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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 this note, we apply Transition Path Theory (TPT) from Markov chains to shed light on the problem of Iceland-Scotland Overflow Water (ISOW) equatorward export. A recent analysis of observed trajectories of submerged floats demanded revision of the traditional abyssal circulation theory, which postulates that ISOW should steadily flow along a deep boundary current (DBC) around the subpolar North Atlantic prior to exiting it. The TPT analyses carried out here allow to focus the attention on the portions of flow from the origin of ISOW to the region where ISOW exits the subpolar North Atlantic and suggest that insufficient sampling may be biasing the aforementioned demand. The analyses, appropriately adapted to represent a continuous input of ISOW, are carried out on three time-homogeneous Markov chains modeling the ISOW flow. One is constructed using a high number of simulated trajectories homogeneously covering the flow domain. The other two use much fewer trajectories which heterogeneously cover the domain. The trajectories in the latter two chains are observed trajectories or simulated trajectories subsampled at the observed frequency. While the densely sampled chain supports a well-defined DBC, the more heterogeneously sampled chains do not, irrespective of whether observed or simulated trajectories are used. Studying the sampling sensitivity of the Markov chains, we can give recommendations for enlarging the existing float dataset to improve the significance of conclusions about time-asymptotic aspects of the ISOW circulation., Submitted to JPO. Comments welcomed!

The impacts of a tropical cyclone after landfall depend not only on storm intensity but also on the size and structure of the wind field. Hence, a simple predictive model for the wind field after landfall has significant potential value. This work tests existing theory for wind structure and size over the ocean against idealized axisymmetric landfall experiments in which the surface beneath a mature storm is instantaneously dried and roughened individually or simultaneously. Structure theory captures the response of the low-level wind field to different types of idealized landfalls, given the intensity and size response. Storm size, modeled to follow the ratio of simulated time-dependent storm intensity to the Coriolis parameter , can generally predict the transient response of the storm gale wind radii r34kt to inland surface forcings, particularly for at least moderate surface roughening regardless of the level of drying. Given knowledge of the intensity evolution, the above results combine to yield a theoretical model that can predict the full tangential wind field response to idealized landfalls. Significance Statement A theoretical model that can predict the time-dependent wind field structure of landfalling tropical cyclones (TCs) with a small number of physical, observable input parameters is essential for mitigating hazards and allocating public resources. This work provides a first-order prediction of storm size and structure after landfall, which can be combined with existing intensity predictions to form a simple model describing the inland wind field evolution. Results show its potential utility for modeling idealized inland TC wind fields.

Over the past few years, quantum computers and quantum algorithms have attracted considerable interest and attention from numerous scientific disciplines. In this article, we aim to provide a nontechnical yet informative introduction to key aspects of quantum computing. We discuss whether quantum computers one day might become useful tools for numerical weather and climate prediction. Using a recently developed quantum algorithm for solving nonlinear differential equations, we integrate a simple nonlinear model. In addition to considering the advantages that quantum computers have to offer, we shall also discuss the challenges one faces when trying to use quantum computers for real-world problems involving “big data,” such as weather prediction.

Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting), and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective "average event." We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT's capacities on a wave-mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass (1976), which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also \emph{backward in time} to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the \emph{reactive current}. The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions., Comment: 18 pages, 7 figures (main text), 19 pages, 1 figure (supplement). Accepted for publication in the Journal of the Atmospheric Sciences

To close the overturning circulation, dense bottom water must upwell via turbulent mixing. Recent studies have identified thin bottom boundary layers (BLs) as locations of intense upwelling, yet it remains unclear how they interact with and shape the large-scale circulation of the abyssal ocean. The current understanding of this BL–interior coupling is shaped by 1D theory, suggesting that variations in locally produced BL transport generate exchange with the interior and thus a global circulation. Until now, however, this picture has been based on a 1D theory that fails to capture the local evolution in even highly idealized 2D geometries. The present work applies BL theory to revised 1D dynamics, which more naturally generalizes to two and three dimensions. The BL is assumed to be in quasi-equilibrium between the upwelling of dense water and the convergence of downward buoyancy fluxes. The BL transport, for which explicit formulas are presented, exerts an influence on the interior by modifying the bottom boundary condition. In 1D, this BL transport is independent of the interior evolution, but in 2D the BL and interior are fully coupled. Once interior variables and the bottom slope are allowed to vary in the horizontal, the resulting convergences and divergences in the BL transport exchange mass with the interior. This framework allows for the analysis of previously inaccessible problems such as the BL–interior coupling in the presence of an exponential interior stratification, laying the foundation for developing a full theory for the abyssal circulation.

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

Many atmosphere and climate phenomena lie in the gray zone between weather and climate: they are not amenable to deterministic forecast, but they still depend on the initial condition. A natural example is medium-range forecasting, which is inherently probabilistic because it lies beyond the deterministic predictability time of the atmosphere, but for which statistically significant prediction can be made, which depends on the current state of the system. Similarly, one may ask the probability of occurrence of an El Niño event several months ahead of time. We introduce a quantity that corresponds precisely to this type of prediction problem: the committor function is the probability that an event takes place within a given time window, as a function of the initial condition. We compute it in the case of a low-dimensional stochastic model for El Niño, the Jin and Timmermann model. In this context, we show that the ability to predict the probability of occurrence of the event of interest may differ strongly depending on the initial state. The main result is the new distinction between probabilistic predictability (when the committor function is smooth and probability can be computed, which does not depend sensitively on the initial condition) and probabilistic unpredictability (when the committor function depends sensitively on the initial condition). We also demonstrate that the Jin and Timmermann model might be the first example of a stochastic differential equation with weak noise for which transition between attractors does not follow the Arrhenius law, which is expected based on large deviation theory and generic hypothesis. Significance Statement A key problem for atmospheric and climate phenomena is to predict events beyond the time scale over which deterministic weather forecast is possible. In a simple model of El Niño, we demonstrate the existence of two regimes, depending on initial conditions. For initial conditions in the “probabilistic predictability” regime, the system is unpredictable deterministically because of chaos, but the probability of occurrence of the event can still be predicted because it depends only weakly on the initial condition. In the “probabilistic unpredictability” regime, even predicting probabilities is difficult, because the probability depends strongly on initial conditions. These new concepts of probabilistic predictability and unpredictability should be key in understanding the predictability potential for rare events in climate problems, as well as in other complex dynamics.

