Nearly-Lagrangian observations from two EM-APEX velocity and density profiling floats on concentric trajectories determine profiles of ocean eddy vorticity and velocity, and the intrinsic frequency, energy, vertical wavenumber, vertical phase velocity, and vertical group velocity of a near-inertial-wave (NIW). For the first time, NIW 𝐶 and 𝐶 are computed from firs principles (by direct computation of 𝜔⁄𝑚 and 𝜕𝜔⁄𝜕𝑚 vs. depth without a dispersion relation). This novel experiment, moving with turbulent zone produced by a downgoing NIW packet observed stalling at 135-m depth. KE flux convergence initiates KE dissipation at 115 m. Below 135 m, shear production is hypothesized to support dissipation. These observations compare well with a theory of NIW interaction with the eddy, not in solid-body rotation. Theory and observations show that the observed turbulent zone arises from wave stalling and instability in a critical or caustic layer as the vertical group velocity of the wave goes toward zero.

The Earth surface Mineral dust source InvesTigation (EMIT) is a visible-to- shortwave infrared imaging spectrometer currently aboard the International Space Station. Derivations of fractional cover from spectral unmixing algorithms have provided insights into various ecosystem functions. In the case of EMIT, they will be used by multiple global Earth systems models to constrain the sign of dust-related radiative forcing. This study aims to evaluate the efficacy of different approaches for estimating fractional cover and quantifying the corresponding uncertainty, and serves as a model to encapsulate the true error budget for EMIT. We simulated surface reflectance from a spectral library compiled from various drylands to generate millions of candidate spectra made up of different random fractions of nonphotosynthetic vegetation (NPV), green vegetation (GV), and soil. Simulated spectra were used as-is but we also tested the impact of atmospheric conditions/surface reflectance retrieval by using them to calculate top-of-atmosphere radiance then using the current EMIT surface reflectance retrieval algorithm to estimate apparent surface reflectance. We tested approaches to unmixing these simulated spectra using multiple strategies for dealing with spectrum brightness, within-class spectral variability, and library selection. We also incorporated a Monte Carlo approach to stabilize fractional cover retrievals and quantify uncertainty. The best spectral unmixing approaches produced mean absolute error <0.10 for NPV and soil and <0.06 for GV with uncertainties ≤ ± 0.02 for all classes. We named this innovative approach EndMember Combination Monte Carlo, E(MC) ², unmixing and found that our fractional cover retrievals are insensitive to atmospheric residuals in the surface reflectance data.

Seven profiling floats equipped with oxygen sensors deployed in the Sea of Okhotsk provide time series data for 33 cases of spring phytoplankton bloom, including nine cases in which sea ice existed just before the bloom (prior-ice case). As an index of biological productivity, we calculated Net Community Production (NCP) based on the increasing oxygen rate using the Redfield ratio. The total NCP in the euphotic layer averaged for prior-ice cases is 31.3 mmolCm-2day-1), ~4 times higher than that of non-ice cases. In addition to intensification of surface stratification, other factors of sea-ice melt likely enhance the bloom. The influence of sea-ice melt is particularly large in the southwestern region, where the iron availability likely limits phytoplankton growth. A suggested scenario is that when the sea ice containing sediment/iron is transported from the northern shelves, a prominent bloom is induced via the iron supply by sea-ice melt.

A document by Bruce E. Wilson. Click on the document to view its contents.

Public earthquake early warning systems have the potential to reduce individual risk by warning people of an incoming tremor, but their development is hampered by costly infrastructure. Furthermore, users’ understanding of such a service and their reactions to warnings remains poorly studied. The Earthquake Network app turns users’ smartphones into motion detectors and provides rapid information about felt earthquakes in multiple countries. It offers an alternative without the need of dedicated infrastructure in the many regions unlikely to be covered by conventional early warning systems in the foreseeable future. We show here that it already provides an early warning service, including for damaging shaking levels and although warnings are appreciated and understood by users, only a fraction follow the “drop, cover and hold” advice.

Accurate precipitation forecasting can better reflect climate change trends, provide timely and effective environmental information for management decisions, and prevent flood and drought disasters. In this paper, we propose a short-term regional precipitation prediction model based on wind-improved spatiotemporal convolutional network. Among them, the improved Graph Convolution Network (GCN) integrates the effects of wind direction and geographic location at past moments to capture the spatial dependence, whilst the Gated Recurrent Unit (GRU) captures the temporal dependence by learning the dynamic changes of data. The spatio-temporal memory flow module and attention module are added to capture spatial deformation and temporal variation more accurately, thereby better matching the physical properties of precipitation. Experimental results on real data sets show that the proposed model can handle complex spatial dependence and temporal dynamic changes, better learn the temporal and spatial characteristics of precipitation data, and achieve better prediction results.

