Variability and Predictability of Summer Monsoon Rainfall over Pakistan

Adnan, Muhammad Akram; Khan, Firdos; Rehman, Nadia; Ali, Shaukat; Hassan, Sher Shah; Dogar, Muhammad Mubashar; Mehmood, Shahbaz; Hasson, Shabeh ul

doi:10.1007/s13143-020-00178-2

Cited by 19 publications

(11 citation statements)

References 28 publications

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“…There are number of studies on the monsoon precipitation variability over Pakistan (Del Río et al ., 2013; Iqbal et al ., 2016; Latif and Syed, 2016; Iqbal and Athar, 2018; Adnan et al ., 2020), but how the large‐scale forcing from ENSO influences the early summer (premonsoon) seasonal (May–June) SAT variability over Pakistan is not well established. The monsoon onsets over Pakistan in early July (Latif and Syed, 2016) and the early summer (premonsoon) period is considered as dry and the hottest season over Pakistan, where the temperature peaks during its annual cycle.…”

Section: Introductionmentioning

confidence: 99%

Early summer surface air temperature variability over Pakistan and the role of El Niño–Southern Oscillation teleconnections

Rashid¹,

Abid

Almazroui

et al. 2022

Intl Journal of Climatology

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Early summer (May–June) is the season where the surface air temperature (SAT) variability is largest and may result in extreme temperature conditions over Pakistan. Therefore, we analysed the early summer interannual SAT variability over Pakistan for the period 1981–2018 using observational dataset and model simulations. We noted that upper‐level anticyclonic circulation anomalies over Pakistan, which are associated with upper to the middle tropospheric descending motion, favour clear skies with an increase in net shortwave radiation that leads to extreme surface warming over the region. Moreover, the El Niño–Southern Oscillation (ENSO) based on Niño3.4 SST index is negatively associated with SAT anomalies over Pakistan. The cold ENSO phase favours the positive geopotential height anomalies due to the strengthening of the Walker circulation that enhance the sinking motion and result in less cloudiness leading to the extreme higher surface temperature conditions over Pakistan, while the opposite happens in the warm ENSO phase. Moreover, the Saudi‐KAU Atmospheric Global Climate Model (AGCM) simulated large‐scale patterns that are in good agreement with the observations. Sensitivity experiments with the AGCM confirm that cold SST anomalies in the ENSO region significantly favour above‐normal SAT anomalies over Pakistan, while the opposite happens in the warm ENSO phase. These results are important to understand and potentially predict regional heatwaves over the South Asian region, particularly over Pakistan.

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Section: Introductionmentioning

confidence: 99%

Early summer surface air temperature variability over Pakistan and the role of El Niño–Southern Oscillation teleconnections

Rashid¹,

Abid

Almazroui

et al. 2022

Intl Journal of Climatology

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“…The positive/negative Phase of ENSO (EL-Nino/La Nina) encourageous/ discourages Hadley Cell, which consequently has significant impact on South Asian Region . Adnan et al (2020) used highly correlated PCs to input in multiple regression model to study the variability and predictability of Monsoon precipitation over Pakistan on inter-and intra-annual basis. The model proficiently validated the result for 2014-2015 and produced profound outputs in predicting inter-annual Monsoon precipitation.…”

Section: Introductionmentioning

confidence: 99%

The impact of climate indices on precipitation variability in Baluchistan, Pakistan

Aamir

Hassan

2020

Tellus A: Dynamic Meteorology and Oceanography

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“…The inter‐annual variability of the summer monsoon precipitation over South Asia is affected remotely by the sea surface temperature (SST) anomalies in the Pacific (Shukla & Paolina, 1983; Webster & Yang, 1992; Kumar, Rajagopalan, & Cane, 1999, among others) related to El Niño–Southern Oscillation (ENSO) and Atlantic (Cherchi, Kucharski, & Colleoni, 2018; Dogar, Kucharski, & Azharuddin, 2017; Syed & Kucharski, 2016; Yadav, Srinivas, & Chowdary, 2018), and it is also locally influenced by SST anomalies in the Indian Ocean (Cherchi & Navarra, 2013; Yuan, Yang, Zhou, & Li, 2008). For the boreal summer monsoon precipitation over Pakistan, several studies focused on the long‐term trend and variability using observational data and empirical models (e.g., Hartmann & Andresky, 2013; Iqbal & Athar, 2018; Khan, Ali, & Pilz, 2018; Adnan et al, 2020, among others). Specifically, Webster et al (2011) examined the predictability of the intra‐seasonal summer monsoon rainfall over Pakistan using ECMWF 15‐day ensemble prediction system.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, Webster et al (2011) examined the predictability of the intra‐seasonal summer monsoon rainfall over Pakistan using ECMWF 15‐day ensemble prediction system. They concluded that “Pakistan rainfall is highly predictable out to 6–8 days.” Recently, Adnan et al (2020) also obtained a very high skill of monsoon rainfall over Pakistan on an inter‐annual timescale using a linear statistical forecast model based on the highly correlated principal components of sea level pressure, horizontal and meridional winds over the period 2001–2013. Besides these empirical studies, the seasonal forecast data produced by the initialized seasonal forecast models has never been explored for the predictability and forecast skill assessment for the peak summer monsoon precipitation over Pakistan.…”

Section: Introductionmentioning

confidence: 99%

Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

Ehsan

Tippett

Kucharski

et al. 2020

Intl Journal of Climatology

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The potential and actual forecast skill of peak summer monsoon precipitation (July–August [JA]) over the core summer monsoon region of Pakistan (CSMRP: 68°–76°E, 30°–36°N) is investigated. The predictions from the latest version of the European Centre for Medium‐Range Weather Forecast System5 (SEAS5) and the North American Multimodel Ensemble (NMME) initialized in the beginning of June, May, and April are utilized. The potential skill is estimated by signal‐to‐noise ratio and perfect model correlation. The forecast skill is evaluated using the temporal anomaly correlation as well as the ranked probability skill score for probabilistic forecasts. The SEAS5 generally captures the observed climatological mean and variability patterns of peak summer monsoon precipitation over the region, with some biases mainly located over the complex topography zones. Observed SST anomalies in the El Niño–Southern Oscillation (ENSO) region are negatively correlated with observed peak summer monsoon precipitation over the region. The ENSO–precipitation connection is weakly reproduced in SEAS5 at Lead‐1 and fades gradually with increasing lead time. The potentially predictable component (signal) is small compared to the unpredictable part (noise). Nonetheless, statistically significant potential skill is found over the CSMRP. However, the forecast skill of peak summer monsoon precipitation over the region is lower than the potential skill in SEAS5. A multimodel ensemble average of five models shows little benefit in predicting peak monsoon precipitation over Pakistan. Extended spatial averaging has some impact on the skill; however, temporal averaging has no effect on the skill of the summer monsoon precipitation in SEAS5. Together these results show limitations of the current seasonal prediction models and demonstrate their inadequate performance in predicting peak summer monsoon precipitation over Pakistan.

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Variability and Predictability of Summer Monsoon Rainfall over Pakistan

Cited by 19 publications

References 28 publications

Early summer surface air temperature variability over Pakistan and the role of El Niño–Southern Oscillation teleconnections

Early summer surface air temperature variability over Pakistan and the role of El Niño–Southern Oscillation teleconnections

The impact of climate indices on precipitation variability in Baluchistan, Pakistan

Predicting peak summer monsoon precipitation over Pakistan in ECMWF SEAS5 and North American Multimodel Ensemble

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