Brolley, J. M. (2007).
Effects of ENSO, NAO (PVO), and PDO on Monthly Extreme Temperatures and Precipitation. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: The El Nino-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), and the Polar Vortex Oscillation (PVO) produce conditions favorable for monthly extreme temperatures and precipitation. These climate modes produce upper-level teleconnection patterns that favor regional droughts, floods, heat waves, and cold spells, and these extremes impact agriculture, energy, forestry, and transportation. The above sectors prefer the knowledge of the worst (and sometimes the best) case scenarios. This study examines the extreme scenarios for each phase and the combination of phases that produce the greatest monthly extremes. Data from Canada, Mexico, and the United States are gathered from the Historical Climatology Network (HCN). Monthly data are simulated by the utilization of a Monte Carlo model. This Monte Carlo method simulates monthly data by the stochastic selection of daily data with identical ENSO, PDO, and PVO (NAO) characteristics. In order to test the quality of the Monte Carlo simulation, the simulations are compared with the observations using only PDO and PVO. It has been found that temperatures and precipitation in the simulation are similar to the model. Statistics tests have favored similarities between simulations and observations in most cases. Daily data are selected in blocks of four to eight days in order to conserve temporal correlation. Because the polar vortex occurs only during the cold season, the PVO is used during January, and the NAO is used during other months. The simulated data are arranged, and the tenth and ninetieth percentiles are analyzed. The magnitudes of temperature and precipitation anomalies are the greatest in the western Canada and the southeastern United States during winter, and these anomalies are located near the Pacific North American (PNA) extrema. Western Canada has its coldest (warmest) Januaries when the PDO and PVO are low (high). The southeastern United States has its coldest Januaries with high PDO and low PVO and warmest Januaries with low PDO and high PVO. Although extremes occur during El Nino or La Nina, many stations have the highest or lowest temperatures during neutral ENSO. In California and the Gulf Coast, the driest (wettest) Januaries tend to occur during low (high) PDO, and the reverse occurs in Tennessee, Kentucky, Ohio, and Indiana. Summertime anomalies, on the other hand, are weak because temperature variance is low. Phase combinations that form the wettest (driest) Julies form spatially incoherent patterns. The magnitudes of the temperature and precipitation anomalies and the corresponding phase combinations vary regionally and seasonally. Composite maps of geopotential heights across North America are plot for low, median, and high temperatures at six selected sites and for low, median, and high precipitation at the same sites. The greatest fluctuations occur near the six sites and over some of the loci of the PNA pattern. Geopotential heights tend to decrease (increase) over the target stations during the cold (warm) cases, and the results for precipitation are variable.
Bunge, L., & Clarke, A. J. (2014). On the Warm Water Volume and Its Changing Relationship with ENSO.
J. Phys. Oceanogr., 44(5), 1372–1385.
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.
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.
Gilford, D. M., Smith, S. R., Griffin, M. L., & Arguez, A. (2013). Southeastern U.S. Daily Temperature Ranges Associated with the El Niño-Southern Oscillation.
J. Appl. Meteor. Climatol., 52(11), 2434–2449.
Kumar, V., Jana, S., Bhardwaj, A., Deepa, R., Sahu, S. K., Pradhan, P. K., et al. (2018). Greenhouse Gas Emission, Rainfall and Crop Production Over North-Western India.
TOECOLJ, 11(1), 47–61.
This study is based on datasets acquired from multi sources e.g. rain-gauges, satellite, reanalysis and coupled model for the region of Northwestern India. The influence of rainfall on crop production is obvious and direct. With the climate change and global warming, greenhouse gases are also showing an adverse impact on crop production. Greenhouse gases (e.g. CO2, NO2 and CH4) have shown an increasing trend over Northwestern Indian region. In recent years, rainfall has also shown an increasing trend over Northwestern India, while the production of rice and maize are reducing over the region. From eight selected sites, over Northwestern India, where rice and maize productions have reduced by 40%, with an increase in CO2, NO2 and CH4 gas emission by 5% from 1998 to 2011.
The correlation from one year to another between rainfall, gas emission and crop production was not very robust throughout the study period, but seemed to be stronger for some years than others.
Such trends and crop yield are attributed to rainfall, greenhouse gas emissions and to the climate variability.
Lu, J., Hu, A., & Zeng, Z. (2014). On the possible interaction between internal climate variability and forced climate change.
Geophys. Res. Lett., 41(8), 2962–2970.
Morey, S. L., Dukhovskoy, D. S., & Bourassa, M. A. (2009). Connectivity of the Apalachicola River flow variability and the physical and bio-optical oceanic properties of the northern West Florida Shelf.
Continental Shelf Research, 29(9), 1264–1275.
Perron, M., & Sura, P. (2013). Climatology of Non-Gaussian Atmospheric Statistics.
J. Climate, 26(3), 1063–1083.