This limited-scope project tackled the challenge of predicting water-cycle extremes in Texas and Oklahoma as severe as the 2015 and 2016 floods beyond seasonal timescale. The impact of extreme floods during spring and the hurricane season on Department of Defense (DoD) facilities has elevated, straining infrastructural limits and operational capability. The project’s main objectives were (1) tracking the 4-6-year ENSO (El Niño-Southern Oscillation) and teleconnections and (2) characterizing uncertainty in the 4-6-year prediction, testing the hypothesis that predictability within the reportedly strengthened 4-6-year ENSO cycle and its teleconnection can be extracted to provide extended outlook for extreme flood risk.

The project utilized long simulations produced by the Community Earth System Model (CESM) of the Large Ensemble Project (LEP) and observational data sets. Diagnostics of the LEP simulations of the 4-6-year ENSO cycle and its precursor patterns across the three major oceans were conducted to examine the evolution and regional impacts. Furthermore, an add-on analysis for Hurricane Harvey in the context of climate attribution and implication to prediction was performed. The guiding principle was to understand the differing synoptic processes embedded in the large-scale variability. Extremely wet and dry seasons in Texas and Oklahoma were objectively identified within a global reanalysis, the CESM, and the suite of climate models from the Coupled Model Intercomparison Project (CMIP), and their connection with ENSO was evaluated. The synoptic elements that could either produce consecutive rainstorms or stall a tropical storm were investigated in the model projections, to assess their role in the prediction uncertainty.

The LEP simulations revealed stable signal in the 4-6-year ENSO cycle with an amplified signal after 2010, suggesting increased predictability in the future, warmer climate. While both excessive precipitation and intense drought in Texas were projected to increase towards 2050, their association with the energized 4-6-year ENSO mode will likely strengthen and become increasingly predictable. However, groundwater storage in Texas and Oklahoma will likely decrease due to diminishing recharge caused by concurrent increases in drought, which offsets the effect of added rains. A greater implication of these results is that the alternation between excessive wet years and severe drought years will amplify. Prediction of ENSO impacts could be improved by capturing the western and north Pacific precursors showing strong air-sea interactions a year before a mature ENSO phase develops. These variations are manifest in the advection process of the sea surface heat budget, which acts in concert with the air-sea heat fluxes to foster ENSO development. However, these processes were compounded in the December 2015 Missouri flood, as the El Niño teleconnection interfered with the Madden Julian Oscillation, which contributes to uncertainty. Finally, through the analysis of Hurricane Harvey, it was found that the stalling characteristics of tropical storms will enhance moderately, but prediction of hurricanes beyond a season is not feasible due to the lack of ENSO connection. 

A physically-based approach to anticipating extreme floods in Texas and Oklahoma was developed within this limited-scope project; one that could be straightforwardly adapted for other states and international bases. The project result implied an impending drought in 2018 and potential risk of widespread flood in 2019, along with a long-term outlook for water resources planning. Additionally, a mesoscale modeling approach adds value to quantifying extreme weather threat around Texas and Oklahoma, where precipitation is becoming more intense. Model data and forecast information generated within this project were made available through a public university’s webserver, to be widely available for DoD and civilian use.