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Lim, Y. - K., Cai, M., Kalnay, E., & Zhou, L. (2008). Impact of Vegetation Types on Surface Temperature Change. J. Appl. Meteor. Climatol., 47(2), 411–424.
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Lim, Y. - K., Cocke, S., Shin, D. W., Schoof, J. T., LaRow, T. E., & O'Brien, J. J. (2010). Downscaling large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States. Clim Dyn, 35(2-3), 449–471.
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Lim, Y. - K., LaRow, T. E., O'Brien, J. J., & Shin, D. W. (2006). Statistical downscaling of the FSUGSM temperature over the southeast United States. Research Activities in Atmospheric and Ocean Modeling, CAS/JSC Working Group on Numerical Experimentation.
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Lim, Y. - K., Shin, D. W., Cocke, S., LaRow, T. E., Schoof, J. T., O'Brien, J. J., et al. (2007). Dynamically and statistically downscaled seasonal simulations of maximum surface air temperature over the southeastern United States. J. Geophys. Res., 112(D24).
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Lim, Y. - K., Stefanova, L. B., Chan, S. C., Schubert, S. D., & O'Brien, J. J. (2011). High-resolution subtropical summer precipitation derived from dynamical downscaling of the NCEP/DOE reanalysis: how much small-scale information is added by a regional model? Clim Dyn, 37(5-6), 1061–1080.
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Lim, Y. - K., D. W. Shin, T. E. LaRow, and S. Cocke. (2007). Categorical predictability of regionalized surface temperature and precipitation over the southeast United States. CAS/JSC Working Group on Numerical Experimentation.
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Lindstrom, E. J., M. A. Bourassa, L.-A. Breivik, C. J. Donlon, Lee-Lueng Fu, P. Hacker, G. Lagerloef, T. Lee, C. Le Quere, V. Swail, W. S. Wilson, V. Zlotnicki. (2010). Research Satellite Missions. In D. D.E. and Stammer Harrison J. Hall (Ed.), Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society (Vol. 1).
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Liu, J., Curry, J. A., Clayson, C. A., & Bourassa, M. A. (2011). High-Resolution Satellite Surface Latent Heat Fluxes in North Atlantic Hurricanes. Mon. Wea. Rev., 139(9), 2735–2747.
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Liu, M., Lin, J., Wang, Y., Sun, Y., Zheng, B., Shao, J., et al. (2018). Spatiotemporal variability of NO2 and PM2.5 over Eastern China: observational and model analyses with a novel statistical method. Atmos. Chem. Phys., 18(17), 12933–12952.
Abstract: Eastern China (27-41 degrees N, 110-123 degrees E) is heavily polluted by nitrogen dioxide (NO2), particulate matter with aerodynamic diameter below 2.5 mu m (PM2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF-EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes over Eastern China in fall-winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north-south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another.
We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO2 by about 17 mu g m(-3) and PM2.5 by 35 mu g m(-3 )on average over fall-winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north-south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO2). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO2 by about 30 mu g m(-3) and PM2.5 by 60 mu g m(-3). For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north-south opposing mode of NO2. Both models capture the day-to-day variability of PM2.5 better than that of NO2. These results shed light on model improvement. The EOF-EEMD package is freely available for noncommercial uses.
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Liu, Q., Tan, Z. - M., Sun, J., Hou, Y., Fu, C., & Wu, Z. (2020). Changing rapid weather variability increases influenza epidemic risk in a warming climate. Environmental Research Letters, 15(4).
Abstract: The continuing change of the Earth's climate is believed to affect the influenza viral activity and transmission in the coming decades. However, a consensus of the severity of the risk of influenza epidemic in a warming climate has not been reached. It was previously reported that the warmer winter can reduce influenza epidemic-caused mortality, but this relation cannot explain the deadly influenza epidemic in many countries over northern mid-latitudes in the winter of 2017-2018, one of the warmest winters in recent decades. Here we reveal that the widely spread 2017-2018 influenza epidemic can be attributed to the abnormally strong rapid weather variability. We demonstrate, from historical data, that the large rapid weather variability in autumn can precondition the deadly influenza epidemic in the subsequent months in highly populated northern mid-latitudes; and the influenza epidemic season of 2017-2018 was a typical case. We further show that climate model projections reach a consensus that the rapid weather variability in autumn will continue to strengthen in some regions of northern mid-latitudes in a warming climate, implying that the risk of influenza epidemic may increase 20% to 50% in some highly populated regions in later 21st century.
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