In addition to remote SST forcing, realistic representation of land forcing (i.e., soil moisture) over the United States is critical for a prediction of U.S. severe drought events approximately one season in advance. Using “identical twin” experiments with different land initial conditions (ICs) in the 32-yr (1979–2010) CFSv2 reforecasts (NASA GLDAS-2 reanalysis versus NCEP CFSR), sensitivity and skill of U.S. drought predictions to land ICs are evaluated. Although there is no outstanding performer between the two sets of forecasts with different land ICs, each set shows greater skill in some regions, but their locations vary with forecast lead time and season. The 1999 case study demonstrates that although a pattern of below-normal SSTs in the Pacific in the fall and winter is realistically reproduced in both reforecasts, GLDAS-2 land initial states display a stronger east–west gradient of soil moisture, particularly drier in the eastern United States and more consistent with observations, leading to warmer surface temperature anomalies over the United States. Anomalies lasting for one season are accompanied by more persistent barotropic (warm core) anomalous high pressure over CONUS, which results in better prediction skill of this drought case up to 4 months in advance in the reforecasts with GLDAS-2 land ICs. Therefore, it is essential to minimize the uncertainty of land initial states among the current land analyses for improving U.S. drought prediction on seasonal time scales.

The NCEP CFSv2 ensemble reforecasts initialized with different land surface analyses for the period of 1979–2010 have been conducted to assess the effect of uncertainty in land initial states on surface air temperature prediction. The two observation-based land initial states are adapted from the NCEP CFS Reanalysis (CFSR) and the NASA GLDAS-2 analysis; atmosphere, ocean, and ice initial states are identical for both reforecasts. This identical-twin experiment confirms that the prediction skill of surface air temperature is sensitive to the uncertainty of land initial states, especially in soil moisture and snow cover. There is no distinct characteristic that determines which set of the reforecasts performs better. Rather, the better performer varies with the lead week and location for each season. Estimates of soil moisture between the two land initial states are significantly different with an apparent north–south contrast for almost all seasons, causing predicted surface air temperature discrepancies between the two sets of reforecasts, particularly in regions where the magnitude of initial soil moisture difference lies in the top quintile. In boreal spring, inconsistency of snow cover between the two land initial states also plays a critical role in enhancing the discrepancy of predicted surface air temperature from week 5 to week 8. Our results suggest that a reduction of the uncertainty in land surface properties among the current land surface analyses will be beneficial to improving the prediction skill of surface air temperature on subseasonal time scales. Implications of a multiple land surface analysis ensemble are also discussed.

The impact of initial land-surface states on monthly to seasonal prediction skill of the Indian summer monsoon (June- September) is investigated using a suite of hindcasts made with the Climate Forecast System version 2 (CFSv2) operational forecast model. The modern paradigm of land-atmosphere coupling is applied to quantify biases in different components of the land-atmosphere coupled system and their effect on systematic errors. Three sets of hindcasts are performed for the period spanning 1982-2009 initialized at the start of April, May and June. For a particular initial date of a given year, one member (Control run) has the analyzed land initial state consistent with the atmosphere, sea ice and ocean states for that year; the other 27 members have land states taken from each of the remaining 27 years. There is significant improvement in the deterministic prediction skill of near surface temperature and soil moisture on monthly and seasonal time scales due to realistic land initial conditions. The improvement occurs in those areas where the land-atmosphere coupling is strongest. Improvements in the prediction skill of precipitation are confined to relatively small areas. The pattern of skill differences resembles patterns of land-atmosphere coupling strength, while biases in the representation of land-atmosphere coupling affect the skill of temperature and rainfall. The re-emergence of skill in temperature and precipitation towards the end of the season over northwest India within April and June IC hindcasts may be attributed to better simulation of the withdrawal phase of the monsoon as well as increased land-atmosphere coupling. For May IC hindcasts, increased skill in air temperature on the sub-seasonal time scales could be due to other large-scale factors. Errors in the parameterization of radiation, convection, boundary layer processes, surface moisture fluxes and the representation of vegetation contribute to decay in potential predictability and skill attributable to land initial conditions. Furthermore, incorrect representation of daily and sub-daily precipitation statistics over land also likely lead to errors in land-atmosphere coupling. Above all, the importance of accurate land surface initialization and land-atmosphere coupling in improving the Indian summer monsoon prediction on sub-seasonal to seasonal time scales is emphasized.

