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Bellow, J. G., Shin, D. W., Schoof, J., Jones, J., & O'Brien, J. J. (2006). Contribution of Temperature, Precipitation, and Solar Radiation from Dynamically Downscaled Global Climate Model Output to Predicting Peanut Yields and Phenology in the SE USA. In 2006 Annual Meeting of Southern Branch ASA Feb. 4-8, Orlando, Florida, United States.
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Bellow, J., A. Mokssit, J. O'Brien, and R. Sebbari. (2008). Building national and specialised climate services. In A. Troccoli, M. Harrison, D. L. T. Anderson, & S. Mason (Eds.), Seasonal Climate: Forecasting and Managing Risk (pp. 315–349). Springer.
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Bellow, J. G., Hudson, R. F., & Nair, P. K. R. (2008). Adoption potential of fruit-tree-based agroforestry on small farms in the subtropical highlands. Agroforest Syst, 73(1), 23–36.
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Belyaev, K. P., Tanajura, C. A. S., & O'Brien, J. J. (2001). A data assimilation method used with an ocean circulation model and its application to the tropical Atlantic. Applied Mathematical Modelling, 25(8), 655–670.
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Bentamy, A., Piollé, J. F., Grouazel, A., Danielson, R., Gulev, S., Paul, F., et al. (2017). Review and assessment of latent and sensible heat flux accuracy over the global oceans. Remote Sensing of Environment, 201, 196–218.
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Bhardwaj, A., & Misra, V. (2019). The role of air-sea coupling in the downscaled hydroclimate projection over Peninsular Florida and the West Florida Shelf. Clim Dyn, 53(5-6), 2931–2947.
Abstract: A comparative analysis of two sets of downscaled simulations of the current climate and the future climate projections over Peninsular Florida (PF) and the West Florida Shelf (WFS) is presented to isolate the role of high-resolution air-sea coupling. In addition, the downscaled integrations are also compared with the much coarser, driving global model projection to examine the impact of grid resolution of the models. The WFS region is habitat for significant marine resources, which has both commercial and recreational value. Additionally, the hydroclimatic features of the WFS and PF contrast each other. For example, the seasonal cycle of surface evaporation in these two regions are opposite in phase to one another. In this study, we downscale the Community Climate System Model version 4 (CCSM4) simulations of the late twentieth century and the mid-twenty-first century (with reference concentration pathway 8.5 emission scenario) using an atmosphere only Regional Spectral Model (RSM) at 10 km grid resolution. In another set, we downscale the same set of CCSM4 simulations using the coupled RSM-Regional Ocean Model System (RSMROMS) at 10 km grid resolution. The comparison of the twentieth century simulations suggest significant changes to the SST simulation over WFS from RSMROMS relative to CCSM4, with the former reducing the systematic errors of the seasonal mean SST over all seasons except in the boreal summer season. It may be noted that owing to the coarse resolution of CCSM4, the comparatively shallow bathymetry of the WFS and the sharp coastline along PF is poorly defined, which is significantly rectified at 10 km grid spacing in RSMROMS. The seasonal hydroclimate over PF and the WFS in the twentieth century simulation show significant bias in all three models with CCSM4 showing the least for a majority of the seasons, except in the wet June-July-August (JJA) season. In the JJA season, the errors of the surface hydroclimate over PF is the least in RSMROMS. The systematic errors of surface precipitation and evaporation are more comparable between the simulations of CCSM4 and RSMROMS, while they differ the most in moisture flux convergence. However, there is considerable improvement in RSMROMS compared to RSM simulations in terms of the seasonal bias of the hydroclimate over WFS and PF in all seasons of the year. This suggests the potential rectification impact of air-sea coupling on dynamic downscaling of CCSM4 twentieth century simulations. In terms of the climate projection in the decades of 2041-2060, the RSMROMS simulation indicate significant drying of the wet season over PF compared to moderate drying in CCSM4 and insignificant changes in the RSM projection. This contrasting projection is also associated with projected warming of SSTs along the WFS in RSMROMS as opposed to warming patterns of SST that is more zonal and across the WFS in CCSM4.
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Bhardwaj, A., & Misra, V. (2019). Monitoring the Indian Summer Monsoon Evolution at the Granularity of the Indian Meteorological Sub-divisions using Remotely Sensed Rainfall Products. Remote Sensing, 11(9), 1080.
