Prediction Of Indian Summer Monsoon Research Articles

Role of Improved Ocean Initial State in the Seasonal Prediction of Indian Summer Monsoon: A Case Study

This case study has made an effort to show the impact of improved ocean initial conditions (ICs) in a coupled forecast system (CFSv2) simulation on the seasonal prediction of Indian summer monsoon rainfall (ISMR). CFSv2 is used as an operational dynamical model for the seasonal prediction of ISMR. Here, we show an improved ISMR skill by initializing the ocean component of CFSv2 using new improved ocean ICs based on Global Ocean Data Assimilation System (GODAS) analysis. This new analysis is better than the NCEP GODAS, which uses the earlier-generation ocean model MOM4p0d and assimilates observed temperature and synthetic salinity using the 3DVar assimilation scheme. However, the new, improved GODAS analysis uses the MOM4p1 ocean model and assimilates observed salinity instead of synthetic salinity. We performed twin sets of nearly identical model experiments differing only in their ICs, with one set using NCEP ICs and the other using the new ICs (NIC). The NIC experiment consistently shows better El Niño–Southern Oscillation prediction skill than the NCEP IC experiment. This advancement leads to improvement in the ISMR skill. We found that the substantial improvements in both oceanic and atmospheric variables in a coupled feedback system contributed to the improved ISMR skills. The enhanced ISMR skill score of the NIC experiment might be the result of improved teleconnections, better depiction of large-scale monsoon circulations, and reduced model drift.

Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

Inter-comparison of two regional climate models (RegCM and WRF) in downscaling CFSv2 for the seasonal prediction of Indian summer monsoon

The efficacy of two latest versions of regional climate models (RegCM and WRF) for simulating the Indian summer monsoon (JJAS) is tested in this study. The CFSv2 hindcast outputs are downscaled over the Indian monsoon domain for 11 contrasting monsoon seasons using the regional models. The April start ensembles of the CFSv2 are averaged to generate the initial and lateral boundary conditions for driving the WRF and RegCM. The regional models perform better in simulating the Indian summer monsoon features better than the parent CFSv2 model. The rainfall pattern as well as the intensities are improved with the dynamical downscaling and the errors in the rainfall are minimized over the GCM hindcast. On comparing the two regional models, the RegCM overestimates the rainfall during the excess and normal monsoon seasons. The RCMs improve the skill of rainfall prediction as compared to the GCM and WRF shows better skill in particular. One peculiar finding of this study is that the daily rainfall biases averaged over all the years of simulation shows that the two RCMs show similar biases with RegCM showing stronger biases occasionally. It may be implied that the errors from GCM in the form of the ICBC might be influencing the simulation in the RCMs. The upper air and surface parameters analysis shows that the WRF performs better in representing the meridional overturning of the lower atmospheric circulation, monsoon trough and the heat low over north western India which are prominent features of the semi-permanent features of the Indian summer monsoon which may be helping in improving the rainfall over the RegCM. The wind pattern as well as the relative humidity along the vertical column of the atmosphere are captured better in the WRF model. Diagnostics of CAPE & vertically integrated moisture transport supports the finding of the rainfall being simulated better in the WRF model.

Does Increasing Horizontal Resolution Improve Seasonal Prediction of Indian Summer Monsoon?: A Climate Forecast System Model Perspective

AbstractThe seasonal prediction skill of climate forecast system model with two different resolutions, namely T126 and T382, is studied using hindcast data. Using novel diagnostic tools such as total variation distance and two‐state Markov Chain analysis, it is shown that increasing the horizontal resolution of the model has minimal impact on the quality of seasonal prediction. The underlying rain distribution and associated transition probabilities are very similar in both model versions. The Markov chain analysis also provides critical clues about the issues associated with convective processes in the model. Both the models produce longer (shorter) wet (dry) spells compared to the observations. Models are trying to bring the atmosphere closer to convective quasi‐equilibrium, leading to a substantial departure from observed features. Although the conventional error metrics are helpful to assess the prediction skills, the new metrics used in the study provide further insights on possible pathways to improve model moist physics.

Seasonal prediction of Indian summer monsoon in India: The past, the present and the future

Seasonal prediction of Indian summer monsoon in India: The past, the present and the future

Multi-physics schema for sub-seasonal prediction of Indian summer monsoon

Multi-physics schema for sub-seasonal prediction of Indian summer monsoon

On the build-up of dust aerosols and possible indirect effect during Indian summer monsoon break spells using recent satellite observations of aerosols and cloud properties

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.