Real time hydrologic forecasting requires near accurate initial condition of soil moisture; however, continuous monitoring of soil moisture is not operational in many regions, such as, in Ganga basin, extended in Nepal, India and Bangladesh. Here, we examine the impacts of perturbation/error in the initial soil moisture conditions on simulated soil moisture and streamfiow in Ganga basin and its propagation, during the summer monsoon season (June to September). This provides information regarding the required minimum duration of model simulation for attaining the model stability. We use the Variable Infiltration Capacity model for hydrological simulations after validation. Multiple hydrologic simulations are performed, each of 21 days, initialized on every 5th day of the monsoon season for deficit, surplus and normal monsoon years. Each of these simulations is performed with the initial soil moisture condition obtained from long term runs along with positive and negative perturbations. The time required for the convergence of initial errors is obtained for all the cases. We find a quick convergence for the year with high rainfall as well as for the wet spells within a season. We further find high spatial variations in the time required for convergence; the region with high precipitation such as Lower Ganga basin attains convergence at a faster rate. Furthermore, deeper soil layers need more time for convergence. Our analysis is the first attempt on understanding the sensitivity of hydrological simulations of Ganga basin on initial soil moisture conditions. The results obtained here may be useful in understanding the spin-up requirements for operational hydrologic forecasts.

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.

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 1008E and its persistence. This not only leads to prolonged weakening of the subtropical westerly jet but also shifts its position southward of 308N, 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.

A set of ensemble seasonal reforecasts for 1958-2014 is conducted using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2. In comparison with other current reforecasts, this dataset extends the seasonal reforecasts to the 1960s-70s. Direct comparison of the predictability of the ENSO events occurring during the 1960s-70s with the more widely studied ENSO events since then demonstrates the seasonal forecast system's capability in different phases of multidecadal variability and degrees of global climate change. A major concern for a long reforecast is whether the seasonal reforecasts before 1979 provide useful skill when observations, particularly of the ocean, were sparser. This study demonstrates that, although the reforecasts have lower skill in predicting SST anomalies in the North Pacific and North Atlantic before 1979, the prediction skill of the onset and development of ENSO events in 1958-78 is comparable to that for 1979-2014. In particular, the ENSO predictions initialized in April during 1958-78 show higher skill in the summer. However, the skill of the earlier predictions declines faster in the ENSO decaying phase, because the reforecasts initialized after boreal summer persistently predict lingering wind and SST anomalies over the eastern equatorial Pacific during such events. Reforecasts initialized in boreal fall overestimate the peak SST anomalies of strong El Nino events since the 1980s. Both phenomena imply that the model's air-sea feedback is overly active in the eastern Pacific before ENSO event termination. Whether these differences are due to changes in the observing system or are associated with flow-dependent predictability remains an open question.

An updated version of the Heated Condensation Framework (HCF) is implemented as a convective triggering criterion into the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). The new trigger replaces the original criteria in both the deep (Simplified Arakawa-Schubert - SAS) and shallow (SAS based) convective schemes. The performance of the original and new triggering criteria is first compared against radiosonde observations. Then, a series of hindcasts are performed to evaluate the influence of the triggering criterion in the CFSv2 representation of summer precipitation, the diurnal cycle of precipitation, and hurricanes that made landfall. The observational analysis shows that the HCF trigger better captures the frequency of convection, where the original SAS trigger initiates convection too often. When implemented in CFSv2, the HCF trigger improves the seasonal forecast of the Indian summer monsoon rainfall, including the representation of the onset dates of the rainy season over India. On the other hand, the HCF trigger increases error in the seasonal forecast of precipitation over the eastern United States. The HCF trigger also improves the representation of the intensity of hurricanes. Moreover, the simulation of hurricanes provides insights on the mechanism whereby the HCF trigger impacts the representation of convection.

