We investigate the relative importance of ecosystem complexity and phytoplankton light absorption for climate studies. While the complexity of Earth system models with respect to marine biota has increased over the past years, the relative importance of biological processes in driving climate‐relevant mechanisms such as the biological carbon pump and phytoplankton light absorption is still unknown

The assessment of current and future risks for natural and human systems associated with climate change largely relies on numerical simulations performed with state‐of‐the‐art climate models. Various steps are involved in the development of such models, from development of individual components of the climate system up to free parameter calibration of the fully coupled model. Here, we describe the

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

Aerosols can enhance terrestrial productivity through increased absorption of solar radiation by the shaded portion of the plant canopy – the diffuse radiation fertilization effect. Although this process can, in principle, alter surface evaporation due to the coupling between plant water loss and carbon uptake, with the potential to change the surface temperature, aerosol‐climate interactions have

We document the implementation of the Common Representative Intermediates Mechanism version 2, reduction 5 into the United Kingdom Chemistry and Aerosol model (UKCA) version 10.9. The mechanism is merged with the stratospheric chemistry already used by the StratTrop mechanism, as used in UKCA and the UK Earth System Model, to create a new CRI‐Strat mechanism. CRI‐Strat simulates a more comprehensive

The flow of tides over rough bathymetry in the deep ocean generates baroclinic motion including internal waves and bottom‐trapped tides. The stresses generated by this motion feed back on the amplitude and phase of the large‐scale tide. Quantifying the stresses associated with tidal flow over abyssal hills is especially important, as this scale of bathymetry is often unresolved in global baroclinic

It is well known that subtropical shallow convection transports heat and water vapor upwards from the surface. It is less clear if it also transports horizontal momentum upwards to significantly affect the trade winds in which it is embedded. We utilize unique multi‐day large eddy simulations run over the tropical Atlantic with ICON‐LEM to investigate the character of shallow convective momentum transport

Convection is usually parameterized in global climate models, and there are often large discrepancies between results obtained with different convection schemes. Conventional methods of comparing convection schemes using observational cases or directly in three‐dimensional (3D) models do not always clearly identify parameterization strengths and weaknesses. In this paper we evaluate the response of

Deficiencies in convection trigger functions used in deep convection parameterizations in General Circulation Models (GCMs) have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational

In this study, we find significant sensitivity to the choice of time step for the Energy Exascale Earth System Model's atmospheric component, leading to large decreases in the magnitude of cloud forcing when the time step is reduced to 10 seconds. Reducing the time step size for the microphysics increases precipitation, leading to a drying of the atmosphere and an increase in surface evaporation. This

We apply a Bayesian structure learning approach to study interactions between global climate modes, so illustrating its use as a framework for developing process‐based diagnostics with which to evaluate climate models. Homogeneous dynamic Bayesian network models are constructed for time series of empirical indices diagnosing the activity of major tropical, Northern and Southern Hemisphere modes of

We present the results from a global 0.56°‐resolution chemical data assimilation that integrates satellite observations of ozone, NO2, CO, HNO3, and SO2 from OMI, GOME‐2, SCIAMACHY, TES, MOPITT, and MLS. The assimilation is based on an ensemble Kalman filter technique and simultaneously optimizes ozone precursor emissions and concentrations of various species. The data assimilation at 0.56° resolution

In this editorial, we present general principles as well as specific notions that guide the strategy of JAMES′ editors in realizing the journal's mission.

