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Stratocumulus clouds cover about a fifth of Earths surface, and due to their albedo and low-latitude location, they have a strong effect on Earths radiation budget. Previous studies using Large Eddy Simulations have shown that multiple equilibria (both cloud-covered and cloud-free states) exist as a function of fixed-SST, with relevance to equatorward advected air masses. Multiple equilibria have also been found as a function of atmospheric CO2, with a subtropical SST nearly 10 K higher in the cloud-free state, and with suggested relevance to warm-climate dynamics. In this study, we use a mixed-layer model with an added surface energy balance and the ability to simulate both the cloud-covered and cloud-free states to study both types of multiple equilibria and the corresponding hysteresis. The model's simplicity and computational efficiency allow us to explore the mechanisms critical to the stratocumulus cloud instability and hysteresis as well as isolate key processes that allow for multiple equilibria via mechanism denial experiments not possible with a full-complexity model. For the hysteresis in fixed-SST, we find that decoupling can occur due to either enhanced entrainment warming or a reduction in cloud-top longwave cooling. The critical SST at which decoupling occurs is highly sensitive to precipitation and entrainment parameterizations. In the CO2 hysteresis, decoupling occurs in the simple model used even without the inclusion of an active SST and SST-cloud cover feedbacks, and the width of the hysteresis displays the same sensitivities as the fixed-SST case. Overall, the simple model analysis and results motivate further studies using higher complexity models., Submitted to Journal of Climate 06-12-2022

In climate model configurations, standard approaches to the representation of unresolved, or subgrid scales, via deterministic closure schemes are being challenged by stochastic approaches inspired by statistical dynamical theory. Despite gaining popularity, studies of various stochastic subgrid scale parameterizations applied to atmospheric climate and weather prediction systems have revealed a diversity of model responses, including degeneracy in the response to different forcings and compensating model errors, with little reduction in artificial damping of the small scales required for numerical stability. Due to the greater range of spatio-temporal scales involved, how to best sample subgrid fluctuations in a computationally inexpensive manner, with the aim of reduced model error and improvements to the simulated climatological state of the ocean, remains an open question. While previous studies have considered perturbations to the surface forcing or subsurface temperature tendencies, we implement an energetically consistent, simple, stochastic subgrid eddy parameterization of the momentum fluxes in regions of the three-dimensional ocean typically associated with high eddy variability. We consider the changes in the modelled energetics of low-resolution simulations in response to stochastically forced velocity tendencies whose perturbation statistics and amplitudes are calculated from an eddy resolving ocean configuration. Kinetic energy spectra from a triple-decomposition reveal a systematic redistribution from the seasonal (climatological minus mean) potential energy to preferentially generate small scale transient kinetic energy while the total energy spectra remains largely unchanged. We show that stochastic parameterization generally improves model biases, noticeably so for the simulated energetics of the Southern Oceans.

The main energy input to the polar regions in winter is the advection of warm, moist air from lower latitudes. This makes the polar climate sensitive to the temperature and moisture of extrapolar air. Here, we study this sensitivity from an air-mass transformation perspective. We perform simulations of an idealized maritime air mass brought into contact with sea ice employing a three-dimensional large-eddy simulation model coupled to a one-dimensional multilayer sea ice model. We study the response of cloud dynamics and surface warming during the air-mass transformation process to varying initial temperature and humidity conditions of the air mass. We find in all cases that a mixed-phase cloud is formed, initially near the surface but rising continuously with time. Surface warming of the sea ice is driven by downward longwave surface fluxes, which are largely controlled by the temperature and optical depth of the cloud. Cloud temperature, in turn, is robustly constrained by the initial dewpoint temperature of the air mass. Since dewpoint only depends on moisture, the overall result is that surface warming depends almost exclusively on initial humidity and is largely independent of initial temperature. We discuss possible climate implications of this result—in particular, for polar amplification of surface warming and the role played by atmospheric energy transports.

Along-track wavenumber spectral densities of sea surface height (SSH) are estimated from Jason-2 altimetry data as a function of spatial location and calendar month, to understand the seasonality of meso- and submesoscale balanced dynamics across the global ocean. Regions with significant mode-1 and mode-2 baroclinic tides are rejected, restricting the analysis to the extratropics. Where balanced motion dominates, the SSH spectral density is averaged over all pass segments in a region for each calendar month, and is fit to a 4-parameter model consisting of a flat plateau at low wavenumbers, a transition at wavenumber $k_0$ to a red power law spectrum $k^{-s}$, and a white spectrum at high wavenumbers that models the altimeter noise. The monthly time series of the model parameters are compared to the evolution of the mixed layer. The annual mode of the spectral slope $s$ reaches a minimum after the mixed layer deepens, and the annual mode of the bandpassed kinetic energy in the ranges $[2k_0,4 k_0]$ and $[k_0,2 k_0]$ peak $\sim$2 and $\sim$4 months, respectively, after the maximum of the annual mode of the mixed layer depth. This analysis is consistent with an energization of the submesoscale by a winter mixed layer instability followed by an inverse cascade to the mesoscale, in agreement with prior modeling studies and in situ measurements. These results are compared to prior modeling, in situ, and satellite investigations of specific regions, and are broadly consistent with them within measurement uncertainties., Comment: Submitted to the Journal of Physical Oceanography. The code used for analyzing and displaying data can be found at https://github.com/albionlawrence/sm-seasonality

This paper examines the physical controls of extratropical humidity and clouds by isolating the effects of cloud physics factors in an idealized model. The Held–Suarez dynamical core is used with the addition of passive water vapor and cloud tracers, allowing cloud processes to be explored cleanly. Separate saturation adjustment and full cloud scheme controls are used to consider the strength of advection–condensation theory. Three sets of perturbations to the cloud scheme are designed to test the model’s sensitivity to the physics of condensation, sedimentation, and precipitation formation. The condensation and sedimentation perturbations isolate two key differences between the control cases. First, the sub-grid-scale relative humidity distribution assumed for the cloud macrophysics influences the location and magnitude of the extratropical cloud maxima, which interrupt the isentropic transport of moisture to the polar troposphere. Second, within the model’s explicit treatment of cloud microphysics, re-evaporation of hydrometeors moistens and increases clouds in the lower troposphere. In contrast, microphysical processes of precipitation formation (specifically, the ratio of accretion to autoconversion) have negligible effects on humidity, cloudiness, and precipitation apart from the strength of the large-scale condensation and formation cycle. In addition, counterintuitive relationships—such as cloud condensate and cloud fraction responding in opposing directions—emphasize the need for careful dissection of physical mechanisms. In keeping with advection–condensation theory, circulation sets the patterns of humidity, clouds, and precipitation to first order, with factors explored herein providing secondary controls. The results substantiate the utility of such idealized modeling and highlight key cloud processes to constrain.

