Relation of Eurasian Snow Cover and Indian Summer Monsoon Rainfall: Importance of the Delayed Hydrological Effect

Halder, Subhadeep; Dirmeyer, Paul A.

doi:10.1175/jcli-d-16-0033.1

Cited by 61 publications

(50 citation statements)

References 79 publications

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“…As noted previously (Dirmeyer & Halder, ; Roundy et al, ), this causes a wet surface bias over agricultural areas, especially noticeable over the midwestern United States, with excessive ET, very shallow boundary layers, and erroneously weak land‐atmosphere coupling (Dirmeyer et al, ). Furthermore, snowpack and snowmelt biases in this model have some effect in April and May at high latitudes (Halder & Dirmeyer, ).…”

Section: Discussionmentioning

confidence: 99%

On the Harvest of Predictability From Land States in a Global Forecast Model

2018

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Various land surface treatments in a suite of subseasonal-to-seasonal forecasts are applied to diagnose the degree to which potential predictability from the land surface is harvested, where breakdowns occur in the process chains that link land surface states to atmospheric phenomena, and the role played by memory in the climate system. Version 2 of the Coupled Forecast System (CFSv2) is used for boreal summer simulations spanning 28 years. Four types of retrospective forecasts are produced: those where land surface initial states are from the same date and year as the initial atmosphere and ocean states; ensembles where initial land states come from different years than the atmosphere and ocean; simulations where soil moisture is specified from an observationally constrained analysis; and simulations where an alternative triggering mechanism for convection is employed. The specified soil moisture allows estimation of an upper bound for land-driven predictability and prediction skill in boreal summer. Realistic land initialization represents the best possible case with this model in forecast mode, while the simulations with initial land states from different years isolate the impact of atmosphere and ocean initialization on forecasts. Harvested predictability is calculated, and its relationship to memory of initial anomalies is estimated. The pathway of land surface information through the energy and water cycles to the atmosphere, and ultimately its effects on precipitation, is traced, showing a robust propagation of useful signal through land surface fluxes, near-surface meteorological states, and boundary layer properties, but largely disappearing at precipitation, implying problems with the convective parameterization.Plain Language Summary The performance of the National Weather Service's operational climate forecast model is examined to see how the land surface, namely, moisture in the soil, affects the skill of forecasts. We estimate the potential skill derived from the best possible initialization and prediction of land surface states and how much of that potential skill can be realized by the current version of the forecast model. Additionally, we trace the signal of information in the model from the land surface into the atmosphere and find that while good soil moisture information greatly extends the duration of useful temperature and humidity forecasts, much information appears to be lost at the point in the model where clouds and precipitation are simulated. This result suggests that the model could be improved to make better use of land surface data to produce more skillful precipitation forecasts.

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Section: Discussionmentioning

confidence: 99%

On the Harvest of Predictability From Land States in a Global Forecast Model

2018

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“…More snow over western Eurasia and less snow over eastern Eurasia were unfavourable for Indian summer monsoon (ISM) (e.g., Kripalani and Kulkarni, ; Dash et al ., ; Singh and Oh, ) while being favourable for EASM (e.g., Kripalani et al ., ; Yim et al ., ; Wu et al ., ). Halder and Dirmeyer () proposed that winter and spring Eurasian snow cover and snow melt have a delayed hydrological effect on the ensuing ISM. Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

“…More snow over western Eurasia and less snow over eastern Eurasia were unfavourable for Indian summer monsoon (ISM) (e.g., Kripalani and Kulkarni, 1999;Dash et al, 2005;Singh and Oh, 2005) while being favourable for EASM (e.g., Kripalani et al, 2002;Yim et al, 2010;Wu et al, 2014). Halder and Dirmeyer (2017) proposed that winter and spring Eurasian snow cover and snow melt have a delayed hydrological effect on the ensuing ISM. Zhang et al (2017) showed through observations and numerical experiments that the west-east dipole structure of Eurasian snow may significantly affect EASM (especially over China) through anomalous mid-latitude Eurasian wave propagation.…”

