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Sudheer R Bhimireddy and Kiran Bhaganagar

Abstract

A new formulation - bPlume-WRF-LES model - was implemented within the Weather Research and Forecast (WRF) model to simulate the two-way coupling between the atmospheric boundary layer (ABL) and gas plume (denser and lighter) released into the atmosphere. The existing WRF-large eddy simulation (WRF-LES) modeling system was modified by coupling an additional transport equation for the plume gas mixture and by accounting for the buoyant production term in the turbulence kinetic energy equation. The focus of the work was to understand the effect of atmospheric forcings on plume rise, plume mixing and plume dynamics during the early stages of plume development. For this purpose, the bPlume-WRF-LES model was used to simulate the release of buoyant plumes from a large area source into different atmospheric conditions with varying stratification and mean wind forcing values. Plumes released in a convective ABL background rise according to the 2/3 power law with time before reaching the boundary layer top and spreading laterally. The convective ABL eddy turnover time scale (t *) dictates the rate at which plume mixes with the ambient air inside the convective boundary layer (CBL) and the plume dilution rate scales as t*3/2. Both the stratification and wind forcing enhance the plume mixing from the early plume development stage, and dilute the plume much faster. An increase in the mean winds within the CBL contributes to uniform mixing and greater bent-over plume behavior at a shorter downwind distance from the source.

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Douglas E. Miller, Zhuo Wang, Bo Li, Daniel S. Harnos, and Trent Ford

Abstract

Skillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States (US). We use a leading principal component of US soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP’s Climate Forecast System version 2 (CFSv2) at weeks 3-4 in the eastern US. It is found that the North Pacific SST anomalies persist several weeks and are associated with a persistent wave train pattern (WTZ500), which leads to increased occurrences of blocking and extreme temperature over the eastern US. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and decrease in latent heat flux, which may help maintain the overlying anticyclone. The clear sky conditions associated with blocking anticyclones further decrease soil moisture conditions and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, reservoir operation, and provide mitigation of impacts from extreme heat events.

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Xiaojie Yu, Xinyu Guo, Huiwang Gao, and Tao Zou

Abstract

Hydrographic surveys have revealed that the Yellow River plume propagates in the direction opposite to that of a Kelvin wave (upstream) under a low river discharge condition, but turns downstream as the river discharge increases. A numerical model reproduced the upstream extension of the plume under the low river discharge condition and the transition to the downstream direction under the high river discharge condition, and confirmed that the summer wind is not the necessary condition for upstream extension of the plume. With the condition of low river discharge, the model also indicated the dependence of the upstream extension of the plume on the tidal range: extending upstream in spring tide but turning downstream in neap tide. The upstream movement of the plume results from the upstream transport of freshwater that depends on the upstream tide-induced residual current around the river mouth and the downstream density-driven current around the offshore plume front. With the condition of high river discharge, the upstream tide-induced residual current cannot compete with the downstream density-driven current and the plume turns downstream. Momentum analysis confirms the important roles of advection term and viscosity term in the condition of low river discharge and the shift to a Coriolis force-dominated system under high river discharge condition. An idealized model study suggests a dimensionless number for the river discharge changing the river plume extension from upstream to downstream under a specific upstream ambient current around the river mouth.

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Naoki HIROSE, Tianran LIU, Katsumi TAKAYAMA, Katsuto UEHARA, Takeshi TANEDA, and Young Ho KIM

Abstract

This study clarifies the necessity of an extraordinary large coefficient of vertical viscosity for dynamical ocean modeling in a shallow and narrow strait with complex bathymetry. Sensitivity experiments and objective analyses imply that background momentum viscosity is at the order of 100 cm2/s, while tracer diffusivity estimates are on the order of 0.1 cm2/s. The physical interpretation of these estimates is also discussed in the last part of this paper. To obtain reliable solutions, this study introduces cyclic application of the dynamical response to each parameter to minimize the number of long-term sensitivity experiments. The recycling Green’s function method yields weaker bottom friction and enhanced latent heat flux simultaneously with the increased viscosity in high-resolution modeling of the Tsushima/Korea Strait.

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Stephen S. Leroy, Chi O. Ao, Olga P. Verkhoglyadova, and Mayra I. Oyola

Abstract

Bayesian interpolation has previously been proposed as a strategy to construct maps of radio occultation (RO) data, but that proposition did not consider the diurnal dimension of RO data. In this work, the basis functions of Bayesian interpolation are extended into the domain of the diurnal cycle, thus enabling monthly mapping of radio occultation data in synoptic time and analysis of the atmospheric tides. The basis functions are spherical harmonics multiplied by sinusoids in the diurnal cycle up to arbitrary spherical harmonic degree and diurnal cycle harmonic. Bayesian interpolation requires a regularizer to impose smoothness on the fits it produces, thereby preventing the overfitting of data. In this work, a formulation for the regularizer is proposed and the most probable values of the parameters of the regularizer determined. Special care is required when obvious gaps in the sampling of the diurnal cycle are known to occur in order to prevent the false detection of statistically significant high-degree harmonics of the diurnal cycle in the atmosphere. Finally, this work probes the ability of Bayesian interpolation to generate a valid uncertainty analysis of the fit. The postfit residuals of Bayesian interpolation are dominated not by measurement noise but by unresolved variability in the atmosphere, which is statistically nonuniform across the globe, thus violating the central assumption of Bayesian interpolation. The problem is ameliorated by constructing maps of RO data using Bayesian interpolation that partially resolve the temporal variability of the atmosphere, constructing maps for approximately every 3 days of RO data.

