Predicting summer monsoon of Bhutan based on SST and teleconnection indices

Dorji, Singay; Herath, Srikantha; Mishra, Binaya Kumar; Chophel, Ugyen

doi:10.1007/s00703-018-0589-2

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…Due to differences in methods and data sources, different reanalysis datasets have demonstrated a wide range of estimated results, ranging from 1000 mm/year (for instance, He et al [11] estimated the LYZV annual mean precipitation at 1001 mm/year by combining upstream ground station data with satellite reanalysis data) to 4000 mm/year (the estimation by Ma et al [12] is 3011 mm/year using interpolation of downstream gauge data; the estimation by Li et al [8] is from 1000 to 4000 mm/year at different altitudes using grid data from the Global Precipitation Measurement, GPM). We tend to use the higher precipitation estimates for LYZV because they are more consistent with precipitation characteristics in the southern Himalayas, which are close to the same altitude regions in Bhutan [45] and corroborate early findings by Yang et al [91]. Nonetheless, given the QTP's extensive area and complex topography, different reanalysis datasets offer distinct advantages across various regions [20].…”

Section: Availability Of Reanalysis Precipitationsupporting

confidence: 82%

“…Because of water vapor transport by monsoons and the uplift effect of the valley, precipitation in LYZV is greater than in other regions [44]. Although LYZV is an ungauged region, we can estimate its annual precipitation ranging from 2500 mm to 3500 mm by using observations from similar neighborhoods in the south of the Himalayas and early investigation findings [43,[45][46][47]. Thus, it is essential to provide reasonable estimations in LYZV.…”

Section: Definition and Division Of Study Areamentioning

confidence: 99%

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CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century

Wang,

Song,

et al. 2024

Atmosphere

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The Qinghai-Tibetan Plateau (QTP), the source of many major Asian rivers, is sensitive to climate change, affecting billions of people’s livelihoods across Asia. Here, we developed high-resolution projections of precipitation and daily maximum/minimum temperatures at 0.1° spatial resolution over the QTP. The projections are based on the output from seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for historical (1979–2013) and projected (2015–2100) climates across four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). An updated nonstationary cumulative distribution function matching method (called CNCDFm) was used to remove model systemic bias. We verify the necessity of taking into account altitude in downscaling processes and the validity of nonstationary bias correction. Compared to the historical period, the climate in the QTP in the 21st century is warmer (1.2–5.1 °C, for maximum surface temperature) and wetter (3.9–26.8%) according to the corrected GCM projection. For precipitation, the Indus River (IDR), Tarim River (TMR), Inner of Qiangtang Basin (IQTB), Yarlung Zangbo (YLZBR), and Qaidam Basin (QDB) showed growth well above the global average across high radiative forcing scenarios, which could have a profound impact on the regional hydrological cycle. However, there is great uncertainty in precipitation prediction, which is demonstrated by a very low signal-to-noise ratio (SNR) and a large difference between Bayesian model averaging (BMA) and multi-model averages (MMAs). This bias-corrected dataset is available for climate change impact research in the QTP at the subregion scale.

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Section: Availability Of Reanalysis Precipitationsupporting

confidence: 82%

Section: Definition and Division Of Study Areamentioning

confidence: 99%

CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century

Wang,

Song,

et al. 2024

Atmosphere

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“…The point predictions generated from linear or nonlinear regression models were also converted to probabilistic predictions that are often of greater usefulness in decisionmaking. Similar to current operational forecasts such as those from the South Asian Climate Outlook Forum [58], the probabilities generated were those for placing in each tercile of precipitation from the training period. The point predictions were converted to per-tercile probabilities that minimized the Kullback-Leibler divergence from an equal-chances climatology probability distribution while having the mean indicated by the point prediction (a 'maximum entropy' approach [59,60]), but with the tercile probabilities all constrained to the range 25%p50%.…”

Section: Probabilistic Prediction and Information Skill Score (Iss)mentioning

confidence: 99%

Year-ahead predictability of South Asian Summer Monsoon precipitation

Krakauer

2019

Environ. Res. Lett.