Thermodynamical and dynamical aspects of the climate system response to anthropogenic forcing are often considered in two distinct frameworks: the former in the context of the forcing–feedback framework, and the latter in the context of eddy–mean flow feedbacks and large-scale thermodynamic constraints. Here we use experiments with the dynamical core of a general circulation model (GCM) to provide insights into the relationships between these two frameworks. We first demonstrate that the climate feedbacks and climate sensitivity in a dynamical core model are determined by its prescribed thermal relaxation time scales. We then perform two experiments: one that explores the relationships between the thermal relaxation time scale and the climatological circulation, and a second that explores the relationships between the thermal relaxation time scale and the circulation response to a global warming–like forcing perturbation. The results indicate that shorter relaxation time scales (i.e., lower climate sensitivities in the context of a dynamical core model) are associated with 1) a more vigorous large-scale circulation, including increased thermal diffusivity and stronger and more poleward storm tracks and eddy-driven jets, and 2) a weaker poleward displacement of the storm tracks and eddy-driven jets in response to the global warming–like forcing perturbation. Interestingly, the circulation response to the forcing perturbation effectively disappears when the thermal relaxation time scales are spatially uniform, suggesting that the circulation response to homogeneous forcing requires spatial inhomogeneities in the local feedback parameter. Implications for anticipating the circulation response to global warming and thermodynamic constraints on the circulation are discussed.

This study explores the dynamics of intense coherent vortices in large-scale vertically sheared flows. We develop an analytical theory for vortex propagation and validate it by a series of numerical simulations. Simulations are conducted using both stable and baroclinically unstable zonal background flows. We find that vortices in stable westward currents tend to adjust to an equilibrium state characterized by quasi-uniform zonal propagation. These vortices persist for long periods, during which they propagate thousands of kilometers from their points of origin. The adjustment tendency is realized to a much lesser extent in eastward background flows. These findings may help to explain the longevity of the observed oceanic vortices embedded in predominantly westward flows. Finally, we examine the influence of background mesoscale variability induced by baroclinic instability of large-scale flows on the propagation and persistence of isolated vortices.

The neighborhood-based ensemble evaluation using the continuous ranked probability score is based on the pooling of the cumulative density function (CDF) for all the points inside a neighborhood. This methodology can be applied to the forecast CDF for measuring the predictive input of neighboring points in the center of the neighborhood. It can also be applied at the same time to forecast CDF and observed CDF so as to quantify the quality of the pooled ensemble forecast at the scale of the neighborhood. Fair versions of these two neighborhood scores are also defined in order to reduce their dependencies on the size of ensemble forecasts. The borderline case of deterministic forecasts is also explored so as to be able to compare them with ensemble forecasts. The information of these new scores is analyzed on idealized and real cases of rain accumulated during 3 h and of 2-m temperature forecast by four deterministic and probabilistic forecasting systems operational at Météo-France.

Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations. Significance Statement Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.

The weak temperature gradient (WTG) approximation is extended to the basic equations on a rotating plane. The circulation is decomposed into a diabatic component that satisfies WTG balance exactly and a deviation from this balance. Scale analysis of the decomposed basic equations reveals a spectrum of motions, including unbalanced inertio-gravity waves and several systems that are in approximate WTG balance. The balanced systems include equatorial moisture modes with features reminiscent of the MJO, off-equatorial moisture modes that resemble tropical depression disturbances, “mixed systems” in which temperature and moisture play comparable roles in their thermodynamics, and moist quasigeostrophic motions. In the balanced systems the deviation from WTG balance is quasi nondivergent, in nonlinear balance, and evolves in accordance to the vorticity equation. The evolution of the strictly balanced WTG circulation is in turn described by the divergence equation. WTG balance restricts the flow to evolve in the horizontal plane by making the isobars impermeable to vorticity and divergence, even in the presence of diabatically driven vertical motions. The vorticity and divergence equations form a closed system of equations when the irrotational circulation is in WTG balance and the nondivergent circulation is in nonlinear balance. The resulting “WTG equations” may elucidate how interactions between diabatic processes and the horizontal circulation shape slowly evolving tropical motions. Significance Statement Many gaps in our understanding of tropical weather systems still exist and there are still many opportunities to improve their forecasting. We seek to further our understanding of the tropics by extending a framework known as the “weak temperature gradient approximation” to all of the equations for atmospheric flow. Doing this reveals a variety of motions whose scales are similar to observed tropical weather systems. We also show that two equations describe the evolution of slow systems: one that describes tropical thunderstorms and one for the rotating horizontal winds. The two equations may help us understand the dynamics of slowly evolving tropical systems.

Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Δx) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Δx ≈ 100–1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional mesoscale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation. Significance Statement The spatial resolution of weather models continues to increase at a rapid rate in accordance with the enhancement of computing power. As a result, smaller-scale atmospheric features become more explicitly resolved. However, most numerical models still ignore the impact of horizontal weather variations on boundary layer flows, which becomes more important at these smaller spatial scales. To address this issue, we have implemented a new modeling approach, using fundamental principles, which accounts for horizontal variability. Our results show that including three-dimensional effects of turbulence is necessary to achieve realistic boundary layer characteristics. This novel technique may be useful for many applications including complex terrain flows, pollutant dispersion, and surface–atmosphere interaction studies.

We identify and characterize an error in the National Data Buoy Center (NDBC) wave records due to the sustained tilt of a buoy under high winds. We use a standard, operational 3-m aluminum discus buoy from NDBC with two wave systems, one gimballed, and the other strapped down but uncorrected. By comparing the two, we find that the most extreme significant wave heights are systematically overestimated. The overestimation is shown to be confined to a region around the peak frequency in the spectra: 0.05–0.15 Hz. Wave direction and directional spread are unaffected. A bias due to tilt error can be observed starting at winds of 10 m s−1 or wave heights of 4 m. The bias increases as a function of wind speed and wave height, i.e., the bias is +10% when winds are 20 m s−1. Very high waves and winds are relatively rare, so while the tilt error does not affect overall statistics and basic analyses it could potentially affect analysis sensitive to the extremes. A correction is derived for significant wave height, which is a quadratic function of wind speed. The correction is shown to reduce wave heights in uncorrected records, but is found inadequate for general use. There is evidence of tilt error at other NDBC stations, but the full extent of prevalence in the record is not known at this time.

The Antarctic Slope Current (ASC) plays a central role in redistributing water masses, sea ice, and tracer properties around the Antarctic margins, and in mediating cross-slope exchanges. While the ASC has historically been understood as a wind-driven circulation, recent studies have highlighted important momentum transfers due to mesoscale eddies and tidal flows. Furthermore, momentum input due to wind stress is transferred through sea ice to the ASC during most of the year, yet previous studies have typically considered the circulations of the ocean and sea ice independently. Thus, it remains unclear how the momentum input from the winds is mediated by sea ice, tidal forcing, and transient eddies in the ocean, and how the resulting momentum transfers serve to structure the ASC. In this study the dynamics of the coupled ocean–sea ice–ASC circulation are investigated using high-resolution process-oriented simulations and interpreted with the aid of a reduced-order model. In almost all simulations considered here, sea ice redistributes almost 100% of the wind stress away from the continental slope, resulting in approximately identical sea ice and ocean surface flows in the core of the ASC in a fully spun-up equilibrium state. This ice–ocean coupling results from suppression of vertical momentum transfer by mesoscale eddies over the continental slope, which allows the sea ice to accelerate the ocean surface flow until the speeds coincide. Tidal acceleration of the along-slope flow exaggerates this effect and may even result in ocean-to-ice momentum transfer. The implications of these findings for along- and across-slope transport of water masses and sea ice around Antarctica are discussed.

