[1] A mechanism of internal variability of Indian summer monsoon through the modulation of intraseasonal oscillation (ISO) by land-atmosphere feedback is proposed. Evidence of feedback between surface soil moisture and ISOs is seen in the soil moisture data from GSWP-2 and rainfall data from observations. Using two sets of internal simulation by a regional climate model (RCM), it is shown that internally generated anomalous soil moisture interacts with the following ISO and generates interannual variability. To gain further insight, 27 years of sensitivity experiment by prescribing wet (dry) soil moisture condition during break (active) period along with a control simulation are carried out. The sensitivity experiment reveals the large-scale nature of soil moisture and ISO feedback which takes place through the changes in atmospheric stability by altering lower-level atmospheric conditions. The feedback is inherent to the monsoon system and a part of it acts through the intraseasonal varying memory of soil moisture. The RCM used to test the hypothesis is constrained by one-way interactions at the lateral boundary. Experiments with a much larger domain upheld the findings and hence suggest the true nature of soil moisture and ISO feedback present in the monsoon system.

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.

This observationally based study demonstrates the importance of the delayed hydrological response of snow cover and snowmelt over the Eurasian region and Tibet for variability of Indian summer monsoon rainfall during the first two months after onset. Using snow cover fraction and snow water equivalent data during 1967–2003, it is demonstrated that, although the snow-albedo effect is prevalent over western Eurasia, the delayed hydrological effect is strong and persistent over the eastern part. Long soil moisture memory and strong sensitivity of surface fluxes to soil moisture variations over eastern Asia and Tibet provide a mechanism for soil moisture anomalies generated by anomalies in winter and spring snowfall to affect rainfall during the initial months in summer. Dry soil moisture anomalies over the eastern Eurasian region associated with anomalous heating at the surface and midtroposphere help in anchoring of an anomalous upper-tropospheric “blocking” ridge around 100°E and its persistence. This not only leads to prolonged weakening of the subtropical westerly jet but also shifts its position southward of 30°N, followed by penetration of anomalous troughs in the westerlies into the Indian region. Simultaneously, intrusion of cold and dry air from the midlatitudes can reduce the convective instability and hence rainfall over India after the onset. Such a southward shift of the jet can also significantly weaken the vertical easterly wind shear over the Indian region in summer and lead to decrease in rainfall. This delayed hydrological effect also has the potential to modulate the snow–atmosphere coupling strength for temperature and precipitation in operational forecast models through soil moisture–evaporation–precipitation feedbacks.

Retrospective forecasts from CFSv2 are evaluated in terms of three elements of land–atmosphere coupling at subseasonal to seasonal time scales: sensitivity of the atmosphere to variations in land surface states, the magnitude of variability of land states and fluxes, and the memory or persistence of land surface anomalies. The Northern Hemisphere spring and summer seasons are considered for the period 1982–2009. Ensembles are constructed from all available pairings of initial land and atmosphere/ocean states taken from the Climate Forecast System Reanalysis at the start of April, May, and June among the 28 years, so that the effect of initial land states on the evolving forecasts can be assessed. Finally, improvement and continuance of forecast skill derived from accurate land surface initialization is related to the three coupling elements. It is found that soil moisture memory is the most broadly important element for significant improvement of realistic land initialization on forecast skill. However, coupling strength manifested through the elements of sensitivity and variability are necessary to realize the potential predictability provided by memory of initial land surface anomalies. Even though there is clear responsiveness of surface heat fluxes, near-surface temperature, humidity, and daytime boundary layer development to variations in soil moisture over much of the globe, precipitation in CFSv2 is unresponsive. Failure to realize potential predictability from land surface states could be due to unfavorable atmospheric stability or circulation states; poor quality of what is considered realistic soil moisture analyses; and errors in the land surface model, atmospheric model, or their coupled interaction.

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