Fraisse, C. W., Breuer, N. E., Zierden, D., Bellow, J. G., Paz, J., Cabrera, V. E., et al. (2006). AgClimate: A climate forecast information system for agricultural risk management in the southeastern USA.
Computers and Electronics in Agriculture, 53(1), 13–27.
Fu, C. B., Qian, C., & Wu, Z. H. (2011). Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach.
Sci. China Earth Sci., 54(9), 1400–1406.
Selman, C., Misra, V., Stefanova, L., Dinapoli, S., & Smith III, T. J. (2013). On the twenty-first-century wet season projections over the Southeastern United States.
Reg Environ Change, 13(S1), 153–164.
Michael, J. - P., Misra, V., & Chassignet, E. P. (2013). The El Niño and Southern Oscillation in the historical centennial integrations of the new generation of climate models.
Reg Environ Change, 13(S1), 121–130.
Cammarano, D., Stefanova, L., Ortiz, B. V., Ramirez-Rodrigues, M., Asseng, S., Misra, V., et al. (2013). Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US.
Reg Environ Change, 13(S1), 101–110.
Parfitt, R., Ummenhofer, C. C., Buckley, B. M., Hansen, K. G., & D'Arrigo, R. D. (2020). Distinct seasonal climate drivers revealed in a network of tree-ring records from Labrador, Canada.
Clim Dyn, 54(3-4), 1897–1911.
Abstract: Traditionally, high-latitude dendroclimatic studies have focused on measurements of total ring width (RW), with the maximum density of the latewood (MXD) serving as a complementary variable. Whilst MXD has typically improved the strength of the growing season climate connection over that of RW, its measurements are costly and time-consuming. Recently, a less costly and more time-efficient technique to extract density measurements has emerged, based on lignin's propensity to absorb blue light. This Blue Intensity (BI) methodology is based on image analyses of finely-sanded core samples, and the relative ease with which density measurements can be extracted allows for significant increases in spatio-temporal sample depth. While some studies have attempted to combine RW and MXD as predictors for summer temperature reconstructions, here we evaluate a systematic comparison of the climate signal for RW and latewood BI (LWBI) separately, using a recently updated and expanded tree ring database for Labrador, Canada. We demonstrate that while RW responds primarily to climatic drivers earlier in the growing season (January-April), LWBI is more responsive to climate conditions during late spring and summer (May-August). Furthermore, RW appears to be driven primarily by large-scale atmospheric dynamics associated with the Pacific North American pattern, whilst LWBI is more closely associated with local climate conditions, themselves linked to the behaviour of the Atlantic Multidecadal Oscillation. Lastly, we demonstrate that anomalously wide or narrow growth rings consistently respond to the same climate drivers as average growth years, whereas the sensitivity of LWBI to extreme climate conditions appears to be enhanced.
Li, H., & Misra, V. (2014). Thirty-two-year ocean-atmosphere coupled downscaling of global reanalysis over the Intra-American Seas.
Clim Dyn, 43(9-10), 2471–2489.
Stefanova, L., Misra, V., Chan, S., Griffin, M., O'Brien, J. J., & Smith III, T. J. (2012). A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses.
Clim Dyn, 38(11-12), 2449–2466.
Wu, Z., Huang, N. E., Wallace, J. M., Smoliak, B. V., & Chen, X. (2011). On the time-varying trend in global-mean surface temperature.
Clim Dyn, 37(3-4), 759–773.
Qian, C., Wu, Z., Fu, C., & Zhou, T. (2010). On multi-timescale variability of temperature in China in modulated annual cycle reference frame.
Adv. Atmos. Sci., 27(5), 1169–1182.