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order to fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted for the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods are discussed. Specifically this paper covers perceptrons, artificial neural networks, convolutional neural networks and U-networks. Like the part 1 paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets.

Large-eddy simulations (LES) are employed to investigate the role of time-varying currents on the form drag and vortex dynamics of submerged 3D topography in a stratified rotating environment. The current is of the form Uc + Utsin(2πftt), where Uc is the mean, Ut is the tidal component, and ft is its frequency. A conical obstacle is considered in the regime of low Froude number. When tides are absent, eddies are shed at the natural shedding frequency fs,c. The relative frequency is varied in a parametric study, which reveals states of high time-averaged form drag coefficient. There is a twofold amplification of the form drag coefficient relative to the no-tide (Ut = 0) case when lies between 0.5 and 1. The spatial organization of the near-wake vortices in the high drag states is different from a Kármán vortex street. For instance, the vortex shedding from the obstacle is symmetric when and strongly asymmetric when . The increase in form drag with increasing stems from bottom intensification of the pressure in the obstacle lee which we link to changes in flow separation and near-wake vortices.

Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the mid-latitude jet stream's latitudinal position, often referred to as a "tug-of-war". Studies that investigate the jet's response to these thermal forcings show that it is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Much of this past work focuses on studying a simulation's response to external manipulation. In contrast, we explore the potential to train a convolutional neural network (CNN) on internal variability alone and then use it to examine possible nonlinear responses of the jet to tropospheric thermal forcing that more closely resemble anthropogenic climate change. Our approach leverages the idea behind the fluctuation-dissipation theorem, which relates the internal variability of a system to its forced response but so far has been only used to quantify linear responses. We train a CNN on data from a long control run of the CESM dry dynamical core and show that it is able to skillfully predict the nonlinear response of the jet to sustained external forcing. The trained CNN provides a quick method for exploring the jet stream sensitivity to a wide range of tropospheric temperature tendencies and, considering that this method can likely be applied to any model with a long control run, could lend itself useful for early stage experiment design., Comment: 24 pages, 9 figures, submitted for consideration for publication in Artificial Intelligence for the Earth Systems

A gradient-wind balanced flow with an elliptic streamline parametrically excites internal inertia-gravity waves through ageostrophic anticyclonic instability (AAI). This study numerically investigates the breaking of internal waves and the following turbulence generation resulting from the AAI. In our simulation, we periodically distort the calculation domain following the streamlines of an elliptic vortex and integrate the equations of motion using a Fourier spectral method. This technique enables us to exclude the overall structure of the large-scale vortex from the computation and concentrate on resolving the small-scale waves and turbulence. From a series of experiments, we identify two different scenarios of wave breaking conditioned on the magnitude of the instability growth rate scaled by the buoyancy frequency, $\lambda/N$. First, when $\lambda/N\gtrsim0.008$, the primary wave amplitude excited by AAI quickly goes far beyond the overturning threshold and directly breaks. The resulting state is thus strongly nonlinear turbulence. Second, if $\lambda/N\lesssim0.008$, weak wave-wave interactions begin to redistribute energy across frequency space before the primary wave reaches a breaking limit. Then, after a sufficiently long time, the system approaches a Garrett-Munk-like stationary spectrum, in which wave breaking occurs at finer vertical scales. Throughout the experimental conditions, the growth and decay time scales of the primary wave energy are well correlated. However, since the primary wave amplitude reaches a prescribed limit in one scenario but not in the other, the energy dissipation rates exhibit two types of scaling properties. This scaling classification has similarities and differences with D'Asaro and Lien's (2000) wave-turbulence transition model., Comment: 55 pages, 18 figures. Journal of Physical Oceanography (2023)

Boundary layer turbulence in coastal regions differs from that in deep ocean because of bottom interactions. In this paper, we focus on the merging of surface and bottom boundary layers in a finite-depth coastal ocean by numerically solving the wave-averaged equations using a large eddy simulation method. The ocean fluid is driven by combined effects of wind stress, surface wave, and a steady current in the presence of stable vertical stratification. The resulting flow consists of two overlapping boundary layers, i.e. surface and bottom boundary layers, separated by an interior stratification. The overlapping boundary layers evolve through three phases, i.e. a rapid deepening, an oscillatory equilibrium and a prompt merger, separated by two transitions. Before the merger, internal waves are observed in the stratified layer, and they are excited mainly by Langmuir turbulence in the surface boundary layer. These waves induce a clear modulation on the bottom-generated turbulence, facilitating the interaction between the surface and bottom boundary layers. After the merger, the Langmuir circulations originally confined to the surface layer are found to grow in size and extend down to the sea bottom (even though the surface waves do not feel the bottom), reminiscent of the well-organized Langmuir supercells. These full-depth Langmuir circulations promote the vertical mixing and enhance the bottom shear, leading to a significant enhancement of turbulence levels in the vertical column., 20 pages, 15 figures

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.