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Since the South Asia Summer Monsoon is the main source of water for a densely cultivated and climate-sensitive region, its predictability has long been the target of research. This work estimates the predictability horizon of monsoon precipitation amount by systematically comparing statistical forecasts made using information from different lead times before the monsoon start. Linear and nonlinear prediction methods are considered that use the leading modes of the global sea surface temperature field to forecast monsoon-season (June-September) total precipitation on a 0.5°grid over South Asia, where each method is trained on data from 1901 to 1996 and evaluated on data from 1997 to 2017. Forecasts were found to outperform a climatology baseline up to at least 1 year ahead, with a nonlinear method (random forest) on average outperforming linear regression with group lasso, although with greater variability in skill across locations and years. Forecast performance measures (fractional reduction in root mean square error and information skill score) decreased with increasing lead time following exponential decay timescales of 5-12 months, depending on the performance measure and forecast method. Even at lead times of several years, there was some forecast skill compared to climatology, as a result of the impact of long-term climate change on monsoon precipitation. The results suggest that monsoon prediction is possible with longer lead times than generally attempted now.

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“…Numerous teleconnection studies have revealed that South Asian Monsoon (SAM) is modulated by many large-scale circulation patterns, including the El Niño-Southern Oscillation (ENSO) [64,63,47,40,37], Indian Ocean Dipole (IOD) [56,8,9,69], Atlantic Multidecadal Oscillation (AMO) [79,23,36], and Pacific Decadal Oscillation (PDO) [34,35,38,60,59]. Among these patterns, ENSO generally affects the SAM variability through the Walker circulation, subject to the change in spatial configurations of sea surface temperature (SST) anomalies in the Pacific Ocean [19]. However, weaker ENSO influence has been recently reported due to more active IOD events [56,74,69], the south-eastward shift in the Walker circulation anomalies [40,41], and forcing from the Atlantic circulations [16].…”

Section: Introductionmentioning

confidence: 99%

Space- and time-varying associations between Bangladesh's seasonal rainfall and large-scale climate oscillations

Mahmud

Chen

2021

Preprint

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Understanding teleconnections of a region's climate can be beneficial to seasonal outlooks and hydro-climate services. This study aims at analyzing the teleconnections of seasonal rainfall over Bangladesh with selected climate indices, including El Niño/Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) indices. Rainfall data spanning from 1965–2017 in the seven hydrological regions are used to derive three seasonal rains, namely the pre-monsoon (March–May), monsoon (June–September), and post-monsoon (October and November) rains, for correlation- and wavelet coherence (WC)-based teleconnection analyses. Among the three seasonal rains, the post-monsoon rain shows the negative correlations, strongest with the IOD and ENSO indices. Correlations between the pre-monsoon/monsoon rain and climate indices are subject to notable spatial and temporal variations. For instance, correlations between the pre-monsoon (monsoon) rain in the South Central (South West) region and the IOD (ENSO) index shift from negative to positive after the 1980s, whereas the comprehensive negative correlations of the post-monsoon rain with the IOD and ENSO indices further enhanced from the early to recent epochs. WC analysis not only corroborates the findings of correlation analysis at shorter time scales (e.g., 1–4 years), but also reveals significant coherence at longer time scales (e.g., 8–16 years). We find that the pre-monsoon and monsoon rains experience the phase change in WC from shorter to longer scales. In contrast, the post-monsoon rain shows the consistent anti-phase WC, more dominant at the longer time scale. Both correlation and WC analyses indicate that the association patterns of the PDO mimic those of ENSO. Lastly, the analysis results of the AMO suggest quite distinct and significant association between Bangladesh's rainfall and the Atlantic Ocean.

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Predicting summer monsoon of Bhutan based on SST and teleconnection indices

Cited by 5 publications

References 33 publications

CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century

CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century

Year-ahead predictability of South Asian Summer Monsoon precipitation

Space- and time-varying associations between Bangladesh's seasonal rainfall and large-scale climate oscillations

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