Abstract
Many global climate models, including the Climate Forecast System version 2 (CFSv2), have a biased representation of subseasonal modes of variability of the Indian summer monsoon. For ...instance, they simulate a weaker summer mean monsoon low‐pressure systems (LPS) climatology, faster than observed northward propagation of monsoon intraseasonal oscillations (MISOs), and a systematic dry bias over Indian landmass. The Bay of Bengal (BoB), with its shallow mixed layers and unique thermal stratification, significantly modulates the convective activity in this region at subseasonal‐to‐seasonal timescales through modulation of sea surface temperature. The highly stratified upper ocean in the BoB is due to the enormous freshwater it receives from rains and rivers. A river routing model is coupled to the CFSv2 to account for the riverine freshwater and the improvements in modelling the upper‐ocean structure are analysed. Model simulations indicate that inclusion of temporally varying riverine freshwater improves the upper‐ocean state in the BoB and the observed mixed‐layer temperature gradients in the Bay are simulated reasonably after incorporating the time varying river runoff. This resulted in increased LPS lifetime and track density, and enhanced rainfall over central India. Better representation of the upper‐ocean stratification in the model leads to larger post‐convection shoaling of mixed layers at intraseasonal timescales, thereby forming thick barrier layers. Enhanced air–sea interactions restricted to the shallow mixed layer are associated with stronger vorticity, specific humidity and low‐level convergence to the north of the intraseasonal convection band. This enhanced low‐level moisture convergence north of the convection centre results in realistic northward propagation of MISO and aids LPS activity. It is demonstrated that better simulation of the upper‐ocean structure in coupled climate models can improve the representation of subseasonal modes of monsoon variability. These results bear important implications for operational forecasting.
State-of-the-art coupled models have several limitations in representing the phase and amplitude characteristics of monsoon intra-seasonal oscillations (MISO). Specifically, the models’ deficiencies ...in predicting stronger active spells have been widely reported in earlier studies. In the present study, we endeavour to overcome this limitation by improving the representation of the diurnal cycle of the sea surface temperature and the associated feedback processes. In the present study, we demonstrate that resolving the diurnal cycle rectification along with implementing a modern bulk surface-flux algorithm in a global coupled model improves the simulation of MISO characteristics. The present analysis showcases how rectification in the presence of a revised turbulent flux algorithm and diurnal skin temperature parameterisation can modulate the oceanic, atmospheric, and interfacial properties so that the coupled model can better simulate stronger active monsoon spells. The essential requirements for the coherent northward propagation mechanisms of MISOs are pronounced in the presence of intra-seasonal rectification by diurnal SSTs and air–sea interactive flux feedbacks.
El Niño Modoki and its possible teleconnection Ashok, Karumuri; Behera, Swadhin K.; Rao, Suryachandra A. ...
Journal of Geophysical Research - Oceans,
November 2007, Letnik:
112, Številka:
C11
Journal Article
Recenzirano
Using observed data sets mainly for the period 1979–2005, we find that anomalous warming events different from conventional El Niño events occur in the central equatorial Pacific. This unique warming ...in the central equatorial Pacific associated with a horseshoe pattern is flanked by a colder sea surface temperature anomaly (SSTA) on both sides along the equator. empirical orthogonal function (EOF) analysis of monthly tropical Pacific SSTA shows that these events are represented by the second mode that explains 12% of the variance. Since a majority of such events are not part of El Niño evolution, the phenomenon is named as El Niño Modoki (pseudo‐El Niño) (“Modoki” is a classical Japanese word, which means “a similar but different thing”). The El Niño Modoki involves ocean‐atmosphere coupled processes which include a unique tripolar sea level pressure pattern during the evolution, analogous to the Southern Oscillation in the case of El Niño. Hence the total entity is named as El Niño–Southern Oscillation (ENSO) Modoki. The ENSO Modoki events significantly influence the temperature and precipitation over many parts of the globe. Depending on the season, the impacts over regions such as the Far East including Japan, New Zealand, western coast of United States, etc., are opposite to those of the conventional ENSO. The difference maps between the two periods of 1979–2004 and 1958–1978 for various oceanic/atmospheric variables suggest that the recent weakening of equatorial easterlies related to weakened zonal sea surface temperature gradient led to more flattening of the thermocline. This appears to be a cause of more frequent and persistent occurrence of the ENSO Modoki event during recent decades.
This 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.