We use an analog method, based on displacements of Argo floats at their parking depth (nominally located around 1000 dbar) from the ANDRO dataset, to compute continuous, likely trajectories and estimate the Lagrangian dispersion. From this, we find that the horizontal diffusivity coefficient has a median value around 500 m2 s−1 but is highly variable in space, reaching values from 100 m2 s−1 in the gyre interior to 40 000 m2 s−1 in a few specific locations (in the Zapiola Gyre and in the Agulhas Current retroflection). Our analysis suggests that the closure for diffusivity is proportional to eddy kinetic energy (or square of turbulent velocity) rather than (absolute) turbulent velocity. It is associated with a typical turbulent time scale of 4–5.5 days, which is noticeably quite constant over the entire globe, especially away from coherent intense currents. The diffusion is anisotropic in coherent intense currents and around the equator, with a primary direction of diffusion consistent with the primary direction of horizontal velocity variance. These observationally based horizontal diffusivity estimations, and the suggested eddy kinetic energy closure, can be used for constraining, testing, and validating eddy turbulence parameterization.

Advanced microphysics schemes (such as Eulerian bin and Lagrangian superdroplet) are becoming standard tools for cloud physics research and parameterization development. This study compares a double-moment bin scheme and a Lagrangian superdroplet scheme via large-eddy simulations of nonprecipitating and precipitating cumulus congestus clouds. Cloud water mixing ratio in the bin simulations is reduced compared to the Lagrangian simulations in the upper part of the cloud, likely from numerical diffusion, which is absent in the Lagrangian approach. Greater diffusion in the bin simulations is compensated by more secondary droplet activation (activation above cloud base), leading to similar or somewhat higher droplet number concentrations and smaller mean droplet radius than the Lagrangian simulations for the nonprecipitating case. The bin scheme also produces a significantly larger standard deviation of droplet radius than the superdroplet method, likely due to diffusion associated with the vertical advection of bin variables. However, the spectral width in the bin simulations is insensitive to the grid spacing between 50 and 100 m, suggesting other mechanisms may be compensating for diffusion as the grid spacing is modified. For the precipitating case, larger spectral width in the bin simulations initiates rain earlier and enhances rain development in a positive feedback loop. However, with time, rain formation in the superdroplet simulations catches up to the bin simulations. Offline calculations using the same drop size distributions in both schemes show that the different numerical methods for treating collision–coalescence also contribute to differences in rain formation. The stochastic collision–coalescence in the superdroplet method introduces more variability in drop growth for a given rain mixing ratio.

The influence of four different immersion freezing parameterizations on Arctic clouds and the top-of-the atmosphere (TOA) and surface radiation fluxes is investigated in the fifth version of the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS-5) with sea surface temperature, sea ice fraction, and aerosol emissions held fixed. The different parameterizations were derived from a variety of sources, including classical nucleation theory and field and laboratory measurements. Despite the large spread in the ice-nucleating particle (INP) concentrations in the parameterizations, the cloud properties and radiative fluxes had a tendency to form two groups, with the lower INP concentration category producing larger water path and low-level cloud fraction during winter and early spring, whereas the opposite occurred during the summer season. The stability of the lower troposphere was found to strongly correlate with low-cloud fraction and, along with the effect of ice nucleation, ice sedimentation, and melting rates, appears to explain the spring-to-summer reversal pattern in the relative magnitude of the cloud properties between the two categories of simulations. The strong modulation effect of the liquid phase on immersion freezing led to the successful simulation of the characteristic Arctic cloud structure, with a layer rich in supercooled water near cloud top and ice and snow at lower levels. Comparison with satellite retrievals and in situ data suggest that simulations with low INP concentrations more realistically represent Arctic clouds and radiation.

The microphysical quasi-equilibrium in an ascending adiabatic parcel is elucidated by an analytical theory in 0D with drastic simplifications. The theory predicts how in-cloud activation is most likely to be triggered by the onset of precipitation during sufficient ascent, with the ascent only needing to approach almost twice the cloud-base updraft speed aloft. The precipitation and cloud condensate mass fields are coupled in a closed system in a simplified version of the model. The initial state of no precipitation is unstable with respect to a perturbation. In the 2D phase space of both mass fields, there is a neutral line. Unstable growth of precipitation mass drives the system to cross the line into a regime of stability. An attractor is then approached consisting of precipitation mass balanced by accretion of cloud mass and fallout. The cloud-particle number concentration also approaches a stable equilibrium governed by the balance between in-cloud activation aloft from the inexorably increasing supersaturation during vertical acceleration and accretion of cloud droplets by precipitation. Dimensionless numbers characterizing the microphysical equilibria and their stability are derived mathematically, including a condensation–precipitation efficiency and an in-cloud activation efficiency. The theory explains common observations of the orders of magnitude of liquid water content in convective and stratiform clouds. Finally, sensitivity tests of the numerically integrated theoretical equations are documented with respect to variations in cloud condensation nucleus (CCN) aerosols and updraft speed. It is shown that this theory of in-cloud activation, with an increasing supersaturation during ascent, applies to both ice-only and liquid-only cloud. Significance Statement The purpose of the study is to create a theory for how equilibria among cloud-microphysical properties are attained during ascent. Continual initiation of fresh cloud droplets far above cloud base in the free troposphere is explained. The theory proposes a balance between creation and depletion of such droplets, in relation to a balance between creation and fallout of precipitation. The transition from positive feedbacks of instability toward negative feedbacks of stability when the equilibria are approached is elucidated. The theory explains why the mass content of cloud liquid in regions of ascent is typically about an order of magnitude lower in layer clouds with weak ascent than in convective clouds with faster ascent. Any future work could extend the theory to treat dilute parcels or coexistence of supercooled liquid and ice.

Future projections of the poleward eddy heat flux by the atmosphere are often regarded as being uncertain because of the competing effect between surface and upper-tropospheric meridional temperature gradients. Previous idealized modeling studies showed that eddy heat flux response is more sensitive to the variability of lower-tropospheric temperature gradient. However, observational evidence is lacking. In this study, observational data analyses are performed to examine the relationships between eddy heat fluxes and temperature gradients during boreal winter by constructing daily indices. On the intraseasonal time scale, the surface temperature gradient is found to be more effective at regulating the synoptic-scale eddy heat flux (SF) than is the upper-tropospheric temperature gradient. Enhancements in surface temperature gradient, however, are subject to an inactive planetary-scale eddy heat flux (PF). The PF in turn is dependent on the zonal gradient in tropical convective heating. Consistent with these interactions, over the past 40 winters, the zonal gradient in tropical heating and PF have been trending upward, while the surface temperature gradient and SF have been trending downward. These results indicate that for a better understanding of eddy heat fluxes, attention should be given to zonal convective heating gradients in the tropics as much as to meridional temperature gradients.

The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core—the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25 km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the stochastically perturbed physics tendencies (SPPT) scheme and stochastic kinetic energy backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error–spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over North America. GEFSv12 was implemented on 23 September 2020.

Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) Model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared with runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3-km/750-m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared with observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Last, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length-scale formulation in the 3D PBL parameterization is also explored. Significance Statement In this article, we demonstrate how a new framework for modeling atmospheric turbulence improves cold pool predictions, using a case study from January 2017 in the Columbia River basin (U.S. Pacific Northwest). Cold pools are regions of cold, stagnant air that form within valleys or basins, and improved forecasts could help to mitigate the risks they pose to air quality, transportation, and wind energy production. For the chosen case study, our tests show a reduction in temperature and wind speed errors by up to a factor of 2–3 relative to standard model options. These results strongly motivate continued development of the framework as well as its application to other complex weather events.

