Response of the upper ocean to northeast Pacific atmospheric rivers under climate change
Not Available
Shields, Christine A. (ORCID:0000000344811377)↗
Engineering topics
Liu, Xue
Publications and source records attributed to Liu, Xue.
How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?
Abstract Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.
54 ENVIRONMENTAL SCIENCES↗
Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity
Abstract It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.
Meteorology & Atmospheric Sciences↗
Direct Imaging of Antiferromagnet-Ferromagnet Phase Transition in van der Waals Antiferromagnet CrSBr
The advent of van der Waals (vdW) ferromagnetic (FM) and antiferromagnetic (AFM) materials offers unprecedented opportunities for spintronics and magneto-optic devices. Combining magnetic Kerr microscopy and density functional theory calculations, the AFM-FM transition is investigated and a surprising abnormal magneto-optic anisotropy in vdW CrSBr associated with different magnetic phases (FM, AFM, or paramagnetic state) is discovered. This unique magneto-optic property leads to different anisotropic optical reflectivity from different magnetic states, permitting direct imaging of the AFM Néel vector orientation and the dynamic process of the AFM-FM transition within a magnetic field. Using Kerr microscopy, not only the domain nucleation and propagation process is imaged but also the intermediate spin-flop state in the AFM-FM transition is identified. In conclusion, the unique magneto-optic property and clear identification of the dynamics process of the AFM-FM phase transition in CrSBr demonstrate the promise of vdW magnetic materials for future spintronic technology.
36 MATERIALS SCIENCE↗
Bioinspired Macrocyclic Molecule Supported Two‐Dimensional Lamellar Membrane with Robust Interlayer Structure for High‐Efficiency Nanofiltration
Abstract 2D lamellar membranes (2DLMs) are used for efficient desalination and nanofiltration. However, weak interactions between adjacent stacked nanosheets result in susceptibility to swelling that limits practical applicability. Inspired by the super adhesion of multi‐point suction cups on octopus tentacles, a 2DLM is constructed from Ti 3 C 2 T x MXene supported by the macrocyclic “multi‐point” molecule cucurbit[5]uril (CB5) and demonstrated for nanofiltration of methyl blue (MB) and enrichment of uranyl carbonate. Experimental results and density functional theory calculations indicate that CB5 rivets to the surface of the nanoflakes through strong stable interactions between its multiple binding sites and surface hydroxyl functional groups on MXene nanosheets. This novel 2DLM exhibits excellent nanofiltration performance (69 L m −2 h −1 bar −1 permeance with 93.6% rejection for MB) and can be recycled at least 30 times without significant degradation. The 2DLM exhibits excellent swelling resistance at high salinity, with a demonstration of selective enrichment of uranyl carbonate from artificial water and natural seawater. The results provide a new strategy for constructing highly stable 2DLMs with interlayer spacing controllable from sub‐nano to nanometer scales, for size‐selective sieving of molecules and ions, high‐efficiency nanofiltration, and other applications.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa
Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827-2807 km2 for Senegal and 245-1271 km2 for The Gambia with one outlier for each country. We further report "Places of Agreement" (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making.
Mondal, Pinki↗