Key Points
Compare the simulation and prediction skills of AISMR at two atmospheric horizontal resolutions
AISMR variance and prediction skill are better represented in the high‐resolution CFSv2
Improved simulation of mean climate and IOD‐ISM teleconnection in high resolution is reported
Present work uses 1979-2005 monthly observational data to study the impacts of El Nino Modoki on dry/wet conditions in the Pacific rim during boreal summer. The El Nino Modoki phenomenon is ...characterized by the anomalously warm central equatorial Pacific flanked by anomalously cool regions in both west and east. Such zonal SST gradients result in anomalous two-cell Walker Circulation over the tropical Pacific, with a wet region in the central Pacific. There are two mid-tropospheric wave trains passing over the extratropical and subtropical North Pacific. They contain a positive phase of a Pacific-Japan pattern in the northwestern Pacific, and a positive phase of a summertime Pacific-North American pattern in the northeastern Pacific/North America region. The western North Pacific summer monsoon is enhanced, while the East Asian summer monsoon is weakened. In the South Pacific, there is a basin-wide low in the mid-latitude with enhanced Australian high and the eastern South Pacific subtropical high. Such an atmospheric circulation pattern favors a dry rim surrounding the wet central tropical Pacific. The El Nino Modoki and its climate impacts are very different from those of El Nino. Possible geographical regions for dry/wet conditions influenced by El Nino Modoki and El Nino are compared. The two phenomena also have very different temporal features. El Nino Modoki has a large decadal background while El Nino is predominated by interannual variability. Mixing-up the two different phenomena may increase the difficulty in understanding their mechanisms, climate impacts, and uncertainty in their predictions.
A dry bias in climatological Central Indian rainfall plagues Indian summer monsoon (ISM) simulations in multiple generations of climate models. Here, using observations and regional climate modeling, ...we focus on a warm coastal Bay of Bengal sea surface temperature (SST) front and its impact on Central Indian rainfall. The SST front, featuring sharp gradients as large as 0.5 °C/100 km, is colocated with a mixed layer depth (MLD) front, in a region where salinity variations are known to control MLD. Regional climate simulations coupling a regional atmospheric model with an ocean mixed layer model are performed. A simulation with observed MLD climatology reproduces SST, rainfall, and atmospheric circulation associated with ISM reasonably well; it also eliminates the dry bias over Central India significantly. Perturbing MLD structure in the simulations, we isolate the SST front's impact on the simulated ISM climate state. This experiment offers insights into ISM climatological biases in the coupled NCEP Climate Forecast System version-2. We suggest that the warm SST front is essential to Central Indian rainfall as it helps to sustain deep and intense convection in its vicinity, which may be a source for the vortex cores seeding the monsoon low-pressure systems.
The sensitivity of seasonal prediction (June to September) of Indian monsoon to initial state from two variants of coupled data assimilation (CDA) products, viz. the Climate Forecast System (CFS) ...Reanalysis (CFSR) and Indian Institute of Tropical Meteorology, University of Maryland‐ Weakly Coupled Analysis (IWCA) is explored in this study. The IWCA implements the local ensemble transform Kalman filter, and incorporates theoretically advanced features of flow‐dependency and ensemble‐based analysis compared to CFSR. The CFS version‐2 predictions using IWCA simulate the large‐scale monsoon features, and convection centers well, and improve prediction skills compared to CFSR predictions. The enhanced analysis quality and Ocean‐Atmospheric cross‐domain equilibrium in IWCA reduce initial shocks in springtime predictions. Further, the sustained ensemble consistency aided to simulate the variability better and improved the seasonal predictions. The study strongly advocates the adaptation of advanced CDA methods for seasonal monsoon and probable seamless predictions.
Plain Language Summary
Early prediction of sub‐seasonal to inter‐annual variations in Indian summer monsoon rainfall has multifaceted benefits (e.g., agriculture, economy, etc.). Hence any significant improvement in the prediction skill could be highly appreciated. The Ocean and Atmosphere observations are increased tremendously during the recent decades due to satellites and improved observational networks. Since monsoon prediction partly depends on the state of the Ocean and Atmosphere (i.e., model starting point or “analysis”), these observations can be used to improve the analysis, thereby the predictions. Even though sophisticated data assimilation techniques have been demonstrated to strengthen the analysis quality, their operational utilization in the context of monsoon prediction is still far from the reality due to the difficulty in adaptation and computational limitations. The recent improvements in high‐performance computing and data assimilation research under Monsoon Mission have aided us in implementing an advanced data assimilation method to the operational monsoon prediction model. Using this new analysis, the seasonal monsoon predictions improved. The present study reports the enhancements and attempts to explore probable mechanisms responsible for the improvement. The study is vital to operational agencies in adopting advanced data assimilation methods, particularly to boost monsoon predictions and elsewhere.