Daily moderate rainfall events, which constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 through 2005. On the other hand, mean and extreme near-surface daily temperature during the monsoon season have increased by a maximum of 1–1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) and prescribed land cover of years 1950 and 2005, it is demonstrated that part of the changes in moderate rainfall events and temperature have been caused by land-use/land-cover change (LULCC), which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over central India coincides with the region of decrease in forest and increase in crop cover. Our results also show that LULCC alone causes warming in the extremes of daily mean and maximum temperatures by a maximum of 1–1.2 °C, which is comparable with the observed increasing trend in the extremes. Decrease in forest cover and simultaneous increase in crops not only reduces the evapotranspiration over land and large-scale convective instability, but also contributes toward decrease in moisture convergence through reduced surface roughness. These factors act together in reducing significantly the moderate rainfall events and the amount of rainfall in that category over central India. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures over the Indian Ocean. As a result, enhanced warming at the surface and greater decrease in moderate rainfall events over central India compared to the earlier set of simulations are noticed. Results from these additional experiments corroborate our initial findings and confirm the contribution of LULCC in the decrease in moderate rainfall events and increase in daily mean and extreme temperature over India. Therefore, this study demonstrates the important implications of LULCC over India during the monsoon season. Although, the regional climate model helps in better resolving land–atmosphere feedbacks over the Indian region, the inferences do depend on the fidelity of the model in capturing the features of Indian monsoon realistically. It is proposed that similar studies using a suite of climate models will further enrich our understanding about the role of LULCC in the Indian monsoon climate.

When initial soil moisture is perturbed among ensemble members in the operational NWS global forecast model, surface latent and sensible fluxes are immediately affected much more strongly, systematically, and over a greater area than conventional land-atmosphere coupling metrics suggest. Flux perturbations are likewise transmitted to the atmospheric boundary layer more formidably than climatology-based metrics would indicate. Impacts are not limited to the traditional land-atmosphere coupling hot spots, but extend over nearly all ice-free land areas of the globe. Key to isolating this effect is that initial atmospheric states are identical among quantities correlated, pinpointing soil moisture and snow cover. A consequence of this high sensitivity is that significant positive impacts of realistic land surface initialization on the skill of deterministic near-surface temperature and humidity forecasts are also immediate and nearly universal during boreal spring and summer (the period investigated) and persist for at least 3 days over most land areas. Land surface initialization may be more broadly important for weather forecasts than previously realized, as the research focus historically has been on subseasonal-to-seasonal time scales. This study attempts to bridge the gap between climate studies with their associated coupling assessments and weather forecast time scales. Furthermore, errors in land surface initialization and shortcomings in the parameterization of atmospheric processes sensitive to surface fluxes may have greater consequences than previously recognized, the latter exemplified by the lack of impact on precipitation forecasts even though the simulation of boundary layer development is shown to be greatly improved with realistic soil moisture initialization.

A relatively simple land surface model, the Biosphere‐Atmosphere Transfer Scheme (BATS) and the more complex Community Land Model (CLM3.5) coupled to RegCM4 are used to investigate land‐atmosphere feedback processes during the Indian summer monsoon. Model simulations for 27 years show that the mean and interannual variability of rainfall and surface air temperature are affected significantly due to differences in the formulation of evapotranspiration and hydrological processes between BATS and CLM3.5, prescribed land use, land cover data and changes in net radiation. RegCM4 with CLM3.5 (RCLM) shows a reduction in surface moisture flux and precipitation but an increase in surface air temperature over most parts of India as compared to RegCM4 with BATS (RBAT). In terms of the mean and interannual variability of rainfall over central India, RBAT performs better than RCLM. Evapotranspiration over central India is found to be less (more) sensitive to soil wetness variations in RCLM (RBAT) compared to the multimodel estimate from the Global Soil Wetness Project, that is mainly attributed to the differences in ground evaporation and transpiration. Changes in evaporation efficiency between the models also lead to a reduction in the land‐atmosphere coupling strength for precipitation in RCLM. Furthermore, such changes decrease the convective instability over central and eastern India in RCLM leading to weakened convection, reduced large‐scale moisture convergence and precipitation over land. Observations of soil moisture, surface fluxes, and radiation are needed for better understanding and improvement of coupled land‐atmosphere feedbacks in models during the Indian summer monsoon.

