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Free access
Ayumi Fujisaki-Manome
,
Haoguo Hu
,
Jia Wang
,
Joannes J. Westerink
,
Damrongsak Wirasaet
,
Guoming Ling
,
Mindo Choi
,
Saeed Moghimi
,
Edward Myers
,
Ali Abdolali
,
Clint Dawson
, and
Carol Janzen

Abstract

In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system.

Significance Statement

Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.

Open access
D. A. Cherian
,
Y. Guo
, and
F. O. Bryan

Abstract

We assess the representation of mesoscale stirring in a suite of models against an estimate derived from microstructure data collected during the North Atlantic Tracer Release Experiment (NATRE). We draw heavily from the approximate temperature variance budget framework of Ferrari and Polzin. This framework assumes two sources of temperature variance away from boundaries: first, the vertical stirring of the large-scale mean vertical gradient by small-scale turbulence; and second, the lateral stirring of large-scale mean along-isopycnal gradients by mesoscale eddies. Temperature variance so produced is transformed and on average transferred down scales for ultimate dissipation at the microscale at a rate χ estimated using microstructure observations. Ocean models represent these pathways by a vertical mixing parameterization, and an along-isopycnal lateral mixing parameterization (if needed). We assess the rate of variance production by the latter as a residual from the NATRE dataset and compare against the parameterized representations in a suite of model simulations. We find that variance production due to lateral stirring in a Parallel Ocean Program version 2 (POP2) 1/10° simulation agrees well, to within the estimated error bars, with that inferred from the NATRE estimate. A POP2 1° simulation and the Estimating the Circulation and Climate of the Ocean Version 4 release 4 (ECCOV4r4) simulation appear to dissipate an order of magnitude too much variance by applying a lateral diffusivity, when compared to the NATRE estimate, particularly below 1250 m. The ECCOV4r4-adjusted lateral diffusivities are elevated where the microstructure suggests elevated χ sourced from mesoscale stirring. Such elevated values are absent in other diffusivity estimates suggesting the possibility of compensating errors and caution in interpreting ECCOV4r4’s adjusted lateral diffusivities.

Significance Statement

We look at whether microstructure turbulence observations can provide a useful metric for judging the fidelity of representation of mesoscale stirring in a suite of models. We focus on the region of the North Atlantic Tracer Release Experiment (NATRE), the site of a major ocean turbulence observation campaign, and use an approximate variance budget framework for the region with observational estimates from . The approach provides a novel framework to evaluate the approximate representation of mesoscale stirring in a variety of models.

Open access
Alejandra Sanchez-Rios
,
R. Kipp Shearman
,
Craig M. Lee
,
Harper L. Simmons
,
Louis St. Laurent
,
Andrew J. Lucas
,
Takashi Ijichi
, and
Sen Jan

Abstract

The Kuroshio occasionally carries warm and salty North Pacific Water into fresher waters of the South China Sea, forming a front with a complex temperature–salinity (TS) structure to the west of the Luzon Strait. In this study, we examine the TS interleavings formed by alternating layers of North Pacific Water with South China Sea Water in a front formed during the winter monsoon season of 2014. Using observations from a glider array following a free-floating wave-powered vertical profiling float to calculate the fine-scale parameters Turner angle, Tu, and Richardson number, Ri, we identified areas favorable to double-diffusion convection and shear instability observed in a TS interleaving. We evaluated the contribution of double-diffusion convection and shear instabilities to the thermal variance diffusivity, χ, using microstructure data and compared it with previous parameterization schemes based on fine-scale properties. We discover that turbulent mixing is not accurately parameterized when both Tu and Ri are within critical ranges (Tu > 60; Ri < ¼). In particular, χ associated with salt finger processes was an order of magnitude higher (6.7 × 10−7 K2 s−1) than in regions where only velocity shear was likely to drive mixing (8.7 × 10−8 K2 s−1).

