The rapidly increasing computing powers allow global atmospheric simulations with aggressively high resolutions, challenging traditional model design principles. This study presents a Low Mach number Approximate Riemann Solver (LMARS) based unstaggered finite‐volume model for solving the shallow‐water equations on arbitrary gnomonic cubed‐sphere grids. Using a novel reference line‐based grid‐generation

Satellite altimeters provide global observations of sea surface height (SSH) and present a unique data set for advancing our theoretical understanding of upper‐ocean dynamics and monitoring its variability. Considering that mesoscale SSH patterns can evolve on timescales comparable to or shorter than satellite return periods, it is challenging to accurately reconstruct the continuous SSH evolution

Specific yield is a key parameter for estimating water table depth in land surface models, which can strongly modulate the interaction between soil moisture and groundwater and further affect the water budget between the land surface and the atmosphere. The error in water table simulation comes mainly from the uncertainty in determining the specific yield. The determination of specific yield in land

Vegetation is an important component in the Earth system, providing a direct link between the biosphere and atmosphere. As such, a representative vegetation pattern is needed to accurately simulate climate. We attempt to model global vegetation (biomes) with a data driven approach, to test if this allows us to create robust global and regional vegetation patterns. This not only provides quantitative

Surface waves modify upper ocean dynamics through Stokes drift related processes. Stokes drift estimated from a discrete wave spectrum is compared to Stokes drift approximations as a monochromatic profile based on bulk surface wave parameters, and to two additional super‐exponential functional forms. The impact of these different methods on ocean processes is examined in two test‐bed cases of a wave‐current

Simulations of the CMIP6 historical period 1850–2014, characterized by the emergence of anthropogenic climate drivers like greenhouse gases, are presented for different configurations of the NASA Goddard Institute for Space Studies (GISS) Earth System ModelE2.1. The GISS‐E2.1 ensembles are more sensitive to greenhouse gas forcing than their CMIP5 predecessors (GISS‐E2) but warm less during recent decades

It is difficult for coarse‐resolution global models of the atmosphere to accurately simulate the observed distribution of low clouds. In particular, it is difficult for moist turbulence closure models to simulate sufficiently bright near‐coastal stratocumulus (Sc) without simulating overly bright marine shallow cumuli (Cu). To parameterize bright Sc, a turbulence parameterization must damp the turbulent

Seasonal prediction is one important element in a seamless prediction chain between weather forecasts and climate projections. After several years of development in collaboration with Universität Hamburg and Max Planck Institute for Meteorology, the Deutscher Wetterdienst performs operational seasonal forecasts since 2016 with the German Climate Forecast System, now in Version 2 (GCFS2.0). Here, the

In the atmosphere, convection can organize from smaller scale updrafts into more coherent structures on various scales. In bulk‐plume cumulus convection parameterizations, this type of organization has to be represented in terms of how the resolved flow would “feel” convection if more coherent structures were present on the subgrid. This type of subgrid organization acts as building blocks for larger

This study examines the large‐scale factors that govern global tropical cyclone (TC) formation and an upper bound on the annual number of TCs. Using idealized simulations for an aquaplanet tropical channel, it is shown that the tropical atmosphere has a maximum capacity in generating TCs, even under ideal environmental conditions. Regardless of how favorable the tropical environment is, the total number

There is increasing evidence that local rainfall extremes can increase with warming at a higher rate than expected from the Clausius‐Clapeyron (CC) relation. The exact mechanisms behind this super‐CC scaling phenomenon are still unsolved. Recent studies highlight invigorated local dynamics as a contributor to enhanced precipitation rates with warming. Here, cold pools play an important role in the

A large‐eddy simulation (LES) tool is developed for simulating the dynamics of atmospheric boundary layers using lattice Boltzmann method (LBM), which is an alternative approach for computational fluid dynamics and proved to be very well suited for the simulation of low‐Mach flows. The equations of motion are coupled with the global complex physical models considering the coupling among several mechanisms

The representation of stable boundary layers (SBLs) still challenges turbulence parameterizations implemented in current weather or climate models. The present work assesses whether these model deficiencies reflect calibration choices or intrinsic limits in currently‐used turbulence parameterization formulations and implementations. This question is addressed for the CNRM atmospheric model ARPEGE‐Climat

Ensemble weather forecasts enable a measure of uncertainty to be attached to each forecast, by computing the ensemble's spread. However, generating an ensemble with a good spread‐error relationship is far from trivial, and a wide range of approaches to achieve this have been explored – chiefly in the context of numerical weather prediction models. Here, we aim to transform a deterministic neural network

No abstract is available for this article.

