Interannual variability of Indian summer monsoon rainfall onset date at local scale

Moron, Vincent; Robertson, Andrew W.

doi:10.1002/joc.3745

Cited by 52 publications

(63 citation statements)

References 31 publications

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“…Moreover, enhanced skill for starts during cold ENSO phases might be related to increased predictability of monsoon onset dates. Over the AM region for instance, the onset is hastened during La Niña episodes, while the pattern of latent heat release that drives the monsoon flow (Slingo and Annamalai, 2000) is noisier during the core season, when the monsoon is then less sensitive to, and thus less predictible from, large-scale SST forcings (Moron and Robertson, 2014;Moron et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Vigaud

Robertson

Tippett

et al. 2017

Front. Environ. Sci.

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Subseasonal forecast skill over the broadly defined North American (NAM), West African (WAM) and Asian (AM) summer monsoon regions is investigated using three Ensemble Prediction Systems (EPS) at sub-monthly lead times. Extended Logistic Regression (ELR) is used to produce probabilistic forecasts of weekly and week 3-4 averages of precipitation with starts in May-Aug, over the 1999-2010 period. The ELR tercile category probabilities for each model gridpoint are then averaged together with equal weight. The resulting Multi-Model Ensemble (MME) forecasts exhibit good reliability, but have generally low sharpness for forecasts beyond 1 week; Multi-model ensembling largely removes negative values of the Ranked Probability Skill Score (RPSS) seen in individual forecasts, and broadly improves the skill obtained in any of the three individual models except for the AM. The MME week 3-4 forecasts have generally higher RPSS and comparable reliability over all monsoon regions, compared to week 3 or week 4 forecast separately. Skill is higher during La Niña compared to El Niño and ENSO-neutral conditions over the 1999-2010 period, especially for the NAM. Regionally averaged RPSS is significantly correlated with the Maden-Julian Oscillation (MJO) for the AM and WAM. Our results indicate potential for skillful predictions at subseasonal time-scales over the three summer monsoon regions of the Northern Hemisphere.

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Section: Discussionmentioning

confidence: 99%

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Vigaud

Robertson

Tippett

et al. 2017

Front. Environ. Sci.

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“…In both WHI and CNI, increased rainfall is illustrated in most days during the entire period. However, the rainfall response does not change significantly in CNI until late June or early July, which is due to a late monsoon onset in CNI at the end of June (Moron and Robertson, 2014). The daily ensemble mean of the modeled area-averaged AOD in DST is also shown in Figures 9c and 9d for comparison with the rainfall response.…”

Section: Modeled Ism Rainfall Response To Dustmentioning

confidence: 95%

Consistent response of Indian summer monsoon to Middle East dust in observations and simulations

Jin

Wei

et al. 2015

Atmos. Chem. Phys.

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Abstract. The response of the Indian summer monsoon (ISM) circulation and precipitation to Middle East dust aerosols on sub-seasonal timescales is studied using observations and the Weather Research and Forecasting model coupled with online chemistry (WRF-Chem). Satellite data show that the ISM rainfall in coastal southwest India, central and northern India, and Pakistan is closely associated with the Middle East dust aerosols. The physical mechanism behind this dust-ISM rainfall connection is examined through ensemble simulations with and without dust emissions. Each ensemble includes 16 members with various physical and chemical schemes to consider the model uncertainties in parameterizing short-wave radiation, the planetary boundary layer, and aerosol chemical mixing rules. Experiments show that dust aerosols increase rainfall by about 0.44 mm day −1 (∼ 10 % of the climatology) in coastal southwest India, central and northern India, and north Pakistan, a pattern consistent with the observed relationship. The ensemble mean rainfall response over India shows a much stronger spatial correlation with the observed rainfall response than any other ensemble members. The largest modeling uncertainties are from the boundary layer schemes, followed by short-wave radiation schemes. In WRF-Chem, the dust aerosol optical depth (AOD) over the Middle East shows the strongest correlation with the ISM rainfall response when dust AOD leads rainfall response by about 11 days. Further analyses show that increased ISM rainfall is related to enhanced southwesterly monsoon flow and moisture transport from the Arabian Sea to the Indian subcontinent, which are associated with the development of an anomalous low-pressure system over the Arabian Sea, the southern Arabian Peninsula, and the Iranian Plateau due to dust-induced heating in the troposphere. The dust-induced heating in the mid-upper troposphere is mainly located in the Iranian Plateau rather than the Tibetan Plateau. This study demonstrates a thermodynamic mechanism that links remote desert dust emissions in the Middle East to ISM circulation and precipitation variability on subseasonal timescales, which may have implications for ISM rainfall forecasts.

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“…Some are specific to agriculture and include a representation of breaks in the monsoon (Moron and Robertson, 2014). More general metrics include a combination of meteorological variables, such as 850 hPa wind and precipitation as in Martin et al (2000), or only use precipitation, such as in Sperber et al (2013) and the Normalized Pentad Precipitation Index (NPPI) (Lucas-Picher et al, 2011).…”

Section: Estimating Monsoon Onset and Retreatmentioning

confidence: 99%

“…The A1B scenario specifically represents this future world where there is balance across energy sources, i.e. a mixture of fossil and non-fossil fuels (Nakicenovic et al, 2000).…”

Section: Appendix A: Details Of the Models Usedmentioning

confidence: 99%

Estimating sowing and harvest dates based on the Asian summer monsoon

et al. 2018

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Abstract. Sowing and harvest dates are a significant source of uncertainty within crop models, especially for regions where high-resolution data are unavailable or, as is the case in future climate runs, where no data are available at all. Global datasets are not always able to distinguish when wheat is grown in tropical and subtropical regions, and they are also often coarse in resolution. South Asia is one such region where large spatial variation means higher-resolution datasets are needed, together with greater clarity for the timing of the main wheat growing season. Agriculture in South Asia is closely associated with the dominating climatological phenomenon, the Asian summer monsoon (ASM). Rice and wheat are two highly important crops for the region, with rice being mainly cultivated in the wet season during the summer monsoon months and wheat during the dry winter. We present a method for estimating the crop sowing and harvest dates for rice and wheat using the ASM onset and retreat. The aim of this method is to provide a more accurate alternative to the global datasets of cropping calendars than is currently available and generate more representative inputs for climate impact assessments.We first demonstrate that there is skill in the model prediction of monsoon onset and retreat for two downscaled general circulation models (GCMs) by comparing modelled precipitation with observations. We then calculate and apply sowing and harvest rules for rice and wheat for each simulation to climatological estimates of the monsoon onset and retreat for a present day period. We show that this method reproduces the present day sowing and harvest dates for most parts of India. The application of the method to two future simulations demonstrates that the estimated sowing and harvest dates are successfully modified to ensure that the growing season remains consistent with the internal model climate. The study therefore provides a useful way of modelling potential growing season adaptations to changes in future climate.

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Interannual variability of Indian summer monsoon rainfall onset date at local scale

Cited by 52 publications

References 31 publications

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Subseasonal Predictability of Boreal Summer Monsoon Rainfall from Ensemble Forecasts

Consistent response of Indian summer monsoon to Middle East dust in observations and simulations

Estimating sowing and harvest dates based on the Asian summer monsoon

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