A DRP-4DVar based 4DEnVar data assimilation system with the flow-dependent background error covariance was developed for global numerical weather prediction

The deterministic forecast initialized from the 4DEnVar ensemble mean analysis has performance comparable to 4DVar in the Extratropics

Higher quality of analyses and ensemble forecasts can be produced by the 4DEnVar system relative to the 4DVar system

In this study a wind-farm parameterization is implemented in the numerical weather prediction model HARMONIE-AROME

A model evaluation of a full year reveals the wind-farm parameterization improves wind-speed forecasts close to offshore wind farms

The presence of wind farms in the model also alters temperature and humidity profiles due to the enhanced turbulent mixing by the turbines

The Geophysical Fluid Dynamics Laboratory (GFDL) cloud microphysics scheme (GFDL MP) has been thoroughly updated for better physical realism and consistency

The upgraded GFDL MP significantly improves large-scale weather prediction within the GFDL System for High-resolution prediction on Earth-to-Local Domains model

Changes to the particle size distributions and cloud droplet number concentrations show significant impacts on weather prediction

Sensitivity of microphysics model state to stochastic parameter perturbations is highly time dependent

Parameter perturbations informed by state-dependent covariance reduce model bias and introduce more variability than uncorrelated sampling

Neural networks and their explainability tools can be harnessed to identify patterns of forced change within combined fields

Combined fields of input allow for earlier detection of the emergence of a forced climate response

Explainable AI techniques can be used to identify patterns that describe the emergence and evolution of forced climate change

A nine-mode version of the modal aerosol model has been developed to improve the dust representation in Community Atmosphere Model

The dust aerosols simulated by this new implement in remote regions better agree with observational data

The increased coarse dust burden has increased the dust direct and indirect radiative effects resulting in a warmer atmosphere

The distribution of routing-related parameters in the Hillslope Link Model (HLM) has a significant impact on estimating peak flow magnitude and timing

We developed a runoff controller that separates hillslope and routing processes allowing the identification of routing-related parameters

Our machine learning procedure obtained routing parameters that match the Iowa landforms and improved HLM peak flow estimation

Quantification of multi-parameter uncertainty of cloud microphysical evolution of WCB trajectories using algorithmic differentiation

Uncertainty at every time step derived with algorithmic differentiation representative for key uncertainty over at least 30 min intervals

Parameterization of CCN activation, diameter size, and fall velocity of hydrometeors have the largest mean impact on water vapor contents

A methane emission-driven configuration of the UK community Earth system model UKESM1, UKESM1-ems, has been developed

In UKESM1-ems global wetlands are interactively coupled to the atmosphere at every timestep via methane emissions

The UKESM1-ems performs well simulating the global methane cycle including feedbacks; the global budget compares well with observations

Energy Exascale Earth System Model version 1 (E3SMv1) captures spatial and temporal variability in the observed dust aerosol optical depth, but underestimates long-range transport

The net direct radiative effect of dust simulated by E3SMv1 is −0.42 Wm⁻² with a smaller longwave warming than other recent studies

In addition to emission, dry removal of dust are highly sensitive to the increase of horizontal or vertical model resolution

Representation of the congestus mode in RCE varies greatly across models

The congestus mode is invigorated by large-scale convective aggregation

Tropospheric stability increases with aggregation due to congestus invigoration and reduced entrainment cooling

The matrix approach unifies land carbon cycle models in a matrix form and thus helps gain simplicity in model structure

The matrix approach provides a theoretical framework to understand the general behavior of land carbon cycle

It helps address contemporary issues in land carbon cycle modeling, including pinning down model uncertainty and accelerating spin-up

Accounting for the snow over sea-ice enables strong cooling events and extreme low temperatures in the Arctic to be better represented

Accounting for the snow over sea-ice improves the representation of Arctic winter states in the ECMWF Integrated Forecasting System

Compensating errors between cloud and surface processes are a key factor affecting the near-surface forecast biases in the Arctic

Calibration of poorly constrained parameters in higher-resolution Earth system models (ESMs) is computationally expensive

A machine learning technique from computer vision can replace the ESM during calibration, even for complex variables like precipitation

Our machine learning approach reduces the computational costs of emulating a higher-resolution ESM by 20%–40%

Including a land-hydrology model component in a forced global ocean/sea-ice configuration allows for a more realistic closure of the freshwater cycle

Improved freshwater closure can substantially reduce ocean temperature and salinity initialization drift - without the need for artificial salinity restoring fluxes

A mid-latitude poleward moisture transport constraint derived from CMIP6 historical simulations is consistent with further increase in hindcast performance

The impact of rotation on simulations of idealized simulations of moist convection is studied

The formation and maintenance of hurricane-like vortices involve a combination of conditional instability and rotation

Rotating moist convection in a conditionally unstable environment should spontaneously generate hurricane-like vortices

We use a novel laboratory experiment to investigate zonal jet dynamics and distinguish between standing meanders and transient eddies

Flows occupy two distinct regimes; predominantly zonal, or predominantly meandering, depending on the timescales of forcing and dissipation

For predominantly meandering flow, the standing meanders perform 79% of meridional tracer transport, with 18% by transient eddies

We developed a land surface-hydrological model that describes irrigation processes by incorporating an irrigation scheme

We used the Global Crop Water Model method to more realistically estimate irrigation and described water extraction and irrigation processes

We conducted verification in terms of river discharge, evapotranspiration, and irrigation water amount to evaluate model performance

GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experiments

GISS-E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate response

Both coupled models, E2.1-G and E2.1-H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5-8.5 in the E2.1-G model

A stochastic cloud population model is coupled with a mesoscale model for applications as a parameterization of cumulus convection

The model predicts the evolution of the cloud-base mass flux distribution via a transition function obtained using machine learning

The potential of the approach as a path to improvement of statistics and variability of precipitation in models is demonstrated