The Atlantic meridional overturning circulation (AMOC) is one of the most important characteristics of an ocean model run. Using the depth (z) and density frameworks, we analyze how the sinking and diapycnal transformations defining the AMOC as well as AMOC strength and variability react to mesh refinement from low to higher resolution in two model runs driven by the CORE‐II forcing. Both runs can

The Walker circulation connects the regions with deep atmospheric convection in the western tropical Pacific to the shallow‐convection, tropospheric subsidence, and stratocumulus cloud decks of the eastern Pacific. The purpose of this study is to better understand the multi‐scale interactions between the Walker circulation, cloud systems, and interactive radiation. To do this we simulate a mock‐Walker

The second version of Chinese Academy of Sciences Earth System Model (CAS‐ESM 2) is described with emphasis on the development process, strength and weakness, and climate sensitivities in simulations of the Coupled Model Intercomparison Project (CMIP6) DECK experiments. CAS‐ESM 2 was built as a numerical model to simulate both the physical climate system as well as atmospheric chemistry and carbon

The equilibrium climate sensitivity in the latest version of CNRM climate model, CNRM‐CM6‐1, and in its high resolution counterpart, CNRM‐CM6‐1‐HR, is significantly larger than in the previous version (CNRM‐CM5.1). The traceability of this climate sensitivity change is investigated using coupled ocean‐atmosphere model climate change simulations. These simulations show that the increase in equilibrium

The present study aims to estimate effective diahaline turbulent salinity fluxes and diffusivities in numerical model simulations of estuarine scenarios. The underlying method is based on a quantification of salinity mixing per salinity class, which is shown to be twice the turbulent salinity transport across the respective isohaline. Using this relation, the recently derived universal law of estuarine

This study presents a description of the El Niño–Southern Oscillation (ENSO) and Pacific Decadal Variability (PDV) in a multicentury preindustrial simulation of the Community Earth System Model Version 2 (CESM2). The model simulates several aspects of ENSO relatively well, including dominant timescale, tropical and extratropical precursors, composite evolution of El Niño and La Niña events, and ENSO

The usage of four‐dimensional variational (4DVar) scheme is limited by the static background error covariance and the adjoint model. In a hybrid frame of the four‐dimensional ensemble‐variational data assimilation scheme (4DEnVar), being able to avoid the tangent linear and adjoint models in the 4DVar and nowadays developed into a cutting‐edge research topic of the next‐generation data assimilation

We develop a novel framework for rigorously evaluating land surface models (LSMs) against observations by recognizing the asymmetry between verification‐ and falsification‐oriented approaches. The former approach cannot completely verify LSMs even though it exhausts every case of consistency between the model predictions and observations, whereas the latter only requires a single case of inconsistency

This article presents a new deep convective parameterization that determines cloud characteristics based on a specified cloud size distribution. The vertical profiles of cloud properties are determined by analytic equations, which formulate entrainment with an inverse relationship to cloud width. In line with recent studies of large eddy simulations, cloud widths are assumed to be constant with height

We investigate how mesoscale circulations associated with convective aggregation can modulate the sensitivity of the hydrologic cycle to warming. We quantify changes in the full distribution of rain across radiative‐convective equilibrium states in a cloud‐resolving model. For a given SST, the shift in mean rainfall between disorganized and organized states is associated with a shift in atmospheric

The land‐surface model of the Chinese Academy of Sciences (CAS‐LSM), which includes lateral flow, water use, nitrogen discharge and river transport, soil freeze‐thaw front dynamics, and urban planning, was implemented in the Flexible Global Ocean‐Atmosphere‐Land System model, grid‐point version 3 (CAS‐FGOALS‐g3) to investigate the climatic effects of eco‐hydrological processes and human activities

The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or ‘tuning’ the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM‐Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time‐series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes

Secondary organic aerosols (SOA) are large contributors to fine particle mass loading and number concentration and interact with clouds and radiation. Several processes affect the formation, chemical transformation, and removal of SOA in the atmosphere. For computational efficiency, global models use simplified SOA treatments, which often do not capture the dynamics of SOA formation. Here we test more

In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating

We demonstrate a new approach for climate model tuning in a realistic situation. Our approach, the mathematical foundations and technical details of which are given in Part I, systematically uses a single‐column configuration of a global atmospheric model on test cases for which reference large‐eddy‐simulations are available. The space of free parameters is sampled running the single‐column model from

