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Jing Ming and Jianqi Sun

Abstract

This study investigates the relationship between the central tropical Pacific (CTP) sea surface temperature (SST) and the surface air temperature (SAT) variability un-related to canonical El Niño-Southern Oscillation (ENSO) over mid-to-high latitude Eurasia during boreal summers over the past half-century. The results show that their relationship experienced a decadal shift around the early 1980s. Before the early 1980s, the Eurasian SAT-CTP SST connection was weak; after that time, the relationship became stronger, and the SAT anomalies exhibited a significant wave-like pattern over Eurasia. Such a decadal change in the Eurasian SAT-CTP SST relationship could be attributed to decadal changes in the mean state and variability of CTP SST. The warmer mean state and enhanced SST variability after the early 1980s reinforced the convective activities over the tropical Pacific, leading to significantly anomalous divergence/convergence and Rossby wave sources over the North Pacific. This outcome further excited the wave train propagating along the Northern Hemisphere zonal jet stream to northern Eurasia and then affected the surface heat fluxes and atmospheric circulations over the region, resulting in wave-like SATs over Eurasia. However, during the period before the early 1980s, the CTP SST had a weak impact on the North Pacific atmospheric circulation and was consequently not able to excite the wave train pattern to impact the Eurasian atmospheric circulation and SATs. The physical processes linking the CTP SST and Eurasian SAT are further confirmed by numerical simulations. The results of this study are valuable to understanding the variability of summer Eurasian SATs.

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Eviatar Bach, Safa Mote, V. Krishnamurthy, A. Surjalal Sharma, Michael Ghil, and Eugenia Kalnay

Abstract

Oscillatory modes of the climate system are among its most predictable features, especially at intraseasonal time scales. These oscillations can be predicted well with data-driven methods, often with better skill than dynamical models. However, since the oscillations only represent a portion of the total variance, a method for beneficially combining oscillation forecasts with dynamical forecasts of the full system was not previously known. We introduce Ensemble Oscillation Correction (EnOC), a general method to correct oscillatory modes in ensemble forecasts from dynamical models. We compute the ensemble mean—or the ensemble probability distribution—with only the best ensemble members, as determined by their discrepancy from a data-driven forecast of the oscillatory modes. We also present an alternate method which uses ensemble data assimilation to combine the oscillation forecasts with an ensemble of dynamical forecasts of the system (EnOCDA). The oscillatory modes are extracted with a time-series analysis method called multi-channel singular spectrum analysis (M-SSA), and forecast using an analog method. We test these two methods using chaotic toy models with significant oscillatory components, and show that they robustly reduce error compared to the uncorrected ensemble. We discuss the applications of this method to improve prediction of monsoons as well as other parts of the climate system.

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Eric Rappin, Rezaul Mahmood, Udaysankar Nair, Roger A. Pielke Sr., William Brown, Steve Oncley, Joshua Wurman, Karen Kosiba, Aaron Kaulfus, Chris Phillips, Emilee Lachenmeier, Joseph Santanello Jr., Edward Kim, and Patricia Lawston-Parker

Abstract

Extensive expansion in irrigated agriculture has taken place over the last half century. Due to increased irrigation and resultant land use land cover change, the central United States has seen a decrease in temperature and changes in precipitation during the second half of 20th century. To investigate the impacts of widespread commencement of irrigation at the beginning of the growing season and continued irrigation throughout the summer on local and regional weather, the Great Plains Irrigation Experiment (GRAINEX) was conducted in the spring and summer of 2018 in southeastern Nebraska. GRAINEX consisted of two, 15-day intensive observation periods. Observational platforms from multiple agencies and universities were deployed to investigate the role of irrigation in surface moisture content, heat fluxes, diurnal boundary layer evolution, and local precipitation.

This article provides an overview of the data collected and an analysis of the role of irrigation in land-atmosphere interactions on time scales from the seasonal to the diurnal. The analysis shows that a clear irrigation signal was apparent during the peak growing season in mid-July. This paper shows the strong impact of irrigation on surface fluxes, near-surface temperature and humidity, as well as boundary layer growth and decay.

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Hui W. Christophersen, Brittany A. Dahl, Jason P. Dunion, Robert F. Rogers, Frank D. Marks, Robert Atlas, and William J. Blackwell

Abstract

As part of the NASA Earth Venture-Instrument program, the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, to be launched in January 2022, will deliver unprecedented rapid-update microwave measurements over the tropics that can be used to observe the evolution of the precipitation and thermodynamic structure of tropical cyclones (TCs) at meso- and synoptic scales. TROPICS consists of six CubeSats, each hosting a passive microwave radiometer that provides radiance observations sensitive to atmospheric temperature, water vapor, precipitation, and precipitation-size ice particles. In this study, the impact of TROPICS all-sky radiances on TC analyses and forecasts is explored through a regional mesoscale observing system simulation experiment (OSSE). The results indicate that the TROPICS all-sky radiances can have positive impacts on TC track and intensity forecasts, particularly when some hydrometeor state variables and other state variables of the data assimilation system that are relevant to cloudy radiance assimilation are updated. The largest impact on the model analyses is seen in the humidity fields, regardless of whether or not there are radiances assimilated from other satellites. TROPICS radiances demonstrate large impact on TC analyses and forecasts when other satellite radiances are absent. The assimilation of the all-sky TROPICS radiances without default radiances leads to a consistent improvement in the low- and mid-tropospheric temperature and wind forecasts throughout the five-day forecasts, but only up to 36 h lead time in the humidity forecasts at all pressure levels. This study illustrates the potential benefits of TROPICS data assimilation for TC forecasts and provides a potentially streamlined pathway for transitioning TROPICS data from research to operations post-launch.

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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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