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Powell, M. D., & Cocke, S. (2012). Hurricane wind fields needed to assess risk to offshore wind farms. Proc Natl Acad Sci U S A, 109(33), E2192; author reply E2193–4.
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Proshutinsky, A., Dukhovskoy, D., Timmermans, M. - L., Krishfield, R., & Bamber, J. L. (2015). Arctic circulation regimes. Philos Trans A Math Phys Eng Sci, 373(2052).
Abstract: Between 1948 and 1996, mean annual environmental parameters in the Arctic experienced a well-pronounced decadal variability with two basic circulation patterns: cyclonic and anticyclonic alternating at 5 to 7 year intervals. During cyclonic regimes, low sea-level atmospheric pressure (SLP) dominated over the Arctic Ocean driving sea ice and the upper ocean counterclockwise; the Arctic atmosphere was relatively warm and humid, and freshwater flux from the Arctic Ocean towards the subarctic seas was intensified. By contrast, during anticylonic circulation regimes, high SLP dominated driving sea ice and the upper ocean clockwise. Meanwhile, the atmosphere was cold and dry and the freshwater flux from the Arctic to the subarctic seas was reduced. Since 1997, however, the Arctic system has been under the influence of an anticyclonic circulation regime (17 years) with a set of environmental parameters that are atypical for this regime. We discuss a hypothesis explaining the causes and mechanisms regulating the intensity and duration of Arctic circulation regimes, and speculate how changes in freshwater fluxes from the Arctic Ocean and Greenland impact environmental conditions and interrupt their decadal variability.
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Wu, Z., Feng, J., Qiao, F., & Tan, Z. - M. (2016). Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets. Philos Trans A Math Phys Eng Sci, 374(2065), 20150197.
Abstract: In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.
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Stukel, M. R., Aluwihare, L. I., Barbeau, K. A., Chekalyuk, A. M., Goericke, R., Miller, A. J., et al. (2017). Mesoscale ocean fronts enhance carbon export due to gravitational sinking and subduction. Proc Natl Acad Sci U S A, 114(6), 1252–1257.
Abstract: Enhanced vertical carbon transport (gravitational sinking and subduction) at mesoscale ocean fronts may explain the demonstrated imbalance of new production and sinking particle export in coastal upwelling ecosystems. Based on flux assessments from 238U:234Th disequilibrium and sediment traps, we found 2 to 3 times higher rates of gravitational particle export near a deep-water front (305 mg Cm-2d-1) compared with adjacent water or to mean (nonfrontal) regional conditions. Elevated particle flux at the front was mechanistically linked to Fe-stressed diatoms and high mesozooplankton fecal pellet production. Using a data assimilative regional ocean model fit to measured conditions, we estimate that an additional approximately 225 mg Cm-2d-1 was exported as subduction of particle-rich water at the front, highlighting a transport mechanism that is not captured by sediment traps and is poorly quantified by most models and in situ measurements. Mesoscale fronts may be responsible for over a quarter of total organic carbon sequestration in the California Current and other coastal upwelling ecosystems.
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Zhang, M., Zhang, Y., Shu, Q., Zhao, C., Wang, G., Wu, Z., et al. (2018). Spatiotemporal evolution of the chlorophyll a trend in the North Atlantic Ocean. Sci Total Environ, 612, 1141–1148.
Abstract: Analyses of the chlorophyll a concentration (chla) from satellite ocean color products have suggested the decadal-scale variability of chla linked to the climate change. The decadal-scale variability in chla is both spatially and temporally non-uniform. We need to understand the spatiotemporal evolution of chla in decadal or multi-decadal timescales to better evaluate its linkage to climate variability. Here, the spatiotemporal evolution of the chla trend in the North Atlantic Ocean for the period 1997-2016 is analyzed using the multidimensional ensemble empirical mode decomposition method. We find that this variable trend signal of chla shows a dipole pattern between the subpolar gyre and along the Gulf Stream path, and propagation along the opposite direction of the North Atlantic Current. This propagation signal has an overlapping variability of approximately twenty years. Our findings suggest that the spatiotemporal evolution of chla during the two most recent decades is part of the multidecadal variations and possibly regulated by the changes of Atlantic Meridional Overturning Circulation, whereas the mechanisms of such evolution patterns still need to be explored.
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Coles, V. J., Stukel, M. R., Brooks, M. T., Burd, A., Crump, B. C., Moran, M. A., et al. (2017). Ocean biogeochemistry modeled with emergent trait-based genomics. Science, 358(6367), 1149–1154.
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Shin, D. W., G. A. Baigorria, Y.-K. Lim, S. Cocke, T. E. LaRow, J. J. O'Brien, and J. W. Jones. (2009). Assessing Crop Yield Simulations with Various Seasonal Climate Data. Science and Technology Infusion Climate Bulletin, .
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González-Rodríguez, E., Trasviña-Castro, A., Gaxiola-Castro, G., Zamudio, L., & Cervantes-Duarte, R. (2012). Net primary productivity, upwelling and coastal currents in the Gulf of Ulloa, Baja California, México. Ocean Sci., 8(4), 703–711.
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Cammarano, D., Basso, B., Stefanova, L., & Grace, P. (2012). Adapting wheat sowing dates to projected climate change in the Australian subtropics: analysis of crop water use and yield. Crop Pasture Sci., 63(10), 974.
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Krishnamurti, T. N., Kishtawal, C., LaRow, T. E., Bachiochi, D., Zhang, Z., Williford, C., et al. (1999). Improved Skill for Weather and Seasonal Climate Forecasts from Multi-Model Super Ensemble. Science, 285(5433), 1548–1550.
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