Flow over a surface can be stratified by imposing a fixed mean vertical temperature (density) gradient profile throughout or via cooling at the surface. These distinct mechanisms can act simultaneously to establish a stable stratification in a flow. Here, we perform a series of direct numerical simulations of open-channel flows to study adaptation of a neutrally stratified turbulent flow under the combined or independent action of the aforementioned mechanisms. We force the fully developed flow with a constant mass flow rate. This flow forcing technique enables us to keep the bulk Reynolds number constant throughout our investigation and avoid complications arising from the acceleration of the bulk flow when a constant pressure gradient approach were to be adopted to force the flow instead. When both stratification mechanisms are active, the dimensionless stratification perturbation number emerges as an external flow control parameter, in addition to the Reynolds, Froude, and Prandtl numbers. We demonstrate that significant deviations from the Monin-Obukhov similarity formulation are possible when both types of stratification mechanisms are active within an otherwise weakly stable flow, even when the flux Richardson number is well below 0.2. An extended version of the similarity theory due to Zilitinkevich and Calanca shows promise in predicting the dimensionless shear for cases where both types of stratification mechanisms are active, but the extended theory is less accurate for gradients of scalar. The degree of deviation from neutral dimensionless shear as a function of the vertical coordinate emerges as a qualitative measure of the strength of stable stratification for all the cases investigated in this study., Comment: revised manuscript submitted to the Journal of the Atmospheric Sciences

Convective available potential energy (CAPE) is of strong interest in climate modeling because of its role in both severe weather and in model construction. Extreme levels of CAPE ($>$ 2000 J/kg) are associated with high-impact weather events, and CAPE is widely used in convective parametrizations to help determine the strength and timing of convection. However, to date no study has systematically evaluated CAPE biases in models in a climatological context, in an assessment large enough to characterize the high tail of the CAPE distribution. This work compares CAPE distributions in over 200,000 summertime proximity soundings from four sources: the observational radiosonde network (IGRA), 0.125 degree reanalysis (ERA-Interim and ERA5), and a 4 km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and model consistently show too-narrow distributions of CAPE, with the high tail ($>$ 95th percentile) systematically biased low by up to 10% in surface-based CAPE and 20% at the most unstable layer. This "missing tail" corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by bias in surface temperature and humidity: reanalyses and model undersample observed cases of extreme heat and moisture. These results suggest that reducing inaccuracies in land surface and boundary layer models is critical for accurately reproducing CAPE., Submitted to AMS Journal of Climate (15 December 2020)

Recent observations have indicated significant modulation of the Madden Julian Oscillation (MJO) by the phase of the stratospheric Quasi-Biennial Oscillation (QBO) during boreal winter. Composites of the MJO show that upper tropospheric ice cloud fraction and water vapor anomalies are generally collocated, and that an eastward tilt with height in cloud fraction exists. Through radiative transfer calculations, it is shown that ice clouds have a stronger tropospheric radiative forcing than do water vapor anomalies, highlighting the importance of incorporating upper tropospheric/lower stratospheric processes into simple models of the MJO. The coupled troposphere-stratosphere linear model previously developed by the authors is extended by including a mean wind in the stratosphere and a prognostic equation for cirrus clouds, which are forced dynamically and allowed to modulate tropospheric radiative cooling, similar to the effect of tropospheric water vapor in previous formulations. Under these modifications, the model still produces a slow, eastward propagating mode that resembles the MJO. The sign of zonal mean wind in the stratosphere is shown to control both the upward wave propagation and tropospheric vertical structure of the mode. Under varying stratospheric wind and interactive cirrus cloud radiation, the MJO-like mode has weaker growth rates under stratospheric westerlies than easterlies, consistent with the observed MJO-QBO relationship. These results are directly attributable to an enhanced barotropic mode under QBO easterlies. It is also shown that differential zonal advection of cirrus clouds leads to weaker growth rates under stratospheric westerlies than easterlies. Implications and limitations of the linear theory are discussed.

Current statistical post-processing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 hours ahead, based on KNMI's deterministic Harmonie-Arome NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using 3 different density estimation methods (quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution), and found the probabilistic forecasts based on the QS method to be best., 44 pages, 5 figures

The dynamics and momentum budget of the quasi-biennial oscillation (QBO) are examined in ERA5. Because of ERA5’s higher spatial resolution compared to its predecessors, it is capable of resolving a broader spectrum of atmospheric waves and allows for a better representation of the wave–mean flow interactions, both of which are of crucial importance for QBO studies. It is shown that the QBO-induced mean meridional circulation, which is mainly confined to the winter hemisphere, is strong enough to interrupt the tropical upwelling during the descent of the westerly shear zones. Since the momentum advection tends to damp the QBO, the wave forcing is responsible for both the downward propagation and for the maintenance of the QBO. It is shown that half the required wave forcing is provided by resolved waves during the descent of both westerly and easterly regimes. Planetary-scale waves account for most of the resolved wave forcing of the descent of westerly shear zones and small-scale gravity (SSG) waves with wavelengths shorter than 2000 km account for the remainder. SSG waves account for most of the resolved forcing of the descent of the easterly shear zones. The representation of the mean fields in the QBO is very similar in ERA5 and ERA-Interim but the resolved wave forcing is substantially stronger in ERA5. The contributions of the various equatorially trapped wave modes to the QBO forcing are documented in Part II.

This work develops a theoretical model for steady thermodynamic and kinematic profiles for severe convective storm environments, building off of the two-layer static energy framework developed in Agard and Emanuel (2017). The model is phrased in terms of static energy, and it allows for independent variation of the boundary layer and free troposphere separated by a capping inversion. An algorithm is presented to apply the model to generate a sounding for numerical simulations of severe convective storms, and the model is compared and contrasted with that of Weisman and Klemp. The model is then fit to a case-study sounding associated with the 3 May 1999 tornado outbreak, and its potential utility is demonstrated via idealized numerical simulation experiments. A long-lived supercell is successfully simulated with the historical sounding but not the analogous theoretical sounding. Two types of example experiments are then performed that do simulate a long-lived supercell: 1) a semi-theoretical experiment in which a portion of the theoretical sounding is modified to match the real sounding (low-level moisture); 2) a fully-theoretical experiment in which a model physical parameter is modified (free-tropospheric relative humidity). Overall, the construction of this minimal model is flexible and amenable to additional modifications as needed. The model offers a novel framework that may be useful for testing how severe convective storms depend on the vertical structure of the hydrostatic environment, as well as for linking variability in these environments to the physical processes that produce them within the climate system., Comment: 21 pages, 7 figures, accepted in Journal of the Atmospheric Sciences 2020-10-26

