Building on the results from the observing system simulation experiments (OSSEs) in Part I, this study investigates the impact of assimilating Geostationary Operational Environmental Satellites – 16 (GOES-16) derived Atmospheric Motion Vector (AMV) data on the convective scale numerical weather prediction (NWP) by using the National Severe Storms Laboratory (NSSL) three-dimensional variational (3DVAR)

Tropical deep convection can aggregate into large clusters, which can have impacts on the local humidity and precipitation. Sea surface temperature (SST) gradients have been shown to organise convection, yet there has been little work done to investigate the impact of diabatic heating perturbations in the atmosphere on the aggregation of convection. Here we investigate how anomalous diabatic heating

This study further evaluates the modeling approach of Jia et al. (2019), https://doi.org/10.1029/2019gl081960 (JEA19) to investigate the potential effects of mesoscale sea surface temperature (SST) variability on the atmospheric circulation. The approach employs a global atmospheric circulation model coupled to a slab ocean model to produce two ensembles of simulations: one in which the SST exhibits

Reliable projections of sea-level rise depend on accurate representations of how fast-flowing glaciers slip along their beds. The mechanics of slip are often parameterized as a constitutive relation (or ‘sliding law’) whose proper form remains uncertain. Here, we present a novel deep learning-based framework for learning the time evolution of drag at glacier beds from time-dependent ice velocity and

A ∼ 50 km resolution atmospheric general circulation model (GCM) is used to investigate the impact of radiative interactions on spatial organization of convection, the model’s mean state, and extreme precipitation events in the presence of realistic boundary conditions. Mechanism-denial experiments are performed in which synoptic-scale feedbacks between radiation and dynamics are suppressed by overwriting

This study investigates the direct radiative-convective processes that drive and maintain aggregation within convection-permitting elongated channel (and smaller square) simulations of the UK Met Office Unified Model. Our simulations are configured using three fixed sea surface temperatures (SSTs) following the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) protocol. By defining

We assess the ability of neural network emulators of physical parametrization schemes in numerical weather prediction models to aid in the construction of linearized models required by four-dimensional variational (4D-Var) data assimilation. Neural networks can be differentiated trivially, and so if a physical parametrization scheme can be accurately emulated by a neural network then its tangent-linear

Water isotopologues, as natural tracers of the hydrological cycle on Earth, provide a unique way to assess the skill of climate models in representing realistic atmospheric-terrestrial water pathways. This study presents the newly developed WRF-Hydro-iso, which is a version of the coupled atmospheric-hydrological WRF-Hydro model enhanced with a joint soil-vegetation-atmospheric description of water

Using the depth (z) and density (ϱ) frameworks, we analyze local contributions to AMOC variability in a 900-year simulation with the AWI climate model. Both frameworks reveal a consistent interdecadal variability; however, the correlation between their maxima deteriorates on year-to-year scales. We demonstrate the utility of analyzing the spatial patterns of sinking and diapycnal transformations through

The hydrologic cycle couples the Earth’s energy and carbon budgets through evaporation, moisture transport, and precipitation. Despite a wealth of observations and models, fundamental limitations remain in our capacity to deduce even the most basic properties of the hydrological cycle, including the spatial pattern of the residence time of water in the atmosphere and the mean distance traveled from

This study investigates the mechanisms by which small-scale turbulence and cloud physics determine the organization of large-scale convection in radiative-convective equilibrium (RCE), an idealization of the tropical atmosphere. Under uniform forcings similar to typical tropical conditions, the atmosphere in RCE might spontaneously separate into dry and moist regions on scales of 100-1000 km, with

The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place in January–February, 2020. It was designed to understand the relationship between shallow convection and the large-scale environment in the trade-wind regime. A Lagrangian large eddy simulation, following the trajectory of a boundary-layer airmass, can reproduce a transition of trade cumulus organization from

Global models are an essential tool for climate projections, but conventional coarse-resolution atmospheric general circulation models suffer from errors both in their parameterized cloud physics and in their representation of climatically important circulation features. A notable recent study by Terai et al. (2020, https://doi.org/10.1029/2020ms002274) documents a global model capable of reproducing

