Velocity Bias Research Articles

Exploring the squeezed three-point galaxy correlation function with generalized halo occupation distribution models

We present the GeneRalized ANd Differentiable Halo Occupation Distribution (GRAND-HOD) routine that generalizes the standard 5 parameter halo occupation distribution model (HOD) with various halo-scale physics and assembly bias. We describe the methodology of 4 different generalizations: satellite distribution generalization, velocity bias, closest approach distance generalization, and assembly bias. We showcase the signatures of these generalizations in the 2-point correlation function (2PCF) and the squeezed 3-point correlation function (squeezed 3PCF). We identify generalized HOD prescriptions that are nearly degenerate in the projected 2PCF and demonstrate that these degeneracies are broken in the redshift-space anisotropic 2PCF and the squeezed 3PCF. We also discuss the possibility of identifying degeneracies in the anisotropic 2PCF and further demonstrate the extra constraining power of the squeezed 3PCF on galaxy-halo connection models. We find that within our current HOD framework, the anisotropic 2PCF can predict the squeezed 3PCF better than its statistical error. This implies that a discordant squeezed 3PCF measurement could falsify the particular HOD model space. Alternatively, it is possible that further generalizations of the HOD model would open opportunities for the squeezed 3PCF to provide novel parameter measurements. The GRAND-HOD Python package is publicly available at https://github.com/SandyYuan/GRAND-HOD .

Coarse large-eddy simulations in a transitional wake flow with flow models under location uncertainty

The focus of this paper is to perform coarse-grid large-eddy simulation (LES) using recently developed sub-grid scale (SGS) models of cylinder wake flow at Reynolds number (Re) of 3900. As we approach coarser resolutions, a drop in accuracy is noted for all LES models but more importantly, the numerical stability of classical models is called into question. The objective is to identify a statistically accurate, stable sub-grid scale (SGS) model for this transitional flow at a coarse resolution. The proposed new models under location uncertainty (MULU) are applied in a deterministic coarse LES context and the statistical results are compared with variants of the Smagorinsky model and various reference data-sets (both experimental and Direct Numerical Simulation (DNS)). MULU are shown to better estimate statistics for coarse resolution (at 0.46% the cost of a DNS) while being numerically stable. The performance of the MULU is studied through statistical comparisons, energy spectra, and sub-grid scale (SGS) contributions. The physics behind the MULU are characterised and explored using divergence and curl functions. The additional terms present (velocity bias) in the MULU are shown to improve model performance. The spanwise periodicity observed at low Reynolds is achieved at this moderate Reynolds number through the curl function, in coherence with the birth of streamwise vortices.

CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site

AbstractAll the weather and climate models participating in the Clouds Above the United States and Errors at the Surface project show a summertime surface air temperature (T2 m) warm bias in the region of the central United States. To understand the warm bias in long‐term climate simulations, we assess the Atmospheric Model Intercomparison Project simulations from the Coupled Model Intercomparison Project Phase 5, with long‐term observations mainly from the Atmospheric Radiation Measurement program Southern Great Plains site. Quantities related to the surface energy and water budget, and large‐scale circulation are analyzed to identify possible factors and plausible links involved in the warm bias. The systematic warm season bias is characterized by an overestimation of T2 m and underestimation of surface humidity, precipitation, and precipitable water. Accompanying the warm bias is an overestimation of absorbed solar radiation at the surface, which is due to a combination of insufficient cloud reflection and clear‐sky shortwave absorption by water vapor and an underestimation in surface albedo. The bias in cloud is shown to contribute most to the radiation bias. The surface layer soil moisture impacts T2 m through its control on evaporative fraction. The error in evaporative fraction is another important contributor to T2 m. Similar sources of error are found in hindcast from other Clouds Above the United States and Errors at the Surface studies. In Atmospheric Model Intercomparison Project simulations, biases in meridional wind velocity associated with the low‐level jet and the 500 hPa vertical velocity may also relate to T2 m bias through their control on the surface energy and water budget.