Severe local storm (SLS) activity is known to occur within specific thermodynamic and kinematic environments. These environments are commonly associated with key synoptic-scale features--including southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones--that link the large-scale climate to SLS environments. This work analyzes spatiotemporal distributions of both the environmental parameters and synoptic-scale features in ERA5 reanalysis and in Community Atmosphere Model version 6 (CAM6) during 1980--2014 over North America. Compared to radiosondes, ERA5 successfully reproduces SLS environments, with strong spatiotemporal correlations and low biases, especially over the Great Plains. Both ERA5 and CAM6 reproduce the climatology of SLS environments over the central United States as well as its strong seasonal and diurnal cycles. ERA5 and CAM6 also reproduce the climatological occurrence of the synoptic-scale features, with the distribution pattern similar to that of SLS environments. Compared to ERA5, CAM6 exhibits a high bias in Convective Available Potential Energy over the eastern United States primarily due to a high bias in surface moisture, and to a lesser extent, storm-relative helicity due to enhanced low-level winds. Composite analysis indicates consistent synoptic anomaly patterns favorable for significant SLS environments over much of the eastern half of the United States in both ERA5 and CAM6, though the pattern differs for the southeastern United States. Overall, results indicate that both ERA5 and CAM6 are capable of reproducing SLS environments as well as the synoptic-scale features and transient events that generate them., 25 pages, 15 figures

Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. (Hodyss et al 2013, 2014) demonstrated that a correction term to various deterministic numerical schemes, known in stochastic analysis as the It\^o correction, can help improve solution accuracy and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the It\^o correction is valid only when the stochasticity is represented by {\it white} noise. In this study, a generalized formulation of the It\^o correction is derived for noises of any color. The formulation is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized It\^o correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized It\^o correction allows for the use of significantly longer time steps and hence helps to reduce the computational cost of the numerical simulation., Comment: 18 pages, 2 figures; v2 includes section rearrangement and added details for the numerical implementation; v3 includes addition of sections, references and one figure

The combined effectiveness of model reduction and the quasilinear approximation for the reproduction of the low-order statistics of oceanic surface boundary-layer turbulence is investigated. Idealized horizontally homogeneous problems of surface-forced thermal convection and Langmuir turbulence are studied in detail. Model reduction is achieved with a Galerkin projection of the governing equations onto an subset of modes determined by proper orthogonal decomposition. For less than 0.2% of the modes retained, the reduced quasilinear model is able to reproduce vertical profiles of horizontal mean fields as well as certain energetically important second-order turbulent transport statistics and energies to within 30% error. For intermediate sizes of the basis truncation some statistics approach those found in the fully nonlinear simulations. Thus basis reduction can actually improve upon the accuracy of quasilinear dynamics. A test model with a small total number of modes demonstrates the non-monotonic convergence toward the correct statistics as the size of the basis is increased., 24 pages, 14 figures

The redistribution of matter in the deep-sea depends on water-flow currents and turbulent exchange, for which breaking internal waves are an important source. As internal waves and turbulence are essentially three-dimensional 3D, their dynamical development should ideally be studied in a volume of seawater. However, this is seldom done in the ocean where 1D-observations along a single vertical line are already difficult. We present the design, construction and successful deployment of a half-cubic-hectometer (480,000 m3) 3D-T mooring array holding 2925 high-resolution temperature sensors to study weakly density-stratified waters of the 2500-m deep Western Mediterranean. The stand-alone array samples temperature at a rate of 0.5 Hz, with precision, 44 pages, 16 figures

We relate about the custom-made modification of a Benthos deep-ocean acoustic release into a deep caller, a hydrophone for calling ocean acoustic releases that cannot be reached from a standard deck-unit. The self-contained deep caller can be lowered down to 12 km on any non-conducting winch cable. It may prove useful to retrieve sub-surface instrumentation like a seafloor-lander hidden behind large rocks or in a narrow canyon, or moorings in very deep topographic depressions. We used it to retrieve a 7 km long mooring from 10,910 m depth in the Challenger Deep, Mariana Trench., Comment: 7 pages, 0 figures

Vertically banded zonal jets are frequently observed in weakly or non-rotating stratified turbulence, with the quasi-biennial oscillation in the equatorial stratosphere and the ocean's equatorial deep jets being two examples. Explaining the formation of jets in stratified turbulence is a fundamental problem in geophysical fluid dynamics. Statistical state dynamics (SSD) provides powerful methods for analyzing turbulent systems exhibiting emergent organization, such as banded jets. In SSD, dynamical equations are written directly for the evolution of the turbulence statistics, enabling direct analysis of the statistical interactions between the incoherent component of the turbulence and the coherent large-scale structure component that underlie jet formation. A second-order closure of SSD, known as S3T, has previously been applied to show that meridionally banded jets emerge in barotropic beta-plane turbulence via a statistical instability referred to as the zonostrophic instability. Two-dimensional Boussinesq turbulence provides a simple model of non-rotating stratified turbulence analogous to the beta-plane model of planetary turbulence. Jets known as vertically sheared horizontal flows (VSHFs) often emerge in simulations of Boussinesq turbulence, but their dynamics is not yet clearly understood. In this work S3T analysis of the zonostrophic instability is extended to study VSHF emergence in two-dimensional Boussinesq turbulence using an analytical formulation of S3T amenable to perturbation stability analysis. VSHFs are shown to form via an instability that is analogous in stratified turbulence to the zonostrophic instability in beta-plane turbulence. This instability is shown to be strikingly similar to the zonostrophic instability, suggesting that jet emergence in both geostrophic and non-rotating stratified turbulence may be understood as instances of the same generic phenomenon., Re-submitted to Journal of the Atmospheric Sciences