Gain of one-month lead time in seasonal prediction of Indian summer monsoon prediction: comparison of initialization strategies

Reasonable seasonal prediction skill for the Indian summer monsoon rainfall has been achieved using the Monsoon Mission (MM) Seasonal Forecast model, at a lead time of 3 months. The ensembles in the MM model are generated by utilizing lagged initial conditions. The possibility of enhancing the lead time is explored by using the burst ensemble approach. Comprehensive seasonal hindcast experiments carried out in this study reveal that the two methods exhibit similar skill scores for the major tropical phenomenon which govern ISMR variability. In general, the model forecasts are slightly under-dispersive but satisfactorily represent the spread-error relationship for major tropical oceanic climate modes. The ratio between the spread and RMSE is small for ISMR forecasts. Though the skill scores for the majority of indices are similar, the monsoon teleconnections seem to be quite sensitive to the initialization strategy. It is found that the burst initialization method provides a gain of 1-month lead time compared to lagged initialization strategy employed in previous studies without compromising the prediction skill. The gain of a months’ lead time with the burst ensemble approach is a tempting and useful proposition, which can be crucial for the policy- and decision-makers.

Boreal Spring El Niño Convective State and Its Impact on Monsoon Onset

AbstractA lesser known relationship between boreal spring El Niño state and the Indian summer monsoon (ISM) is explored, considering the differences in the evolution of sea surface temperature and convection over the Pacific. Rossby wave tracing analysis reveals that the boreal spring El Niño convective state permits two distinct eastward waveguides from the central Pacific to the ISM domain. The first waveguide reaches the northern Indian region roughly along the African‐Asian jet, and the second waveguide crosses over to the Southern Hemisphere over the eastern Pacific and extends up to the south Indian Ocean region. The net result is an altered free tropospheric temperature distribution over the ISM domain in the month of May leading to a delayed monsoon onset. The existence of the two waveguides and the modulation of monsoon onset makes the boreal spring El Niño state an important component to be accounted for in ISM prediction.

Reply to Comment by E. T. Swenson, D. Das, and J. Shukla on “Unraveling the Mystery of Indian Summer Monsoon Prediction: Improved Estimate of Predictability Limit”

AbstractSwenson et al. (2020, https://doi.org/10.1029/2020JD033037) (hereafter SN20) raise some technical issues on observed correlations between the synoptic variances and the seasonal mean of area averaged (all‐India or central India) Indian summer monsoon rainfall (ISMR) reported in Saha et al. (2019, https://doi.org/10.1029/2018JD030082) (hereafter SA19). SN20 did not comment on the other major finding of SA19 on model‐based prediction and predictability of the ISMR. Therefore, we focus in this reply only on the observational part of SA19 even though the modeling part is closely related to the observations. While we disagree with SN20 in all three aspects of their comments, we present additional analysis to clarify the scientific basis for our selection of area averaging, the selection of 30‐year period for the study and argue that the relevant probability density function of daily rainfall is not that of the individual stations (or grid points) but that of the daily averaged rainfall over a comparable area. On the debate of small area averaging (major point of SN20), we argue that it is physically meaningless in the context of ISMR.

Comment on “Unraveling the Mystery of Indian Summer Monsoon Prediction: Improved Estimate of Predictability Limit” by Saha et al.

AbstractIn a recent study, Saha et al. (2019, https://doi.org/10.1029/2018JD030082) examine the correlation between boreal summer seasonal mean rainfall over India and rainfall variance as a function of subseasonal time scale and find that the correlation has a local maximum (exceeding a value of 0.6) for synoptic time scales (2–5 day periods). They claim these results to be a major advancement in understanding monsoon predictability but do not provide a clear physical explanation. Here we examine the sensitivity of this relationship to the details of the analysis and only consider the observed correlation identified by Saha et al. (2019, https://doi.org/10.1029/2018JD030082). There is large sensitivity in the correlation maximum between spatially averaged seasonal mean rainfall and synoptic scale rainfall variance averaged over the same domain. The correlation maximum is weaker over the longer period of 1901–2015, and more notably it is highly sensitive to the domain of prior averaging. A correlation peak is not found outside central India and is neither found within central India when averaging over a smaller domain. Averaging over a larger domain results in a disproportionate reduction in synoptic variance (that is of most interest). The peak in correlation between seasonal mean and 2–5 day variance only emerges after first averaging over a sufficiently large region; thus, the maximum appears to be an artifact of spatial averaging. It is further pointed out that a positive mean‐variance relationship is an intrinsic property of rainfall, and thus, its existence alone does not necessarily imply any physical, causal, and/or predictive connection between time scales.