Conventional long-range weather prediction is an initial value problem that uses the current state of the atmosphere to produce ensemble forecasts. Purely stochastic predictions for long-memory processes are “past value” problems that use historical data to provide conditional forecasts. Teleconnection patterns, defined from cross-correlations, are important for identifying possible dynamical interactions, but they do not necessarily imply causation. Using the precise notion of Granger causality, we show that for long-range stochastic temperature forecasts, the cross-correlations are only relevant at the level of the innovations - not temperatures. This justifies the Stochastic Seasonal to Interannual Prediction System (StocSIPS) that is based on a (long memory) fractional Gaussian noise model. Extended here to the multivariate case, (m-StocSIPS) produces realistic space-time temperature simulations. Although it has no Granger causality, emergent properties include realistic teleconnection networks and El Niño events and indices.

The November 2017 magnitude (MW) 5.5 Pohang, Korea, earthquake, induced by an Engineered 24 Geothermal Systems (EGS) project, caused one fatality and ~US$300M of economic 25 consequences. It has been proposed that a significant probability of such losses was predictable 26 beforehand, from the small earthquakes caused by well-stimulation, so the project should have 27 been suspended, implying that its developer was remiss for not doing so. This argument depends 28 on the low (~0.61) estimated b-value of this earthquake population. However, it is shown that 29 many of the magnitude determinations are inaccurate (underestimated) and the true b-value is 30 higher (1.12 for one subset). The probability of any earthquake as large as MW=5.5, predicted 31 beforehand by extrapolation, was thus much lower than has been claimed. This analysis 32 highlights the necessity of taking care over accuracy when reporting datasets like this, especially 33 in situations where such analyses might influence criminal trials of EGS developers.

Modeling sea ice microwave emissivities at large scales presents challenges, due to complex interactions between the microwave signal and the sea ice environment. For the preparation of the Copernicus Imaging Microwave Radiometer mission (CIMR) that focusses on the pole monitoring, a pragmatic parameterization of the sea ice emissivity over the Arctic is proposed, providing consistent emissivity parameterizations between 1.4 and 36~GHz, for V and H polarizations. Satellite-derived microwave emissivities are calculated from AMSR2, SMAP, and SMOS observations, subtracting the atmospheric contributions and the surface temperature modulation using ERA5 meteorological reanalysis. The resulting Arctic sea ice emissivities are analyzed, alongside sea ice geophysical parameters Mercator model outputs and ERA5, to identify the pertinent variables for the emissivity parameterization. Sea ice thickness emerges as a crucial factor, particularly at 18 and 36~GHz. A large training database of coincident satellite-derived emissivities and geophysical parameters is set up, to develop a Neural Network parameterization of the emissivities based on the geophysical parameters. This pragmatic methodology establishes a direct link between calculated emissivities and physical sea ice properties, eliminating the need for a priori assumptions. Promising emissivity results are obtained, with RMSE below 0.02 for most channels, reaching 0.04 at 36~GHz. Part of the error is expected to come from uncertainties in the input geophysical parameters, especially the sea ice thickness and the snow depth above sea ice. The emissivity frequency dependence is checked, and the emissivity angular variation of the 1.4~GHz is derived from the analysis of the SMOS-derived emissivities.

Shale contains a certain amount of natural fractures, which affects the mechanical properties of shale. In spite that significant progress has been made, how natural fractures interfere the failure behaviour of layered shale remains unclear and need to be examined. In this paper, a bonded-particle model in particle flow code (PFC) is established to simulate the failure process of layered shale under Brazilian tests, under the complex relationship of layer plane and natural fracture. First, a shale model without natural fractures is verified against the experimental results. Simultaneously, the impact of the layer plane angle (marked as α) on the failure process in intact shale is exposed. Then, a natural fracture is embedded in the shale model, where the outcomes indicate that α and the angle (marked as β) of embedded fracture prominently interfere the failure strength anisotropy and fracture pattern. Finally, sensitivity evaluations suggest that variable tensile/cohesion strength has a changeable influence on failure mechanism of shale, even for same α or/and β. To serve this work, the stimulated fractures are innovatively and concisely categorized into two patterns based on whether they relate to natural fracture or not. Meanwhile, four damage modes (matrix shear, matrix tension, layer shear and layer tension) and the number of micro cracks during the loading process are recognized quantitatively to study the mechanism of shale failure behaviour. Considering the failure mechanism determines the outcome of hydraulic fracturing in shale, this work is supposed to provide a significant implication in theory for the engineering operation.