A global model with superparameterized physics is used to shed light on the observed regionality of convectively coupled Kelvin waves and the Madden-Julian Oscillation (MJO). A series of aquaplanet simulations over zonally uniform sea-surface temperatures is performed, in which the axisymmetric structure of the background zonal flow is altered through nudging, while maintaining a quasi-fixed rainfall

Biases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top-of-atmosphere (TOA) clear-sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the S–N mean SW flux bias for the multi-model

The Institut Pierre-Simon Laplace Climate Modeling Center has produced an ensemble of extended historical simulations using the IPSL-CM6A-LR climate model. This ensemble (referred to as IPSL-EHS) is composed of 32 members over the 1850–2059 period that share the same external forcings but differ in their initial conditions. In this study, we assess the simulated decadal to multidecadal climate variability

Soil water and heat transfer is especially complicated during the freezing and thawing processes over the high-altitude cold regions. In this study, four sensitivity tests of soil water and heat transfer parameterizations including replacing soil property data (SP1), soil resistance scheme modification (SP2), soil thermal conductivity scheme (SP3) and virtual temperature scheme (SP4), and four combination

Contemporary Earth system models mostly ignore the sub-grid scale (SGS) heterogeneous coupling between the land surface and atmosphere. To aid in the development of coupled land and atmosphere SGS parameterizations for global models, we present a study of different aspects of highly-realistic sub-100 km scale land-surface heterogeneity. The primary experiment is a set of simulations of September 24

The dust emission parameterization in the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM2) is described with emphasis on the implementation process and simulations of global dust cycle and East Asia dust event statistics. The parameterization is based on the scheme of Shao (2004; http://doi.org/10.1029/2003JD004372) which considers two major dust emission mechanisms, namely, saltation

Snow water equivalent (SWE), temperature, and precipitation biases and trends are evaluated in the atmosphere-land simulations of the Energy Exascale Earth System Model version 1 (E3SMv1) in comparison to the Community Earth System Model version 2 (CESM2) and two other models using the ground measurement-based University of Arizona (UA) snow product. SWE, temperature, and precipitation biases are highest

We describe a new way to apply a spatial filter to gridded data from models or observations, focusing on low-pass filters. The new method is analogous to smoothing via diffusion, and its implementation requires only a discrete Laplacian operator appropriate to the data. The new method can approximate arbitrary filter shapes, including Gaussian filters, and can be extended to spatially varying and anisotropic

Climate predictions using coupled models in different time scales, from intraseasonal to decadal, are usually affected by initial shocks, drifts, and biases, which reduce the prediction skill. These arise from inconsistencies between different components of the coupled models and from the tendency of the model state to evolve from the prescribed initial conditions toward its own climatology over the

Coupled climate simulations that span several hundred years cannot be run at a high-enough spatial resolution to resolve mesoscale ocean dynamics. Recently, several studies have considered Deep Learning to parameterize subgrid forcing within macroscale ocean equations using data from ocean-only simulations with idealized geometry. We present a stochastic Deep Learning parameterization that is trained

Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncertainty quantification are too expensive for climate models; they are also not robust in the presence of internal climate variability. For example, Markov

We present a comprehensive inter-comparison of linear regression (LR), stochastic, and deep-learning approaches for reduced-order statistical emulation of ocean circulation. The reference data set is provided by an idealized, eddy-resolving, double-gyre ocean circulation model. Our goal is to conduct a systematic and comprehensive assessment and comparison of skill, cost, and complexity of statistical

The role of stomata in regulating photosynthesis and transpiration, and hence governing global biogeochemical cycles and climate, is well-known. Less well-understood, however, is the importance of stomatal control to the exchange of other trace gases between terrestrial vegetation and the atmosphere. Yet these gases determine atmospheric composition, and hence air quality and climate, on scales ranging

To extend the nonhydrostatic global Model for Prediction Across Scales (MPAS) for deep-atmosphere (geospace) applications, we have modified the model equations and numerics to include variable atmospheric composition and (potentially large) molecular viscosity and thermal conductivity. The split-explicit numerical integration techniques in MPAS remain stable in idealized test cases for atmospheric