The Missouri River Basin (MORB) has experienced a resurgence of grassland conversion to crop production, which raised concerns on water quality. We applied the Soil and Water Assessment Tool (SWAT) to address how this conversion would impact water quality. We designed three crop production scenarios representing conversion of grassland to: 1) continuous corn; 2) corn/soybean rotation; and 3) corn/wheat

This study updates the multi‐layered Community Land Model (CLM‐ml) for hillslopes and compares predictions from against observations collected in tropical montane rainforest, Costa Rica. Modifications are made in order to capture a wider array of vertical leaf area distributions, predict CO2 profiles, account for soil respiration, and adjust wind forcings for difficult topographic settings. Test results

A generic shortcoming of constant‐depth (or “z‐coordinate”) ocean models such as MOM5 and Nucleus for European Models of the Ocean (NEMO) is a tendency for the advection scheme to produce unphysical numerical diapycnal mixing, which may exceed the explicitly parameterized mixing based on observed physical processes. Megann (2018, https://doi.org/10.1016/j.ocemod.2017.11.001) estimated the effective

We have equipped the unstructured‐mesh global sea‐ice and ocean model FESOM2 with a set of physical parameterizations derived from the single‐column sea‐ice model Icepack. The update has substantially broadened the range of physical processes that can be represented by the model. The new features are directly implemented on the unstructured FESOM2 mesh, and thereby benefit from the flexibility that

We modify the Zhang‐McFarlane (ZM) deep convection scheme in the Community Atmosphere Model version 5 to couple it with a unified parameterization for boundary‐layer turbulence and shallow convection, that is, Cloud Layers Unified by Binormals (CLUBB). By assuming a lognormal distribution of entrainment rate across the entire moist convective regimes, we link mass fluxes between shallow and deep convection

We explore the potential of feed‐forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ∼250 trials. Our DNN explains over 70 percent of the temporal variance at the 15‐minute

No abstract is available for this article.

As the editors of a scientific journal, we have the honor of front‐row seats to the peer review process. We watch as reviewers bring fresh perspectives to authors' work, identifying weakness in conception or expression, suggesting refinements, and urging authors to clarify a point or better support an argument. The process is occasionally uncomfortable but almost always respectful and constructive

Plants are expected to face increasing water stress under future climate change. Most land surface models, including Noah‐MP, employ an idealized “big‐leaf” concept to regulate water and carbon fluxes in response to soil moisture stress through empirical soil hydraulics schemes (SHSs). However, such schemes have been shown to cause significant uncertainties in carbon and water simulations. In this

Process‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose a strategy for process‐based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of single‐column versions of climate models with explicit

Large scale flood risk analyses are fundamental to many applications requiring national or international overviews of flood risk. While large‐scale climate patterns such as teleconnections and climate change become important at this scale, it remains a challenge to represent the local hydrological cycle over various watersheds in a manner that is physically consistent with climate. As a result, global

The coupled data assimilation (CDA) system consisting of ocean, sea‐ice, and atmosphere data assimilation components with the Beijing Climate Center (BCC) Climate System Model has been developed to provide reliable analyses of the atmosphere, ocean, and sea‐ice states. It incorporates ocean temperature/salinity profiles, sea surface temperature, sea level height, and sea‐ice concentration observations

Accurate simulations of high‐latitude ecosystems are critical for confident Earth system model (ESM) projections of carbon cycle feedbacks to global climate change. Land surface model components of ESMs, including the E3SM Land Model (ELM), simulate vegetation growth and ecosystem responses to changing climate and atmospheric CO2 concentrations by grouping heterogeneous vegetation into like sets of

An advanced aerosol treatment, with a focus on semivolatile nitrate formation, is introduced into the Community Atmosphere Model version 5 with interactive chemistry (CAM5‐chem) by coupling the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) with the 7‐mode Modal Aerosol Module (MAM7). An important feature of MOSAIC is dynamic partitioning of all condensable gases to the different

A description and assessment of the first release of the Arctic Subpolar gyre sTate Estimate (ASTE_R1), a data‐constrained ocean‐sea ice model‐data synthesis, is presented. ASTE_R1 has a nominal resolution of 1/3° and spans the period 2002‐2017. The fit of the model to an extensive (O(109)) set of satellite and in situ observations was achieved through adjoint‐based nonlinear least‐squares optimization