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confidence: 96%

Combined impact of Greenland sea ice, Eurasian snow, and El Niño–Southern Oscillation on Indian and Korean summer monsoons

Kim

Prabhu

et al. 2019

Intl Journal of Climatology

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The combined impact of Greenland sea ice, Eurasian snow, and the El Niño–Southern Oscillation (ENSO) on the out‐of‐phase relationship between the Indian summer monsoon (ISM) and Korean summer monsoon (KSM) were investigated through numerical experiments. The results revealed that Indian and Korean summer rainfalls showed nonlinear responses to ENSO and Greenland sea ice forcing when the events co‐occurred. Above‐normal Greenland sea ice and a concurrent La Niña showed a distinct in‐phase relationship with ISM and out‐of‐phase relationship with KSM. Below‐normal and above‐normal Greenland sea ice during boreal autumn surrounded the Greenland region with anomalous low pressure and high pressure, respectively. These were associated with a barotropic +west/−east or –west/+east dipole pattern, respectively, over Eurasia during the subsequent winter and spring seasons. Furthermore, these patterns led to positive and negative snow depth anomalies, respectively, over western Eurasia and the opposite snow tendency over eastern Eurasia during the subsequent spring. This variability in Eurasian snow patterns may play a crucial role in ISM and KSM. The co‐occurrence of ENSO variability also generates high‐ and low‐pressure anomaly patterns over the Indian Ocean that may be related to unfavourable or favourable ISM, respectively, while influencing the negative or positive phases of a Pacific Japan (PJ)‐like teleconnection pattern that may be related to unfavourable or favourable KSM, respectively. Therefore, coexisting ENSO forcing may play a dominant role in ISM and KSM, but Greenland sea ice forcing and Eurasian snow variation intensify the out‐of‐phase relationship between ISM and KSM.

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“…Eurasian snow is one of the major land surface processes, which also has an inverse relationship with the ISMR (Hahn and Shukla, 1976;Vernekar et al, 1995;Kripalani and Kulkarni, 1999;Fasullo, 2004;Singh and Oh, 2005;Halder and Dirmeyer, 2017). The pioneering study by Blanford (1884) explained the negative correlation between winter snow over the Himalaya and subsequent summer monsoon rainfall over India.…”

Section: Introductionmentioning

confidence: 99%

Effects of a multilayer snow scheme on the global teleconnections of the Indian summer monsoon

Sujith

Saha

Rai

et al. 2019

Quart J Royal Meteoro Soc

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Eurasian snow is one of the slowly varying boundary forcings known to have significant influences on the mean and variability of the Indian summer monsoon rainfall (ISMR). A multilayer complex snow scheme, incorporated into the state‐of‐the‐art coupled Climate Forecast System version 2 (CFSv2) showed significant improvements in the simulation of mean ISMR, snow, and Northern Hemisphere surface and tropospheric temperature. Here we show that a realistic simulation of high‐latitude snow decreases the north–south temperature gradient, which in turn decreases the meridional transport of energy from the Equator to the Pole, consequently affecting the tropical sea surface temperature (SST) and air–sea interactions. The global teleconnections of the ISMR with SST and 2 m temperature over land are also improved considerably in association with improved simulation of the oceanic natural modes of variability. Our findings provide new insights for the relationship between the winter Eurasian snow and the following ISMR, namely that the same relationship may be understood through a framework of meridional atmospheric energy transport and its effects on the tropical air–sea interactions. The improvements in the global teleconnection in the modified version of CFSv2 may have implications in the ISMR predictability and prediction skill.

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Relation of Eurasian Snow Cover and Indian Summer Monsoon Rainfall: Importance of the Delayed Hydrological Effect

Cited by 61 publications

References 79 publications

On the Harvest of Predictability From Land States in a Global Forecast Model

On the Harvest of Predictability From Land States in a Global Forecast Model

Combined impact of Greenland sea ice, Eurasian snow, and El Niño–Southern Oscillation on Indian and Korean summer monsoons

Effects of a multilayer snow scheme on the global teleconnections of the Indian summer monsoon

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