Sea ice is a complex media composed of discrete interacting elements of various sizes and thicknesses (floes), and at sufficiently small lengthscales it can not be approximated as a continuous media as routinely done at large scales. While the Eulerian data assimilation is a relatively mature field, techniques for assimilation of satellite-derived Lagrangian trajectories of sea ice floes remain poorly

Based on 10 pairs of atmospheric general circulation models from the Coupled Model Intercomparison Project Phase 6 High Resolution Model Intercomparison Project, we find that high-resolution models (HRMs, ∼50 km) perform better than low-resolution models (LRMs, ∼100 km) in simulating the East Asian summer monsoon (EASM) rain belt. We attribute the increased rainfall to enhanced vertical advection of

Sea ice surface temperature is an important variable for short-range numerical weather prediction systems operating in the Arctic. However, when provided by numerical sea ice models, this variable is seldomly constrained by the observations, thus introducing errors and biases in the simulated near-surface atmospheric fields. In the present study a new sea ice data assimilation framework is introduced

No abstract is available for this article.

The seasonal prediction of the Indian summer monsoon (ISM) and Monsoon Intraseasonal Oscillations (MISO), as well as the Madden Julian Oscillations (MJO) that strongly modulate MISO, is of much importance to the agriculture sectors of the Indian Sub-continent. This study highlights the importance of selecting proper ‘autoconversion’ coefficients for precipitation formation in CGCM’s convective and

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence

We develop a generalized interpolation material point method (GIMPM) for the shallow shelf approximation (SSA) of ice flow. The GIMPM, which can be viewed as a particle version of the finite element method, is used here to solve the shallow shelf approximations of the momentum balance and ice thickness evolution equations. We introduce novel numerical schemes for particle splitting and integration

Ice shelf fracture is responsible for roughly half of Antarctic ice mass loss in the form of calving and can weaken buttressing of upstream ice flow. Large uncertainties associated with the ice sheet response to climate variations are due to a poor understanding of these fracture processes and how to model them. Here, we address these problems by implementing an anisotropic, nonlocal integral formulation

Convection observed in the OTREC field program in the tropical east Pacific and southwest Caribbean is simulated using a cloud-resolving model employing the weak temperature gradient approximation. Simulations are made using reference profiles derived from three-dimensional variational analyses of dropsonde data selected for different ranges of saturation fraction, a kind of column relative humidity

Global wave hindcasts are developed using the third generation spectral wave model WAVEWATCH III with the observation-based source terms (ST6) and a hybrid rectilinear-curvilinear, irregular-regular-irregular grid system (approximately at ). Three distinct global hindcasts are produced: (a) a long-term hindcast (1979–2019) forced by the ERA5 conventional winds and (b) two short-term hindcasts (2011–2019)

The correct simulation of fog and low stratus (FLS) is a difficult task for numerical weather prediction (NWP) models. The Swiss Plateau experiences many days with FLS in winter. Most NWP models employ terrain-following vertical coordinates. As a consequence, the typically flat cloud top is intersected by sloping coordinate surfaces above hilly terrain such as the Swiss Plateau. Horizontal advection

The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning (ML) method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of

Weather prediction and climate simulations need reliable parameterizations of turbulent fluxes in the stable surface layer. Especially in these conditions, the uncertainties of such parametrizations are still large. Most of them rely on the Monin-Obukhov similarity theory (MOST), for which universal stability functions (SFs) represent important ingredients. The SFs are nonlinear, if so, a numerical

Geophysical models make predictions relying on parameter values to be estimated from data. However, existing methods are costly because they require either many runs of the complex geophysical model or to implement an adjoint model. Here, we propose an alternative approach based on optimal control theory which is the simultaneous perturbations stochastic approximation (SPSA). This gradient-descent

Elevated concentrations of atmospheric bromine are known to cause ozone depletion in the Arctic, which is most frequently observed during springtime. We implement a detailed description of bromine and chlorine chemistry within the WRF-Chem 4.1.1 model, and two different descriptions of Arctic bromine activation: (1) heterogeneous chemistry on surface snow on sea ice, triggered by ozone deposition to

As one of the effective ways to study urban climate and environmental problems, the performance of numerical model could be improved in urban areas by enhancing the description of urban morphology. One of the largest challenges faced by researchers is the lack of real urban canopy parameters (UCPs) to represent urban morphological characteristics. In this study, the urban morphological parameters were

No abstract is available for this article.