Statistical postprocessing is routinely applied to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only processing the high-resolution NWP. We calibrate and combine 2-m air temperature predictions for a large set of Swiss weather stations at the hourly time-scale. The multi-model EMOS approach ('Mixed EMOS') is able to improve forecasts by 30\% with respect to direct model output from the high-resolution NWP. A detailed evaluation of Mixed EMOS reveals that it outperforms either single-model EMOS version by 8-12\%. Valley location profit particularly from the model combination. All forecast variants perform worst in winter (DJF), however calibration and model combination improves forecast quality substantially., 16 pages, 7 figures, submitted to Weather and Forecasting

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many National Meteorological Services (NMS), with for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS towards running ensemble Numerical Weather Prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations; the necessity to preserve space time correlation of high-dimensional corrected fields; the need to reduce the impact of model changes affecting the parameters of the corrections; the necessity for techniques to merge different types of forecasts and ensembles with different behaviors; and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues will also be discussed., Comment: This work has been submitted to the Bulletin of the American Meteorological Society. Copyright in this work may be transferred without further notice

Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring prespecified link functions. In a case study of 2-m temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark postprocessing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables, thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical postprocessing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the postprocessing of numerical weather forecasts in the coming decade.

Vortex-split sudden stratospheric warmings (S-SSWs) are investigated by using the Japanese 55-year Reanalysis (JRA-55), a spherical barotropic quasi-geostrophic (QG) model, and equilibrium statistical mechanics. The QG model reproduces well the evolution of the composite potential vorticity (PV) field obtained from JRA-55 by considering a time-dependent effective topography given by the composite height field of the 550 K potential temperature surface. The zonal-wavenumber-2 component of the effective topography is the most essential feature required to observe the vortex splitting. The statistical-mechanics theory predicts a large-scale steady state as the most probable outcome of turbulent stirring, and such a state can be computed without solving the QG dynamics. The theory is applied to a disk domain, which is modeled on the north polar cap in the stratosphere. The equilibrium state is obtained by computing the maximum of an entropy functional. In the range of parameters relevant to the winter stratosphere, this state is anticyclonic. By contrast, cyclonic states are quasi-stationary states corresponding to saddle points of the entropy functional. The theoretical calculations are compared with the results of the quasi-static experiment in which the wavenumber-2 topographic amplitude is increased linearly and slowly with time. The results suggest that S-SSWs can be qualitatively interpreted as the transition from the cyclonic quasi-stationary state toward the anticyclonic equilibrium state. The polar vortex splits during the transition toward the equilibrium state. Without any forcing such as radiative cooling, the anticyclonic equilibrium state would be realized sufficiently after an S-SSW., Comment: 23 pages, 11 figures

We assess the skill and reliability of forecasts of winter and summer temperature, wind speed and irradiance over China, using the GloSea5 seasonal forecast system. Skill in such forecasts is important for the future development of seasonal climate services for the energy sector, allowing better estimates of forthcoming demand and renewable electricity supply. We find that although overall the skill from the direct model output is patchy, some high-skill regions of interest to the energy sector can be identified. In particular, winter mean wind speed is skilfully forecast around the coast of the South China Sea, related to skilful forecasts of the El Ni\~no--Southern Oscillation. Such information could improve seasonal estimates of offshore wind power generation. Similarly, forecasts of winter irradiance have good skill in eastern central China, with possible use for solar power estimation. Much of China shows skill in summer temperatures, which derives from an upward trend. However, the region around Beijing retains this skill even when detrended. This temperature skill could be helpful in managing summer energy demand. While both the strengths and limitations of our results will need to be considered when developing seasonal climate services in the future, the outlook for such service development in China is promising., Comment: 14 pages, 9 figures; maps at reduced quality due to arXiv size limits. This is the original pre-print version as submitted to J. Appl. Met. Climatol., and prior to peer review. Please read the final, peer-reviewed article by following the DOI link below

The polar vortices play a crucial role in the formation of the ozone hole and can cause severe weather anomalies. Their boundaries, known as the vortex `edges', are typically identified via methods that are either frame-dependent or return non-material structures, and hence are unsuitable for assessing material transport barriers. Using two-dimensional velocity data on isentropic surfaces in the northern hemisphere, we show that elliptic Lagrangian Coherent Structures (LCSs) identify the correct outermost material surface dividing the coherent vortex core from the surrounding incoherent surf zone. Despite the purely kinematic construction of LCSs, we find a remarkable contrast in temperature and ozone concentration across the identified vortex boundary. We also show that potential vorticity-based methods, despite their simplicity, misidentify the correct extent of the vortex edge. Finally, exploiting the shrinkage of the vortex at various isentropic levels, we observe a trend in the magnitude of vertical motion inside the vortex which is consistent with previous results.

The correlation between increased Sahel rainfall and reduced Saharan surface pressure is well established in observations and global climate models, and has been used to imply that increased Sahel rainfall is caused by a stronger shallow meridional circulation (SMC) over the Sahara. This study uses two atmospheric reanalyses to examine interannual variability of Sahel rainfall and the Saharan SMC, which consists of northward near-surface flow across the Sahel into the Sahara and southward flow near 700 hPa out of the Sahara. During wet Sahel years, the Saharan SMC shifts poleward, producing a drop in low-level geopotential and surface pressure over the Sahara. Statistically removing the effect of the poleward shift from the low-level geopotential eliminates significant correlations between this geopotential and Sahel precipitation. As the Saharan SMC shifts poleward, its mid-tropospheric divergent outflow decreases, indicating a weakening of its overturning mass flux. The poleward shift and weakening of the Saharan SMC during wet Sahel years is reproduced in an idealized model of West Africa; a wide range of imposed sea surface temperature and land surface albedo perturbations in this model produce a much larger range of SMC variations that nevertheless have similar quantitative associations with Sahel rainfall as in the reanalyses. These results disprove the idea that enhanced Sahel rainfall is caused by strengthening of the Saharan SMC. Instead, these results are consistent with the hypothesis that the a stronger SMC inhibits Sahel rainfall, perhaps by advecting mid-tropospheric warm and dry air into the precipitation maximum., Submitted to Journal of Climate

