Influence of Eurasian snow on Indian summer monsoon in NCEP CFSv2 freerun

Saha, Subodh Kumar; Pokhrel, Samir; Chaudhari, Hemantkumar S.

doi:10.1007/s00382-012-1617-4

Cited by 61 publications

(52 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is also a region of positive snow depth anomaly over west central Eurasia in March in ERA-Interim and April in Series 1. However, we do not find an indication of the east-west dipole reported in some previous studies that have explored the relationship between the strength of the ISM rainfall and Eurasia snow cover (Peings and Douville 2010) or snow depths (Saha et al 2013). It is to be noted that the central and northern Eurasian regions appear larger because of the distortion due to the map projection.…”

Section: Composite Analysis Of Snow Depthcontrasting

confidence: 50%

“…However, Fasullo (2004) confirmed the Blanford mechanism during non-ENSO years, but noted that ENSO overwhelmed the influence of snow in other years. Turner and Slingo (2011) and Saha et al (2013) demonstrated that snow forcing reduced tropospheric heating and weakened the monsoon development. In particular, Turner and Slingo (2011) found that the effect of snow-albedo feedback was the major contributor to tropospheric cooling over the HTP and thereby more critical to the Blanford mechanism in their model than the snow-hydrology effect.…”

Section: Introductionmentioning

confidence: 99%

“…Kripalani et al (2003) also used satellite snow cover data for 1986-2000 and found that the inverse relationship between spring snow cover and ISMR remained robust. Saha et al (2013) focused on mid and high latitudes and found a correlation between ISMR and an east-west dipole pattern in snow depth across Eurasia in the assimilated European Space Agency (ESA) GlobSnow dataset. Concordant conclusions on a high snow/delayed ISM linkage were not found in all observational studies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of springtime Himalayan–Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts

et al. 2016

View full text Add to dashboard Cite

their Seasonal Forecasting System 4. The reforecasts were initialized on 1 April every year for the period 1981-2010. In these seasonal reforecasts, the snow is initialized "realistically" with ERA-Interim/Land Reanalysis. In addition, we carried out an additional set of forecasts, identical in all aspects except that initial conditions for snow-related land surface variables over the HTP region are randomized. We show that high snow depth over HTP influences the meridional tropospheric temperature gradient reversal that marks the monsoon onset. Composite difference based on a normalized HTP snow index reveal that, in high snow years, (1) the onset is delayed by about 8 days, and (2) negative precipitation anomalies and warm surface conditions prevail over India. We show that about half of this delay can be attributed to the realistic initialization of snow over the HTP region. We further demonstrate that high April snow depths over HTP are not uniquely influenced by El Nino-Southern Oscillation, the Indian Ocean Dipole or the North Atlantic Oscillation.

show abstract

Section: Composite Analysis Of Snow Depthcontrasting

confidence: 50%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of springtime Himalayan–Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts

et al. 2016

View full text Add to dashboard Cite

show abstract

“…43 and references therein) that link the Eurasian snow cover and its teleconnection to Indian monsoon rainfall: Increased snow cover is associated with reduced rainfall over India. In our Global, Boreal, and Temperate deforestation simulations, there is an increase in Eurasian snow depth due to NH cooling (SI Appendix, Fig.…”

Section: Significancementioning

confidence: 99%

Effects of large-scale deforestation on precipitation in the monsoon regions: Remote versus local effects

Devaraju

Bala

Modak

2015

Proc. Natl. Acad. Sci. U.S.A.

149

136

View full text Add to dashboard Cite

In this paper, using idealized climate model simulations, we investigate the biogeophysical effects of large-scale deforestation on monsoon regions. We find that the remote forcing from largescale deforestation in the northern middle and high latitudes shifts the Intertropical Convergence Zone southward. This results in a significant decrease in precipitation in the Northern Hemisphere monsoon regions (East Asia, North America, North Africa, and South Asia) and moderate precipitation increases in the Southern Hemisphere monsoon regions (South Africa, South America, and Australia). The magnitude of the monsoonal precipitation changes depends on the location of deforestation, with remote effects showing a larger influence than local effects. The South Asian Monsoon region is affected the most, with 18% decline in precipitation over India. Our results indicate that any comprehensive assessment of afforestation/ reforestation as climate change mitigation strategies should carefully evaluate the remote effects on monsoonal precipitation alongside the large local impacts on temperatures.deforestation | biogeophysical effects | Hadley Cell movement | ITCZ shift | monsoon regions H istorical land cover change has been one of the major drivers of climate change. By the 1750s, ∼6-7% of the global land surface area had been deforested for agriculture. Today, croplands and pasture lands make up approximately one third of the global land surface (1-4). In terms of area, croplands and pasture lands increased globally from 620 million ha in 1700 to 4,960 million ha by 2000 (1). This large-scale conversion of forests to croplands or grasslands can impact climate through biogeochemical (changes in atmospheric composition) and biogeophysical (changes in physical land surface characteristics such as albedo, evapotranspiration, and roughness length) processes.The impacts of past, present, and future biogeochemical and biogeophysical effects from land use change have been investigated by numerous studies (5-10). These studies find that the biogeochemical process primarily causes global effects while biogeophysical processes cause strong local effects. The combined biogeochemical and biogeophysical effects from land cover change in the Holocene before 1850 were modeled as a global mean warming of 0.73 K (9).During the historical period (1750 to present day), deforestationassociated CO 2 emissions have contributed ∼180 ± 80 PgC to the cumulative anthropogenic CO 2 emissions (11) and a warming of ∼0.16-0.30 K (biogeochemical effect) to anthropogenic climate change (5, 6). This warming is probably partly offset by the biogeophysical effect of albedo increase, which may have caused a global mean cooling by ∼0.03-0.27 K (5, 7, 8). However, other major biogeophysical processes, such as reduction in evapotranspiration and roughness length due to deforestation, could result in warming (12).Several studies have investigated the link between land cover change and local climate change (13-16). For example, deforestation (16) in the tropics (18....

show abstract

“…Saha et al 2014b;Pokhrel et al 2012b). Saha et al (2013) have shown that positive bias in seasonal mean of the Eurasian snow is one of the reasons of the dry bias over India. The proper vertical representation of relative humidity and modified cloud microphysics can reduce systematic dry bias over the South Asian monsoon region .…”

mentioning

confidence: 98%