We present a neutral diffusion operator appropriate for an ocean model making use of general vertical coordinates. The diffusion scheme uses polynomial reconstructions in the vertical, along with a horizontally local but vertically nonlocal stencil for estimates of tracer fluxes. These fluxes are calculated on a vertical grid that is the superset of model columns in a neutral density space. Using flux‐limiters

In order to enhance our understanding of clouds and their microphysical processes, it is crucial to exploit both observations and models. Local observations from ground‐based remote sensing sites provide detailed information on clouds, but as they are limited in dimension, there is no straightforward way to use them to guide large‐scale model development. We show that large‐eddy simulations (LES) performed

Comprehensive land surface models are very important for climate and weather forecasting and for improving our understanding of the relationships between humans and the Earth system. This work presents a land surface model of the Chinese Academy of Sciences (CAS‐LSM) that considers groundwater lateral flow, human water regulation, soil freeze‐thaw front dynamics, riverine dissolved inorganic nitrogen

The present paper presents results of seasonal‐to‐decadal climate predictions based on a coupled climate model called the Model for Interdisciplinary Research on Climate version 6 (MIROC6) contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). MIROC6 is initialized every year for 1960–2018 by assimilating observed ocean temperature and salinity anomalies and full fields of sea ice

Vegetation carbon stock (Cveg) in global forests, which is important for C cycle‐climate feedbacks, commonly increases with forest age. Due to the allometric growth of plants, the nonlinear increase in Cveg with woody fraction (fw) is expected across space. However, it remains unclear whether such a nonlinear relationship between Cveg and fw can be constrained by observations and further used to benchmark

Atmospheric radiation is the main driver of weather and climate, yet due to a complicated absorption spectrum, the precise treatment of radiative transfer in numerical weather and climate models is computationally unfeasible. Radiation parameterizations need to maximize computational efficiency as well as accuracy, and for predicting the future climate many greenhouse gases need to be included. In

We present an unprecedented set of high‐resolution climate simulations, consisting of a 500‐year pre‐industrial control simulation and a 250‐year historical and future climate simulation from 1850 to 2100. A high‐resolution configuration of the Community Earth System Model version 1.3 (CESM1.3) is used for the simulations with a nominal horizontal resolution of 0.25° for the atmosphere and land models

Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) and climate prediction conducted with state‐of‐the‐art, comprehensive high‐resolution general circulation models. In a data assimilation framework, recent advances in the context of weak‐constraint 4D‐Var have shown that it is possible to estimate and correct for a large fraction of systematic

Parameterizations of unresolved turbulent processes often compromise the fidelity of large‐scale ocean models. In this work, we argue for a Bayesian approach to the refinement and evaluation of turbulence parameterizations. Using an ensemble of large eddy simulations of turbulent penetrative convection in the surface boundary layer, we demonstrate the method by estimating the uncertainty of parameters

The Intertropical Convergence Zone (ITCZ) is a discontinuous, zonal precipitation band that plays a crucial role in the global hydrological cycle. Previous studies using prescribed sea surface temperature (SST) aquaplanets show the ITCZ is sensitive to convective mixing, but such a framework is energetically inconsistent. Studies also show that atmosphere‐ocean coupling reduces the sensitivity of the

Motivated by an observed relationship between marine low cloud cover and surface wind speed, this study investigates how vertical wind shear affects trade‐wind cumulus convection, including shallow cumulus and congestus with tops below the freezing level. We ran large‐eddy simulations for an idealized case of trade‐wind convection using different vertical shears in the zonal wind. Backward shear, whereby

The use of machine learning based on neural networks for cloud microphysical parameterizations is investigated. As an example, we use the warm‐rain formation by collision‐coalescence, that is, the parameterization of autoconversion, accretion, and self‐collection of droplets in a two‐moment framework. Benchmark solutions of the kinetic collection equations are performed using a Monte Carlo superdroplet

Coupling 3‐D hydrodynamics with climate models is necessary but difficult for resolving multiscale interactions and has been rarely implemented in predicting Great Lakes' water level fluctuations because of issues in treating net basin supply (NBS) components and connecting channel flows. This study developed an interactive lake‐atmosphere‐hydrology modeling system by coupling the regional Climate‐Weather

Ocean models at eddy‐permitting resolution are generally overdissipative, damping the intensity of the mesoscale eddy field. To reduce overdissipation, we propose a simplified, kinematic energy backscatter parametrization built into the viscosity operator in conjunction with a new flow‐dependent coefficient of viscosity based on nearest neighbor velocity differences. The new scheme mitigates excessive

The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system

Unresolved temperature and salinity fluctuations interact with a nonlinear seawater equation of state to produce significant errors in the ocean model evaluation of the large‐scale density field. It is shown that the impact of temperature fluctuations dominates the impact of salinity fluctuations and that the error in density is, to leading order, proportional to the product of a subgrid‐scale temperature

No abstract is available for this article.