One of the proposed mechanisms for energy loss in the ocean is through dissipation of internal waves, in particular above rough topography where internal lee waves are generated. Rates of dissipation and diapycnal mixing are often estimated using linear internal wave generation theory and a constant value for mixing efficiency. However, previous oceanographic measurements found that nonlinear dynamics may be important close to topography. To investigate the role of nonlinear interactions, we conduct idealized 3D direct numerical simulations (DNS) of steady flow over 1D topography and vary the topographic height, which correlates to the degree of flow nonlinearity. We analyze the spatial distribution of energy transfer rates between internal waves and the nongeostrophic portion of the time-mean flow, and of dissipation and diapycnal mixing rates. In our simulations with taller, more nonlinear topographies, energy transfer rates are similar to previously unexplained oceanographic observations near topography: internal waves gain energy from time-mean flow through horizontal straining and lose energy through vertical shearing. In the tall topography simulations, buoyancy fluxes also play a significant role, consistent with observations but contrary to linear wave theory, suggesting that quasigeostrophy-based approximations and linear theory may not hold in some regions above rough topography. Both dissipation and mixing rates increase with topographic height, but their vertical distributions differ between topographic regimes. As such, the vertical profile of mixing efficiency is different for linear and nonlinear topographic regimes, which may need to be incorporated into parameterizations of small-scale processes in models and estimates of ocean energy loss.

In a recent paper, we argued that ocean dynamics increase the variability of midlatitude sea surface temperatures (SSTs) on monthly to interannual time scales, but act to damp lower-frequency SST variability over broad midlatitude regions. Here, we use two configurations of a simple stochastic climate model to provide new insights into this important aspect of climate variability. The simplest configuration includes the forcing and damping of SST variability by observed surface heat fluxes only, and the more complex configuration includes forcing and damping by ocean processes, which are estimated indirectly from monthly observations. It is found that the simple model driven only by the observed surface heat fluxes generally produces midlatitude SST power spectra that are too red compared to observations. Including ocean processes in the model reduces this discrepancy by whitening the midlatitude SST spectra. In particular, ocean processes generally increase the SST variance on 2-yr time scales. This happens because oceanic forcing increases the midlatitude SST variance across many time scales, but oceanic damping outweighs oceanic forcing on >2-yr time scales, particularly away from the western boundary currents. The whitening of midlatitude SST variability by ocean processes also operates in NCAR’s Community Earth System Model (CESM). That is, midlatitude SST spectra are generally redder when the same atmospheric model is coupled to a slab rather than dynamically active ocean model. Overall, the results suggest that forcing and damping by ocean processes play essential roles in driving midlatitude SST variability.

Diffusive theories for the meridional atmospheric energy transport can summarize our understanding of this central aspect of the general circulation. They can also be utilized in simple models of Earth’s energy balance to help interpret the response of the system to perturbations. A theory for this diffusivity of eddy heat transport is described based on Rhines scaling and the global entropy budget, each of which provides a constraint between the kinetic energy dissipation and the diffusivity. An expression for the diffusivity is then obtained by eliminating the dissipation from this set of two constraints. The theory can be thought of as a generalization of the theories of Held–Larichev and Barry–Craig–Thuburn. The theory is compared to simulations of the Held–Suarez idealized dry atmospheric model. Limitations of the theory are emphasized. The form of the theory allows it to be readily generalized to a moist atmosphere.

Mesoscale eddies are one of the most prominent processes in the world’s ocean. The eddy-induced transport of water mass, heat, and energy has a great impact on the ocean and atmosphere. The study of global mass transport by mesoscale eddies is important. However, most existing studies have used Eulerian eddy detection methods. Compared with Lagrangian methods, Eulerian methods fail to distinguish the coherent transport from the incoherent transport induced by eddies. Using a Lagrangian-averaged vorticity deviation (LAVD)-based coherent eddy detection method, this study identifies global coherent mesoscale eddies in the upper 1000 m of the ocean. Based on the eddy dataset, the eddy-induced coherent mass transport is calculated. Compared with Eulerian estimates, the Lagrangian results shown in this study are one order of magnitude smaller. This means that roughly only about 10% of eddy-induced global water mass transport is coherent. The cumulative eddy-induced coherent transport across each latitude or longitude is only around 1 Sv (1 Sv ≡ 106 m3 s−1), which is much less than the transport induced by wind-driven gyres and thermohaline circulation.

The physical processes of heat exchange between lakes and the surrounding atmosphere are important in simulating and predicting terrestrial surface energy balance. Latent and sensible heat fluxes are the dominant physical process controlling ice growth and decay on the lake surface, as well as having influence on regional climate. While one-dimensional lake models have been used in simulating environmental changes in ice dynamics and water temperature, understanding the seasonal to daily cycles of lake surface energy balance and its relationship to lake thermal properties, atmospheric conditions, and how those are represented in models is still an open area of research. We evaluated a pair of one-dimensional lake models, Freshwater Lake (FLake) and the General Lake Model (GLM), to compare modeled latent and sensible heat fluxes against observed data collected by an eddy covariance tower during a 1-yr period in 2017, using Lake Mendota in Madison, Wisconsin, as our study site. We hypothesized transitional periods of ice cover as a leading source of model uncertainty, and we instead found that the models failed to simulate accurate values for large positive heat fluxes that occurred from late August into late December. Our results ultimately showed that one-dimensional models are effective in simulating sensible heat fluxes but are considerably less sensitive to latent heat fluxes than the observed relationships of latent heat flux to environmental drivers. These results can be used to focus future improvement of these lake models especially if they are to be used for surface boundary conditions in regional numerical weather models. Significance Statement While lakes consist of a small amount of Earth’s surface, they have a large impact on local climate and weather. A large amount of energy is stored in lakes during the spring and summer, and then removed from lakes before winter. The effect is particularly noticeable in high latitudes, when the seasonal temperature difference is larger. Modeling this lake energy exchange is important for weather models and measuring this energy exchange is challenging. Here we compare modeled and observed energy exchange, and we show there are large amounts of energy exchange happening in the fall, which models struggle to capture well. During periods of partial ice coverage in early winter, lake behavior can change rapidly.

Two analytical models with different starting points of convective parameterizations, the Fuchs and Raymond model on one hand and the Khairoutdinov and Emanuel model on the other, are used to develop “minimal difference” models for the MJO. The main physical mechanisms that drive the MJO in both models are wind-induced surface heat exchange (WISHE) and cloud–radiation interactions (CRI). The dispersion curves for the modeled eastward-propagating mode, the MJO mode, are presented for an idealized case with zero meridional wind and for the realistic cases with higher meridional numbers. In both cases, the two models produce eastward-propagating modes with the growth rate greatest at the largest wavelengths despite having different representations of cumulus convection. We show that the relative contributions of WISHE and CRI are sensitive to how the convection and entropy/moisture budgets are represented in models like these. Significance Statement The Madden–Julian oscillation is the largest weather disturbance on our planet. It propagates eastward encompassing the whole tropical belt. It influences weather all around the globe by modulating hurricanes, atmospheric rivers, and other phenomena. Numerical models that forecast the Madden–Julian oscillation need improvement. Here we explore the physics behind the Madden–Julian oscillation using simple analytical models. Our models are based on the assumption that surface enthalpy fluxes and cloud–radiation interactions are responsible for the Madden–Julian oscillation but it should be borne in mind that other physical mechanisms have been proposed for the MJO. The impact of this research is to better understand the Madden–Julian oscillation mechanism.