The landscapes across much of the Midwestern United States are characterized by glacial activity that left water‐holding kettles, depressions, and potholes. Until recently, traditional watershed algorithms assumed these depressions to be errors in the elevation data and filled them as a means of correction, when many of these features may rarely fill and have the potential to dramatically affect surface

Ocean observations are expensive and difficult to collect. Designing effective ocean observing systems therefore warrants deliberate, quantitative strategies. We leverage adjoint modeling and Hessian uncertainty quantification (UQ) within the ECCO (Estimating the Circulation and Climate of the Ocean) framework to explore a new design strategy for ocean climate observing systems. Within this context

Saturated hydraulic conductivity (Ksat) is a key soil hydraulic parameter for representing infiltration and drainage in land surface models. For large scale applications, Ksat is often estimated from pedotransfer functions (PTFs) based on easy‐to‐measure soil properties like soil texture and bulk density. The reliance of PTFs on data from uniform arable lands and the omission of soil structure limits

We aim at developing a simple model as an interpretative framework for the water vapor isotopic variations in the tropical troposphere over the ocean. We use large‐eddy simulations of disorganized convection in radiative‐convective equilibrium to justify the underlying assumptions of this simple model, to constrain its input parameters and to evaluate its results. We also aim at interpreting the depletion

Since rock glaciers are believed to be more resilient to climate change, water stores therein may become important water reservoirs in future, in particular in dry regions, which currently rely on glacial runoff. In order to estimate and evaluate the future runoff potential from permafrost and rock glaciers, distributed runoff models suitable for high Alpine catchments are needed. An extension to the

This study starts from a two‐layer 1D Estuary Box Model (EBM), the so called Centro Euro‐Mediterraneo sui Cambiamenti Climatici (CMCC) EBM (Verri et al., 2020, https://doi.org/10.1016/j.ocemod.2020.101587), devised to offer a proper representation of the net river release in a coupled modeling framework with hydrology models and ocean models reaching the mesoscale. Two key advances are proposed in

Idealized models can reveal insights into Earth's climate system by reducing its complexities. However, their potential is undermined by the scarcity of fully coupled idealized models with components comparable to contemporary, comprehensive Earth System Models. To fill this gap, we compare and contrast the climates of two idealized planets which build on the Simpler Models initiative of the Community

A scalable semi‐implicit barotropic mode solver for the ocean component of the model for prediction across scales has been implemented as a competitor to an existing explicit‐subcycling scheme to allow faster and more stable simulations while not sacrificing accuracy. The semi‐implicit solver adopts the pipelined preconditioned bi‐conjugate gradient stabilization algorithm as an iterative solver in

The root water uptake (RWU) rate of plants is influenced by various environmental factors. Low soil temperature stress can reduce RWU rate by inhibiting the growth of plant roots and increasing the hydraulic resistance of water transport among the soil‐plant‐atmosphere continuum. Given that low soil temperature stress is not accounted for in current land surface models (LSMs); in this study, we introduce

The Goddard Earth Observing System composition forecast (GEOS‐CF) system is a high‐resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS‐CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space‐based and in‐situ observations. GEOS‐CF expands on the GEOS weather and aerosol modeling

No abstract is available for this article.

The societal benefits of numerical weather prediction (NWP) forecasts is most evident in populated areas. An urban representation within NWP models should provide improved forecast accuracy. Here we present the preliminary implementation of an urban scheme within the Integrated Forecasting System (IFS) using a simplified single‐layer urban canopy model. The scheme makes assumptions of canyon geometry

Interior oceanic motions occur predominantly along, rather than across, the neutral tangent plane. These planes do not link together to form well‐defined surfaces, so oceanographers use approximately neutral surfaces. To date, the most accurate such surface is the ω‐surface, but its practical utility was limited because its numerical implementation was slow and sometimes unstable. This work upgrades