A stochastic view on column efficiency

A stochastic model of transcolumn eddy dispersion along packed beds was derived. It was based on the calculation of the mean travel time of a single analyte molecule from one radial position to another. The exchange mechanism between two radial positions was governed by the transverse dispersion of the analyte across the column. The radial velocity distribution was obtained by flow simulations in a focused-ion-beam scanning electron microscopy (FIB-SEM) based 3D reconstruction from a 2.1 mm × 50 mm column packed with 2 μm BEH-C18 particles. Accordingly, the packed bed was divided into three coaxial and uniform zones: (1) a 1.4 particle diameter wide, ordered, and loose packing at the column wall (velocity uw), (2) an intermediate 130 μm wide, random, and dense packing (velocity ui), and (3) the bulk packing in the center of the column (velocity uc).First, the validity of this proposed stochastic model was tested by adjusting the predicted to the observed reduced van Deemter plots of a 2.1 mm × 50 mm column packed with 2 μm BEH-C18 fully porous particles (FPPs). An excellent agreement was found for ui = 0.93uc, a result fully consistent with the FIB-SEM observation (ui = 0.95uc). Next, the model was used to measure ui = 0.94uc for 2.1 mm × 100 mm column packed with 1.6 μm Cortecs-C18 superficially porous particles (SPPs). The relative velocity bias across columns packed with SPPs is then barely smaller than that observed in columns packed with FPPs (+6% versus + 7%). uw=1.8ui is measured for a 75 μm × 1 m capillary column packed with 2 μm BEH-C18 particles. Despite this large wall-to-center velocity bias (+80%), the presence of the thin and ordered wall packing layer has no negative impact on the kinetic performance of capillary columns. Finally, the stochastic model of long-range eddy dispersion explains why analytical (2.1–4.6 mm i.d.) and capillary (<400 μm i.d.) columns can all be packed efficiently (1 <hmin< 2) with particles of size larger than 2 μm. In contrast, the model predicts that 0.4–1.2 mm i.d. columns and 2.1 mm i.d. columns cannot be packed well (hmin>3) with sub-2 μm particles and with 1 μm particles, respectively.

Treatment of vestibular disorders with weak asymmetric base-in prisms: An hypothesis with a focus on Ménière's disease.

BACKGROUND:Regular treatments of Ménière’s disease (MD) vary largely, and no single satisfactory treatment exists. A complementary treatment popular among Dutch and Belgian patients involves eyeglasses with weak asymmetric base-in prisms, with a perceived high success rate. An explanatory mechanism is, however, lacking.OBJECTIVE:To speculate on a working mechanism explaining an effectiveness of weak asymmetric base-in prims in MD, based on available knowledge.METHODS:After describing the way these prisms are prescribed using a walking test and its effect reported on, we give an explanation of its underlying mechanism, based on the literature.RESULTS:The presumed effect can be explained by considering the typical star-like walking pattern in MD, induced by a drifting after-image comparable to the oculogyral illusion. Weak asymmetric base-in prisms can furthermore eliminate the conflict between a net vestibular angular velocity bias in the efferent signal controlling the VOR, and a net re-afferent ocular signal.CONCLUSIONS:The positive findings with these glasses reported on, the fact that the treatment itself is simple, low-cost, and socially acceptable, and the fact that an explanation is at hand, speak in favour of elaborating further on this treatment.

Spectroscopic Confirmation and Velocity Dispersions for 20 Planck Galaxy Clusters at 0.16 &lt; z &lt; 0.78

Abstract We present Gemini and Keck spectroscopic redshifts and velocity dispersions for 20 clusters detected via the Sunyaev–Zel’dovich (SZ) effect by the Planck space mission, with estimated masses in the range . Cluster members were selected for spectroscopic follow-up with Palomar, Gemini, and Keck optical and (in some cases) infrared imaging. Seven cluster redshifts were measured for the first time with this observing campaign, including one of the most distant Planck clusters confirmed to date, at , PSZ2 G085.95+25.23. The spectroscopic redshift catalogs of members of each confirmed cluster are included as online tables. We show the galaxy redshift distributions and measure the cluster velocity dispersions. The cluster velocity dispersions obtained in this paper were used in a companion paper to measure the Planck mass bias and to constrain the cluster velocity bias.