Association of ENSO and IOD with Summer Monsoon Rainfall Variability over Central India

Present study aims to unfold the trend of summer monsoon (JJAS) rainfall for past 114 years (1901–2014) over central India (16.5°N to 26.5° N, 74.5°E to 86.5°E). Further its variability over a long-term time-scale and association with ENSO (El Nino Southern Oscillation) and IOD (Indian Ocean Dipole) like events and further investigated that has more significant association. Due to a significant contribution of summer monsoon (JJAS) rainfall to annual rainfall, all investigation has been performed for summer monsoon rainfall by using high resolution (0.5°×0.5°) CRU TS3.23 gridded; monthly rainfall global data set for 114 years. Further to this, the association of ENSO and IOD has been investigated and it is observed that ENSO has a strong correlation with Indian summer monsoon rainfall as compared to IOD however for some of the monsoon seasons IOD appears to override the ENSO effect. It is noted that the ENSO cycle is significantly associated with summer monsoon rain, but the role of IOD in modulating monsoon rainfall cannot be ignored. The uncertain and irregular behavior of Indian monsoon causes a substantial impact on the economy and livelihood. Thus results may be used for the better understanding and predictability of Indian summer monsoon. Keywords: Trend, summer monsoon, variability, ENSO, IOD, significance, correlation, rainfall index, El Nino

Evaluation of Indian Summer Monsoon Rainfall Using the NCEP Global Model: An SST Impact Study

The study evaluates the Indian summer monsoon prediction skill of the Atmospheric General Circulation Model (AGCM) and the impact of sea surface temperature (SST) boundary forcing on the model performance. The National Center for Environmental Prediction's (NCEP's) T170/L42 AGCM model configured with a horizontal resolution of 75 × 75 km, with 42 vertical levels is used for the study. The SST-rainfall relationship is examined in the coupled Climate Forecast System version 2 (CFSv2) model, as CFSv2-predicted SST is used as input for the T170 model. The NCEP Global Forecast System-T170 (GFS-T170) simulations are carried out with boundary forcing of observed SST, CFSv2-predicted SST and the bias-corrected CFSv2 SST. An ensemble of seasonal runs was made using the initial conditions of May to September, and integrated up to September 30th. The significance of discontinuity in the initial conditions due to climate forecast system reanalysis (CFSR) is assessed based on the two-period approach of climatology for the two time scales of 1985–1998 and 1998–2009. CFSv2 predicted climatological summer monsoon rainfall with a significant dry bias over the three convection zones; Western Ghats, Central India and North-east India, and cold bias over the Indian ocean basin and central equatorial Pacific, with strong cold bias over a narrow region of equatorial Pacific. The model could capture 64% (16 out of 25) of the year’s rainfall anomaly signal. The skill of the model is improved in the recent period (1999–2009). The model could simulate the negative Nino 3 and excess rainfall and the La Nina event realistically for the year 1988. The model shows a large difference in Nino indices for the years 1987 and 1998, which led to the unrealistic rainfall simulation. The model has a low skill for indicating the relationship between the Indian Ocean Dipole (IOD) and Indian summer monsoon rainfall (ISMR). The CFSv2 model could not capture the strong positive correlation of the IOD and strong negative correlation of Nino 3 with the ISMR for the period 1999–2009 realistically, suggesting improvement of SST simulation in the CFSv2 model. The T170 model forced with observed SST shows wet bias in peninsular India and dry bias over North-east India, whereas that of CFSv2-predicted SST simulated a wet bias in peninsular India and widespread dry bias in North and Central India. When the model was forced with bias-corrected CFSv2 SST, the dry bias improved in North and Central India, and the intensity of wet bias increased in peninsular India. The model could capture 56, 48 and 64% of the year’s rainfall anomaly signal (positive or negative) correctly in the same sign for being forced with observed SST, CFSv2-predicted SST, and bias-corrected CFSv2 SST, respectively.