Thirteen years of precipitation d2H, d18O, and 3H data for three western United States continental interior weather stations, supplemented with 60 years of precipitation data, have been analyzed. The stations are located 1,000 to 2,000 km from four ocean moisture sources. Precipitation was evaluated relative to storm track trajectory, the El Niño-Southern Oscillation Oceanic Niño Index (INO), orography, precipitation amount, air temperature, month, and season. The INO was not fond to correlate with precipitation flux or isotopic composition. Tritium deposition was evaluated relative to the ‘spring leak’, thunderstorms, surface evaporation, storm tracks, and seasons. Local meteoric water lines and the Global Meteoric Water Line were compared. Winter precipitation is isotopically depleted and summer precipitation is isotopically enriched. Factors affecting the stable isotopes include winter cold cloud temperature, summer rain droplet partial evaporation, gradual rain out, and multiple episodes of soil moisture re-evaporation and subsequent re-precipitation.

Cloud optical property retrievals from passive satellite imagers tend to be most accurate during the daytime due to the availability of visible and near-infrared solar reflectances. Infrared (IR) channels have a relative lack of spectral sensitivity to optically thick clouds and are heavily influenced by cloud-top temperature making accurate retrievals of cloud optical depth, cloud effective radius, and cloud water path more difficult at night. In this work, we examine whether the use of spatial context—information about the local structure and organization of cloud features—can help overcome these limitations of IR channels and provide more accurate estimates of nighttime cloud optical properties. We trained several neural networks to emulate the Advanced Baseline Imager (ABI) NOAA Daytime Cloud Optical and Microphysical Properties (DCOMP) algorithm using only IR channels. We then compared the neural networks to the NOAA operational daytime and nighttime products, and the Nighttime Lunar Cloud Optical and Microphysical Properties (NLCOMP) algorithm, which utilizes the low-light visible band on VIIRS in collocated imagery. These comparisons show that the use of spatial context can significantly improve estimates of nighttime cloud optical properties. The primary model we trained, U-NetCOMP, can reasonably match DCOMP during the day and significantly reduces artifacts associated with day/night terminator. We also find that U-NetCOMP estimates align more closely with NLCOMP at night compared to the nighttime NOAA operational products for ABI.

Phenology and its interannual variability are altered through anthropogenic climate change. Feedbacks of plant phenology to the regional climate system affect fluxes of energy, water, CO2, biogenic volatile organic compounds as well as canopy conductance, surface roughness length, and are influencing the seasonality of albedo. We performed simulations with the regional climate model COSMO-CLM (CCLM) with 3km horizontal resolution over Germany covering the period 1999 to 2015 to study the sensitivity of grass phenology to different environmental conditions by implementing a new phenology module. We provide new evidence that the standard annually-recurring phenology of CCLM is improved by the new calculation of leaf area index (LAI) dependent upon surface temperature, day length, and water availability. Results with the new phenology implemented in the model showed a significantly higher correlation with observations than simulations with the standard phenology. The interannual variability of LAI, the representation of years with extremely warm spring or extremely dry summer, and the start of the growing season also improved with the new phenology module. The number of hot days with maximum temperature exceeding the 90th percentile and heavy precipitation events (> 20mm) with the new phenology are in very good agreement with the observations. We also show that lower LAI values in summer lead to a decrease of latent heat flux in the model due to less evapotranspiration. The CCLM simulation with improved representation of the phenology should be used in future applications with an extension on more plant functional types.

Twenty six years of MF radar wind measurements made from 1994 to 2019 at Davis Station (68.6◦S, 77.9◦E) are used to study the mean re- sponse of the mesosphere-lower thermosphere to stratospheric warmings in the southern hemisphere. Warming events were detected using Modern- Era Retrospective Analysis for Research and Applications (MERRA)-2 data with a systematic search for reductions in the zonal-mean circulation at 60◦S and corresponding increases in polar temperatures. Some 38 events were identified, including the major warmings of 2002 and 2019, with an average of 1 to 2 warmings per year. At the 10 hPa level, the polar cap temperature increases ranged from 5 to 30 K, with a mean value of 11 K, while the zonal wind speed reductions varied between -7 to -43 ms−1, with −1 a mean value of -15 ms . Peak values occurred near 40 km. Warmings occurred mainly between August and October, with a small peak in oc- currence in April/May. The MF radar data showed an average reduction in the mesospheric eastward winds of about 5-7 ms−1 at heights near 75 km that occurred some 3-4 days prior to the changes in the stratosphere. Warming events were driven by episodic intensifications in planetary waves amplitudes, with quasi-stationary PW 1 being especially important. Plane- tary wave Eliassen-Palm flux divergences show a systematic behavior with time and height that is consistent with a poleward residual circulation and downwelling over the pole prior to the warming events and an equatorward flow and upwelling after the peak of the events.

