This study examines the causes of observed sea surface temperature (SST) anomalies in the tropical North Atlantic between 1982 and 2015. The emphasis is on the boreal winter and spring seasons, when tropical Atlantic SSTs project strongly onto the Atlantic meridional mode (AMM). Results from a composite analysis of satellite and reanalysis data show important forcing of SST anomalies by wind-driven changes in mixed layer depth and shortwave radiation between 5° and 10°N, in addition to the well-known positive wind–evaporation–SST and shortwave radiation–SST feedbacks between 5° and 20°N. Anomalous surface winds also drive pronounced thermocline depth anomalies of opposite signs in the eastern equatorial Atlantic and intertropical convergence zone (ITCZ; 2°–8°N). A major new finding is that there is strong event-to-event variability in the impact of thermocline depth on SST in the ITCZ region, in contrast to the more consistent relationship in the eastern equatorial Atlantic. Much stronger anomalies of meridional wind stress, thermocline depth, and vertical turbulent cooling are found in the ITCZ region during a negative AMM event in 2009 compared to a negative event in 2015 and a positive event in 2010, despite SST anomalies of similar magnitude in the early stages of each event. The larger anomalies in 2009 led to a much stronger and longer-lived event. Possible causes of the inconsistent relationship between thermocline depth and SST in the ITCZ region are discussed, including the preconditioning role of the winter cross-equatorial SST gradient.
This paper describes development of a method for discriminating high ice water content (HIWC) conditions that can disrupt jet-engine performance in commuter and large transport aircraft. Using input data from satellites, numerical weather prediction models, and ground-based radar, this effort employs machine learning to determine optimal combinations of available information using fuzzy logic. Airborne in situ measurements of ice water content (IWC) from a series of field experiments that sampled HIWC conditions serve as training data in the machine-learning process. The resulting method, known as the Algorithm for Prediction of HIWC Areas (ALPHA), estimates the likelihood of HIWC conditions over a three-dimensional domain. Performance statistics calculated from an independent subset of data reserved for verification indicate that the ALPHA has skill for detecting HIWC conditions, albeit with significant false alarm rates. Probability of detection (POD), probability of false detection (POFD), and false alarm ratio (FAR) are 86%, 29% (60% when IWC below 0.1 g m−3 are omitted), and 51%, respectively, for one set of detection thresholds using in situ measurements. Corresponding receiver operating characteristic (ROC) curves give an area under the curve of 0.85 when considering all data and 0.69 for only points with IWC of at least 0.1 g m−3. Monte Carlo simulations suggest that aircraft sampling biases resulted in a positive POD bias and the actual probability of detection is between 78.5% and 83.1% (95% confidence interval). Analysis of individual case studies shows that the ALPHA output product generally tracks variation in the measured IWC.
High ice water content (HIWC; defined herein as at least 1.0 g m−3) conditions are often found in the anvils of convective systems and can cause engine damage and/or failure in aircraft. We use ice water content (IWC) retrievals from satellite-borne radar and lidar (CloudSat and CALIOP) to provide the first analysis of global HIWC frequency using 11 years of data (2007 to 2017). Results show HIWC is generally present in 1 to 2% of CloudSat and CALIOP IWC retrievals between flight levels 270 (27,000 ft or 8.230 km) and 420 (FL420; 42,000 ft or 12.801 km) in areas with frequent convection. Similar rates of HIWC are found over midlatitude oceans at relatively low altitudes (below FL270). Possible non-convective mechanisms for the formation of this low-level HIWC are discussed, as are the uncertainties suggesting the results at these low altitudes are an overestimation of the true threat of HIWC to aircraft engines. The satellite IWC retrievals are also used to validate a HIWC diagnostic tool which provides storm-scale statistics on HIWC over the Contiguous United States (CONUS) during the summer convective season (May through August, 2012 to 2019). Results over the CONUS suggest HIWC over the Great Plains is highest in June, when a point in the region is under HIWC conditions about 25 hours out of 30 days on average. The mean area-equivalent diameters of HIWC conditions in some areas of the Great Plains exceeds 350 km and the conditions can persist for 4 to 5 hours.
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