Impact of Land Surface Initialization and Land-Atmosphere Coupling on the Prediction of the Indian Summer Monsoon with the CFSv2

The impact of initial land-surface states on monthly to seasonal prediction skill of the Indian summer monsoon (June- September) is investigated using a suite of hindcasts made with the Climate Forecast System version 2 (CFSv2) operational forecast model. The modern paradigm of land-atmosphere coupling is applied to quantify biases in different components of the land-atmosphere coupled system and their effect on systematic errors. Three sets of hindcasts are performed for the period spanning 1982-2009 initialized at the start of April, May and June. For a particular initial date of a given year, one member (Control run) has the analyzed land initial state consistent with the atmosphere, sea ice and ocean states for that year; the other 27 members have land states taken from each of the remaining 27 years. There is significant improvement in the deterministic prediction skill of near surface temperature and soil moisture on monthly and seasonal time scales due to realistic land initial conditions. The improvement occurs in those areas where the land-atmosphere coupling is strongest. Improvements in the prediction skill of precipitation are confined to relatively small areas. The pattern of skill differences resembles patterns of land-atmosphere coupling strength, while biases in the representation of land-atmosphere coupling affect the skill of temperature and rainfall. The re-emergence of skill in temperature and precipitation towards the end of the season over northwest India within April and June IC hindcasts may be attributed to better simulation of the withdrawal phase of the monsoon as well as increased land-atmosphere coupling. For May IC hindcasts, increased skill in air temperature on the sub-seasonal time scales could be due to other large-scale factors. Errors in the parameterization of radiation, convection, boundary layer processes, surface moisture fluxes and the representation of vegetation contribute to decay in potential predictability and skill attributable to land initial conditions. Furthermore, incorrect representation of daily and sub-daily precipitation statistics over land also likely lead to errors in land-atmosphere coupling. Above all, the importance of accurate land surface initialization and land-atmosphere coupling in improving the Indian summer monsoon prediction on sub-seasonal to seasonal time scales is emphasized.

Predictability and prediction of Indian summer monsoon by CFSv2: implication of the initial shock effect

This study evaluates the seasonal predictability of the Indian summer monsoon (ISM) rainfall using the Climate Forecast System, version 2 (CFSv2), the current operational forecast model for subseasonal-to-seasonal predictions at the National Centers for Environmental Prediction (NCEP). From a 50-year CFSv2 simulation, 21 wet, dry and normal ISM cases are chosen for a set of seasonal “predictions” with initial states in each month from January to May to conduct predictability experiments. For each prediction, a five-member ensemble is generated with perturbed atmospheric initial states and all predictions are integrated to the end of September. Based on the measures of correlation and root mean square error, the prediction skill decreases with lead month, with the initial states with the shortest lead (May initial states) generally showing the highest skill for predicting the summer mean (June to September; JJAS) rainfall, zonal wind at 850 hPa and sea surface temperature over the ISM region in the perfect model scenario. These predictability experiments are used to understand the finding reported by some recent studies that the NCEP CFSv2 seasonal retrospective forecasts generally have higher skill in predicting the ISM rainfall anomalies from February initial states than from May ones. Comparing the May climatologies generated by the February and May initialized CFSv2 retrospective forecasts, it is found that the latter shows larger bias over the Arabian Sea, with stronger monsoon winds, precipitation and surface latent heat flux. Although the atmospheric bias diminishes quickly after May, an accompanying cold bias persists in the Arabian Sea for several months. It is argued that a similar phenomenon does not occur in the predictability experiments in the perfect model scenario, because the initial shock is negligible in these experiments by design. Therefore, it is possible that the stronger model bias and initial shock in the May CFSv2 retrospective forecasts over the Arabian Sea may be a major factor in affecting ISM prediction skill.

Significance of high resolution GHRSST on prediction of Indian Summer Monsoon

In this study, the Weather Research and Forecasting (WRF) model was used to assess the importance of very high resolution sea surface temperature (SST) on seasonal rainfall prediction. Two different SST datasets available from the National Centers for Environmental Prediction (NCEP) global model analysis and merged satellite product from Group for High Resolution SST (GHRSST) are used as a lower boundary condition in the WRF model for the Indian Summer Monsoon (ISM) 2010. Before using NCEP SST and GHRSST for model simulation, an initial verification of NCEP SST and GHRSST are performed with buoy measurements. It is found that approximately 0.4K root mean square difference (RMSD) in GHRSST and NCEP SST when compared with buoy observations available over the Indian Ocean during 01 May to 30 September 2010. Our analyses suggest that use of GHRSST as lower boundary conditions in the WRF model improve the low level temperature, moisture, wind speed and rainfall prediction over ISM region. Moreover, temporal evolution of surface parameters such as temperature, moisture and wind speed forecasts associated with monsoon is also improved with GHRSST forcing as a lower boundary condition. Interestingly, rainfall prediction is improved with the use of GHRSST over the Western Ghats, which mostly not simulated in the NCEP SST based experiment.

How distinct are the two flavors of El Niño in retrospective forecasts of Climate Forecast System version 2 (CFSv2)?