As one kind of submesoscale instability, symmetric instability (SI) of the ocean surface mixed layer (SML) plays a significant role in modulating the SML energetics and material transport. The small spatial scales of SI, O(10 m–1 km), are not resolved by current climate ocean models and most regional models. This study describes comparisons in an idealized configuration of the SI parameterization scheme

Understanding the coupling between convective clouds and the general circulation, as well as addressing the gray zone problem in convective parameterization, requires insight into the genesis and maintenance of spatial patterns in cumulus cloud populations. In this study, a simple toy model for recreating populations of interacting convective objects as distributed over a two‐dimensional Eulerian grid

The IPSL‐CM6‐LR atmosphere‐ocean coupled model exhibits a pronounced multicentennial variability of the Atlantic meridional overturning circulation (AMOC), driven by delayed freshwater accumulation and release in the Arctic. The AMOC fluctuations are preceded by salinity‐driven density anomalies in the main deep convection sites in the Labrador and Greenland seas. During a strong AMOC, a combination

Modeling of the land surface water‐, energy‐, and carbon balance provides insight into the behavior of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large‐scale land surface, hydrological, and crop models,

Higher order upwind‐biased advection schemes are often used for potential temperature advection in dynamical cores of atmospheric models. The inherent diffusive and antidiffusive fluxes are interpreted here as the effect of irreversible sub‐gridscale dynamics. For those, total energy conservation and positive internal entropy production must be guaranteed. As a consequence of energy conservation, the

This paper presents a sea ice prognostic model involving a one‐dimensional thermodynamic diffusion model, nudging satellite‐derived snow/ice temperatures, and two‐dimensional Lagrangian ice tracking. The aim of the model is to produce the evolvement of the physical properties of the snow and ice over the Arctic Ocean during the winter season. While the one‐dimensional column process solves the solution

Ocean dynamic sea level (DSL) change is a key driver of relative sea level (RSL) change. Projections of DSL change are generally obtained from simulations using atmosphere‐ocean general circulation models (GCMs). Here, we develop a two‐layer climate emulator to interpolate between emission scenarios simulated with GCMs and extend projections beyond the time horizon of available simulations. This emulator

A large‐eddy simulation tool is developed for simulating the dynamics of atmospheric boundary layers (ABLs) 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

No abstract is available for this article.

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

No abstract is available for this article.

[Stochastic representation of the influence of the subgrid‐scales on the resolved scales in weather and climate models has been shown to improve ensemble spread and resolved variability. We propose a statistical scale‐aware space‐time model to characterize the contribution of mesoscale wind variability to air‐sea exchanges. In an earlier study, we analyzed the difference between “true” fluxes computed

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

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 superexponential functional forms. The impact of these different methods on ocean processes is examined in two test‐bed cases of a wave‐current

Coupled general circulation models (GCM), and their atmospheric, oceanic, land, and sea‐ice components have many parameters. Some parameters determine the numerics of the dynamical core, while others are based on our current understanding of the physical processes being simulated. Many of these parameters are poorly known, often globally defined, and are subject to pragmatic choices arising from a

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

Climate change will affect both the mean state and seasonality of marine physical and biogeochemical properties, with important implications for the oceanic sink of atmospheric CO2. Here, we investigate the seasonal cycle of the air‐sea exchange of CO2 and pCO2,sw (surface seawater pCO2) and their long term changes using the CMIP6 submission of the NASA‐GISS modelE (GISS‐E2.1‐G). In comparison to the

Clouds are critical for weather and climate prediction. The multiple scales of cloud processes make simulation difficult. Often models and measurements are used to develop empirical relationships for large‐scale models to be computationally efficient. Machine learning provides another potential tool to improve our empirical parameterizations of clouds. To explore these opportunities, we replace the

Numerical weather prediction has traditionally been based on the models that discretize the dynamical and physical equations of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purely data‐driven medium‐range weather forecasting with first studies exploring the feasibility of such an approach. To accelerate progress in this area, the WeatherBench benchmark