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Han Wang and Oliver Bühler

Abstract

We present a new method to estimate second-order horizontal velocity structure functions, as well as their Helmholtz decomposition into rotational and divergent components, from sparse data collected along Lagrangian observations. The novelty compared to existing methods is that we allow for anisotropic statistics in the velocity field and also in the collection of the Lagrangian data. Specifically, we assume only stationarity and spatial homogeneity of the data and that the cross covariance between the rotational and divergent flow components is either zero or a function of the separation distance only. No further assumptions are made and the anisotropy of the underlying flow components can be arbitrarily strong. We demonstrate our new method by testing it against synthetic data and applying it to the Lagrangian Submesoscale Experiment (LASER) dataset. We also identify an improved statistical angle-weighting technique that generally increases the accuracy of structure function estimations in the presence of anisotropy.

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Jessica S. Kenigson and M.-L. Timmermans

Abstract

The Beaufort high (BH) and its accompanying anticyclonic winds drive the Arctic Ocean’s Beaufort Gyre, the major freshwater reservoir of the Arctic Ocean. The Beaufort Gyre circulation and its capacity to accumulate or release freshwater rely on the BH intensity. The migration of Nordic seas cyclones into the Arctic has been hypothesized to moderate the strength of the BH. We explore this hypothesis by analyzing reanalysis sea level pressure fields to characterize the BH and identify and track cyclones north of 60°N during 1948–2019. A cluster analysis of Nordic seas cyclone trajectories reveals a western pathway (through the Arctic interior) associated with a relatively weak BH and an eastern pathway (along the Arctic periphery) associated with a relatively strong BH. Furthermore, we construct cyclone activity indices (CAIs) in the Arctic and Nordic seas that take into account multiple cyclone parameters (number, strength, and duration). There are significant correlations between the BH and the CAIs in the Arctic and Nordic seas during 1948–2019, with anomalously strong cyclone activity related to an anomalously weak BH, and vice versa. We show how the Arctic and Nordic seas CAIs experienced a regime shift toward increased cyclone activity between the first four decades analyzed (1948–88) and the most recent three decades (1989–2019). Over the same two time periods, the BH exhibits a weakening. Increased cyclone activity and an accompanying weakening of the BH may be consistent with expectations in a warming Arctic and have implications for Beaufort Gyre dynamics and freshwater.

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Yannick Peings, Zachary M. Labe, and Gudrun Magnusdottir

Abstract

This study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two general circulation models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100 ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the midlatitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean–atmosphere PAMIP experiments are analyzed. Substantial differences in the midlatitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean–atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s t and false discovery rate tests. This is problematic for the multimodel assessment of the response, as some of the spread may be attributed to different model sensitivities whereas it is due to internal variability. We propose a method to overcome this consistency issue that allows for more robust conclusions when only 100 ensemble members are used.

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Bor-Ting Jong, Mingfang Ting, and Richard Seager

Abstract

During the summer when an El Niño event is transitioning to a La Niña event, the extratropical teleconnections exert robust warming anomalies over the U.S. Midwest threatening agricultural production. This study assesses the performance of current climate models in capturing the prominent observed extratropical responses over North America during the transitioning La Niña summer, based on atmospheric general circulation model experiments and coupled models from the North American Multimodel Ensemble (NMME). The ensemble mean of the SST-forced experiments across the transitioning La Niña summers does not capture the robust warming in the Midwest. The SST-forced experiments do not produce consistent subtropical western Pacific (WP) negative precipitation anomalies and this leads to the poor simulations of extratropical teleconnections over North America. In the NMME models, with active air–sea interaction, the negative WP precipitation anomalies show better agreement across the models and with observations. However, the downstream wave train pattern and the resulting extratropical responses over North America exhibit large disagreement across the models and are consistently weaker than in observations. Furthermore, in these climate models, an anomalous anticyclone does not robustly translate into a warm anomaly over the Midwest, in disagreement with observations. This work suggests that, during the El Niño to La Niña transitioning summer, active air–sea interaction is important in simulating tropical precipitation over the WP. Nevertheless, skillful representations of the Rossby wave propagation and land–atmosphere processes in climate models are also essential for skillful simulations of extratropical responses over North America.

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Chad W. Thackeray, Alex Hall, Mark D. Zelinka, and Christopher G. Fletcher

Abstract

An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAFsnow) and sea ice (SAFice). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAFsnow EC is equally strong in CMIP6 as it was in past generations, while the SAFice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 ± 0.05 W m−2 K−1, or ~61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. The NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAFsnow and SAFice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAFice, and to a lesser extent SAFsnow.

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