Iceberg drift and decay and the associated freshwater release are increasingly seen as important processes in Earth's climate system, yet a detailed understanding of their dynamics has remained elusive. Here, an idealized model of iceberg drift is presented. The model is designed to include the most salient physical processes that determine iceberg motion while remaining sufficiently simple to facilitate physical insight into iceberg drift dynamics. We derive an analytical solution of the model, which helps build understanding and also enables the rapid computation of large numbers of iceberg trajectories. The long-standing empirical rule of thumb that icebergs drift at 2% of the wind velocity, relative to the ocean current, is derived here from physical first principles, and it is shown that this relation only holds in the limit of strong winds or small icebergs, which approximately applies for typical icebergs in the Arctic. It is demonstrated that the opposite limit of weak winds or large icebergs approximately applies for typical Antarctic tabular icebergs, and that in this case the icebergs simply move with the ocean surface current. It is furthermore found that when winds are strong, wind drag drives icebergs in the direction the wind blows, whereas weak winds drive icebergs at a 90 degree angle to the wind direction., Comment: 10 pages, 7 figures

A theoretical framework is developed for understanding the transient growth and propagation characteristics of thermodynamically coupled, meridional mode-like structures in the tropics. The model consists of a Gill-Matsuno type steady atmosphere under the longwave approximation coupled via a wind-evaporation-sea surface temperature (WES) feedback to a "slab" ocean model. When projected onto basis functions for the atmosphere the system simplifies to a non-normal set of equations that describes the evolution of individual sea surface temperature (SST) modes, with clean separation between symmetric and anti-symmetric modes. The following major findings result from analysis of the system: (i) a transient growth process exists whereby specific SST modes propagate toward lower order modes at the expense of the higher-order modes; (ii) the same dynamical mechanisms govern the evolution of symmetric and anti-symmetric SST modes except for the lowest-order wave number, where for symmetric structures the atmospheric Kelvin wave plays a critically different role in enhancing decay; and (iii) the WES feedback is positive for all modes (with a maximum for the most equatorially confined antisymmetric structure) except for the most equatorially confined symmetric mode where the Kelvin wave generates a negative WES feedback. Taken together, these findings explain why equatorially anti-symmetric "dipole"-like structures may dominate thermodynamically coupled ocean / atmosphere variability in the tropics. The role of non-normality as well as the role of realistic mean states in meridional mode variability are discussed., WES feedback, Ocean-Atmosphere coupling, Tropical Dynamics, Tropical-Extratropical Interactions, Non-normal growth

The last decade has seen the success of stochastic parameterizations in short-term, medium-range and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to better represent model inadequacy and improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing longstanding climate biases and relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups which show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface and cryosphere of comprehensive weather and climate models (a) gives rise to more reliable probabilistic forecasts of weather and climate and (b) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics and turbulence is reviewed, its relevance for the climate problem demonstrated, and future research directions outlined., Comment: 26 pages, 15 figures. Final published version

A simple analytical model is developed for the current induced by the wind and modified by surface wind-waves in the oceanic surface layer, based on a first-order turbulence closure and including the effect of a vortex force representing the Stokes drift of the waves. The shear stress is partitioned between a component due to shear in the current, which is reduced at low turbulent Langmuir number ($La_t$), and a wave-induced component, which decays over a depth proportional to the dominant wavelength. The model reproduces the apparent reduction of the friction velocity and enhancement of the roughness length estimated from current profiles, detected in a number of studies. These effects are predicted to intensify as $La_t$ decreases, and are entirely attributed to non-breaking surface waves. The current profile becomes flatter for low $La_t$ owing to a smaller fraction of the total shear stress being supported by the current shear. Comparisons of the model with the comprehensive dataset provided by the laboratory experiments of Cheung and Street show encouraging agreement, with the current speed decreasing as the wind speed increases (corresponding to decreasing $La_t$), if the model is adjusted to reflect the effects of a full wave spectrum on the intensity and depth of penetration of the wave-induced stress. A version of the model where the shear stress decreases to zero over a depth consistent with the measurements accurately predicts the surface current speed. These results contribute towards developing physically-based momentum flux parameterizations for the wave-affected boundary layer in ocean circulation models., Comment: 13 pages, 8 figures, submitted to Journal of Physical Oceanography

The feature-based spatial verification method named for its three score components: structure, amplitude, and location (SAL) is applied to cloud data, that is, two-dimensional spatial fields of total cloud cover and spectral radiance. Model output is obtained from the German-focused Consortium for Small-Scale Modeling (COSMO-DE) forward operator Synthetic Satellite Simulator (SynSat) and compared with SEVIRI satellite data. The aim of this study is twofold: first, to assess the applicability of SAL to this kind of data and, second, to analyze the role of external object identification algorithms (OIA) and the effects of observational uncertainties on the resulting scores. A comparison of three different OIA shows that the threshold level, which is a fundamental part of all studied algorithms, induces high sensitivity and unstable behavior of object-dependent SAL scores (i.e., even very small changes in parameter values lead to large changes in the resulting scores). An in-depth statistical analysis reveals significant effects on distributional quantities commonly used in the interpretation of SAL, for example, median and interquartile distance. Two sensitivity indicators that are based on the univariate cumulative distribution functions are derived. They make it possible to assess the sensitivity of the SAL scores to threshold-level changes without computationally expensive iterative calculations of SAL for various thresholds. The mathematical structure of these indicators connects the sensitivity of the SAL scores to parameter changes with the effect of observational uncertainties. Last, the discriminating power of SAL is studied. It is shown that—for large-scale cloud data—changes in the parameters may have larger effects on the object-dependent SAL scores (i.e., the S and L2 scores) than does a complete loss of temporal collocation.