Constraining the optical depth of galaxies and velocity bias with cross-correlation between the kinetic Sunyaev–Zeldovich effect and the peculiar velocity field

We calculate the cross-correlation function $\langle (\Delta T/T)(\mathbf{v}\cdot \mathbf{n}/\sigma_{v}) \rangle$ between the kinetic Sunyaev-Zeldovich (kSZ) effect and the reconstructed peculiar velocity field using linear perturbation theory, to constrain the optical depth $\tau$ and peculiar velocity bias of central galaxies with Planck data. We vary the optical depth $\tau$ and the velocity bias function $b_{v}(k)=1+b(k/k_{0})^{n}$, and fit the model to the data, with and without varying the calibration parameter $y_{0}$ that controls the vertical shift of the correlation function. By constructing a likelihood function and constraining $\tau$, $b$ and $n$ parameters, we find that the quadratic power-law model of velocity bias $b_{v}(k)=1+b(k/k_{0})^{2}$ provides the best-fit to the data. The best-fit values are $\tau=(1.18 \pm 0.24) \times 10^{-4}$, $b=-0.84^{+0.16}_{-0.20}$ and $y_{0}=(12.39^{+3.65}_{-3.66})\times 10^{-9}$ ($68\%$ confidence level). The probability of $b>0$ is only $3.12 \times 10^{-8}$ for the parameter $b$, which clearly suggests a detection of scale-dependent velocity bias. The fitting results indicate that the large-scale ($k \leq 0.1\,h\,{\rm Mpc}^{-1}$) velocity bias is unity, while on small scales the bias tends to become negative. The value of $\tau$ is consistent with the stellar mass--halo mass and optical depth relation proposed in the previous literatures, and the negative velocity bias on small scales is consistent with the peak background-split theory. Our method provides a direct tool to study the gaseous and kinematic properties of galaxies.

Mathematical Modeling of Marine Oil Spills in the Luanjiakou District, near the Port of Yantai

This paper presents a simulation method for oil spills in a multi-island area. The simulation considers three parts, which consist of(1)the spreading of an oil slick on its edge as well as the diffusion and drift under dynamic actions,(2)the evaporation and spreading thickness of an oil slick in its interior, and(3)the adsorption and emulsification near shorelines and islands. The Euler-Lagrange method is adopted to track the spill location and particles positions on the edge of oil slicks. A mathematical model of marine oil spills is established for the Luanjiakou District of the Port of Yantai. The flow field verification shows that the BIAS of tidal level, flow velocity, and flow direction is below ±10 cm, 0.11 m/s, and ±2°, respectively, and the oil spill verification captures satisfactory results. Hence, the proposed model could reproduce the oil spill process in this region. Then, we simulate oil spills under various operating conditions. It is concluded that the transport of oil slicks is mainly influenced by flood/ebb currents, whereas the wind plays a major role in the drift and thickness of oil slicks. The study provides an important reference to controlling and handling of accidental oil spills.

The Cluster-EAGLE project: velocity bias and the velocity dispersion–mass relation of cluster galaxies

We use the Cluster-EAGLE simulations to explore the velocity bias introduced when using galaxies, rather than dark matter particles, to estimate the velocity dispersion of a galaxy cluster, a property known to be tightly correlated with cluster mass. The simulations consist of 30 clusters spanning a mass range $14.0 \le \log_{10}(M_{\rm 200c}/\mathrm{M_\odot}) \le 15.4$, with their sophisticated sub-grid physics modelling and high numerical resolution (sub-kpc gravitational softening) making them ideal for this purpose. We find that selecting galaxies by their total mass results in a velocity dispersion that is 5-10 per cent higher than the dark matter particles. However, selecting galaxies by their stellar mass results in an almost unbiased ($<5$ per cent) estimator of the velocity dispersion. This result holds out to $z=1.5$ and is relatively insensitive to the choice of cluster aperture, varying by less than 5 per cent between $r_{\rm 500c}$ and $r_{\rm 200m}$. We show that the velocity bias is a function of the time spent by a galaxy inside the cluster environment. Selecting galaxies by their total mass results in a larger bias because a larger fraction of objects have only recently entered the cluster and these have a velocity bias above unity. Galaxies that entered more than $4 \, \mathrm{Gyr}$ ago become progressively colder with time, as expected from dynamical friction. We conclude that velocity bias should not be a major issue when estimating cluster masses from kinematic methods.