Applying composite analysis to ERA-Interim data, the surface air temperature (SAT) anomaly pattern of the Pacific–North American (PNA) teleconnection is shown to include both symmetric and asymmetric SAT anomalies with respect to the PNA phase. The symmetric SAT anomalies, overlying the Russian Far East and western and eastern North America, grow through advection of the climatological temperature by the anomalous meridional wind and vertical mixing. The asymmetric SAT anomalies, overlying Siberia during the positive PNA and the subtropical North Pacific during the negative PNA, grow through vertical mixing only. For all SAT anomalies, vertical mixing relocates the temperature anomalies of the PNA teleconnection pattern from higher in the boundary layer downward to the level of the SAT. Above the level of the SAT, temperature anomaly growth is caused by horizontal temperature advection in all locations except for the subtropical North Pacific, where adiabatic cooling dominates. SAT anomaly decay is caused by longwave radiative heating/cooling, except over Siberia, where SAT anomaly decay is caused by vertical mixing. Additionally, temperature anomaly decay higher in the boundary layer due to nonlocal mixing contributes indirectly to SAT anomaly decay by weakening downgradient diffusion. These results highlight a diverse array of mechanisms by which individual anomalies within the PNA pattern grow and decay. Furthermore, with the exception of Siberia, throughout the growth and decay stages, horizontal temperature advection and/or vertical mixing is nearly balanced by longwave radiative heating/cooling, with the former being slightly stronger during the growth stage and the latter during the decay stage.

Key processes associated with the leading intraseasonal variability mode of wintertime surface air temperature (SAT) over Eurasia and the Arctic region are investigated in this study. Characterized by a dipole distribution in SAT anomalies centered over north Eurasia and the Arctic, respectively, and coherent temperature anomalies vertically extending from the surface to 300 hPa, this leading intraseasonal SAT mode and associated circulation have pronounced influences on global surface temperature anomalies including the East Asian winter monsoon region. By taking advantage of realistic simulations of the intraseasonal SAT mode in a global climate model, it is illustrated that temperature anomalies in the troposphere associated with the leading SAT mode are mainly due to dynamic processes, especially via the horizontal advection of winter mean temperature by intraseasonal circulation. While the cloud–radiative feedback is not critical in sustaining the temperature variability in the troposphere, it is found to play a crucial role in coupling temperature anomalies at the surface and in the free atmosphere through anomalous surface downward longwave radiation. The variability in clouds associated with the intraseasonal SAT mode is closely linked to moisture anomalies generated by similar advective processes as for temperature anomalies. Model experiments suggest that this leading intraseasonal SAT mode can be sustained by internal atmospheric processes in the troposphere over the mid- to high latitudes by excluding forcings from Arctic sea ice variability, tropical convective variability, and the stratospheric processes.

Accurate representation of heterogeneous surface layer processes is essential for numerical weather prediction (NWP) with sub-kilometer grid spacing. NWP models such as the Weather Research and Forecasting (WRF) Model generally use second-moment turbulent models for parameterizing the planetary boundary layer (PBL). The most common parameterizations follow Mellor–Yamada and account for the vertical turbulent mixing only; that is, standard PBL parameterizations are one-dimensional (1DPBL). The horizontal diffusion of momentum is parameterized based on Smagorinsky’s model for numerical stability. Although the combination of 1DPBL and 2D Smagorinsky parameterizations is successful at coarse grid resolutions (e.g., grid-sizedx∼ 12–2 km), it does not represent well the effect of horizontal turbulence as gridcell size decreases (

For the Community Atmosphere Model version 6 (CAM6), an adjustment is needed to conserve dry air mass. This adjustment exposes an inconsistency in how CAM6’s energy budget incorporates water—in CAM6 water in the vapor phase has energy, but condensed phases of water do not. When water vapor condenses, only its latent energy is retained in the model, while its remaining internal, potential, and kinetic energy are lost. A global fixer is used in the default CAM6 model to maintain global energy conservation, but locally the energy tendency associated with water changing phase violates the divergence theorem. This error in energy tendency is intrinsically tied to the water vapor tendency, and reaches its highest values in regions of heavy rainfall, where the error can be as high as 40 W m−2 annually averaged. Several possible changes are outlined within this manuscript that would allow CAM6 to satisfy the divergence theorem locally. These fall into one of two categories: 1) modifying the surface flux to balance the local atmospheric energy tendency and 2) modifying the local atmospheric tendency to balance the surface plus top-of-atmosphere energy fluxes. To gauge which aspects of the simulated climate are most sensitive to this error, the simplest possible change—where condensed water still does not carry energy and a local energy fixer is used in place of the global one—is implemented within CAM6. Comparing this experiment with the default configuration of CAM6 reveals precipitation, particularly its variability, to be highly sensitive to the energy budget formulation. Significance Statement This study examines and explains spurious regional sources and sinks of energy in a widely used climate model. These energy errors result from not tracking energy associated with water after it transitions from the vapor phase to either liquid or ice. Instead, the model used a global fixer to offset the energy tendency related to the energy sources and sinks associated with condensed water species. We replace this global fixer with a local one to examine the model sensitivity to the regional energy error and find a large sensitivity in the simulated hydrologic cycle. This work suggests that the underlying thermodynamic assumptions in the model should be revisited to build confidence in the model-simulated regional-scale water and energy cycles.

The interaction between the Antarctic Circumpolar Current (ACC) jets and mesoscale eddies plays a prominent role in the dynamics of the ACC. However, few studies have investigated the influence of the jets on the statistics of the mesoscale turbulence field. This study combines a mesoscale eddy detection algorithm with the jet-detection technique to extract their interaction in the Southern Ocean from 1993 to 2016. It is found that stronger jet filaments can effectively shorten eddy lifetime, thus introducing a negative correlation between eddy lifetime and matched jet filament velocities. It can also be seen along the pathway of the ACC whether the eddy is located upstream or downstream of topography. A series of numerical experiments suggest that the stronger zonal jet plays the role of a waveguide to facilitate the stronger and faster eastward linear Rossby wave field induced by the mesoscale eddy. A stronger eastward Rossby wave phase speed, enabled by the jet-induced Doppler shift, results in a more rapid loss of eddy energy and shortens the eddy lifetime.

An analytical model is presented for the generation of a Blackadar-like nocturnal low-level jet in a broad baroclinic zone. The flow is forced from below (flat ground) by a surface buoyancy gradient and from above (free atmosphere) by a constant pressure gradient force. Diurnally varying mixing coefficients are specified to increase abruptly at sunrise and decrease abruptly at sunset. With attention restricted to a surface buoyancy that varies linearly with a horizontal coordinate, the Boussinesq-approximated equations of motion, thermal energy, and mass conservation reduce to a system of one-dimensional equations that can be solved analytically. Sensitivity tests with southerly jets suggest that (i) stronger jets are associated with larger decreases of the eddy viscosity at sunset (as in Blackadar theory); (ii) the nighttime surface buoyancy gradient has little impact on jet strength; and (iii) for pure baroclinic forcing (no free-atmosphere geostrophic wind), the nighttime eddy diffusivity has little impact on jet strength, but the daytime eddy diffusivity is very important and has a larger impact than the daytime eddy viscosity. The model was applied to a jet that developed in fair weather conditions over the Great Plains from southern Texas to northern South Dakota on 1 May 2020. The ECMWF Reanalysis v5 (ERA5) for the afternoon prior to jet formation showed that a broad north–south-oriented baroclinic zone covered much of the region. The peak model-predicted winds were in good agreement with ERA5 winds and lidar data from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) central facility in north-central Oklahoma.