In a recent paper, Romps (JAS, vol.72, p.3639-3646, 2015, hereafter R15) argues that the moist-air static energy (MSE) is only approximately conserved for an adiabatically lifted parcel, and that the quantity "MSE - CAPE" could be used as a true conserved variable, where CAPE is the convective available energy. It is shown in this comment that the quantity denoted by CAPE in R15 is the opposite of the convective available energy. It is explained that the vertical adiabatic ascent considered in R15 is not realistic, since it generates condensed water of the order of 10 to 20 g/kg at height above 6 km. Moreover, the thermodynamic equations are written in R15 by making several assumptions, not all of which are explicitly mentioned. This comment aims to clarify the hypotheses made in R15. It will show that these assumptions call into question the validity of the moist-air internal energy, enthalpy and entropy functions in R15. It also demonstrates that it is possible to obtain more precise and general formulations for moist-air energy, enthalpy and entropy functions, in particular by using the third law of thermodynamics. The large differences between the thermodynamics formulas derived in R15 and those depending on the third law are illustrated by studying a realistic pseudo-adiabatic vertical profile. The same notations as in R15 will be used as far as possible in this comment., Comment: 13 pages, 5 figure, 1 Table. Revised version 4 December 2015

Three-dimensional (3D) finite-time Lyapunov exponents (FTLEs) are computed from numerical simulations of a freely evolving mixed layer (ML) front in a zonal channel undergoing baroclinic instability. The 3D FTLEs show a complex structure, with features that are less defined than the two-dimensional (2D) FTLEs, suggesting that stirring is not confined to the edges of vortices and along filaments and posing significant consequences on mixing. The magnitude of the FTLEs is observed to be strongly determined by the vertical shear. A scaling law relating the local FTLEs and the nonlocal density contrast used to initialize the ML front is derived assuming thermal wind balance. The scaling law only converges to the values found from the simulations within the pycnocline, while it displays differences within the ML, where the instabilities show a large ageostrophic component. The probability distribution functions of 2D and 3D FTLEs are found to be non-Gaussian at all depths. In the ML, the FTLEs wavenumber spectra display −1 slopes, while in the pycnocline, the FTLEs wavenumber spectra display −2 slopes, corresponding to frontal dynamics. Close to the surface, the geodesic Lagrangian coherent structures (LCSs) reveal a complex stirring structure, with elliptic structures detaching from the frontal region. In the pycnocline, LCSs are able to detect filamentary structures that are not captured by the Eulerian fields.

Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, the performance of nine operational global ensemble prediction systems (EPSs) is analyzed relative to climatology-based forecasts for 1–5-day accumulated precipitation based on the monsoon seasons during 2007–14 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, state-of-the-art statistical postprocessing methods were applied in the form of Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), and results were verified against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated and unreliable, and often underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. The differences between raw ensemble and climatological forecasts are large and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles but, somewhat disappointingly, typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007–14, but overall they have little added value compared to climatology. The suspicion is that parameterization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

A low Mach number model for moist atmospheric flows is introduced that accurately incorporates reversible moist processes in flows whose features of interest occur on advective rather than acoustic time scales. Total water is used as a prognostic variable, so that water vapor and liquid water are diagnostically recovered as needed from an exact Clausius–Clapeyron formula for moist thermodynamics. Low Mach number models can be computationally more efficient than a fully compressible model, but the low Mach number formulation introduces additional mathematical and computational complexity because of the divergence constraint imposed on the velocity field. Here, latent heat release is accounted for in the source term of the constraint by estimating the rate of phase change based on the time variation of saturated water vapor subject to the thermodynamic equilibrium constraint. The authors numerically assess the validity of the low Mach number approximation for moist atmospheric flows by contrasting the low Mach number solution to reference solutions computed with a fully compressible formulation for a variety of test problems.

A linearized energy-balance model for global temperature is formulated, featuring a scale-invariant long-range memory (LRM) response and stochastic forcing representing the influence on the ocean heat reservoir from atmospheric weather systems. The model is parameterized by an effective response strength, the stochastic forcing strength, and the memory exponent. The instrumental global surface temperature record and the deterministic component of the forcing are used to estimate these parameters by means of the maximum-likelihood method. The residual obtained by subtracting the deterministic solution from the observed record is analyzed as a noise process and shown to be consistent with a long-memory time series model and inconsistent with a short-memory model. By decomposing the forcing record in contributions from solar, volcanic, and anthropogenic activity one can estimate the contribution of each to twentieth-century global warming. The LRM model is applied with a reconstruction of the forcing for the last millennium to predict the large-scale features of Northern Hemisphere temperature reconstructions, and the analysis of the residual also clearly favors the LRM model on millennium time scale. The decomposition of the forcing shows that volcanic aerosols give a considerably greater contribution to the cooling during the Little Ice Age than the reduction in solar irradiance associated with the Maunder Minimum in solar activity. The LRM model implies a transient climate response in agreement with IPCC projections, but the stronger response on longer time scales suggests replacing the notion of equilibrium climate sensitivity by a time scale–dependent sensitivity.

This paper presents a simple analytical framework for the dynamic response of cirrus to a local radiative flux convergence, expressible in terms of three independent modes of cloud evolution. Horizontally narrow and tenuous clouds within a stable environment adjust to radiative heating by ascending gradually across isentropes while spreading sufficiently fast that isentropic surfaces stay nearly flat. Alternatively, optically dense clouds experience very concentrated heating, and if they are also very broad, they develop a convecting mixed layer. Along-isentropic spreading still occurs, but in the form of turbulent density currents rather than laminar flows. A third adjustment mode relates to evaporation, which erodes cloudy air as it lofts, regardless of its optical density. The dominant mode is determined from two dimensionless numbers, whose predictive power is shown in comparisons with high-resolution numerical cloud simulations. The power and simplicity of the approach hints that fast, subgrid-scale radiative–dynamic atmospheric interactions might be efficiently parameterized within slower, coarse-grid climate models.