This study evaluates the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison Phase 6 (CMIP6). TaiESM1 is developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The

The regional System of Multigrid Nonlinear Least Squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP) was recently established based on the multigrid NLS-4DVar assimilation scheme, Weather Research and Forecasting numerical model, and Gridpoint Statistical Interpolation (GSI)-based observation quality control and observation operator modules. The analysis variables

We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts six key atmospheric variables with six-hour time resolution. This computationally efficient model uses convolutional neural networks (CNNs) on a cubed sphere grid to produce global forecasts. The trained model requires just three minutes on a single GPU to produce a 320-member set

Numerical mixing, defined here as the physically spurious tracer diffusion due to the numerical discretization of advection, is known to contribute to biases in ocean models. However, quantifying numerical mixing is nontrivial, with most studies utilizing targeted experiments in idealized settings. Here, we present a water mass transformation-based method for quantifying numerical mixing that can be

In this study, the impact of gravel was taken into account in the regional climate model version 4.7, which was driven by the ERA-Interim with a resolution of 1.5 km in this numerical experiment. The gravel parameterization schemes and the original soil hydrothermal parameterization schemes were used in the regional soil hydrothermal simulations on the Tibetan Plateau (TP), respectively. The performance

The interactions between crops and the atmosphere significantly impact surface energy and hydrology budgets, climate, crop yield, and agricultural management. In this study, a multipass land data assimilation scheme (MLDAS) is proposed based on the Noah-MP-Crop model. The ensemble Kalman filter (EnKF) method is used to jointly assimilate the leaf area index (LAI), soil moisture (SM), and solar-induced

A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high-resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity)

We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate

In this study, a conceptual model to define convective density currents is proposed. Based on theory, observations and modeling studies, we define convective density currents as 3D coherent structures with an anomalously cold core, an adjacent wind gust, and a vertical structure made of two layers: a well-mixed one near the surface and a stratified one above. With this definition, a methodology is

For the past two decades, precipitation radars (PR) onboard low-orbiting satellites such as Tropical Rainfall Measuring Mission (TRMM) have provided invaluable insight into global precipitation variability and led to advancements in numerical weather prediction through data assimilation. Building upon this success, planning has begun on the next generation of satellite-based PR instruments, with the

The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is

To understand and predict large, complex, and chaotic systems, Earth scientists build simulators from physical laws. Simulators generalize better to new scenarios, require fewer tunable parameters, and are more interpretable than nonphysical deep learning, but procedures for obtaining their derivatives with respect to their inputs are often unavailable. These missing derivatives limit the application

The mechanical interactions between ice floes in the polar sea-ice packs play an important role in the state and predictability of the sea-ice cover. We use a Lagrangian-based numerical model to investigate such floe-floe interactions. Our simulations show that elastic and reversible deformation offers significant resistance to compression before ice floes yield with brittle failure. Compressional

We investigate how ocean feedbacks and the diurnal cycle impact convective aggregation using a slab ocean coupled to a cloud resolving model. With a 20 meter mixed layer ocean, aggregation occurs after 25 days. Thinner ocean layers slow the onset of clustering, with a 1 meter ocean layer needing around 43 days. The delay is due to anomalous solar radiation in clear sky regions, causing a relative surface

Previous studies have shown that atmospheric models with a spectral element grid can benefit from putting physics calculations on a relatively coarse finite volume grid. Here we demonstrate an alternative high-order, element-based mapping approach used to implement a quasi-equal-area, finite volume physics grid in E3SM. Unlike similar methods, the new method in E3SM requires topology data purely local

No abstract is available for this article.