The fast decline of Arctic sea ice necessitates a stronger focus on understanding the Arctic sea ice predictability and developing advanced forecast methods for all seasons and for pan-Arctic and regional scales. In this study, the operational forecasting system combining an advanced eddy-permitting ocean–sea ice ensemble reanalysis ORAS5 and state-of-the-art seasonal model-based forecasting system SEAS5 is used to investigate effects of sea ice dynamics and thermodynamics on seasonal (growth-to-melt) Arctic sea ice predictability in 1993–2020. We demonstrate that thermodynamics (growth/melt) dominates the seasonal evolution of mean sea ice thickness at pan-Arctic and regional scales. The thermodynamics also dominates the seasonal predictability of sea ice thickness at pan-Arctic scale; however, at regional scales, the predictability is dominated by dynamics (advection), although the contribution from ice growth/melt remains perceptible. We show competing influences of sea ice dynamics and thermodynamics on the temporal change of ice thickness predictability from 1993–2006 to 2007–20. Over these decades, there was increasing predictability due to growth/melt, attributed to increased winter ocean heat flux in both Eurasian and Amerasian basins, and decreasing predictability due to advection. Our results demonstrate an increasing impact of advection on seasonal sea ice predictability as the region of interest becomes smaller, implying that correct modeling of sea ice drift is crucial for developing reliable regional sea ice predictions. This study delivers important information about sea ice predictability in the “new Arctic” conditions. It increases awareness regarding sea ice state and implementation of sea ice forecasts for various scientific and practical needs that depend on accurate seasonal sea ice forecasts.

We analyze the role of mesoscale heat advection in a mixed layer (ML) heat budget, using a regional high-resolution coupled model with realistic atmospheric forcing and an idealized ocean component. The model represents two regions in the Southern Ocean, one with strong ocean currents and the other with weak ocean currents. We conclude that heat advection by oceanic currents creates mesoscale anomalies in sea surface temperature (SST), while the atmospheric turbulent heat fluxes dampen these SST anomalies. This relationship depends on the spatial scale, the strength of the currents, and the mixed layer depth (MLD). At the oceanic mesoscale, there is a positive correlation between the advection and SST anomalies, especially when the currents are strong overall. For large-scale zonal anomalies, the ML-integrated advection determines the heating/cooling of the ML, while the SST anomalies tend to be larger in size than the advection and the spatial correlation between these two fields is weak. The effects of atmospheric forcing on the ocean are modulated by the MLD variability. The significance of Ekman advection and diabatic heating is secondary to geostrophic advection except in summer when the MLD is shallow. This study links heat advection, SST anomalies, and air–sea heat fluxes at ocean mesoscales, and emphasizes the overall dominance of intrinsic oceanic variability in mesoscale air–sea heat exchange in the Southern Ocean.

Supersaturation fluctuations in the atmosphere are critical for cloud processes. A nonlinear dependence on two scalars—water vapor and temperature—leads to different behavior than single scalars in turbulent convection. For modeling such multiscalar processes at subgrid scales (SGS) in large-eddy simulations (LES) or convection-permitting models, a new SGS scheme is implemented in CM1 that solves equations for SGS water vapor and temperature fluctuations and their covariance. The SGS model is evaluated using benchmark direct-numerical simulations (DNS) of turbulent Rayleigh–Bénard convection with water vapor as in the Michigan Tech Pi Cloud Chamber. This idealized setup allows thorough evaluation of the SGS model without complications from other atmospheric processes. DNS results compare favorably with measurements from the chamber. Results from LES using the new SGS model compare well with DNS, including profiles of water vapor and temperature variances, their covariance, and supersaturation variance. SGS supersaturation fluctuations scale appropriately with changes to the LES grid spacing, with the magnitude of SGS fluctuations decreasing relative to those at the resolved scale as the grid spacing is decreased. Sensitivities of covariance and supersaturation statistics to changes in water vapor flux relative to thermal flux are also investigated by modifying the sidewall conditions. Relative changes in water vapor flux substantially decrease the covariance and increase supersaturation fluctuations even away from boundaries.

Wind-wave development is governed by the fetch- or duration-limited growth principle that is expressed as a pair of similarity functions relating the dimensionless elevation variance (wave energy) and spectral peak frequency to fetch or duration. Combining the pair of similarity functions, the fetch or duration variable can be removed to form a dimensionless function of elevation variance and spectral peak frequency, which is interpreted as the wave energy evolution with wave age. The relationship is initially developed for quasi-neural stability and quasi-steady wind forcing conditions. Further analyses show that the same fetch, duration, and wave-age similarity functions are applicable to unsteady wind forcing conditions, including rapidly accelerating and decelerating mountain gap wind episodes and tropical cyclone (TC) wind fields. Here it is shown that with the dimensionless frequency converted to dimensionless wavenumber using the surface wave dispersion relationship, the same similarity function is applicable in all water depths. Field data collected in shallow to deep waters and mild to TC wind conditions and synthetic data generated by spectrum model computations are assembled to illustrate the applicability. For the simulation work, the finite-depth wind-wave spectrum model and its shoaling function are formulated for variable spectral slopes. Given wind speed, wave age, and water depth, the measured and spectrum-computed significant wave heights and the associated growth parameters are in good agreement in forcing conditions from mild to TC winds and in all depths from deep ocean to shallow lake. Significance Statement This paper presents a growth function and spectrum model to describe wind-wave development in all water depths. Their applicability covers a wide range of wind forcing conditions including steady, accelerating, decelerating, and tropical cyclone events. Support for the unified spectrum model and growth function is presented with field observations and numerical computations.

The wavenumber filtering of the Kelvin waves (KWs) is performed by a multivariate projection of the ERA5 data on the horizontal structures of the KWs from linear theory and vertical structure functions (VSFs) spanning the troposphere and stratosphere. The associated equivalent depths are consistent with solutions expected for the bounded atmosphere. The zonal wavenumber spectra of the KW energy and temporal variance are shown to be continuous and red with the rotational kinetic energy being dominant over the divergent kinetic energy at zonal wavenumber k = 1. Spatial and temporal variance in wavenumber space is analyzed in terms of periods from linear theory. The strongest variability is found in periods of 8–12 days at k = 1 and VSFs with a single zero-crossing in the troposphere and a structure resembling upward propagating waves across the tropopause and higher up. Secondary maxima are associated with VSFs that have multiple zero crossings in the troposphere and small equivalent depths. A comparison of the wavenumber and wavenumber–frequency methods shows a disagreement between frequencies from the Fourier time series and linear theory. A phase-locking experiment, in which the KW phases are replaced by linear theory estimates, demonstrates that power spectra from wavenumber–frequency filtering in the troposphere are largely defined by wave phases.