Restricted access
Catharina Elisabeth Graafland
,
Swen Brands
, and
José Manuel Gutiérrez

Abstract

The different phases of the Coupled Model Intercomparison Project (CMIP) provide ensembles of past, present, and future climate simulations crucial for climate change impact and adaptation activities. These ensembles are produced using multiple global climate models (GCMs) from different modeling centers with some shared building blocks and interdependencies. Applications typically follow the “model democracy” approach which might have significant implications in the resulting products (e.g., large bias and low spread). Thus, quantifying model similarity within ensembles is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. The classical methods used for assessing GCM similarity can be classified into two groups. The a priori approach relies on expert knowledge about the components of these models, while the a posteriori approach seeks similarity in the GCMs’ output variables and is thus data-driven. In this study, we apply probabilistic network models (PNMs), a well-established machine learning technique, as a new a posteriori method to measure intermodel similarities. The proposed methodology is applied to surface temperature fields of the historical experiments from the CMIP5 multimodel ensemble and different reanalysis gridded datasets. PNMs are able to learn the complex spatial dependency structures present in climate data, including teleconnections operating on multiple spatial scales, characteristic of the underlying GCM. A distance metric building on the resulting PNMs is applied to characterize GCM model dependencies. The results of this approach are in line with those obtained with more traditional methods but have further explanatory potential building on probabilistic model querying.

Significance Statement

The present study proposes the use of probabilistic network models (PNMs) to quantify model similarity within ensembles of global climate models (GCMs). This is crucial for interpreting model agreement and multimodel uncertainty in climate change studies. When applied to climate data (gridded global surface temperature in this study), PNMs encode the relevant spatial dependencies (local and remote connections). Similarities among the PNMs resulting from different GCMs can be quantified and are shown to capture similar GCM formulations reported in previous studies. Differently to other machine learning methods previously applied to this problem, PNMs are fully explainable (allowing probabilistic querying) and are applicable to high-dimensional gridded raw data.

Open access
Chen Liu
,
Lei Chen
, and
Stefan Liess

Abstract

The features of large-scale atmospheric circulations, storm tracks, and the mean flow-eddy interaction during winter Pacific-North American (PNA) events are investigated using National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis data at subseasonal timescale from 1979 to 2022. The day-to-day variations of storm-track activity and stream function reveal that storm-track activity varies along the evolution of mean flow. To better understand storm track variability with the mean flow-eddy interaction, further exploration is made by analyzing local energy energetics. The changes in horizontal and vertical baroclinic energy conversions from background flow correspond to the storm track anomalies over the North Pacific, indicating that the anomalies in storm tracks are due to the anomalous mean flow associated with PNA patterns impacting energy conversion through mean flow-eddy interaction. Eddy feedback driven by vorticity and heat fluxes is analyzed. This provides a concrete illustration of how eddy feedback serves as a positive factor for the upper-tropospheric circulation anomalies associated with the PNA pattern.

Restricted access
Suqiong Hu
,
Wenjun Zhang
,
Masahiro Watanabe
,
Feng Jiang
,
Fei-Fei Jin
, and
Han-Ching Chen

Abstract

El Niño–Southern Oscillation (ENSO), the dominant mode of interannual variability in the tropical Pacific, is well known to affect the extratropical climate via atmospheric teleconnections. Extratropical atmospheric variability may in turn influence the occurrence of ENSO events. The winter North Pacific Oscillation (NPO), as the secondary dominant mode of atmospheric variability over the North Pacific, has been recognized as a potential precursor for ENSO development. This study demonstrates that the preexisting winter NPO signal is primarily excited by sea surface temperature (SST) anomalies in the equatorial western–central Pacific. During ENSO years with a preceding winter NPO signal, which accounts for approximately 60% of ENSO events observed in 1979–2021, significant SST anomalies emerge in the equatorial western–central Pacific in the preceding autumn and winter. The concurrent presence of local convection anomalies can act as a catalyst for NPO-like atmospheric circulation anomalies. In contrast, during other ENSO years, significant SST anomalies are not observed in the equatorial western–central Pacific during the preceding winter, and correspondingly, the NPO signal is absent. Ensemble simulations using an atmospheric general circulation model driven by observed SST anomalies in the tropical western–central Pacific can well reproduce the interannual variability of observed NPO. Therefore, an alternative explanation for the observed NPO–ENSO relationship is that the preceding winter NPO is a companion to ENSO development, driven by the precursory SST signal in the equatorial western–central Pacific. Our results suggest that the lagged relationship between ENSO and the NPO involves a tropical–extratropical two-way coupling rather than a purely stochastic forcing of the extratropical atmosphere on ENSO.

