Excitation of equatorial Kelvin and Yanai waves by tropical cyclones in an ocean general circulation model

Tropical cyclones (TCs) actively contribute to the dynamics of Earth's coupled climate system. They influence oceanic mixing rates, upper-ocean heat content, and air–sea fluxes, with implications for atmosphere and ocean dynamics on multiple spatial and temporal scales. Using an ocean general circulation model with modified surface wind forcing, we explore how TC winds can excite equatorial ocean waves in the tropical Pacific. We highlight a situation where three successive TCs in the western North Pacific region, corresponding to events in 2003, excite a combination of Kelvin and Yanai waves in the equatorial Pacific. The resultant thermocline adjustment significantly modifies the thermal structure of the upper equatorial Pacific and leads to eastward zonal heat transport. Observations of upper-ocean temperature by the Tropical Atmosphere Ocean (TAO) buoy array and sea-level height anomalies using altimetry reveal wave passage during the same time period with similar properties to the modeled wave, although our idealized model methodology disallows precise identification of the TC forcing with the observed waves. Results indicate that direct oceanographic forcing by TCs may be important for understanding the spectrum of equatorial ocean waves, thus remotely influencing tropical mixing and surface energy budgets. Because equatorial Kelvin waves are closely linked to interannual variability in the tropical Pacific, these findings also suggest TC wind forcing may influence the timing and amplitude of El Niño events

Regional variations in the ocean response to tropical cyclones: Ocean mixing versus low cloud suppression

Tropical cyclones (TCs) tend to cool sea surface temperature (SST) via enhanced vertical mixing and evaporative fluxes. This cooling is substantially reduced in the subtropics, especially in the northeastern Pacific where the occurrence of TCs can warm the ocean surface. Here we investigate the cause of this anomalous warming by analyzing the local oceanic features and TC-induced anomalies of SST, surface fluxes, and cloud fraction using satellite and in situ data. We find that TCs tend to suppress low clouds at the margins of the tropical ocean warm pool, enhancing shortwave radiative surface fluxes within the first week following storm passage, which, combined with spatial variations in ocean thermal structure, can produce a ~1°C near-surface warming in the northeastern Pacific. These findings, supported by high-resolution Earth system model simulations, point to potential connections between TCs, ocean temperature, and low cloud distributions that can influence tropical surface heat budgets

Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems

A new class of algorithms is introduced and analyzed for bound and linearly constrained optimization problems with stochastic objective functions and a mixture of design variable types. The generalized pattern search (GPS) class of algorithms is extended to a new problem setting in which objective function evaluations require sampling from a model of a stochastic system. The approach combines GPS with ranking and selection (R&S) statistical procedures to select new iterates. The derivative-free algorithms require only black-box simulation responses and are applicable over domains with mixed variables (continuous, discrete numeric, and discrete categorical) to include bound and linear constraints on the continuous variables. A convergence analysis for the general class of algorithms establishes almost sure convergence of an iteration subsequence to stationary points appropriately defined in the mixed-variable domain. Additionally, specific algorithm instances are implemented that provide computational enhancements to the basic algorithm. Implementation alternatives include the use modern R&S procedures designed to provide efficient sampling strategies and the use of surrogate functions that augment the search by approximating the unknown objective function with nonparametric response surfaces. In a computational evaluation, six variants of the algorithm are tested along with four competing methods on 26 standardized test problems. The numerical results validate the use of advanced implementations as a means to improve algorithm performance

Closing the sea surface mixed layer temperature budget from in situ observations alone: Operation Advection during BoBBLE

Sea surface temperature (SST) is a fundamental driver of tropical weather systems such as monsoon rainfall and tropical cyclones. However, understanding of the factors that control SST variability is lacking, especially during the monsoons when in situ observations are sparse. Here we use a ground-breaking observational approach to determine the controls on the SST variability in the southern Bay of Bengal. We achieve this through the first full closure of the ocean mixed layer energy budget derived entirely from in situ observations during the Bay of Bengal Boundary Layer Experiment (BoBBLE). Locally measured horizontal advection and entrainment contribute more significantly than expected to SST evolution and thus oceanic variability during the observation period. These processes are poorly resolved by state-of-the-art climate models, which may contribute to poor representation of monsoon rainfall variability. The novel techniques presented here provide a blueprint for future observational experiments to quantify the mixed layer heat budget on longer time scales and to evaluate these processes in models

The Application of Sequential Convex Programming to Large-Scale Structural Optimization Problems