We present a scaling analysis for the stratified turbulent and small-scale turbulent regimes of atmospheric flow with emphasis on the mesosphere. We distinguish rotating-stratified macroturbulence turbulence (SMT), stratified turbulence (ST), and small-scale isotropic Kolmogorov turbulence (KT), and we specify the length and time scales and the characteristic velocities for these regimes. It is shown that the buoyancy scale (Lb) and the Ozmidov scale (Lo) are the main parameters that describe the transition from SMT to KT. We employ the buoyancy Reynolds number and horizontal Froude number to characterize ST and KT in the mesosphere. This theory is applied to simulation results from a high-resolution general circulation model with a Smagorinsky-type turbulent diffusion scheme for the subgrid-scale parameterization. The model allows us to derive the turbulent root-mean-square (rms) velocity in the KT regime. It is found that the turbulent RMS velocity has a single maximum in summer and a double maximum in winter months. The secondary maximum in the winter MLT we associate with a secondary gravity wave–breaking phenomenon. The turbulent rms velocity results from the model agree well with full correlation analyses based on MF-radar measurements. A new scaling for the mesoscale horizontal velocity based on the idea of direct energy cascade in mesoscales is proposed. The latter findings for mesoscale and small-scale characteristic velocities support the idea proposed in this research that mesoscale and small-scale dynamics in the mesosphere are governed by SMT, ST, and KT in the statistical average. Significance Statement Mesoscale dynamics in the middle atmosphere, which consists of atmospheric turbulence and gravity waves, remains a complex problem for atmospheric physics and climate studies. Due to its high nonlinearity, the mesoscale dynamics together with the small-scale turbulence is the primary source of uncertainties and biases in high-altitude general circulation models (GCM) in the middle atmosphere. We use the stratified turbulence theory and the gravity wave–resolving GCM to characterize different scaling regimes and to define various length, time, and velocity scales, that are relevant for the mesoscale and small-scale dynamical regimes. Our results highlight the importance of stratified turbulence in the mesosphere and lower-thermosphere region.

Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.

It is well understood that isolated eddies are presumed to propagate westward intrinsically at the speed of the annual baroclinic Rossby wave. This classic description, however, is known to be frequently violated in both propagation speed and its direction in the real ocean. Here, we present a systematic analysis on the divergence of eddy propagation direction (i.e., global pattern of departure from due west) and dispersion of eddy propagation speed (i.e., zonal pattern of departure from Rossby wave phase speed). Our main findings include the following: 1) A global climatological phase map (the first of its kind to our knowledge) indicating localized direction of most likely eddy propagation has been derived from 28 years (1993–2020) of satellite altimetry, leading to a leaf-like full-angle pattern in its overall divergence. 2) A meridional deflection map of eddy motion is created with prominent equatorward/poleward deflecting zones identified, revealing that it is more geographically correlated rather than polarity determined as previously thought (i.e., poleward for cyclonic eddies and equatorward for anticyclonic ones). 3) The eddy–Rossby wave relationship has a duality nature (waves riding by eddies) in five subtropical bands centered around 27°N and 26°S in the two hemispheres, outside which their relationship has a dispersive nature with dominant waves (eddies) propagating faster in the tropical (extratropical) oceans. Current, wind, and topographic effects are major external forcings responsible for the observed divergence and dispersion of eddy propagations. These results are expected to make a significant contribution to eddy trajectory prediction using physically based and/or data-driven models.

Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

This study presents the characteristics and spatiotemporal structure of near-inertial waves and their interaction with Leeuwin Current eddies in the eastern south Indian Ocean as observed by Electromagnetic Autonomous Profiling Explorer (EM-APEX) floats. The floats sampled the upper ocean during July–October 2013 with a frequency of eight profiles per day down to 1200 m. Near-inertial waves (NIWs) are found to be the dominant signal in the frequency spectra. Complex demodulation is used to estimate the amplitude and phase of the NIWs from the velocity profiles. The NIW energy propagated from the base of the mixed layer downward into the ocean interior, following beam characteristics of linear wave theory. We visually identified a total of 15 near-inertial internal wave packets from the wave amplitudes and phases with a mean vertical wavelength of 89 ± 63 m, a mean horizontal wavelength of 69 ± 85 km, a mean horizontal group velocity of 3 ± 2 cm s−1, and a mean vertical group velocity of 9 ± 7 m day−1. A strong near-inertial packet with a kinetic energy of 20–30 J m−3 found propagating below 700 m suggests that the NIWs can contribute to deep ocean mixing. A blue shift of 10%–15% in the energy spectrum of the NIWs is observed in the upper 1200 m as the floats move toward the equator. The impacts of mesoscale eddies on the characteristics and propagation of the observed NIWs are also investigated. The elevated near-inertial shear variance in anticyclonic eddies suggests trapping of NIWs near the surface. Cyclonic eddies, in contrast, were associated with weak near-inertial shear variance in the upper 400 m.

A dry-core idealized general circulation model with a stratospheric polar vortex in the Northern Hemisphere is run with a combination of simplified topography and imposed tropospheric temperature perturbations, each located in the Northern Hemisphere with a zonal wavenumber of 1. The phase difference between the imposed temperature wave and the topography is varied to understand what effect this has on the occurrence of polar vortex displacements. Geometric moments are used to identify the centroid of the polar vortex for the purposes of classifying whether or not the polar vortex is displaced. Displacements of the polar vortex are a response to increased tropospheric wave activity. Compared to a model run with only topography, the likelihood of the polar vortex being displaced increases when the warm region is located west of the topography peak, and decreases when the cold region is west of the topography peak. This response from the polar vortex is due to the modulation of vertically propagating wave activity by the temperature forcing. When the southerly winds on the western side of the topographically forced anticyclone are collocated with warm- or cold-temperature forcing, the vertical wave activity flux in the troposphere becomes more positive or negative, respectively. This is in line with recent reanalysis studies that showed that anomalous warming west of the surface pressure high, in the climatological standing wave, precedes polar vortex disturbances.

Given the climatic importance of the Madden–Julian oscillation (MJO), this study evaluates the capability of CMIP6 models in simulating MJO eastward propagation in comparison with their CMIP5 counterparts. To understand the representation of MJO simulation in models, a set of diagnostics with respect to MJO-associated dynamic and thermodynamic structures is applied, including large-scale zonal circulation, vertical structures of diabatic heating and equivalent potential temperature, moisture convergence at the planetary boundary layer (PBL), and the east–west asymmetry of moisture tendency relative to the MJO convection. The simulated propagation of the MJO in CMIP6 models shows an overall improvement in realistic speed and longer distance, which displays a robust linear regression relationship against the above-mentioned dynamic and thermodynamic structures. The improved MJO propagation in CMIP6 largely benefits from better representation of premoistening processes that is primarily contributed by improved PBL moisture convergence. In addition, the convergence of moisture and meridional advection of moisture prior to the MJO convection are enhanced in CMIP6, while the zonal advection of moisture is as weak as that in CMIP5. The increased convergence of moisture is a result of enhanced lower-tropospheric moisture and divergence, and the enhanced meridional advection of moisture can be caused by sharpened meridional gradient of mean lower-tropospheric moisture in the western Pacific. Further examination of the lower-tropospheric moisture budget reveals that the enhanced zonal asymmetry of the moisture tendency in CMIP6 is driven by the drying process to the west of the MJO convection, which is attributed to the negative vertical and zonal advections of moisture.