A possible link between preonset land atmospheric conditions and the Indian summer monsoon rainfall (ISMR) is explored. It is shown that, the preonset positive (negative) rainfall anomaly over northwest India, Pakistan, Afghanistan, and Iran is associated with decrease (increase) in ISMR, primarily in the months of June and July, which in turn affects the seasonal mean. ISMR in the months of June and July is also strongly linked with the preonset 2m air temperature over the same regions. The preonset rainfall/2m air temperature variability is linked with stationary Rossby wave response, which is clearly evident in the wave activity flux diagnostics. As the predictability of Indian summer monsoon relies mainly on the El Nino-Southern Oscillation (ENSO), the found link may further enhance our ability to predict the monsoon, particularly during a non-ENSO year.

A new triggering mechanism for deep convection based on the heated condensation framework (HCF) is implemented into the National Centers for Environmental Prediction climate forecast system version 2 (CFSv2). The new trigger is added as an additional criterion in the simplified Arakawa–Schubert scheme for deep convection. Seasonal forecasts are performed to evaluate the influence of the new triggering mechanism in the representation of the Indian summer monsoon in the CFSv2. The HCF trigger improves the seasonal representation of precipitation over the Indian subcontinent. The new triggering mechanism leads to a significant, albeit relatively small, improvement in the bias of seasonal precipitation totals. In addition, the new trigger improves the representation of the seasonal precipitation cycle including the monsoon onset, and the probability distribution of precipitation intensities. The mechanism whereby the HCF improves convection over India seems to be related not only to a better representation of the background state of atmospheric convection but also to an increase in the frequency in which SAS is triggered. As a result, there was an increase in convective precipitation over India favored by the availability of moist convective instability. The increase in precipitation intensity leads to a reduction in the dry bias.

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.

Abstract. The spatio-temporal variability of Indian Summer Monsoon is well studied based on different types of rainfall data. However, very few attempts have been made to study the underlying role of clouds and its hydrometeors on Monsoon Intraseasonal Oscillations. The northward propagating Monsoon Intraseasonal Oscillations and its characteristics remain a challenge for the numerical modelers even today. In view of this, we have set out to analyze the role of cloud hydrometeors and their linkage with northward propagating Monsoon Intraseasonal Oscillations. The science question that we intend to address here is whether the different phases of the cloud hydrometeors show similar propagation characteristics as that of rainfall, and what are the relations of their phases with the convection centre using Tropical Rainfall Measuring Mission data. In answering the question, we have analyzed ten years of Tropical Rainfall Measuring Mission 2A12 hydrometeor data over Indian region. Our analyses show that the cloud water and cloud ice do show a large scale organization during the Indian Summer Monsoon regime of June–September, and systematically progress northward getting initiated over equatorial Indian Ocean. On further analyses, we found that cloud water actually leads the rainfall and cloud ice lags the rainfall. We have further demonstrated the process by analyzing dynamical parameters from Modern Era-Retrospective Analysis for Research and Applications. The presence of cloud water in the lower troposphere in the leading edge of rainfall indicates the lower level moistening and preconditioning of the convective instability due to enhanced moisture convergence. Subsequently, deep convection is triggered, which generates hydrometeor above freezing level and cloud ice in the upper troposphere. To quantify objectively the relation among cloud liquid water, cloud ice and rainfall, the lag correlation is computed with respect to convection center, where the above hypothesis is established that cloud liquid water leads the rainfall and cloud ice lag. This relation among hydrometeors may help the numerical modelers to incorporate such processes for capturing the characteristics of Monsoon Intraseasonal Oscillations.