Velocity reconstruction with nonconvex optimization for low-velocity-encoding phase-contrast MRI.

To introduce and demonstrate a nonconvex optimization method for reconstructing velocity data from low-velocity-encoding (Venc ) phase-contrast MRI data. Solving for velocity values from phase-contrast MRI data was formulated as a nonconvex optimization problem. Weighting was added to account for intravoxel dephasing, and a Laplacian-based regularization was used to account for residual velocity aliasing. The reconstruction was tested with two low-Venc schemes: dual-Venc and a multidirectional high-moment encoding. The reconstruction method was tested in a digital simulation, in flow phantoms, and in healthy volunteers (N = 5). The nonconvex-optimization reconstruction velocity error was lower than the conventional reconstruction in simulations (4.6 versus 3.0 cm/s for multidirectional high moment, 8.3 versus 3.8 cm/s for dual-Venc ) and in flow phantoms (23.9 versus 5.9 cm/s for multidirectional high moment, 15.2 versus 6.4 cm/s for dual-Venc ). Qualitative assessment of velocity fields in all experiments, including healthy volunteers, showed decreased apparent noise in the velocity fields and fewer phase wraps. No additional velocity bias in measured velocities was seen in volunteers with the proposed method. The proposed nonconvex-optimization reconstruction method incorporates additional information to solve for velocities when using any type of low-Venc (high-moment) acquisition. The method reduces the amount of residual phase aliasing, and decreases velocity errors that result from intravoxel dephasing. These improvements allow for more robust acquisitions, and for Venc to be lowered 2 to 4 times relative to conventional acquisitions, thereby increasing the velocity-to-noise ratio. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine. Magn Reson Med 80:42-52, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Constraining the baryon–dark matter relative velocity with the large-scale three-point correlation function of the SDSS BOSS DR12 CMASS galaxies

We search for a galaxy clustering bias due to a modulation of galaxy number with the baryon-dark matter relative velocity resulting from recombination-era physics. We find no detected signal and place the constraint $b_v < 0.01$ on the relative velocity bias for the CMASS galaxies. This bias is an important potential systematic of Baryon Acoustic Oscillation (BAO) method measurements of the cosmic distance scale using the 2-point clustering. Our limit on the relative velocity bias indicates a systematic shift of no more than $0.3\%$ rms in the distance scale inferred from the BAO feature in the BOSS 2-point clustering, well below the $1\%$ statistical error of this measurement. This constraint is the most stringent currently available and has important implications for the ability of upcoming large-scale structure surveys such as DESI to self-protect against the relative velocity as a possible systematic.

Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

Abstract We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution, the conditional luminosity function, abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. The package has an optimized toolkit to make mock observations on a synthetic galaxy population—including galaxy clustering, galaxy–galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others—allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation. Halotools has an automated testing suite and is exhaustively documented on http://halotools.readthedocs.io, which includes quickstart guides, source code notes and a large collection of tutorials. The documentation is effectively an online textbook on how to build and study empirical models of galaxy formation with Python.

Chromatographic Properties of Minimal Aspect Ratio Monolithic Silica Columns.