Large meridional excursions of a jet stream are conducive to blocking and related midlatitude weather extremes, yet the physical mechanism of jet meandering is not well understood. This paper examines the mechanisms of jet meandering in boreal winter through the lens of a potential vorticity (PV)-like tracer advected by reanalysis winds in an advection–diffusion model. As the geometric structure of the tracer displays a compact relationship with PV in observations and permits a linear mapping from tracer to PV at each latitude, jet meandering can be understood by the geometric structure of tracer field that is only a function of prescribed advecting velocities. This one-way dependence of tracer field on advecting velocities provides a new modeling framework to quantify the effects of time mean flow versus transient eddies on the spatiotemporal variability of jet meandering. It is shown that the mapped tracer wave activity resembles the observed spatial pattern and magnitude of PV wave activity for the winter climatology, interannual variability, and blocking-like wave events. The anomalous increase in tracer wave activity for the composite over interannual variability or blocking-like wave events is attributed to weakened composite mean winds, indicating that the low-frequency winds are the leading factor for the overall distributions of wave activity. It is also found that the tracer model underestimates extreme wave activity, likely due to the lack of feedback mechanisms. The implications for the mechanisms of jet meandering in a changing climate are also discussed.

In theoretical models of tropical dynamics, the effects of both surface friction and upward wave radiation through interaction with the stratosphere are oft-ignored, as they greatly complicate mathematical analysis. In this study, we relax the rigid-lid assumption and impose surface drag, which allows the barotropic mode to be excited in equatorial waves. In particular, a previously developed set of linear, strict quasi-equilibrium tropospheric equations is coupled with a dry, passive stratosphere, and surface drag is added to the troposphere momentum equations. Theoretical and numerical model analysis is performed on the model in the limits of an inviscid surface coupled to a stratosphere, as well as a frictional surface under a rigid lid. This study confirms and extends previous research that shows the presence of a stratosphere strongly shifts the growth rates of fast-propagating equatorial waves to larger scales, reddening the equatorial power spectrum. The growth rates of modes that are slowly propagating and highly interactive with cloud radiation are shown to be negligibly affected by the presence of a stratosphere. Surface friction in this model framework acts as purely a damping mechanism and couples the baroclinic mode to the barotropic mode, increasing the poleward extent of the equatorial waves. Numerical solutions of the coupled troposphere–stratosphere model with surface friction show that the stratosphere stratification controls the extent of tropospheric trapping of the barotropic mode, and thus the poleward extent of the wave. The superposition of phase-shifted barotropic and first baroclinic modes is also shown to lead to an eastward vertical tilt in the dynamical fields of Kelvin wave–like modes.

Gravity wave (GW) momentum and energy deposition are large components of the momentum and heat budgets of the stratosphere and mesosphere, affecting predictability across scales. Since weather and climate models cannot resolve the entire GW spectrum, GW parameterizations are required. Tuning these parameterizations is time-consuming and must be repeated whenever model configurations are changed. We introduce a self-tuning approach, called GW parameter retrieval (GWPR), applied when the model is coupled to a data assimilation (DA) system. A key component of GWPR is a linearized model of the sensitivity of model wind and temperature to the GW parameters, which is calculated using an ensemble of nonlinear forecasts with perturbed parameters. GWPR calculates optimal parameters using an adaptive grid search that reduces DA analysis increments via a cost-function minimization. We test GWPR within the Navy Global Environmental Model (NAVGEM) using three latitude-dependent GW parameters: peak momentum flux, phase-speed width of the Gaussian source spectrum, and phase-speed weighting relative to the source-level wind. Compared to a baseline experiment with fixed parameters, GWPR reduces analysis increments and improves 5-day mesospheric forecasts. Relative to the baseline, retrieved parameters reveal enhanced source-level fluxes and westward shift of the wave spectrum in the winter extratropics, which we relate to seasonal variations in frontogenesis. The GWPR reduces stratospheric increments near 60°S during austral winter, compensating for excessive baseline nonorographic GW drag. Tropical sensitivity is weaker due to significant absorption of GW in the stratosphere, resulting in less confidence in tropical GWPR values.

Commonly used parameterization of mixed layer instabilities in general circulation models was developed for temperate oceans and does not take into account the presence of sea ice in any way. However, the ice–ocean drag provides a strong mechanical coupling between the sea ice and the surface ocean currents and hence may affect mixed layer restratification processes. Here we use idealized simulations of mixed layer instabilities to demonstrate that the sea ice dramatically suppresses the eddy-driven overturning in the mixed layer by dissipating the eddy kinetic energy generated during instabilities. Considering the commonly used viscous-plastic sea ice rheology, we developed an improvement to the existing mixed layer overturning parameterization, making it explicitly dependent on sea ice concentration. Below the critical sea ice concentration of about 0.68, the internal sea ice stresses are very weak and the conventional parameterization holds. At higher concentrations, the sea ice cover starts acting as a nearly immobile surface lid, inducing strong dissipation of submesoscale eddies and reducing the intensity of the restratification streamfunction up to a factor of 4 for a fully ice-covered ocean. Our findings suggest that climate projection models might be exaggerating the restratification processes under sea ice, which could contribute to biases in mixed layer depth, salinity, ice–ocean heat fluxes, and sea ice cover.

A linear Ekman model in the stratified atmospheric boundary layer (ABL) is proposed based on the steady-state version of the linearized three-dimensional primitive equations with the inclusion of the vertical diffusivity. Due to the inclusion of the potential temperature equation and hydrostatic equation, pressure and potential temperature couple with wind in the proposed model, and thus are not arbitrarily specified variables as in previous studies on the baroclinicity in the Ekman model. The extended thermal wind balance equation and the Ekman potential vorticity equation are derived to describe the coupling. The two equations, along with the equation describing the constraint on potential temperature, are employed to derive the analytical solutions of the proposed Ekman model. Because potential temperature is not a specified variable but part of the solution, the derived analytical solutions have very different forms from those derived in previous studies. The differences illustrate the impact of the inclusion of the potential temperature equation and hydrostatic equation on wind, pressure, and potential temperature in the proposed Ekman model. It is found that the computed wind profiles based on the proposed model can capture some important features of the observed wind profiles.

Conventional wisdom suggests that the combination of semi-Lagrangian advection and an implicit treatment of gravity wave terms should result in a combined scheme for the shallow-water equations stable for high Courant numbers. This wisdom is well justified by linear analysis of the system about a uniform reference state with constant fluid depth and velocity, but it is only assumed to hold true in more complex scenarios. This work finds that this conventional wisdom no longer holds in more complicated flow regimes, in particular when the background state is given by steady-state flow past topography. Instead, this background state admits a wide range of instabilities that can lead to noise in atmospheric forecasts. Significance Statement This work shows that solutions to the shallow-water equations with a semi-Lagrangian treatment of advection and an implicit, time-centered treatment of gravity wave terms can be unstable when there is a background state of flow over topography. This basic algorithm is used by many operational weather-forecasting models to simulate the meteorological equations, and showing an instability in the simplified, shallow-water system suggests that a similar mechanism may be responsible for “noise” in operational weather forecasts under some circumstances. If this problem can be addressed, it could allow numerical weather models to operate with less dissipation, improving forecast quality.