Particle-resolved direct numerical simulations (PR-DNS) are crucial for unraveling the intricate interplay of aerosol-cloud-turbulence processes. However, such models are challenged by the huge computational cost due to the extremely high resolution. Our prior work showcased that leveraging machine learning emulators could slash computational expenses by two orders of magnitude while maintaining remarkable precision for dynamic fields, and exhibited generalizability across diverse initial conditions and at super-resolution scales without retraining the emulators. Building upon this foundation, this work extends the emulator’s application to thermodynamic in the two spatial dimensions and droplet fields in the three spatial dimensions. Furthermore, to enhance the robust generalizability of the emulator for different initial values and super resolution, we introduce a novel multi-initial learning approach for the neural operator method. For the droplet fields, we introduce a novel loss function tailored to assess distribution differences using the Mallows distance, focusing particularly on droplet size distributions. Our findings indicate that the machine learning emulators hold promising potential to effectively mimic numerical PR-DNS simulations, thereby significantly advancing our understanding of the complex interactions within aerosol-cloud-turbulence processes.

Substorm onset is believed to be a fundamental process for magnetic and plasma energy transport in both magnetosphere and ionosphere.Ballooning instability is one of the key trigger mechanism to examine the substorm onset scenario in the near-Earth plasma sheet. For this subject,the kinetic ballooning instability (KBI) in the near-Earth magnetotail is analyzed using the general type of two-dimensional (2D) kinetic sheet equilibria, the so-called generalized Harris sheet equilibria. The kinetic ballooning mode is unstable in the intermediate range of perpendicular wave number (ky) and the equatorial beta (eq). The growth rate of the ballooning mode reduces signicantly with the increase in electron and ion temperatures ratio (Te=Ti) and wave number ky. The kinetic ballooning mode is found to be most unstable in the thin current sheet region at the equatorial location xe ~(9-10)RE, where the ballooning drive term (βeq/LpRc) is dominant on stiffening effect due to the minimum in the normal magnetic field Bz there.Because of the stabilizing effect through the field line stiffening factor and the strong field line stabilization, the ballooning mode is stable close to the Earth and becomes marginally unstable away from the Earth. This suggests the local current sheet thinning may be also an effective trigger mechanism for the onset of ballooning instability in the near-Earth magnetotail.

Diapycnal mixing in the ocean interior has diverse numerical representations in extant global ocean models. These representations affect the simulated transport and storage of oceanic tracers in ways that remain little studied. Here we present the impacts of three different tidal mixing schemes in thousand-year-long simulations with the NEMO global ocean model at one-degree resolution. The first scheme includes local bottom-intensified mixing at internal tide generation sites and a constant background diffusivity. The second explicitly includes both local and remote tidal mixing, with no background diffusivity. The third scheme is identical to the second but has the added contribution of bottom-trapped (subinertial) internal tides, known to be important in polar regions. The three simulations show broadly similar circulation and stratification but important regional differences. Explicit representation of remote tidal mixing strengthens the Atlantic Meridional Overturning Circulation by up to 1.5 x 106 m3 s-1. Inclusion of bottom-trapped internal tides reduces heat reaching Antarctica by eroding Circumpolar Deep Water at southern high latitudes, and reduces the mean age of the global deep (> 2 km) ocean by 10%. The results call for more observational constraints on polar ocean mixing, and point to multi-faceted climatic repercussions of tidal mixing representations.

The Arctic Ocean is a key component of Earth’s climate system, and an understanding of ocean dynamics in this region is central for predicting how the Arctic is responding to a changing climate. In this study, we examine the ocean circulation in a high-resolution numerical model of the Arctic Ocean and Nordic Seas. Based on what is observed in this simulation, we re-examine an existing idealized linear model predicting the time-variable large-scale circulation in ocean basins, and test it against the highly non-linear numerical model. The idealized model is an integral relation derived from the linear momentum equations and assumes that the circulation around a closed depth contour is driven by surface stresses and regulated by bottom friction. We show that the idealized model estimates agree very well with the numerical simulations. This indicates that much of the variability of the large-scale circulation can be explained by linear processes. In particular, a correct description of the net surface stress over partially ice-covered areas improves the skill significantly in the Arctic Ocean compared to a previous study. However, in contrast to that previous study, we find that the linear model might be lacking a cyclonic tendency.