higher AMIPs/CMIPs. Still, there exists dry bias over a narrow region of the monsoon zone of central India besides wet and cold bias over the surrounding oceans. The persistence of errors in atmosphere-only models hints that the source of errors could be with atmosphere models. Fifteen CMIP6 models selected through objective criteria, perform the best in simulating mean monsoon, IAV of ISMR, the strong inverse relationship between ISMR and Boreal summer El Nino-Southern Oscillation (ENSO), and the inverse relationship between all-India rainfall and north-west tropical Pacific rainfall in June. Several models reproduce the dipole structure of Equatorial Indian Ocean Oscillation (EQUINOO) with the centres over western and eastern equatorial Indian Ocean. But, ISMR-EQUINOO relationship in many of them is opposite to the observed. Our analysis implies the need for capturing ISMR-EQUINOO link to improve the simulation of IAV of ISMR which is crucial for reliable monsoon prediction and projection.

Recent years have witnessed large interannual variation of all-India rainfall (AIR) in June, with intermittent large deficits and excesses. Variability of June AIR is found to have the strongest link with variation of rainfall over northwest tropical Pacific (NWTP), with AIR deficit (excess) associated with enhancement (suppression) of NWTP rainfall. This association is investigated using high-resolution Meteorological Research Institute model which shows high skill in simulating important features of Asian summer monsoon, its variability and the inverse relationship between NWTP rainfall and AIR. Analysis of the variation of NWTP rainfall shows that it is associated with a change in the latitudinal position of subtropical westerly jet over the region stretching from West of Tibetan Plateau (WTP) to NWTP and the phase of Rossby wave steered in it with centres over NWTP and WTP. In years with large rainfall excess/deficit, the strong link between AIR and NWTP rainfall exists through differences in Rossby wave phase steered in the jet. The positive phase of the WTP-NWTP pattern, with troughs over WTP and west of NWTP, tends to be associated with increased rainfall over NWTP and decreased AIR. This scenario is reversed in the opposite phase. Thus, the teleconnection between NWTP rainfall and AIR is a manifestation of the difference in the phase of Rossby wave between excess and deficit years, with centres over WTP and NWTP. This brings out the importance of prediction of phase of Rossby waves over WTP and NWTP in advance, for prediction of June rainfall over India.

Prediction for Indian summer monsoon rainfall (ISMR) is generated by integrating model from initial conditions (ICs) of weather at some time prior to sea- son. We examine the factors responsible for the widely reported highest ISMR forecast skill for February ICs in climate forecast system (CFSv2) model. Skill for February ICs is highest only based on correlation bet- ween observed and predicted year-to-year variation of ISMR, whereas other skill scores indicate highest skill for late-April/early-May ICs having shorter yet useful forecast lead-time. Higher correlation for February ICs arises from correct forecasting of 1983 ISMR excess, which is however due to wrong forecast of La NiA±a and correlation drops to lower value than that for late-April/early-May ICs if 1983 is excluded. Fore- cast skill for sea-surface temperature variation over equatorial central Pacific (ENSO) in Boreal summer is lowest for February ICs indicating role of dynamical drift induced by long forecast lead-time. Model defi- ciencies such as oversensitivity of ISMR to ENSO and unrealistic ENSO influence on variation of convection over equatorial Indian Ocean (EQUINOO) lead to errors in ISMR forecasting. In CFSv2, ISMR is mostly decided by ENSO whereas in observation it is influ- enced by ENSO and EQUINOO independently. © 2021. All Rights Reserved.

Coupled Model Intercomparison Project phase 5 (Fifth Assessment Report of Intergovernmental Panel on Climate Change) coupled global climate model Representative Concentration Pathway 8.5 simulations are analyzed to derive robust signals of projected changes in Indian summer monsoon rainfall (ISMR) and its variability. Models project clear future temperature increase but diverse changes in ISMR with substantial intermodel spread. Objective measures of interannual variability (IAV) yields nearly equal chance for future increase or decrease. This leads to discrepancy in quantifying changes in ISMR and variability. However, based primarily on the physical association between mean changes in ISMR and its IAV, and objective methods such as k-means clustering with Dunn's validity index, mean seasonal cycle, and reliability ensemble averaging, projections fall into distinct groups. Physically consistent groups of models with the highest reliability project future reduction in the frequency of light rainfall but increase in high to extreme rainfall and thereby future increase in ISMR by 0.74 ± 0.36 mm d−1, along with increased future IAV. These robust estimates of future changes are important for useful impact assessments.