As a first step toward achieving full physics urban weather simulation capabilities within the resident‐GPU large‐eddy simulation (LES) FastEddy® model, we have implemented and verified/validated a method for explicit representation of building effects. Herein, we extend the immersed body force method (IBFM) from Chan and Leach (2007, https://doi.org/10.1175/2006JAMC1321.1) to (i) be scale independent

Forced mechanical lifting through cold pool gust fronts can trigger new convection and, as previous work highlights, is enhanced when cold pools collide. However, as shown by conceptual models, the organization of the convective cloud field emerging from two versus three colliding cold pools differs strongly. In idealized dry large‐eddy simulations we therefore compare collisions between two and three

A novel decomposition of the ocean heat energy that contributes to sea ice melt and growth (ocean‐ice and frazil heat) into components that are driven by surface heat flux and ocean circulation changes is used to isolate the evolving roles of the atmosphere and ocean in the Arctic sea ice loss from CO2 increases. A sea ice volume budget analysis is used to separate the impacts of the anomalous frazil/ocean‐ice

This paper introduces a new large‐eddy simulation model, FastEddy®, purpose built for leveraging the accelerated and more power‐efficient computing capacity of graphics processing units (GPUs) toward adopting microscale turbulence‐resolving atmospheric boundary layer simulations into future numerical weather prediction activities. Here a basis for future endeavors with the FastEddy® model is provided

Earth system models (ESMs) have been rapidly developed in recent decades to advance our understanding of climate change‐carbon cycle feedback. However, those models are massive in coding, require expensive computational resources, and have difficulty in diagnosing their performance. It is highly desirable to develop ESMs with modularity and effective diagnostics. Toward these goals, we implemented

Climate models struggle to accurately represent the highly reflective boundary layer clouds overlying the remote and stormy Southern Ocean. We use in situ aircraft observations from the Southern Ocean Clouds, Radiation and Aerosol Transport Experimental Study (SOCRATES) to evaluate Southern Ocean clouds in a cloud‐resolving large‐eddy simulation (LES) and two coarse resolution global atmospheric models

To make weather and climate models computationally affordable, small‐scale processes are usually represented in terms of the large‐scale, explicitly resolved processes using physics‐based/semi‐empirical parameterization schemes. Another approach, computationally more demanding but often more accurate, is super‐parameterization (SP). SP involves integrating the equations of small‐scale processes on

Global water erosion strongly affects the terrestrial carbon balance. However, this process is currently ignored by most global land surface models (LSMs) that are used to project the responses of terrestrial carbon storage to climate and land use changes. One of the main obstacles to implement erosion processes in LSMs is the high spatial resolution needed to accurately represent the effect of topography

We examine the response of the Community Earth System Model Versions 1 and 2 (CESM1 and CESM2) to abrupt quadrupling of atmospheric CO2 concentrations (4xCO2) and to 1% annually increasing CO2 concentrations (1%CO2). Different estimates of equilibrium climate sensitivity (ECS) for CESM1 and CESM2 are presented. All estimates show that the sensitivity of CESM2 has increased by 1.5 K or more over that

An ensemble optimal interpolation (EnOI) data assimilation system for a high‐resolution (0.1° horizontal) version of the Community Earth System Model Version 2 (CESM2) ocean component is presented. For this purpose, a new version of the Data Assimilation Research Testbed (DART Manhattan) that enables large‐state data assimilation by distributing state vector information across multiple processors at

The parametrization of orographic drag processes is a major source of circulation uncertainty in models. The COnstraining ORographic Drag Effects (COORDE) project makes a coordinated effort to narrow this uncertainty by bringing together the modeling community to: explore the variety of orographic drag parametrizations employed in current operational models; assess the resolution sensitivity of resolved

Particle filters are non‐Gaussian filters, which means that the assumption that the error distribution of the ensemble should be Gaussian is unnecessary. Like the ensemble Kalman filter, particle filters are based on the Monte Carlo approximation to represent the distribution of model states. It requires a substantial number of particles to approximate the probability density function of states in