Abstract: We make use of satellite-based rainfall products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to objectively define local onset and demise of the Indian Summer Monsoon (ISM) at the spatial resolution of the meteorological subdivisions defined by the Indian Meteorological Department (IMD). These meteorological sub-divisions are the operational spatial scales for official forecasts issued by the IMD. Therefore, there is a direct practical utility to target these spatial scales for monitoring the evolution of the ISM. We find that the diagnosis of the climatological onset and demise dates and its variations from the TMPA product is quite similar to the rain gauge based analysis of the IMD, despite the differences in the duration of the two datasets. This study shows that the onset date variations of the ISM have a significant impact on the variations of the seasonal length and seasonal rainfall anomalies in many of the meteorological sub-divisions: for example, the early or later onset of the ISM is associated with longer and wetter or shorter and drier ISM seasons, respectively. It is shown that TMPA dataset (and therefore its follow up Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)) could be usefully adopted for monitoring the onset of the ISM and therefore extend its use to anticipate the potential anomalies of the seasonal length and seasonal rainfall anomalies of the ISM in many of the Indian meteorological sub-divisions. View Full-Text
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Bhardwaj, A., Misra, V., Mishra, A., Wootten, A., Boyles, R., Bowden, J. H., et al. (2018). Downscaling future climate change projections over Puerto Rico using a non-hydrostatic atmospheric model. Climatic Change, 147(1-2), 133–147.
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Bhowmick, S. A., Agarwal, N., Ali, M. M., Kishtawal, C. M., & Sharma, R. (2019). Role of ocean heat content in boosting post-monsoon tropical storms over Bay of Bengal during La-Nina events. Climate Dynamics, 52(12), 7225–7234.
Abstract: This study aims to analyze the role of ocean heat content in boosting the post-monsoon cyclonic activities over Bay of Bengal during La-Niña events. In strong La-Niña years, accumulated cyclone energy in Bay of Bengal is much more as compared to any other year. It is observed that during late June to October of moderate to strong La-Nina years, western Pacific is warmer. Sea surface temperature anomaly of western Pacific Ocean clearly indicates the presence of relatively warmer water mass in the channel connecting the Indian Ocean and Pacific Ocean, situated above Australia. Ocean currents transport the heat zonally from Pacific to South eastern Indian Ocean. Excess heat of the southern Indian Ocean is eventually transported to eastern equatorial Indian Ocean through strong geostrophic component of ocean current. By September the northward transport of this excess heat from eastern equatorial Indian Ocean to Bay of Bengal takes place during La-Nina years boosting the cyclonic activities thereafter.
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Boisserie, M. (2010). Generation of an empirical soil moisture initialization and its potential impact on subseasonal forecasting skill of continental precipitation and air temperature. Ph.D. thesis, Florida State University, Tallahassee, FL, FL.
Abstract: The effect of the PAR technique on the model soil moisture estimates is evaluated using the Global Soil Wetness Project Phase 2 (GSWP-2) multimodel analysis product (used as a proxy for global soil moisture observations) and actual in-situ observations from the state of Illinois. The results show that overall the PAR technique is effective; across most of the globe, the seasonal and anomaly variability of the model soil moisture estimates well reproduce the values of GSWP-2 in the top 1.5 m soil layer; by comparing to in-situ observations in Illinois, we find that the seasonal and anomaly soil moisture variability is also well represented deep into the soil. Therefore, in this study, we produce a new global soil moisture analysis dataset that can be used for many land surface studies (crop modeling, water resource management, soil erosion, etc.). Then, the contribution of the resulting soil moisture analysis (used as initial conditions) on air temperature and precipitation forecasts are investigated. For this, we follow the experimental set up of a model intercomparison study over the time period 1986-1995, the Global Land-Atmosphere Coupling Experiment second phase (GLACE-2), in which the FSU/COAPS climate model has participated. The results of the summertime air temperature forecasts show a significant increase in skill across most of the U.S. at short-term to subseasonal time scales. No increase in summertime precipitation forecasting skill is found at short-term to subseasonal time scales between 1986 and 1995, except for the anomalous drought year of 1988. We also analyze the forecasts of two extreme hydrological events, the 1988 U.S. Drought and the 1993 U.S. flood. In general, the comparison of these two extreme hydrological event forecasts shows greater improvement for the summertime of 1988 than that of 1993, suggesting that soil moisture contributes more to the development of a drought than a flood. This result is consistent with Dirmeyer and Brubaker [1999] and Weaver et al. [2009]. By analyzing the evaporative sources of these two extreme events using the back-trajectory methodology of Dirmeyer and Brubaker [1999], we find similar results as this latter paper; the soil moisture-precipitation feedback mechanism seems to play a greater role during the drought year of 1988 than the flood year of 1993. Finally, the accuracy of this soil moisture initialization depends upon the quality of the precipitation dataset that is assimilated. Because of the lack of observed precipitation at a high temporal resolution (3-hourly) for the study period (1986-1995), a reanalysis product is used for precipitation assimilation in this study. It is important to keep in mind that precipitation data in reanalysis sometimes differ significantly from observations since precipitation is often not assimilated into the reanalysis model. In order to investigate that aspect, a similar analysis to that we performed in this study could be done using the 3-hourly Tropical Rainfall Measuring Mission (TRMM) dataset available for a the time period 1998-present. Then, since the TRMM dataset is a fully observational dataset, we expect the soil moisture initialization to be improved over that obtained in this study, which, in turn, may further increase the forecast skill.
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