We report on a study wherein we synthesized TMOS-based silica monolithic skeletons in capillaries with an i.d. of 5 and 10 μm to produce skeleton structures with very low capillary-to-domain size aspect-ratios. These structures include the absolute minimal aspect-ratio case of a monolithic structure whose cross-section only contains a single node point. With domain-sized based reduced plate heights running as low as hmin = 1.3-1.5 for retained coumarin dyes providing a retention factor of k = 0.6-1.0, the study confirms the classic observation that ultralow aspect ratio columns generate a markedly lower dispersion than columns with a larger aspect ratio made in the past by Knox, Jorgenson, and Kennedy for the packed bed of spheres, but now for silica monoliths. The course of the reduced van Deemter curves, and more specifically the ratio of A-term versus C-term band broadening, could be interpreted in terms of the width and persistence length of the velocity bias zones in the columns. Considering the overall kinetic performance, it is found that the two best performing structures are also the structures with the lowest number of domains or node points, that is, with the lowest capillary-to-domain size aspect-ratio and, hence, resembling closest to the open-tubular format, which remains confirmed as the column format with the best kinetic performance. This is quantified by the fact that the minimal impedance values (order of Emin = 100) of the best performing ultralow aspect ratio monolithic columns are still significantly larger than the Emin values for the reference open-tubular columns (order of Emin = 15-20).

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

AbstractThis paper extends the progress of single beacon one‐way‐travel‐time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom‐lock is available as well as the necessity for expensive strap‐down sensors, such as the DVL. Thus, deep water, mid‐water column research has mostly been left untouched, and vehicles that need expensive strap‐down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed‐loop online field results on local waters as well as a real‐time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.

ADCP Bias and Stokes Drift in AUV-Based Velocity Measurements

AbstractA theoretical model is developed to describe how autonomous underwater vehicle (AUV)-based current measurements are influenced by a surface wave field. The model quantifies a quasi-Lagrangian, wave-induced velocity bias as a function of the local wave conditions, and the vehicle’s depth and velocity using a first-order expansion of the linear wave solution. The theoretical bias is verified via field experiments carried out off the coast of Oahu, Hawaii. Spatially averaged along- and cross-track AUV velocity measurements are calculated over one effective wavelength and compared with time-averaged, fixed ADCP measurements in a range of wave and current conditions. The wave-induced bias is calculated using wave directional spectra derived from fixed ADCP data. Ensemble-averaged velocity differences confirm the presence of the wave-induced bias O(1–5) cm s−1 and reveal an additional bias in the direction of the vehicle motion O(1) cm s−1. The analysis considers velocity measurements made using a Remote Environmental Monitoring Units (REMUS) 100 AUV, but the content applies to any small AUV (vehicle size wavelength) immersed in a wave field.

Galaxy clustering dependence on the [O ii] emission line luminosity in the local Universe

We study the galaxy clustering dependence on the $\left[\mathrm{O\scriptsize{II}}\right]$ emission line luminosity in the SDSS DR7 Main galaxy sample at mean redshift $z\sim0.1$. We select volume-limited samples of galaxies with different $\left[\mathrm{O\scriptsize{II}}\right]$ luminosity thresholds and measure their projected, monopole and quadrupole two-point correlation functions. We model these observations using the 1$h^{-1}\rm{Gpc}$ MultiDark Planck cosmological simulation and generate light-cones with the SUrvey GenerAtoR algorithm. To interpret our results, we adopt a modified (Sub)Halo Abundance Matching scheme, accounting for the stellar mass incompleteness of the emission line galaxies. The satellite fraction constitutes an extra parameter in this model and allows to optimize the clustering fit on both small and intermediate scales (i.e. $r_p\lesssim 30h^{-1}\rm{Mpc})$, with no need of any velocity bias correction. We find that, in the local Universe, the $\left[\mathrm{O\scriptsize{II}}\right]$ luminosity correlates with all the clustering statistics explored and with the galaxy bias. This latter quantity correlates more strongly with the SDSS $r$-band magnitude than $\left[\mathrm{O\scriptsize{II}}\right]$ luminosity. In conclusion, we propose a straightforward method to produce reliable clustering models, entirely built on the simulation products, which provides robust predictions of the typical ELG host halo masses and satellite fraction values. The SDSS galaxy data, MultiDark mock catalogues and clustering results are made publicly available.