Global atmospheric reanalyses have become a common tool for both validation of climate models and diagnostic studies, such as assessing climate variability and long-term trends. Presently, the Twentieth Century Reanalysis (20CR), which assimilates only surface pressure reports, sea ice, and sea surface temperature distributions, represents the longest global reanalysis dataset available covering the period from 1871 to the present. Currently the 20CR dataset is extensively used for the assessment of climate variability and trends. Here, the authors compare the variability and long-term trends in northeast Atlantic storminess derived from 20CR and from observations. A well-established storm index derived from pressure observations over a relatively densely monitored marine area is used. It is found that both variability and long-term trends derived from 20CR and from observations are inconsistent. In particular, both time series show opposing trends during the first half of the twentieth century: both storm indices share a similar behavior only for the more recent periods. While the variability and long-term trend derived from the observations are supported by a number of independent data and analyses, the behavior shown by 20CR is quite different, indicating substantial inhomogeneities in the reanalysis, most likely caused by the increasing number of observations assimilated into 20CR over time. The latter makes 20CR likely unsuitable for the identification of trends in storminess in the earlier part of the record, at least over the northeast Atlantic. The results imply and reconfirm previous findings that care is needed in general when global reanalyses are used to assess long-term changes.

The spectral energy density of the internal waves in the open ocean is considered. The Garrett and Munk spectrum and the resonant kinetic equation are used as the main tools of the study. Evaluations of a resonant kinetic equation that suggest the slow time evolution of the Garrett and Munk spectrum is not in fact slow are reported. Instead, nonlinear transfers lead to evolution time scales that are smaller than one wave period at high vertical wavenumber. Such values of the transfer rates are inconsistent with the viewpoint expressed in papers by C. H. McComas and P. Müller, and by P. Müller et al., which regards the Garrett and Munk spectrum as an approximate stationary state of the resonant kinetic equation. It also puts the self-consistency of a resonant kinetic equation at a serious risk. The possible reasons for and resolutions of this paradox are explored. Inclusion of near-resonant interactions decreases the rate at which the spectrum evolves. Consequently, this inclusion shows a tendency of improving of self-consistency of the kinetic equation approach.

Equilibrium statistical mechanics of two-dimensional flows provides an explanation and a prediction for the self-organization of large scale coherent structures. This theory is applied in this paper to the description of oceanic rings and jets, in the framework of a 1.5 layer quasi-geostrophic model. The theory predicts the spontaneous formation of regions where the potential vorticity is homogenized, with strong and localized jets at their interface. Mesoscale rings are shown to be close to a statistical equilibrium: the theory accounts for their shape, their drift, and their ubiquity in the ocean, independently of the underlying generation mechanism. At basin scale, inertial states presenting mid basin eastward jets (and then different from the classical Fofonoff solution) are described as marginally unstable states. These states are shown to be marginally unstable for the equilibrium statistical theory. In that case, considering a purely inertial limit is a first step toward more comprehensive out of equilibrium studies that would take into account other essential aspects, such as wind forcing., 15 pages, submitted to Journal of Physical Oceanography

Equations for the wave-averaged three-dimensional momentum equations have been published in this journal. It appears that these equations are not consistent with the known depth-integrated momentum balance, especially over a sloping bottom. These equations should thus be considered with caution as they can produce erroneous flows, in particular outside of the surf zone. It is suggested that the inconsistency in the equations may arise from the different averaging operators applied to the different terms of the momentum equation. It is concluded that other forms of the momentum equations, expressed in terms of the quasi-Eulerian velocity, are better suited for three dimensional modelling of wave-current interactions., Paper submitted to J. Phys. Oceanogr

The long-term success of climate models that operate on multi-resolution grids will depend on access to subgrid parameterizations that act appropriately across a wide range of spatial and temporal scales. As the first step in a series of efforts to obtain such scale-aware subgrid parametrizations, in this article we focus on the anticipated potential vorticity method (APVM) on quasi-uniform grids with varying resolutions. By a scale analysis technique and the phenomenological theories for two-dimensional turbulent flows, we derive a new formulation of the APVM, which depends on a single parameter that is, in principle, invariant with respect to the time step size, the grid resolution and the flow itself. Results of numerical experiments with this new formulation demonstrate that the optimal parameter of the APVM is invariant with respect to the time step size, insensitive to the flows, and is only weakly dependent on the grid resolution., Comment: Manuscript submitted to Monthly Weather Review. Withdrawn due to publisher's posting policy

An experimental Environmental Watch System, the so-called Observatorio Andino-OA (Observatorio Andino), has been implemented in Venezuela, Colombia, Ecuador, Peru, Bolivia, and Chile over the past two years. The OA is a collaborative and regional network that aims to monitor several environmental variables and develop accurate forecasts based on different scientific tools. Its overall goal is to improve risk assessments, set up Early Warning Systems, support decision-making processes, and provide easily- and intuitively-understandable spatial maps to end-users. The initiative works under the scientific and logistic coordination of the Centro de Modelado Cient\'ifico (CMC) at Zulia University, Venezuela, and the Centro Internacional para la Investigaci\'on del Fen\'omeno 'El Ni\~no' (CIIFEN), and is operated at a local level by the National Weather Services (NWSs) of the aforementioned six Andean nations. The OA provides several freely-available model outputs including meteorological and hydrological forecasts, droughts, fire and flood indices, ecosystems dynamics, climate and health applications, and five-day high-resolution oceanographic predictions for the Eastern Pacific. This article briefly describes the current products, methodologies and dynamical and statistical modeling outputs provided by the OA. Also, a discussion on how these sets of tools have been put together as a coordinated, scientific watch and forecast system for each country and for the entire region is presented. Our experiences over the past two years suggest that this initiative would significantly improve the current decision-making processes in Andean countries., Comment: 20 pages, 5 figures, sent to BAMS