Two different flavors of El Nino-Southern Oscillation (ENSO, canonical east Pacific type and Modoki/central Pacific type) are reported in the recent decades and are found to influence the global climate in different ways. The success of a seasonal prediction system is dependent on its ability to capture these two ENSO flavors accurately, together with associated teleconnections. The present study analyses the ability of Climate Forecast System version 2 (CFSv2) in simulating the two El Nino flavors and their teleconnections. The study uses two versions of CFSv2 in which the atmospheric model horizontal resolutions are different (T126 at 100 km and T382 at 38 km) and are initialized from a calendar month, ranging from February to June. The canonical ENSO pattern is captured as prominent mode of tropical Pacific sea surface temperature (SST) by both resolutions of CFSv2. Even though the tri-polar structure of ENSO Modoki is simulated as second mode, it has some disagreement with observations. The canonical El Nino induced SST, rainfall and atmospheric circulation in the tropical Pacific in summer and fall seasons are comparable with observations in both models. Meanwhile, the teleconnections in the tropical Indian Ocean and Indian monsoon regions are close to observations in T382 only. Teleconnections associated with El Nino Modoki are proper in T382 hindcasts, in which SST bias in Indian Ocean is slightly warm and the cold bias in central Pacific is marginal (mainly for Feb IC hindcasts). The present study indicates that the distinction of ENSO flavors in summer is the major reason for the higher skill of Indian summer monsoon rainfall (ISMR) in CFSv2 T382 Feb IC hindcasts. However, teleconnections associated with two flavors of ENSO are not distinguishable in fall and winter seasons, even in higher resolution model due to the presence of strong cold SST bias in central Pacific and warm SST bias in extreme east Pacific. Thus present study confirms that, higher resolution CFSv2 is required to differentiate the flavors of ENSO and their teleconnections properly. It has better prediction skill for ISMR at a lead time of 4 months, which is significant for seasonal prediction of Indian summer monsoon.

Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution

AbstractThis study compares the simulation and prediction skill of the Indian summer monsoon at two different horizontal resolutions viz., T126 (~100 km) and T382 (~38 km) using 28 years of hindcast runs of the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) model. It is found that the simulation of the mean state of the South Asian summer monsoon, its variance, and prediction skill of the all India summer monsoon rainfall (AISMR) are better represented in the high‐resolution configuration (T382) of the CFSv2 compared to the low‐resolution (T126) configuration. In the high‐resolution run, the systematic bias in the teleconnection between the AISMR and Indian Ocean Dipole (IOD) has considerably reduced and the teleconnections between the AISMR and El Niño–Southern Oscillation (ENSO) remained same. We hypothesize that the better simulation of mean climate and IOD‐AISMR teleconnection in high‐resolution configuration (T382) of CFSv2 are responsible for the improved prediction skill of AISMR in T382 configuration. Although the T382 configuration of CFSv2 has shown a significant improvement in the simulation and prediction of Indian summer monsoon as compared to the T126 configuration, several parallel efforts are still essential to understand the processes controlling some of the systematic biases of CFSv2 and those efforts are underway as part of the Monsoon Mission project.

Autoencoder-based identification of predictors of Indian monsoon

Prediction of Indian summer monsoon uses a number of climatic variables that are historically known to provide a high skill. However, relationships between predictors and predictand could be complex and also change with time. The present work attempts to use a machine learning technique to identify new predictors for forecasting the Indian monsoon. A neural network-based non-linear dimensionality reduction technique, namely, the sparse autoencoder is used for this purpose. It extracts a number of new predictors that have prediction skills higher than the existing ones. Two non-linear ensemble prediction models of regression tree and bagged decision tree are designed with identified monsoon predictors and are shown to be superior in terms of prediction accuracy. Proposed model shows mean absolute error of 4.5 % in predicting the Indian summer monsoon rainfall. Lastly, geographical distribution of the new monsoon predictors and their characteristics are discussed.

Indian summer monsoon prediction and simulation in CFSv2 coupled model

AbstractUsing carefully designed coupled model experiments, we have demonstrated that the prediction skill of the all India summer monsoon rainfall (AISMR) in Climate Forecast System version 2 (CFSv2) model basically comes from the El‐Niňo Southern Oscillation‐Monsoon teleconnection. On the other hand, contrary to observations, the Indian Ocean coupled dynamics do not have a crucial role in controlling the prediction skill of the AISMR in CFSv2. We show that the inadequate representation of the Indian Ocean coupled dynamics in CFSv2 is responsible for this dichotomy. Hence, the improvement of the Indian Ocean coupled dynamics is essential for further improvement of the AISMR prediction skill in CFSv2.