Handover Rate Analysis for K-Tier Heterogeneous Cellular Networks With General Path-Loss Exponents

Network densification will produce frequent handover for mobile users, which could largely diminish the densification gain. We develop a comprehensive analytical model for the handover rate in K-tier heterogeneous cellular networks, where each tier is modeled as an independent Poisson point process with unique transmit power, path-loss exponent, spatial density, and association bias. The spatial randomness of users’ mobility is captured and analyzed by stochastic geometric theory, and we derive closed-form expressions for handover rate with arbitrary mobile trajectories. The relationship between total handover rate and horizontal/vertical handover rate is analyzed, and the effect of the user’s velocity, BS densities, path-loss exponents, and association bias are found. There is an optimum value of BS densities to keep a good tradeoff between handover rate and user’s throughput. Finally, simulation results verify the accuracy of the derivation.

Calibrating the Planck Cluster Mass Scale with Cluster Velocity Dispersions

Abstract We measure the Planck cluster mass bias using dynamical mass measurements based on velocity dispersions of a subsample of 17 Planck-detected clusters. The velocity dispersions were calculated using redshifts determined from spectra that were obtained at the Gemini observatory with the GMOS multi-object spectrograph. We correct our estimates for effects due to finite aperture, Eddington bias, and correlated scatter between velocity dispersion and the Planck mass proxy. The result for the mass bias parameter, , depends on the value of the galaxy velocity bias, , adopted from simulations: . Using a velocity bias of from Munari et al., we obtain , i.e., an error of 17% on the mass bias measurement with 17 clusters. This mass bias value is consistent with most previous weak-lensing determinations. It lies within of the value that is needed to reconcile the Planck cluster counts with the Planck primary cosmic microwave background constraints. We emphasize that uncertainty in the velocity bias severely hampers the precision of the measurements of the mass bias using velocity dispersions. On the other hand, when we fix the Planck mass bias using the constraints from Penna–Lima et al., based on weak-lensing measurements, we obtain a positive velocity bias of at .

Properties and Origin of Galaxy Velocity Bias in the Illustris Simulation

Abstract We use hydrodynamical galaxy formation simulations from the Illustris suite to study the origin and properties of galaxy velocity bias, i.e., the difference between the velocity distributions of galaxies and dark matter inside halos. We find that galaxy velocity bias decreases with increasing ratio of galaxy stellar mass to host halo mass. In general, central galaxies are not at rest with respect to dark matter halos or the core of halos, with a velocity dispersion above 0.04 times that of the dark matter. The central galaxy velocity bias is found to be mostly caused by close interactions between the central and satellite galaxies. For satellite galaxies, the velocity bias is related to their dynamical and tidal evolution history after being accreted onto the host halos. It depends on the time after the accretion and their distances from the halo centers, with massive satellites generally moving more slowly than the dark matter. The results are in broad agreement with those inferred from modeling small-scale redshift-space galaxy clustering data, and the study can help improve models of redshift-space galaxy clustering.

Representation of Arctic Moist Intrusions in CMIP5 Models and Implications for Winter Climate Biases

Abstract This paper examines the wintertime northward moisture flux at 70°N from 1981–2005 in 31 of the CMIP5 models compared with the ERA-Interim reanalysis product. The models’ total zonally integrated northward moisture flux is found to agree reasonably well with the reanalysis, but with large compensating regional biases. Specifically, the models systematically underpredict the moisture flux in the Atlantic sector and overpredict it in the Pacific sector. The biases are predominantly due to misrepresentation of extreme moisture flux events, which are known to exert a significant control on Arctic climate. Biases in these high-intensity fluxes are almost entirely contributed by biases in the meridional velocity, suggesting a link with biases in storm-track activity at lower latitudes. The extent to which the deficit of moisture intrusions in the Atlantic sector and excess in the Pacific sector may account for biases in the climate of the respective sectors is assessed. Biases in the frequency of moisture intrusions explain roughly 17% of surface temperature and 24% of surface downward longwave radiation biases in the Atlantic sector, and about 14% and 16% of the gradient in these respective biases between the two sectors. The predicted bias gradients, while small in amplitude, are very highly correlated with the true bias gradients in the models, suggesting that the temperature bias directly induced by misrepresented intrusion statistics may be strongly amplified by sea ice feedback.