Key Points
Seasonal monsoon predictions improved using Local Ensemble Transform Kalman Filter‐based coupled initial conditions compared to Climate Forecast System Reanalysis
The initial shocks in springtime predictions are reduced due to improved analysis quality and Ocean‐Atmosphere cross‐domain equilibrium
The sustained ensemble consistency further allowed better representation of tropical modes of variability in predictions for a longer time
ABSTRACT
Simulation of Indian summer monsoon features by latest coupled model of National Centers for Environmental Prediction (NCEPs) Climate Forecast System version 2 (CFSv2) is attempted in its ...long run. Improvements in the simulation of Indian summer monsoon as compared with previous version (CFSv1) is accessed and areas which still require considerable refinements are introduced. It is found that, spatial pattern of seasonal mean rainfall and wind circulations are more realistic in CFSv2 as compared with CFSv1. Variance and northward propagation of intraseasonal oscillation (ISO), which also contribute to the seasonal mean rainfall are remarkably improved. However, the central Indian dry bias still persists and amplified. Pervasive cold bias in surface (2 m air temperature) as well as in the whole troposphere is further increased in CFSv2. These cold biases may be partly attributed to the lack of model's ability to realistically simulate the ratio of convective and stratiform rainfall. Sea‐surface temperature (SST) over the Indian Ocean is underestimated in CFSv2. However, CFSv1 shows east–west dipole structure in the bias. The teleconnection of El Nino Southern Oscillation (ENSO) and Indian summer monsoon rainfall (ISMR) in terms of Niño3 SST and monsoon rainfall correlation is more realistic in the latest version of the model. Overall, there are substantial improvements in CFSv2 as compared with CFSv1, but it has to evolve further to realistically simulate the mean and variability of ISMR.
A mechanism of internal variability of Indian summer monsoon through the modulation of intraseasonal oscillation (ISO) by land‐atmosphere feedback is proposed. Evidence of feedback between surface ...soil moisture and ISOs is seen in the soil moisture data from GSWP‐2 and rainfall data from observations. Using two sets of internal simulation by a regional climate model (RCM), it is shown that internally generated anomalous soil moisture interacts with the following ISO and generates interannual variability. To gain further insight, 27 years of sensitivity experiment by prescribing wet (dry) soil moisture condition during break (active) period along with a control simulation are carried out. The sensitivity experiment reveals the large‐scale nature of soil moisture and ISO feedback which takes place through the changes in atmospheric stability by altering lower‐level atmospheric conditions. The feedback is inherent to the monsoon system and a part of it acts through the intraseasonal varying memory of soil moisture. The RCM used to test the hypothesis is constrained by one‐way interactions at the lateral boundary. Experiments with a much larger domain upheld the findings and hence suggest the true nature of soil moisture and ISO feedback present in the monsoon system.
Key Points
Indian summer monsoon and ISO interaction through soil moisture memory
Mechanism of ISO variability through internal feedback
Simulation of monsoon by regional climate model
The present study evaluates the skill of seasonal forecasts of temperatures over India during April to June using the Monsoon Mission Coupled Forecasting System (MMCFS) model hindcasts, which are ...initialized with February initial conditions. Model hindcast data of 1981–2017 period have been used for the analysis. The India Meteorological Department (IMD) gridded temperature dataset has been used for model verifications. The MMCFS model captures the annual cycle of temperatures reasonably well, but with a higher mean and smaller variability compared to observations. The model hindcasts show a significant skill for seasonal forecasts of temperatures over most of northwest and central India. Empirical Orthogonal Function (EOF) analysis suggests that the model captures temporal and spatial characteristics of different modes of maximum temperatures but with less accuracy. The model teleconnections of maximum temperatures with Indian Ocean sea surface temperatures (SSTs) and El Niño–Southern Oscillation (ENSO) are weakly represented. The model is also found capable of predicting the spatial distribution of heat wave characteristics such as heat wave frequency (HWF) and heat wave duration (HWD) reasonably well. The present study suggests that the MMCFS Model can be used to generate a useful outlook of hot weather seasonal temperatures and heat waves over India.
Annual cycle of (a) Tmax (°C), (b) Tmin (°C), (c) diurnal temperature range (DTR; °C) averaged over India (excluding northeast India) for the period 1981–2017, simulated in MMCFS model (in red colour) along with IMD observation (given in black colour). Standard deviation of Tmax and Tmin has been plotted as error bar in (a, b). (d) Ensemble spread of Tmax and Tmin from the MMCFS model.
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