Structural design problems are often modeled using finite element methods. Such models are often characterized by constraint functions that are not explicitly defined in terms of the design variables. These functions are typically evaluated through numerical finite element analysis (FEA). Optimizing large-scale structural design models requires computationally expensive FEAs to obtain function and gradient values. An optimization approach which uses the SCP sequential convex programming algorithm of Zillober, integrated as the optimizer in the Automated Structural Optimization System (ASTROS), is tested. The traditional approach forms an explicitly defined approximate subproblem at each design iteration that is solved using the method of modified feasible directions. In an alternative approach, the SCP subroutine is called to formulate and solve the approximate subproblem. The SCP method is an implementation of the Method of Moving Asymptotes algorithm with five different asymptote determination strategies. This study investigates the effect of different asymptote determination strategies and constraint retention strategies on computational efficiency. The approach is tested on three large-scale structural design models, including one with constraints from multiple disciplines. Results and comparisons to the traditional approach are given. The largest of the three models, which had 1527 design variables and 6124 constraints, was solved to optimality with ASTROS for the first time using a mathematical programming method. The structural weight of the resulting design is 9% lower than the previously recorded minimum weight

Tropical cyclone activity and western North Atlantic stratification over the last millennium: a comparative review with viable connections

Tropical cyclones (TC) are recognized to modify the thermal structure of the upper ocean through the process of vertical mixing. Assessing the role this mixing plays in the overall stratification of the upper ocean is difficult, due to the relatively short and incomplete instrumental record. Proxy records for both TC landfalls and oceanographic stratification are preserved within the geological record and provide insight for how past changes in TC‐induced mixing have potentially affected water column structure prior to the instrumental record. Here we provide the first comparison between previously published paleo‐reconstructions of vertical ocean density and tropical cyclone activity from the western North Atlantic. A prominent lull in TC activity has been observed prior to approximately 1700 CE that extends back several centuries. This interval of low TC activity is shown to be concurrent with the timing of increased ocean stratification near Great Bahama Bank, potentially due in part to reduced TC‐induced mixing. To test whether this relationship is feasible, we present numerical results from a coarse‐resolution ocean general circulation model experiment isolating the effect of TC surface wind forcing on the upper ocean. An anomaly of roughly 0.12 kg m −3 in vertical stratification occurs above and below the mixed layer for model runs with and without TC mixing. This anomaly is roughly 25% of the entire paleo‐density signal observed just prior to 1700 CE. These results suggest that TC mixing alone cannot completely explain the density anomaly observed prior to 1700 CE, but support TC variability as an important contributor to enhancing oceanic stratification during this interval. Copyright © 2011 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91201/1/1551_ftp.pd

Extreme Floods and Droughts under Future Climate Scenarios

Hydroclimatic extremes, such as floods and droughts, affect aspects of our lives and the environment including energy, hydropower, agriculture, transportation, urban life, and human health and safety. Climate studies indicate that the risk of increased flooding and/or more severe droughts will be higher in the future than today, causing increased fatalities, environmental degradation, and economic losses. Using a suite of innovative approaches this book quantifies the changes in projected hydroclimatic extremes and illustrates their impacts in several locations in North America, Asia, and Europe

Multi-model Ensemble Analysis with Neural Network Gaussian Processes

Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called NN-GPR, using Gaussian process regression (GPR) with an infinitely wide deep neural network based covariance function. NN-GPR requires no assumptions about the relationships between models, no interpolation to a common grid, no stationarity assumptions, and automatically downscales as part of its prediction algorithm. Model experiments show that NN-GPR can be highly skillful at surface temperature and precipitation forecasting by preserving geospatial signals at multiple scales and capturing inter-annual variability. Our projections particularly show improved accuracy and uncertainty quantification skill in regions of high variability, which allows us to cheaply assess tail behavior at a 0.44$^\circ$/50 km spatial resolution without a regional climate model (RCM). Evaluations on reanalysis data and SSP245 forced climate models show that NN-GPR produces similar, overall climatologies to the model ensemble while better capturing fine scale spatial patterns. Finally, we compare NN-GPR's regional predictions against two RCMs and show that NN-GPR can rival the performance of RCMs using only global model data as input.Comment: 12 pages, 9 figure

Low Frequency Variability In Globally Integrated Tropical Cyclone Power Dissipation

Surface wind and temperature records from the European Centre for Medium- Range Weather Forecasts 40 Year Reanalysis (ERA-40) Project are used to estimate low-frequency variations in globally integrated tropical cyclone (TC) intensity from 1958 to 2001. For the first time, the annually integrated power dissipation (PD) is explicitly calculated on a global scale, and results show an upward trend in PD during much of the ERA-40 project period, although we argue this is at least partially due to limitations in cyclone representation in ERA-40. Comparing our estimated trend in PD with Emanuel\u27s (2005) approximation to PD reveals good agreement after 1978, coinciding with the onset of a major satellite observing-system epoch in ERA-40. The low pass (\u3e60 months) filtered PD time series correlates with mean annual tropical temperature, thus this result is consistent with the hypothesis that tropical temperatures may directly regulate the integrated intensity of TCs