This study investigates the aerosols-rainfall interaction during Indian summer monsoon and characterizes their difference in drought and excess summer monsoon years, based on MODIS (MODerate Resolution Imaging Spectro-radiometer) derived Aerosol Optical Depth (AOD) at 550 nm. AOD has been estimated using Level-2 MODIS Terra Data Version 6. AOD in drought years is found to be higher over India compared to excess monsoon years. The total effect of aerosols causes reduction of summer rainfall but with distinct differences in their impact during strong and weak summer monsoon years, due to the changes in clouds, radiation, large-scale circulation, and convection. Aerosol and cloud characteristics exhibit strong association to rainfall variability in interannual time scales. Variability in cloud effective radius and cloud optical thickness is found to be consistent with aerosol effect.

Coupled Model Intercomparison Project phase 5 (CMIP5) coupled global climate model (CGCM) Representative Concentration Pathway (RCP) simulations project clear future temperature increase but diverse changes in Indian summer monsoon rainfall (ISMR) with substantial inter-model spread. Robust signals of projected changes are derived based on objective criteria and the physically consistent simulations with the highest reliability suggest future reduction in the frequency of light rainfall but increase in high to extreme rainfall. The role of equatorial Indian and Pacific Oceans on the projected changes in monsoon rainfall is investigated. The results of coupled model projections are also compared with the corresponding projections from high resolution AGCM time-slice, multi-physics and multi-forcing ensemble experiments.

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.

This paper provides a framework for analyzing and comparing timing recovery schemes for higher order partial response (PR) channels. Several classes of timing recovery schemes are analyzed. Timing recovery loops employing timing gradients or phase detectors derived from the minimum mean-square error (MMSE) criterion, the maximum likelihood (ML) criterion, and the timing function approach of Mueller and Muller (1976) (MRI) are analyzed and compared. The paper formulates and analyzes MMSE timing recovery in the context of a slope look-up table (SLT), which is amenable for an efficient implementation. The properties and performance of the SLT-based timing loop are compared with the ML and MM loops. Analysis and time step simulations for a practical 16-state PR magnetic recording channel show that the output noise jitter of the ML phase detector is worse than that of the SLT-based phase detector. This is primarily due to the presence of self-noise in the ML detector. Consequently, the SLT-based phase detector is to be preferred. In comparing the SLT and MM based timing loops, it is found that both schemes have similar jitter performance.

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 multimodal forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed for the multimodel forecasts of global weather, multiseasonal climate simulations, and hurricane track and intensity forecasts. For each type an improvement of the multimodel analysis is demonstrated and compared to the performance of the individual models. Seasonal and multiseasonal simulations demonstrate a major success of this approach for the atmospheric general circulation models where the sea surface temperatures and the sea ice are prescribed. In many instances, a major improvement in skill over the best models is noted.

A number of detection schemes are compared using two partial erasure models in conjunction with a read channel model. The schemes considered are EPR4, EEPR4, TCPR4, Generalized PR and MLDFE. It is shown that the coding gain associated with EEPR4 and TCPR4 are more than offset by the rate loss. The performance difference between EPR4 and more complex schemes is studied for varying amounts of nonlinearities.

A method for improving weather and climate forecast skill has been developed. It is called a superensemble, and it arose from a study of the statistical properties of a low-order spectral model. Multiple regression was used to determine coefficients from multimodel forecasts and observations. The coefficients were then used in the superensemble technique. The superensemble was shown to outperform all model forecasts for multiseasonal, medium-range weather and hurricane forecasts. In addition, the superensemble was shown to have higher skill than forecasts based solely on ensemble averaging.

A generalised construction of rate N/(N+1) run-length limited codes is presented. The (d, k, l, r) constraints are (0, N/4 + 1, N/8, N/8). Encoding and decoding based on the use of multiple pivot bits are described. The encoding and decoding are systematic enough to allow the writing of software to generate efficient logic equations for different N. The logic equations can be further minimised using an automatic synthesis tool.

Using a dynamic state model for the observed upward trend and sinusoidal variation, a Kalman filter is constructed to estimate the atmospheric CO2 concentration, The process noise is assumed to be white with an unknown covariance, so an adaptive scheme is used to estimate the steady-state Kalman gain matrix. Several tests for optimality are performed on the adaptive filter. Measured data are then filtered using the Kalman algorithm. The filtering results are shown to reduce the variability of the airborne fraction of fossil-fuel-produced atmospheric CO2.

A model representing the trends and variations in the Mauna Loa data is presented. The model uses an autoregressive process for low-frequency variations, which enables the noise residuals to pass two tests for whiteness. The analysis of the noise residuals provides a measure of uncertainity for estimates of nonfossil fuel emission of CO2 to the atmosphere. Finally, an adaptive Kalman filter is used to estimate measurement errors contained in the Mauna Loa data. The results obtained agree with previous estimates of the precision and accuracy of the measuring instruments.