Restricted access
Yingkai Sha
,
Ryan A. Sobash
, and
David John Gagne II

Abstract

An ensemble postprocessing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). The method combines conditional generative adversarial networks (CGANs), a type of deep generative model, with a convolutional neural network (CNN) to postprocess convection-allowing model (CAM) forecasts. The CGANs are designed to create synthetic ensemble members from deterministic CAM forecasts, and their outputs are processed by the CNN to estimate the probability of severe weather. The method is tested using High-Resolution Rapid Refresh (HRRR) 1–24-h forecasts as inputs and Storm Prediction Center (SPC) severe weather reports as targets. The method produced skillful predictions with up to 20% Brier skill score (BSS) increases compared to other neural-network-based reference methods using a testing dataset of HRRR forecasts in 2021. For the evaluation of uncertainty quantification, the method is overconfident but produces meaningful ensemble spreads that can distinguish good and bad forecasts. The quality of CGAN outputs is also evaluated. Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the intervariable correlations and the contribution of influential predictors as in the original HRRR forecasts. This work provides a novel approach to postprocess CAM output using neural networks that can be applied to severe weather prediction.

Significance Statement

We use a new machine learning (ML) technique to generate probabilistic forecasts of convective weather hazards, such as tornadoes and hailstorms, with the output from high-resolution numerical weather model forecasts. The new ML system generates an ensemble of synthetic forecast fields from a single forecast, which are then used to train ML models for convective hazard prediction. Using this ML-generated ensemble for training leads to improvements of 10%–20% in severe weather forecast skills compared to using other ML algorithms that use only output from the single forecast. This work is unique in that it explores the use of ML methods for producing synthetic forecasts of convective storm events and using these to train ML systems for high-impact convective weather prediction.

Open access
Zifan Su
,
Yongkun Xie
,
Jianping Huang
,
Guoxiong Wu
,
Yuzhi Liu
, and
Xiaodan Guan

Abstract

The Tibetan Plateau’s (TP) topography has long been recognized for its impact on climate. However, recognition of the influence of the TP on global weather variability remains insufficient. Therefore, this study used numerical simulations to demonstrate the influences of the TP and its mechanical and thermal forcing on global high-frequency temperature variability and eddy kinetic energy (EKE). Despite local influences, the TP influenced the high-frequency temperature variability in far-flung regions like North America. In summer, the TP’s influence on high-frequency temperature variability showed dipole patterns in Eurasia and tripole patterns in North America, which were mainly induced by TP thermal forcing. In winter, the TP’s influence on high-frequency temperature variability was dominated by mechanical forcing and was less significant for remote regions than in summer. Mechanical forcing dominated EKE in both summer and winter. Furthermore, the horizontal temperature advection dominated the TP’s influence on high-frequency temperature variability for both its thermal effect in summer and its mechanical effect in winter, wherein EKE, as the dynamical factor, determined the horizontal temperature advection rather than the thermodynamical factor, the temperature gradient. Our findings suggest that the TP, via its mechanical and thermal forcing, may have an impact on temperature-related weather extremes around the world.

Restricted access
Hairu Ding
,
Li Dong
,
Kaijun Liu
,
Ting Lin
,
Zhiang Xie
,
Bo Zhang
, and
Xiaoxue Wang

Abstract

As the only remaining ice sheet in the Northern Hemisphere, the Greenland ice sheet (GrIS) plays a crucial role in influencing atmospheric circulations, particularly with its rapid melting under global warming. In this paper, the influences of GrIS topography and surface thermal conditions are investigated by a series of aquaplanet experiments. The results show that the GrIS topography induces stationary waves and favors more blocking events through the generation of negative potential vorticity (PV) anomalies, while it tends to suppress local storm activities through the induced stationary waves. The surface cooling center of the GrIS is found to strengthen the jet streams by enhancing the meridional temperature gradient and thermal wind, while it causes the PV and static stability to increase during near-Greenland blocking days, thereby disfavoring blocking onset. Altogether, the topography and surface thermal effects of GrIS appear to compete with each other so that the net effect would determine the final response. Nevertheless, nonlinearity is found in both GrIS-topography alone and GrIS-surface temperature alone experiments, where nonlinear responses of atmospheric circulation are detected when the GrIS topography height or surface temperature exceeds their critical values, respectively. Hence, through this study, the response of the blocking in the vicinity of Greenland to the combined effects of topography and surface thermal conditions may shed light on comprehending the underlying mechanism of blocking aleration in a changing climate.

Restricted access